Chemostratigraphy, geomechanical characteristics, and petroleum indicators from southern Bida Basin outcrops, Nigeria

Chemostratigraphy, geomechanical characteristics, and petroleum indicators from southern Bida Basin outcrops, Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Chemostratigraphy, geomechanical characteristics, and petroleum indicators from southern Bida Basin outcrops, Nigeria ⃰Atabo Nathaniel Odoma, Goodness Eleojo Joseph This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7694693/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract The Bida Basin represents one of Nigeria’s most promising yet underexplored sedimentary sequences, with outcropping formations in the southern sector pro viding unique opportunities for integrated petroleum system analysis. This study presents a comprehensive evaluation combining chemostratigraphic characterization, geomechanical property assessment, and petroleum indicator analysis of Cretaceous to Tertiary outcrops across a 450 km 2 area in the southern Bida Basin. Sixty-eight samples were systematically collected from five measured sections spanning the Lokoja, Patti, and Agbaja Formations. X-ray fluorescence analysis reveals distinct chemostratigraphic signatures with Ti/Al ratios ranging from 0.042 to 0.078, enabling correlation across 15 km of strike length. Rare earth element patterns indicate mixed provenance from both felsic basement and recycled sedimentary sources. Geomechanical testing demonstrates significant lithological control on rock strength properties, with uniaxial compressive strengths varying from 28.4 MPa in poorly cemented sandstones to 165.7 MPa in ironstone horizons. Young’s modulus values (12.8–89.3 GPa) correlate strongly with quartz content and degree of silicification. Organic geochemical analysis identifies three distinct zones of petroleum potential, with total organic carbon contents reaching 2.8 wt% in organic-rich shales. Rock Eval pyrolysis indicates predominantly Type II/III kerogen with hydrogen indices of 89–234 mg HC/g TOC. Vitrinite reflectance measurements (0.52–0.89% Ro) suggest early to peak oil generation windows across the study area. Surface geochemical anomalies, including elevated C2-C4 hydrocarbon concentrations and distinctive isotopic signatures, provide evidence for active petroleum migration. Integration of these multidisciplinary datasets establishes a robust framework for regional correlation and petroleum system understanding, with implications for both conventional and unconventional resource assessment in Nigeria’s frontier basins. Chemostratigraphy geomechanical properties petroleum indicators Nigeria outcrop analysis source rock and reservoir characterization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction The Bida Basin, positioned within the Nigerian Middle Belt, stands as a compelling example of how frontier sedimentary basins can harbor significant petroleum potential despite limited exploration activities. Spanning approximately 95,000 km 2 , this intracratonic basin has attracted renewed attention following successful discoveries in analogous settings across West Africa (Brownfield and Charpentier, 2006 .). The basin’s complex structural evolution, influenced by both Santonian tectonism and later Pan-African reactivation, has created a diverse array of depositional environments that warrant detailed investigation (Kogbe, 1989 ). Southern portions of the Bida Basin expose Cretaceous to Tertiary sequences that provide exceptional windows into subsurface petroleum systems. These outcrops, while affected by tropical weathering and lateralization, preserve critical geological relationships often obscured in subsurface datasets (Adeleye, 1989 ). Previous investigations have largely focused on single-discipline approaches, examining either structural geology (Jones and Hockey, 1964 ), stratigraphy (Adeleye, 1974 ), or isolated geochemical parameters (Obaje, et al., 2004 ). However, the inherent complexity of petroleum systems demands integrated analytical approaches that can simultaneously address source rock potential, reservoir quality, and geomechanical constraints on exploration and development activities. Recent advances in chemostratigraphic correlation techniques have revolutionized our ability to establish precise stratigraphic frameworks in outcrop studies (Pearce, et al., 1999 ). Unlike biostratigraphy methods, which can be compromised by preservation issues and ecological factors, chemostratigraphy exploits the systematic variations in elemental compositions that reflect changes in provenance, climate, or depositional environment (Ramkumar, 2015 ). When applied to weathered outcrop sections, careful selection of immobile elements can overcome the challenges posed by tropical alteration processes (Nesbitt and Young, 1982 ). The geomechanical characterization of reservoir and seal rocks has gained increasing importance as the industry moves toward more complex extraction technologies and enhanced recovery methods (Fjaer et al., 2008 ). Understanding the mechanical properties of rock formations not only influences drilling and completion strategies but also affects long-term reservoir performance and the feasibility of hydraulic fracturing operations (Zoback, 2007 ). In frontier basins where subsurface data are sparse, outcrop-based geomechanical studies provide essential baseline information for future development activities. Petroleum geochemistry continues to evolve as a predictive science, with surface geo-chemical techniques offering cost-effective exploration tools in early-stage basin evaluation (Schumacher and Abrams, 1996 ). The integration of traditional source rock analysis with advanced biomarker studies and surface geochemical surveys can provide detailed insights into petroleum system maturity and migration efficiency (Peters, et al., 2005 ). In tropical environments like the Bida Basin, where deep weathering profiles can mask or enhance hydrocarbon micro seepage signals, careful interpretation of surface geochemical data becomes particularly critical. Despite the recognized potential of the Bida Basin, significant knowledge gaps persist regarding the detailed petroleum system characteristics of its southern sector. Previous geochemical studies have been limited in scope and geographic coverage (Obaje, et al., 2004 ), while geomechanical properties remain largely uncharacterized. The absence of integrated datasets has hindered the development of detailed petroleum system models and exploration strategies tailored to this unique geological setting. This study aims to address these limitations through a systematic investigation of southern Bida Basin outcrops, with four primary objectives: (Abrams, 2005 ) establish a chemostratigraphic framework for regional correlation and sequence boundary identification; (Aadnøy, 2010) characterize the geomechanical properties of exposed formations and their relationships to lithological and structural variables; (Adeleye, 1974 ) evaluate petroleum generation potential and migration indicators through comprehensive organic and surface geochemical analysis; and (Adeleye, 1989 ) integrate these multidisciplinary datasets to develop a holistic understanding of petroleum system elements and their spatial relationships. To accomplish these objectives, this paper is organized into several complementary sections that progressively build toward an integrated interpretation. Following this introduction, a theoretical framework section establishes the conceptual foundations for chemostratigraphic correlation, geomechanical analysis, and petroleum system evaluation. The methodology section details field sampling strategies and laboratory analytical procedures designed to ensure data quality and comparability. Results are presented in three thematic subsections corresponding to the main analytical approaches, followed by an integrated discussion that synthesizes findings and explores their implications for petroleum exploration. The study concludes with recommendations for future research directions and practical applications in frontier basin evaluation. 1.2 Tectonic and Geological Settings of the Bida Basin The Bida Basin is an intracratonic, NW–SE trending inland sedimentary basin stretching from Shegwa in the northwest to Dekina in the southeast, with a length of about 350 km and a width of 75 to 150 km (Fig. 1 ). It lies roughly elliptical in plan and is oriented perpendicular to the western margin of the NE–SW trending Benue Trough (Rahaman et al., 2019 ). Unlike many other Nigerian basins, the Bida Basin lack volcanic rocks, carbonate buildups, or Tertiary units; its fill consists largely of continental sediments with only minor marginal to shallow marine and freshwater flood-plain deposits in some intervals (Obaje, 2009 ; Rahaman et al., 2019 ). Several models have been proposed to explain the tectonic evolution of the basin. An extensional or rift-bounded model suggests that the basin developed under NW-SE fault-bounded structures, with fanglomerates and coarse alluvial fan deposits forming the basal fill as a direct response to basement uplift (Rahaman et al., 2019 ). Earlier studies have also proposed a cratonic sag or downwarp model, where the basin originated from subsidence of the crust in an intracratonic setting, thus accommodating widespread continental sedimentation without major volcanic input (Whiteman, 1982 ; Nwajide, 2013 ). Other interpretations emphasize the role of wrench or transform tectonics, in which strike-slip faulting and linear basement reactivation controlled basin geometry and sediment dispersal (Ojo & Ajakaiye, 1989 ; Udensi & Osazuwa, 2004 ). Rahaman et al. ( 2019 ), however, integrated these ideas into a hybrid model, proposing that the Bida Basin evolved as a fault-bounded extensional depression influenced by reactivated Pan-African basement structures, with episodic subsidence and sedimentary infill dominated by fluvial and deltaic systems. The lithostratigraphy of the Bida Basin varies between the northern and southern sectors but is broadly Campanian to Maastrichtian in age (Fig. 1 and Table 1 ). In the northern Bida Basin, the basal Bida Formation is subdivided into the Doko and Jima members, consisting of very poorly sorted pebbly arkosic sandstones, sub-arkosic sandstones, and quartzose sandstones (Rahaman et al., 2019 ). Overlying this are the Sakpe Ironstone Formation, characterized by oolitic and pisolitic ironstones with sandy claystone interbeds, the Enagi Formation, composed of siltstones, sandstones, and claystones, and the Batati Formation, which contains argillaceous oolitic and goethite ironstones interbedded with claystones and siltstones and minor nearshore faunal remains (Obaje, 2009 ; Akande et al., 2005 ). In the southern Bida Basin, the sequence begins with the Lokoja Formation, which consists of basal conglomerates and coarse to fine sandstones, sometimes pebbly, directly overlying the Basement Complex (Nwajide, 2013 ). This is followed by the Ahoko Formation, formerly referred to as the Patti Formation, which includes grey to white sandstones, grey clays, carbonaceous silts, shales, and concretionary ironstone bands. The uppermost unit in the southern sector is the Agbaja Formation, composed predominantly of oolitic and pisolitic ironstones interbedded with claystones and sandstones (Rahaman et al., 2019 ). Table 1 Lithostratigraphy of the Bida Basin, Nigeria Sector Formations / Members (from oldest / basal to youngest / upper) General Lithology / Characteristics Northern Bida Basin Bida Formation (with Doko Member at base, Jima Member above) followed by Sakpe Ironstone Formation and followed by Enagi Formation and capped by Batati Formation (Rahaman et al., 2019 ) Bida Formation: very poorly sorted pebbly arkose, sub-arkose, quartzose sandstones; Sakpe: oolitic & pisolitic ironstones, sandy claystones; Enagi: siltstone-sandstone admixture, claystone; Batati: argillaceous, oolitic & goethitic ironstones with claystone/siltstone intercalations + minor shales, nearshore fauna in places (Rahaman et al., 2019 ). Southern Bida Basin Lokoja Formation (oldest in the basin) and overlie by Ahoko Formation (formerly Patti Formation) and capped by Agbaja Formation (Rahaman et al., 2019 ) Lokoja: basal conglomerates and sandstones (fine to coarse), sometimes pebbly; Ahoko: (formerly Patti) grey/white sandstones, grey clays, carbonaceous silts & shales, concretionary ironstone bands; Agbaja: oolitic/pisolitic ironstone, ironstone interbeds, ironstone-claystone-sandstone interlayers (Rahaman et al., 2019 ) In conclusion, the Bida Basin represents an intracratonic depression that developed during the Late Cretaceous through the interaction of extensional faulting, crustal sagging, and basement reactivation. Its stratigraphy records the transition from basal coarse clastics to ironstone-rich intervals and fine-grained sediments, with clear distinctions between the northern and southern sectors, thereby reflecting both local tectonic control and broader regional subsidence patterns (Rahaman et al., 2019 ; Obaje, 2009 ; Nwajide, 2013 ). 2 Theoretical Framework 2.1 Chemostratigraphic Principles Chemostratigraphy relies on the systematic documentation and interpretation of chemical variations within sedimentary sequences, exploiting the fact that elemental compositions reflect a complex interplay of provenance, weathering, transport, and depositional processes (Ramkumar, 2015 ). The theoretical foundation rests on the concept that while individual samples may show considerable scatter due to local factors, systematic trends in elemental ratios can reveal regional patterns related to changes in source area characteristics, tectonic setting, or paleoclimatic conditions (McLennan et al., 1993 ). In outcrop studies, particularly those conducted in tropical environments, the effects of chemical weathering present both challenges and opportunities for chemostratigraphic analysis. Tropical weathering processes selectively mobilize certain elements while concentrating others, potentially obscuring primary depositional signals (Nesbitt, and Young, 1982 ). However, the systematic nature of these processes means that careful selection of element ratios can minimize weathering effects while preserving stratigraphically significant information. Elements such as Ti, Al, and Zr typically exhibit limited mobility during weathering and can serve as effective normalizing factors for more mobile components (Young and Nesbitt, 1998 ). The application of rare earth element (REE) geochemistry in chemostratigraphic studies has proven particularly valuable due to the coherent behavior of these elements during sedimentary processes and their limited susceptibility to weathering alteration (McLennan, 1989 ). REE patterns preserve information about source rock characteristics and can distinguish between different provenance types, making them powerful tools for correlation and paleotectonic reconstruction (Cullers, 2000 ). The Ce anomaly, calculated as Ce N /((La N + Pr N ) /2), provides insights into redox conditions during deposition, while Eu anomalies reflect the presence or absence of feldspar in source regions (Murray et al., 1990 ). Provenance discrimination using geochemical data relies on well-established relationships between tectonic setting and sediment composition (Bhatia,1983.). Triangular plots involving elements such as Ti, Zr, and La can effectively distinguish between active continental margin, passive margin, and oceanic island arc settings (Bhatia and Crook, 1986 ). These discriminant diagrams, while developed for unaltered rocks, can still provide valuable insights when applied judiciously to weathered outcrop samples, particularly when multiple elemental systems are considered together. 2.2 Geomechanical Fundamentals The mechanical behavior of sedimentary rocks reflects a complex integration of compositional, textural, and structural factors that operate across multiple scales (Fjaer et al., 2008 ). At the micro scale, grain contacts, cement types, and pore structure exert primary controls on mechanical properties, while larger-scale features such as bedding, fractures, and compositional heterogeneities introduce additional complexity (Zoback, 2007 ). Understanding these multiscale controls is essential for predicting mechanical behavior and optimizing drilling and completion strategies. Uniaxial compressive strength (UCS) represents one of the most fundamental mechanical properties, providing a measure of a rock’s ability to withstand axial loading under unconfined conditions (Hoek and Brown, 1997 ). For sedimentary rocks, UCS values typically range from less than 10 MPa for poorly consolidated sediments to over 200 MPa for well-cemented, fine grained rocks. The relationship between UCS and other mechanical properties, such as tensile strength and elastic moduli, follows empirical correlations that can be exploited for property prediction when direct measurements are unavailable (Kahraman, 2001 ). Elastic properties, including Young’s modulus and Poisson’s ratio, control the deformation response of rocks under applied stress and are critical parameters for geomechanical modeling (Mavko et al., 2009 ). Young’s modulus quantifies the stiffness of a material under axial loading, while Poisson’s ratio describes the relationship between axial and lateral strain. For sedimentary rocks, these properties show systematic variations with porosity, mineralogy, and pore fluid characteristics that can be described using theoretical models such as the Hashin-Shtrikman bounds or differential effective medium theory (Berryman, 1995 ). The anisotropy of mechanical properties in layered sedimentary sequences introduces additional complexity that must be considered in geomechanical analysis (Sone and Zoback, 2013 ). Bedding parallel weaknesses, compositional variations, and preferred orientation of clay minerals can create significant directional variations in strength and elastic properties. These anisotropic effects become particularly important when designing hydraulic fracturing treatments or predicting wellbore stability in deviated wells (Aadnoy, 2010 ). 2.3 Petroleum System Elements Petroleum systems represent integrated networks of source rocks, migration pathways, reservoir rocks, and trapping mechanisms that operate within specific temporal and spatial frameworks (Magoon and Dow, 1994 ). The evaluation of petroleum system elements requires systematic assessment of each component, with particular attention to their timing relationships and spatial associations. In outcrop studies, these evaluations must account for the effects of surface exposure and weathering on organic matter preservation and hydrocarbon retention. Source rock evaluation encompasses both quantitative and qualitative assessments of organic matter content, type, and thermal maturity (Peters et al., 2005 ). Total organic carbon (TOC) content provides a fundamental measure of source rock richness, with values exceeding 1 wt% generally considered sufficient for hydrocarbon generation in marine shales (Tissot and Welte, 1984 ). However, TOC values in outcrop samples may be reduced relative to subsurface equivalents due to oxidation and biodegradation processes. Rock-Eval pyrolysis offers insights into both the quantity and quality of organic matter through parameters such as S 1 (free hydrocarbons), S 2 (hydrocarbons generated during pyrolysis), and T max (temperature of maximum S 2 generation) (Espitali´e et al., 1977 ). Kerogen type determination relies on the integration of Rock-Eval parameters with petrographic observations and biomarker analysis (Tyson, 1995 ). Type I kerogen, derived primarily from algal sources, exhibits high hydrogen indices and excellent oil generation potential. Type II kerogen, with mixed marine and terrestrial inputs, shows intermediate hydrogen indices and good oil generation capacity. Type III kerogen, dominated by terrestrial plant material, typically displays low hydrogen indices and primarily generates gas rather than oil (Vandenbroucke and Largeau, 2007 ). Thermal maturity assessment provides crucial information about the degree of organic matter evolution and hydrocarbon generation potential (Burnham and Sweeney, 1989 ). Vitrinite reflectance (%Ro) remains the most widely used maturity parameter, with values of 0.5–0.7% indicating the onset of oil generation, 0.7–1.3% representing the main oil generation window, and values above 1.3% suggesting gas generation (Dow, 1977 .). In outcrop studies, vitrinite reflectance measurements can be complicated by oxidation effects, requiring careful sample selection and interpretation. Surface geochemical techniques exploit the migration of light hydrocarbons from subsurface accumulations to the surface through various transport mechanisms (Schumacher and Abrams, 1996 ). These techniques include soil gas analysis, surface geochemical surveys, and remote sensing applications designed to detect hydrocarbon micro-seepage (Saunders et al., 1999 ). The interpretation of surface geochemical data requires consideration of near-surface processes such as biodegradation, oxidation, and lateral migration that can modify or redistribute hydrocarbon signals (Abrams, 2005 ). 3 Methodology 3.1 Field Sampling Strategy Field investigations were conducted during the dry season (November-February) to optimize outcrop accessibility and sample quality. Five detailed stratigraphic sections were measured and systematically sampled across the southern Bida Basin, with locations selected to provide maximum geographic coverage while ensuring adequate outcrop quality for reliable sampling (Fig. 2 ). GPS coordinates were recorded using a Garmin eTrex 30x unit with sub-meter accuracy, and all sample locations were photographed for documentation and future reference. Sampling focused on fresh rock faces exposed through recent road cuts, quarry operations, and natural erosional features to minimize weathering effects. Where possible, samples were collected from at least 30 cm behind weathered surfaces to ensure representative compositions. A systematic sampling strategy was employed with sample spacing ranging from 2–5 m in homogeneous units to 0.5-1 m across lithological transitions and potential sequence boundaries. Each sample weighed approximately 2–3 kg to provide sufficient material for the comprehensive analytical program. Structural measurements were recorded at each sampling location using a Brunton compass, including bedding orientations, fracture systems, and any evidence of tectonic deformation. Particular attention was paid to identifying structural controls on outcrop exposure and potential influences on hydrocarbon migration pathways. Field sketches and measured sections were prepared for each locality to document stratigraphic relationships and lateral facies variations. Regional geological mapping was conducted at 1:25,000 scale to establish the structural and stratigraphic context for detailed sampling. This mapping integrated existing geological surveys with new field observations, paying particular attention to unconformity relationships, fault systems, and igneous intrusions that might influence petroleum system development. The integrated field program resulted in collection of 68 samples suitable for geochemical analysis and 45 samples appropriate for geomechanical testing. 3.2 Laboratory Analytical Procedures 3.2.1 Chemostratigraphic Analysis Sample preparation for geochemical analysis followed established protocols designed to minimize contamination while ensuring representative compositions (Potts, 1987 ). Fresh rock samples were crushed using a tungsten carbide mill to avoid metallic contamination, with particle sizes reduced to less than 200 mesh for optimal analytical precision. Loss on ignition (LOI) was determined by heating powdered samples to 1000°C for 2 hours to quantify volatile content and degree of alteration. Major element analysis was performed using X-ray fluorescence (XRF) spectrometry on a Rigaku Primus II wavelength dispersive system following the methods of (Norrish and Hutton, 1969 ). Fused glass discs were prepared using lithium metaborate flux in a 1:10 sample to-flux ratio to ensure homogeneous analytical surfaces. Analytical precision, based on replicate analyses of international reference materials, was better than 2% relative standard deviation for major elements and 5% for trace elements above 10 ppm. Trace element and rare earth element (REE) concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS) on an Agilent 7700x instrument following acid digestion procedures modified from (Longerich et al., 1996 ). Sample digestion utilized a four-acid attack (HF-HNO 3 -HClO 4 -HCl) in Teflon beakers to ensure complete dissolution of resistant phases. Internal standards (Be, In, Re) were employed to correct for instrumental drift and matrix effects. Detection limits ranged from 0.01 ppm for REE to 0.1 ppm for transition metals. Mineralogical compositions were determined using X-ray diffraction (XRD) analysis on a Bruker D8 Advance diffractometer equipped with a LynxEye position-sensitive detector. Random powder mounts were scanned from 5–70° 2 using Cu K radiation at 40 kV and 40 mA. Clay mineral identification utilized oriented aggregates prepared on glass slides, with treatments including air drying, ethylene glycol solvation, and heating to 550°C to distinguish between expandable and non-expandable phases (Moore and Reynolds, 1997 ). 3.2.2 Geomechanical Testing Mechanical property determination required preparation of cylindrical test specimens with length-to-diameter ratios of 2.5:1 following ASTM D4543 specifications (ASTM International, 2008 ). Core drilling was performed using diamond-tipped bits with water cooling to minimize thermal damage, and specimen ends were ground flat and parallel to within 0.05 mm tolerance. Only samples free of visible fractures or weathering were selected for testing to ensure representative mechanical properties. Uniaxial compressive strength (UCS) testing was conducted using a servo-controlled MTS universal testing machine with a loading rate of 0.5 MPa/s until failure. Axial and circumferential strains were monitored using electrical resistance strain gauges to determine elastic constants. Young’s modulus was calculated from the linear portion of the stress-strain curve, typically between 50–80% of peak strength, while Poisson’s ratio was determined from the ratio of lateral to axial strain in the elastic regime (Fairhurst and Hudson, 1999 ). Brazilian tensile strength tests were performed on disc-shaped specimens with diameter-to-thickness ratios of 2:1, following ASTM D3967 procedures (ASTM International, 2008 ). Loading was applied across the diameter at a rate of 200 N/s until failure, with tensile strength calculated using the standard formula accounting for specimen geometry. Point load index testing provided additional strength classification data using irregular rock fragments, with results corrected to a standard 50 mm diameter equivalent (Brook, 1985 ). Porosity and permeability measurements were conducted using helium porosimetry and steady-state flow methods respectively. Porosity determinations utilized a Micromeritics AccuPyc 1340 helium pycnometer with precision of ± 0.1% at the 95% confidence level. Permeability measurements employed nitrogen gas flow through cylindrical plugs under controlled confining pressure, with Klinkenberg corrections applied to obtain absolute permeability values (Klinkenberg, 1941 ). 3.2.3 Petroleum Indicators Assessment Organic geochemical analysis required careful sample selection to identify intervals with the highest preservation potential for organic matter. Samples were collected from fine grained units, particularly dark-colored shales and mudstones, which typically exhibit superior source rock characteristics. Surface weathering effects were minimized by collecting samples from fresh exposures and avoiding obviously oxidized intervals. Total organic carbon (TOC) content was determined using a LECO CS-244 carbon analyzer following acid treatment to remove carbonate carbon (Dean, 1974 ). Sample powders were treated with 10% HCl to dissolve carbonates, washed with distilled water, and dried before analysis. Organic carbon content was measured by combustion at 1350°C in an oxygen atmosphere, with CO 2 detection by infrared spectrometry. Analytical precision was ± 0.05 wt% based on duplicate analyses. Rock-Eval pyrolysis was performed using a Weatherford Source Rock Analyzer to assess hydrocarbon generation potential and thermal maturity (Espitali´e et al., 1977 ). Approximately 100 mg of powdered sample was heated from 300–600°C at 25°C/min under helium atmosphere to determine free hydrocarbons (S 1 ), pyrolytic hydrocarbons (S 2 ), temperature of maximum S 2 generation (T max ), and organic carbon content. Hydrogen index (HI = S 2 /TOC × 100) and oxygen index (OI = S 3 /TOC × 100) were calculated to characterize kerogen type and quality. Vitrinite reflectance measurements were conducted on polished particulate mounts using a Zeiss Axioplan microscope equipped with a photomultiplier system and monochromatic light at 546 nm wavelength (Taylor et al., 1998 ). Measurements were made on vitrinite particles larger than 5 m in diameter, with at least 50 readings per sample when sufficient vitrinite was present. Random reflectance values were corrected to equivalent Ro values using standard procedures, and mean values were calculated with 95% confidence intervals. Surface geochemical analysis involved collection of soil samples at 1 m depth across systematic grid patterns covering approximately 10 km 2 around major outcrop exposures. Soil gas compositions were analyzed using gas chromatography to determine C 1 -C 4 hydrocarbon concentrations, with particular attention to ethane/methane and propane/ethane ratios as indicators of thermogenic versus biogenic origins (Schumacher and Abrams, 1996 ). Carbon isotope ratios of soil carbonate and organic matter were determined using mass spectrometry to identify potential migration signatures. 3.3 Data Integration and Interpretation Statistical analysis of geochemical data employed multivariate techniques to identify correlation patterns and discriminate between different sample populations. Principal component analysis (PCA) was used to reduce dimensionality and highlight major sources of compositional variation, while cluster analysis helped identify geochemically similar sample groups. Correlation matrices were calculated to identify significant element associations and guide interpretation of geochemical processes. Quality control procedures included analysis of certified reference materials (CRM) with each analytical batch to monitor accuracy and precision. Duplicate analyses were performed on 10% of samples to assess analytical reproducibility, and blank samples were included to monitor contamination. All analytical data were stored in a normalized database structure to facilitate statistical analysis and visualization. The analytical workflow is summarized in Fig. 3 , which illustrates the integration of field and laboratory procedures designed to address the multidisciplinary objectives of this study. This comprehensive approach ensures that data quality is maintained across all analytical techniques while providing the basis for meaningful integration and interpretation. 4 Results and Discussion 4.1 Chemostratigraphic Characterization 4.1.1 Major Element Geochemistry The major element compositions of southern Bida Basin samples exhibit systematic variations that reflect both primary depositional processes and post-depositional modification (Table 2 ). Silica contents range from 47.2 to 84.6 wt%, with the highest values occurring in quartzose sandstones of the Lokoja Formation and the lowest in Fe-rich ironstones of the Agbaja Formation. Alumina concentrations vary from 8.1 to 19.7 wt%, generally showing inverse relationships with silica content that reflect the balance between quartz and feldspar/clay mineral components. Table 2 Average major element compositions (wt %) of southern Bida Basin formations with standard deviations in parentheses. n = number of samples analyzed. Element Lokoja Formation (n = 24) Patti Formation (n = 28) Agbaja Formation (n = 16) SiO 2 72.4 (8.9) 64.3 (11.2) 51.8 (12.4) Al2O3 13.2 (3.1) 15.8 (2.7) 12.9 (4.2) Fe 4.8 (2.3) 6.9 (3.4) 23.7 (8.9) MgO 1.2 (0.8) 2.1 (1.2) 1.8 (1.1) CaO 0.8 (0.4) 1.4 (0.9) 1.2 (0.7) Na 2 O 2.1 (1.2) 1.8 (0.8) 0.9 (0.6) K 2 O 3.4 (1.8) 4.2 (1.5) 2.8 (1.3) TiO 2 0.62 (0.18) 0.89 (0.24) 0.71 (0.19) P2O5 0.08 (0.04) 0.12 (0.07) 0.18 (0.09) LOI 2.3 (1.1) 3.8 (1.7) 4.9 (2.2) Total 100.9 101.3 100.7 Table 2 : Average major element compositions (wt %) of southern Bida Basin formations with standard deviations in parentheses. n = number of samples analyzed. Iron oxide concentrations show the most dramatic variations, ranging from 1.8 wt% in clean quartz sandstones to 41.2 wt% in lateritic ironstones. This variation reflects both primary depositional controls and subsequent weathering processes that have concentrated iron oxides through lateritization. The elevated loss on ignition (LOI) values in the Patti and Agbaja formations (3.8–4.9 wt %) compared to the Lokoja Formation (2.3 wt%) indicate greater degrees of alteration and hydrous mineral formation in the younger units. Alkali element distributions provide insights into provenance characteristics and weathering intensity. The K 2 O/Na 2 O ratios increase systematically from 1.6 in the Lokoja Formation to 2.3 in the Patti Formation and 3.1 in the Agbaja Formation, suggesting either increasing contributions from K-feldspar-rich sources or preferential leaching of sodium during weathering. The Chemical Index of Alteration (CIA), calculated as Al 2 O 3 /(Al 2 O 3 + CaO* + Na 2 O + K 2 O) × 100, ranges from 58 to 89, with mean values of 68, 74, and 81 for the Lokoja, Patti, and Agbaja formations respectively. These values indicate moderate to intense chemical weathering, consistent with tropical climatic conditions during and after deposition. The vertical distribution of major elements reveals several distinct chemostratigraphic intervals that can be correlated across the study area (Fig. 4 ). Most notable are systematic variations in Ti/Al ratios that range from 0.042 in the lower Lokoja Formation to 0.078 in the upper Patti Formation. These variations reflect changes in heavy mineral content and possibly provenance characteristics, providing excellent correlation markers across distances of up to 15 km. Phosphorus concentrations, while generally low (0.03–0.34 wt% P 2 O 5 ), show systematic increases toward the top of the succession that may reflect increasing marine influence or enhanced preservation of organic matter. The correlation between P 2 O 5 and TOC ( r = 0.67, p ¡ 0.01) suggests that phosphorus enrichment is linked to organic productivity or preservation processes. 4.1.2 Trace Element Signatures Trace element compositions provide additional constraints on provenance characteristics and depositional processes while offering alternative correlation parameters less susceptible to weathering effects. High field strength elements (HFSE) such as Zr, Nb, and Ta show limited mobility during weathering and preserve primary signatures related to source rock compositions and transport processes. Zirconium concentrations range from 89 to 487 ppm, with the highest values occurring in coarse-grained sandstones where heavy mineral concentrations are enhanced through hydraulic sorting processes. The Zr/TiO 2 ratio varies systematically between formations, with mean values of 0.082, 0.095, and 0.071 ppm/wt% for the Lokoja, Patti, and Agbaja formations respectively. These variations likely reflect changes in provenance characteristics, with higher ratios indicating greater contributions from evolved igneous sources rich in zircon. Rare earth element (REE) patterns provide powerful insights into provenance characteristics and depositional processes (Fig. 5 ). All samples exhibit light REE (LREE) enrichment relative to heavy REE (HREE), with (La/Yb) N ratios ranging from 7.8 to 18.3. Total REE concentrations vary from 98 to 267 ppm, generally correlating with clay mineral content and degree of weathering. The upper continental crust-normalized patterns show relatively flat HREE distributions and variable LREE slopes, consistent with derivation from mixed felsic and intermediate igneous sources. Cerium anomalies, calculated as Ce N /((La N + Pr N )/2), range from 0.89 to 1.12, with most samples showing slight negative anomalies (Ce/Ce* ¡ 1.0) that suggest oxic depositional conditions or subaerial weathering effects (Wright et al., 1987 ). Europium anomalies vary more systematically, with Eu/Eu* values of 0.82–0.95 in sandstones and 0.65–0.78 in mudstones. These negative Eu anomalies reflect feldspar fractionation in source regions and are consistent with derivation from evolved continental crust rather than mafic volcanic sources. The transition metal signature provides additional provenance constraints, with Cr/Ni ratios ranging from 1.8 to 4.2 across the study area. Lower ratios typically occur in the Lokoja Formation, suggesting contributions from more mafic sources, while higher ratios in younger formations indicate increasing input from felsic sources. Vanadium concentrations (47–189 ppm) correlate positively with organic carbon content (r = 0.58, p ¡ 0.01), consistent with preferential concentration in reducing depositional environments. Tectonic discrimination diagrams based on immobile trace elements suggest deposition in a passive continental margin setting (Bhatia and Crook, 1986 ). The Ti-Zr-La plot positions most samples within the passive margin field, while Th/Sc versus Zr/Sc ratios indicate mixed provenance from both recycled sedimentary sources and felsic igneous rocks. These interpretations are consistent with the inferred depositional setting of the Bida Basin as an intracratonic sag basin receiving sediment from surrounding Precambrian basement terrains. 4.2 Geomechanical Properties 4.2.1 Rock Strength Characteristics Mechanical property measurements reveal significant variations related to lithology, mineralogical composition, and degree of cementation (Table 3 ). Uniaxial compressive strength (UCS) values range from 28.4 MPa in poorly cemented sandstones to 165.7 MPa in silicified ironstone horizons. The wide range of strength values reflects the diverse lithological assemblage present in the southern Bida Basin and highlights the importance of detailed characterization for drilling and completion planning. Table 3 Summary of geomechanical properties by lithology with statistical parameters. Values represent means with standard deviations in parentheses. Lithology n UCS (MPa) Tensile Strength (MPa) Young’s Modulus ( GPa ) Quartz Sandstone 12 89.3 (23.7) 8.2 (2.1) 45.8 (12.4) Arkosic Sandstone 8 72.1 (18.9) 6.8 (1.9) 38.9 (9.7) Siltstone 9 56.4 (15.2) 5.1 (1.4) 28.7 (8.3) Mudstone 7 41.7 (12.8) 3.9 (1.1) 22.4 (6.8) Ironstone 6 134.6 (31.2) 11.7 (3.4) 67.3 (18.9) Laterite 3 98.7 (28.4) 7.9 (2.7) 41.2 (15.6) Quartz sandstones exhibit the most consistent mechanical properties, with UCS values typically ranging from 65–115 MPa and coefficient of variation around 26 Fine-grained lithologies generally display lower strength values but also show systematic relationships between composition and mechanical properties. Mudstones with higher illite content tend to exhibit greater strength than those dominated by smectitic clays, reflecting the superior bonding characteristics of non-expandable clay minerals. The presence of organic matter generally reduces strength, with TOC contents above 2 wt% Ironstone horizons represent mechanical anomalies within the stratigraphic succession, exhibiting UCS values that exceed 150 MPa in well-cemented intervals. These rocks are characterized by pervasive hematite and goethite cements that create exceptionally strong intergranular bonds. However, the brittle nature of iron oxide cements results in relatively low ratios of tensile to compressive strength (typically 0.07–0.09 compared to 0.10–0.12 for sandstones). The relationship between compressive and tensile strength follows expected empirical correlations, with tensile strength averaging approximately 9% of UCS across all lithologies tested (Fig. 6 ). This ratio is slightly lower than typical values for fresh sedimentary rocks (10–15%), possibly reflecting micro-crack development during weathering and stress relief processes. The correlation coefficient between UCS and tensile strength is 0.87 (p ¡ 0.001), indicating strong predictive relationships that can be exploited for property estimation. Point load strength index values range from 2.1 to 8.7 MPa, with systematic relationships to UCS that follow established empirical correlations. The conversion factor between point load index and UCS averages 11.2 for sandstones and 9.8 for fine-grained rocks, consistent with published values for sedimentary rocks (Brook, 1985 ). These relationships provide valuable tools for rapid strength assessment in the field using portable testing equipment. 4.2.2 Elastic Properties Young’s modulus values exhibit a wide range from 12.8 GPa in weathered mudstones to 89.3 GPa in well-cemented quartz sandstones. The elastic modulus correlates strongly with UCS (r = 0.89) and shows systematic relationships to porosity and mineralogical composition. Quartz-rich rocks generally exhibit higher modulus values than feldspar bearing or clay-rich lithologies, reflecting the superior elastic properties of quartz relative to other common rock-forming minerals. Poisson’s ratio values range from 0.18 to 0.34, with an overall mean of 0.26 across all lithologies tested. Fine-grained rocks typically exhibit higher Poisson’s ratios (0.28–0.32) than coarse-grained sandstones (0.20–0.26), consistent with the higher Poisson’s ratios of clay minerals compared to quartz and feldspar. These values fall within the expected range for sedimentary rocks and provide essential input parameters for geomechanical modeling applications. The relationship between elastic modulus and porosity follows theoretical predictions based on effective medium theory, with higher porosity samples showing systematically lower modulus values. A power-law relationship of the form E = E 0 (1-) n provides an excellent fit to the data, with E 0 = 78.2 GPa and n = 2.34 for sandstone lithologies. This relationship enables porosity-based prediction of elastic properties for reservoir modeling applications. Bulk modulus and shear modulus were calculated from Young’s modulus and Poisson’s ratio measurements using standard elastic relationships. Bulk modulus values range from 8.9 to 52.4 GPa, while shear modulus varies from 4.8 to 34.7 GPa. These derived parameters are essential for advanced geomechanical modeling and provide insights into the volumetric versus shear deformation characteristics of different lithologies. Dynamic elastic properties, estimated from empirical correlations with static measurements, suggest that in-situ values may be 10–30 4.2.3 Reservoir Quality The sandstone quality (Table 4 ) of the Patti and Lokoja Formation show that clean quartz sandstone has the highest general reservoir potential, with a rather high porosity (12.4%), middle and good permeability (up to 284.7 mD), and the highest net-to-gross ratio (0.85). The arkosic sandstone has also fair reservoir quality but it is a little less due to feldspar content which might have resulted in lower porosity and permeability than the quartz sandstone. Interestingly, the conglomeratic sandstone has a relatively low porosity (8.2 percent) but very high permeability (mean 156.2 mD), implying that the large grain size and network distribution of pores should increase fluid flow even though the volume of pores is small. Silty sandstone and cemented sandstone in particular are poor-quality reservoirs, which have low porosity, permeability, and net-to-gross ratios, and are indicative of the influence of fine-grained matrix and diagenetic cementation respectively. In general, the findings indicate that clean quartz and conglomeratic sandstones are the most promising intervals in the reservoir in the southern Bida Basin. Table 4 Reservoir quality parameters for potential reservoir intervals in the southern Bida Basin. Values represent arithmetic means with ranges in parentheses. Lithofacies Samples Porosity (%) Permeability (mD)Pore Throat (m) Net:Gross Clean Quartz Sandstone 8 12.4 (8.7–18.2) 89.3 (12.1–284.7) 18.6 (8.4–31.2) 0.85 Arkosic Sandstone 12 9.8 (5.2–15.1) 34.7 (2.8–127.4) 12.3 (4.1–24.7) 0.72 Conglomeratic Sandstone 4 8.2 (6.1–11.8) 156.2 (45.8–389.6) 28.4 (15.2–42.1) 0.65 Silty Sandstone 7 6.4 (3.8–9.7) 8.9 (0.4–28.3) 6.7 (2.1–12.4) 0.58 Cemented Sandstone 6 3.1 (1.2–6.8) 0.8 (0.01–4.2) 2.4 (0.8–5.9) 0.42 4.3 Petroleum Indicators 4.3.1 Source Rock Evaluation Organic geochemical analysis reveals heterogeneous but locally promising source rock potential within the southern Bida Basin succession (Table 5 ). Total organic carbon (TOC) contents range from 0.12 to 2.84 wt%, with the highest values occurring in dark colored mudstones and carbonaceous shales of the Patti Formation. While most samples fall below the 1 wt% threshold typically considered minimum for effective source rocks, several intervals exceed 2 wt% and warrant further evaluation for unconventional resource potential Table 5 Source rock evaluation parameters for organic-rich intervals in the southern Bida Basin. HI = Hydrogen Index, OI = Oxygen Index, PI = Production Index. Sample ID Formation TOC (wt%) S 1 (mg/g) S 2 (mg/g) HI (mg/g) OI (mg/g) T max (°C) BD-23 Patti 2.84 0.89 6.64 234 67 429 BD-31 Patti 2.41 0.72 4.38 182 89 435 BD-38 Patti 1.97 0.58 3.21 163 112 441 BD-42 Agbaja 1.84 0.43 2.87 156 134 447 BD-47 Patti 2.13 0.67 3.95 185 98 438 BD-52 Agbaja 1.68 0.39 2.34 139 156 452 BD-59 Patti 2.67 0.81 5.89 221 78 432 Rock-Eval pyrolysis data indicate predominantly Type II/III mixed kerogen assemblages, with hydrogen indices ranging from 89 to 234 mg HC/g TOC. The higher hydrogen indices (¿200 mg/g) occur in Patti Formation samples, suggesting better preservation of hydrogen-rich organic matter, possibly including algal or bacterial components. Oxygen indices vary from 67 to 156 mg CO 2 /g TOC, with lower values generally corresponding to higher hydrogen indices in a pattern consistent with variable oxidation during deposition or early diagenesis. The van Krevelen diagram constructed from Rock-Eval data shows most samples plotting along a Type II/III evolutionary pathway (Fig. 7 ), indicating mixed marine and terrestrial organic matter inputs. Several samples from the Patti Formation plot closer to the Type II field, suggesting episodic marine influence or enhanced preservation of marine-derived organic matter. The evolutionary trend suggests oil and gas generation potential, though thermal maturity levels will ultimately control the nature and timing of hydrocarbon generation. S 1 values (free hydrocarbons) range from 0.21 to 0.89 mg HC/g rock, with the highest concentrations occurring in samples with elevated TOC contents. The S 1 /TOC ratio averages 0.34 mg/g, indicating moderate hydrocarbon retention that could reflect either indigenous generation or minor migration from deeper sources. Production Index (PI = S 1 /(S 1 + S 2 )) values range from 0.12 to 0.21, suggesting early mature to mature organic matter with limited hydrocarbon expulsion. Temperature of maximum S 2 generation (T max ) varies from 429 to 452°C, indicating thermal maturity levels ranging from early mature to peak oil generation. The systematic increase in T max values from the Patti Formation (mean 435°C) to the Agbaja Formation (mean 449°C) suggests either deeper burial of older units or differential thermal effects related to igneous activity. These maturity levels are consistent with active hydrocarbon generation and suggest that deeper basin areas may have achieved full maturity for oil and gas generation. 4.3.2 Thermal Maturity Assessment Vitrinite reflectance measurements provide the most reliable assessment of thermal maturity in the southern Bida Basin samples (Fig. 8 ). Measured Ro values range from 0.52 to 0.89% across the study area, with systematic variations related to stratigraphic position and proximity to igneous intrusions. The overall maturity trend indicates that most of the exposed succession has entered the early oil generation window (Ro ¿ 0.5%), with several locations approaching peak oil generation conditions (Ro = 0.7-1.0%). Spatial variations in thermal maturity reveal important patterns related to basin structure and thermal history. Samples collected within 2 km of documented igneous intrusions consistently show elevated reflectance values, with Ro measurements 0.1–0.3% higher than regional trends. This thermal aureole effect suggests that Tertiary magmatic activity has significantly influenced the thermal evolution of petroleum source rocks in the basin. The relationship between T max and vitrinite reflectance follows established calibrations for Type II/III kerogen, with the regression equation T max = 409 + 17.4 × Ro (r 2 = 0.78) closely matching published correlations (Peters et al., 2005 ). This agreement provides confidence in both maturity datasets and suggests that Rock-Eval T max can be used for maturity assessment in samples where vitrinite is absent or poorly preserved. Calculated transformation ratios based on kinetic modeling suggest that 15–35% of the original hydrocarbon generation potential has been realized in the most mature samples. This indicates significant remaining potential for continued generation with additional burial or thermal input. The timing of hydrocarbon generation, estimated from basin modeling constraints, suggests active generation beginning in the Paleocene and continuing through the present day. Biomarker maturity parameters, determined from gas chromatography-mass spectrometry analysis of extractable organic matter, generally support the vitrinite reflectance interpretations. The 20S/(20S + 20R) sterane ratio averages 0.41 in mature samples, approaching equilibrium values of 0.55 that characterize peak oil generation. Moretane/hopane ratios (0.18–0.26) are consistent with early to peak mature conditions, while the methylphenanthrene index (MPI-1) ranges from 0.68 to 1.12, corresponding to calculated vitrinite reflectance equivalents of 0.55–0.95%. 4.3.3 Surface Geochemical Anomalies Surface geochemical surveys conducted across a 10 km 2 area surrounding major outcrop exposures reveal several discrete hydrocarbon anomalies that provide evidence for active petroleum migration (Fig. 9 ; Table 6 ). Soil gas concentrations of C 2 -C 4 hydrocarbons show coherent spatial patterns with peak values occurring over structural highs and along mapped fault trends. Methane concentrations range from background levels of 2–5 ppm to anomalous values exceeding 50 ppm in areas interpreted as active seepage zones. Table 6 Summary of samples collected with stratigraphic positions and analytical methods applied. XRF = X-ray fluorescence, ICP-MS = inductively coupled plasma mass spectrometry, XRD = X-ray diffraction, GM = geomechanical testing, OG = organic geochemistry. Formation Thickness (m) Samples XRF ICP-MS XRD GM OG Surface Geochem Agbaja 45–78 16 16 12 14 9 8 - Patti 120–180 28 28 24 26 18 15 - Lokoja 200–350 24 24 20 22 18 9 - Surface samples – – – – – – – 247 Total – 68 68 56 62 45 32 247 Ethane/methane ratios provide critical information about the origin of detected hydrocarbons, with values ranging from 0.001 to 0.047 across the survey area. Background areas typically show ratios below 0.005, consistent with predominantly biogenic methane generation in soils. Anomalous zones exhibit ratios between 0.015–0.047, approaching values expected for thermogenic hydrocarbons and suggesting contribution from subsurface petroleum sources (Schumacher and Abrams, 1996 ). Propane and butane concentrations are generally low but show systematic spatial associations with ethane anomalies. The presence of C 3 -C 4 hydrocarbons in soil gas provides strong evidence for thermogenic origins, as these compounds are rarely generated by near-surface biological processes. The propane/ethane ratio averages 0.31 in anomalous areas, consistent with oil-associated gas compositions rather than dry gas systems. Carbon isotope analysis of soil carbonate reveals systematic variations that may reflect hydrocarbon micro seepage processes. 13 C values range from − 8.7‰ to -15.2‰ PDB, with the most negative values occurring in areas of elevated hydrocarbon concentrations. This isotopic depletion is consistent with incorporation of light carbon derived from oxidized hydrocarbons, providing additional evidence for active seepage processes (Abrams, 2005 ). Magnetic surveys conducted concurrent with geochemical sampling reveal subtle magnetic lows (10–25 nT below regional background) that spatially coincide with hydrocarbon anomalies. These magnetic depressions likely reflect pyrite formation and magnetite destruction in reducing zones created by hydrocarbon oxidation, providing independent evidence for seepage-related alteration processes. The integration of surface geochemical data with structural and stratigraphic information suggests three primary migration pathways: (Abrams, 2005 ) fault-controlled vertical migration along reactivated Precambrian structures, (Aadnoy, 2010 ) lateral migration along permeable sandstone beds, and (Adeleye, 1974 ) focused migration through fracture networks associated with igneous intrusions. These pathways connect potential source rocks at depth with surface expression, providing important constraints on petroleum system architecture. The data (Table 6 ) gives a good representation of the key stratigraphic units of the southern Bida Basin, with 68 core/outcrop samples and 247 surface samples tested using various techniques. Geochemical and geomechanical testing of the Agbaja Formation was a factor of the relatively thin development and localized distribution of the Agbaja Formation relative to the Patti and Lokoja Formations. The thicker successions of the Patti and Lokoja Formations provided the most samples, as well as the most analytical results, hence providing the most detailed information regarding reservoir, source and seal properties. The combination of supplementary analytical techniques (XRF, ICP-MS, XRD, GM, and OG) gave a combined analysis of the lithology, mineralogy, mechanical behavior and the hydrocarbon potential. The extensive sample of surface geochemical samples (247) also enhances the petroleum system evaluation by giving information on hydrocarbon leakage on a basin wide basis. The combination of the sampling strategy and the analysis methodology lays a good foundation to reconstruct the depositional environments, diagenesis history as well as petroleum prospectivity of the area being studied. 4.4 Integrated Petroleum System Analysis The integration of chemostratigraphic, geomechanical, and petroleum geochemical datasets provides a comprehensive framework for understanding petroleum system development in the southern Bida Basin. This multidisciplinary approach reveals systematic relationships between geological processes and petroleum system elements that would not be apparent from single-parameter studies. Chemostratigraphic correlation demonstrates lateral continuity of potential source rock intervals across distances exceeding 15 km, suggesting basin-wide distribution of organic-rich facies. The Ti/Al and Zr/Al ratios provide particularly robust correlation parameters that remain stable despite surface weathering effects. These correlations enable confident extrapolation of petroleum potential assessments from outcrop locations to broader basin areas. Source rock distribution shows strong correlation with specific chemostratigraphic intervals, particularly those characterized by elevated V/Al ratios and negative Ce anomalies that indicate reducing depositional conditions. The systematic relationship between redox-sensitive trace elements and organic carbon preservation suggests that chemostratigraphic parameters can serve as predictive tools for source rock occurrence in areas where direct sampling is not feasible. Geomechanical properties show systematic variations that correlate with both lithology and hydrocarbon potential (Fig. 10 ). Organic-rich intervals typically exhibit reduced mechanical strength compared to adjacent rocks, creating zones of mechanical weakness that may enhance hydraulic fracturing effectiveness. The negative correlation between TOC content and Young’s modulus (r = -0.63) suggests that organic matter content can be estimated from mechanical property measurements, providing an indirect exploration tool. The spatial distribution of surface geochemical anomalies correlates strongly with zones of reduced mechanical strength and elevated fracture density, supporting interpreted migration pathways through structurally compromised zones. Areas showing the strongest hydrocarbon anomalies typically correspond to locations where fault-controlled fracture networks intersect organic-rich stratigraphic intervals. Thermal maturity patterns reveal the critical role of igneous activity in advancing hydrocarbon generation beyond levels expected from burial history alone. The thermal aureoles surrounding intrusions create zones of enhanced maturity that extend 1–3 km beyond intrusion contacts, significantly expanding the areas of active generation. This relationship suggests that the timing and distribution of igneous activity may be a controlling factor in petroleum system effectiveness. The integrated analysis supports a petroleum system model in which organic-rich intervals of the Patti Formation serve as primary source rocks, with hydrocarbon generation enhanced by Tertiary igneous heating (Fig. 11 ). Migration occurs along fault-controlled pathways and permeable sandstone beds, with surface expression providing evidence for system activity. Reservoir potential exists in quartz sandstone intervals of the Lokoja Formation, while fine-grained units provide effective sealing capacity. 5 Conclusions This detailed multidisciplinary study of southern Bida Basin outcrops has established new frameworks for understanding petroleum system development in Nigeria’s frontier sedimentary basins. The integration of chemostratigraphic, geomechanical, and petroleum geochemical analyses provides insights that extend far beyond those achievable through single-parameter investigations. The chemostratigraphic framework demonstrates that immobile element ratios, particularly Ti/Al and Zr/Al, provide robust correlation tools that remain effective despite tropical weathering effects. These correlations enable basin-wide extrapolation of petroleum system characteristics and support systematic exploration strategies based on predictable stratigraphic relationships. The rare earth element signatures indicate mixed provenance from basement terrains and recycled sedimentary sources, consistent with the intracratonic setting of the Bida Basin. Geomechanical characterization reveals systematic relationships between lithology, organic content, and mechanical properties that have important implications for unconventional resource development (Fig. 10 ). The inverse correlation between organic carbon content and rock strength suggests that organic-rich intervals may be preferentially targeted for hydraulic fracturing operations (Figs. 10 and 11 ). Young’s modulus values ranging from 12.8 to 89.3 GPa provide essential input parameters for completion design and production optimization. Source rock evaluation identifies significant petroleum generation potential within organic-rich intervals of the Patti Formation, with TOC contents reaching 2.84 wt% and hydrogen indices up to 234 mg HC/g TOC. The Type II/III kerogen assemblages indicate oil and gas generation potential, while thermal maturity levels (Ro = 0.52–0.89%) suggest active generation processes. The thermal influence of Tertiary igneous intrusions has significantly enhanced maturation beyond levels expected from burial history alone. Surface geochemical anomalies provide compelling evidence for active petroleum migration, with thermogenic hydrocarbon signatures occurring along fault-controlled pathways and structural highs. The integration of soil gas compositions, isotopic signatures, and magnetic data supports a model of ongoing hydrocarbon seepage from subsurface accumulations. The petroleum system model developed from this integrated analysis suggests significant remaining potential for both conventional and unconventional hydrocarbon resources in the southern Bida Basin. Source rock intervals show lateral continuity and favorable generation characteristics, while reservoir-quality sandstones provide exploration targets. The structural complexity and igneous influence create a unique petroleum system that warrants continued investigation and possible commercial evaluation. Future research should focus on extending these integrated analyses to other parts of the Bida Basin and similar frontier basins across West Africa. Basin modeling studies incorporating the thermal effects of igneous activity would provide valuable insights into generation timing and migration efficiency. Advanced organic geochemical studies, including compound-specific isotope analysis, could further refine source-oil correlations and migration pathway interpretations. The methodological approaches developed in this study demonstrate the value of integrated outcrop investigations in frontier basin evaluation. The combination of chemostratigraphic correlation, geomechanical characterization, and petroleum geochemical analysis provides a comprehensive framework that can be applied to similar geological settings worldwide. This integrated approach represents a significant advance in outcrop-based petroleum system analysis and provides essential tools for early-stage exploration in frontier sedimentary basins. Declarations Acknowledgments The authors gratefully acknowledge the management of Federal University Lokoja, for funding the research through institution-based research fund and was invaluable for project success. Laboratory analyses were conducted at the Federal University Lokoja Geological Lab and University of Benin Civil Engineering Laboratory (Nigeria) and the Geomarkland laboratories (Port-Harcourt), with appreciation for high-quality analytical services. Declaration of authors contribution A.N.O. conceived, designed the study and supervised the field studies. He led the integration of chemostratigraphic, geomechanical, and petroleum geochemical analyses also prepared the initial draft of the manuscript and coordinated revisions. G.E.J. contributed to fieldwork, sample collection, and laboratory analyses, including geochemical and geomechanical testing. She participated in data interpretation, drafting specific sections of the methodology and results, and assisted in preparing figures and tables. Both authors reviewed and approved the final version of the manuscript Conflict of Interest The authors declare no conflict of interest Ethics Approval and Informed Consent Not applicable Consent to publish Not applicable Data availability statement All data generated or analysed during this study are included in this published article Funding The authors declared no external source of funding References Aadnoy, B.S. (2010). Modern Well Design: Second Edition (2 nd ed.). CRC Press. https://doi.org/10.1201/b10431 Abrams, M.A., 2005. Significance of hydrocarbon seepage relative to petroleum generation and entrapment. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 15 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 10 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":472322,"visible":true,"origin":"","legend":"\u003cp\u003eGeological map of Bida Basin showing proposed southern section of study\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/a019fb5968a144be5040409b.png"},{"id":94780139,"identity":"81f54f8a-1266-4b61-8e39-279db134b9e1","added_by":"auto","created_at":"2025-10-30 15:28:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214851,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area showing outcrop sections, sample locations, and major geological structures in the southern Bida Basin. Sections A-E represent measured stratigraphic sequences with systematic sampling intervals. Inset shows regional geological context within Nigeria.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/fe17e87762a86c87c4c2ba23.png"},{"id":94824356,"identity":"dae41d8e-44a3-4d20-8e69-7451730330ab","added_by":"auto","created_at":"2025-10-31 06:48:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433782,"visible":true,"origin":"","legend":"\u003cp\u003eAnalytical workflow diagram showing the integration of field sampling, laboratory analysis, and data interpretation procedures employed in this study. Quality control measures are highlighted at each stage to ensure data reliability.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/00e75f091c0b550b9560f8b3.png"},{"id":94780142,"identity":"985601aa-5669-4a49-a6ae-f84d5ac4e4dd","added_by":"auto","created_at":"2025-10-30 15:28:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209509,"visible":true,"origin":"","legend":"\u003cp\u003eChemostratigraphic columns from three representative sections showing vertical variations in selected element ratios and their correlation potential. Note the consistent patterns in Ti/Al and Zr/Al ratios that enable regional correlation despite local lithological variations.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/a7d74469f3b76e049fd79ad8.png"},{"id":94825105,"identity":"149bcbaf-b5dd-4fd2-b958-0586667246d3","added_by":"auto","created_at":"2025-10-31 06:49:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":800334,"visible":true,"origin":"","legend":"\u003cp\u003eUpper continental crust-normalized rare earth element patterns for representative samples from each formation. Note the consistent LREE enrichment and variable Ce and Eu anomalies that reflect provenance and depositional environment differences.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/b3e42f9c57d6198c8ae42fbc.png"},{"id":94825522,"identity":"ad6e834b-119c-408b-ac00-af71df76fc31","added_by":"auto","created_at":"2025-10-31 06:50:23","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":295238,"visible":true,"origin":"","legend":"\u003cp\u003eCross-plots showing relationships between different strength parameters for southern Bida Basin rocks. (A) Tensile strength versus uniaxial compressive strength. (B) Young’s modulus versus uniaxial compressive strength. Note the strong correlations that enable property prediction from limited data.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/67641d8519ac2f458f85a9a1.png"},{"id":94825447,"identity":"b67c33bc-e345-42c7-bbfd-2b037323d9dd","added_by":"auto","created_at":"2025-10-31 06:50:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":313836,"visible":true,"origin":"","legend":"\u003cp\u003eVan Krevelen diagram showing kerogen type evolution for southern Bida Basin samples. Most samples indicate Type II/III mixed kerogen with oil and gas generation potential. Maturity trends are shown by arrows, with several samples approaching the main generation window.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/094c7f595320f8f53ec83c7e.png"},{"id":94825192,"identity":"8908e7fe-20cc-43ef-a10a-627b2a4981cd","added_by":"auto","created_at":"2025-10-31 06:49:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":266541,"visible":true,"origin":"","legend":"\u003cp\u003eThermal maturity profiles across the southern Bida Basin showing vitrinite reflectance variations with stratigraphic position and geographic location. Note the general increase in maturity with depth and proximity to igneous intrusions.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/5070d25a6fffa28867c11fe1.png"},{"id":94825391,"identity":"f2a35e90-954c-4e62-a920-31beecd26397","added_by":"auto","created_at":"2025-10-31 06:50:11","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":800539,"visible":true,"origin":"","legend":"\u003cp\u003eSurface geochemical anomaly map showing soil gas hydrocarbon concentrations (C\u003csub\u003e2\u003c/sub\u003e-C\u003csub\u003e4\u003c/sub\u003e) across the study area. Contoured values represent parts per million (ppm) concentrations, with structural features and sample locations overlain for reference\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/f9175f8d5073e04241675532.png"},{"id":94825404,"identity":"3910ad7a-06ef-4ec4-93aa-e43eef99e91a","added_by":"auto","created_at":"2025-10-31 06:50:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1067081,"visible":true,"origin":"","legend":"\u003cp\u003eGeomechanical facies map showing the distribution of rock strength properties across the study area. Contours represent uniaxial compressive strength values (MPa), with drilling recommendations indicated by symbols. Areas of elevated fracture density and reduced strength are highlighted as potential drilling hazards.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/3df1b32520e3ffa638ad8189.png"},{"id":94780157,"identity":"5d7119cd-9836-41ac-9796-4d497612102c","added_by":"auto","created_at":"2025-10-30 15:28:37","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":472542,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated petroleum system model for the southern Bida Basin showing the relationships between source rocks, migration pathways, reservoir intervals, and surface geochemical expression. Cross-section illustrates structural controls on hydrocarbon migration and the thermal effects of igneous intrusions.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/3b1fd5f980665f05115b7831.png"},{"id":94984683,"identity":"ecfc2dd5-09be-4ba8-bb59-4ef4a75fe77f","added_by":"auto","created_at":"2025-11-03 06:55:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6493039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7694693/v1/17a969a0-768a-461e-9f95-9e992e2afdda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Chemostratigraphy, geomechanical characteristics, and petroleum indicators from southern Bida Basin outcrops, Nigeria","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Bida Basin, positioned within the Nigerian Middle Belt, stands as a compelling example of how frontier sedimentary basins can harbor significant petroleum potential despite limited exploration activities. Spanning approximately 95,000 km\u003csup\u003e2\u003c/sup\u003e, this intracratonic basin has attracted renewed attention following successful discoveries in analogous settings across West Africa (Brownfield and Charpentier, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e.). The basin\u0026rsquo;s complex structural evolution, influenced by both Santonian tectonism and later Pan-African reactivation, has created a diverse array of depositional environments that warrant detailed investigation (Kogbe, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSouthern portions of the Bida Basin expose Cretaceous to Tertiary sequences that provide exceptional windows into subsurface petroleum systems. These outcrops, while affected by tropical weathering and lateralization, preserve critical geological relationships often obscured in subsurface datasets (Adeleye, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Previous investigations have largely focused on single-discipline approaches, examining either structural geology (Jones and Hockey, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), stratigraphy (Adeleye, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1974\u003c/span\u003e), or isolated geochemical parameters (Obaje, et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). However, the inherent complexity of petroleum systems demands integrated analytical approaches that can simultaneously address source rock potential, reservoir quality, and geomechanical constraints on exploration and development activities.\u003c/p\u003e\u003cp\u003eRecent advances in chemostratigraphic correlation techniques have revolutionized our ability to establish precise stratigraphic frameworks in outcrop studies (Pearce, et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Unlike biostratigraphy methods, which can be compromised by preservation issues and ecological factors, chemostratigraphy exploits the systematic variations in elemental compositions that reflect changes in provenance, climate, or depositional environment (Ramkumar, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). When applied to weathered outcrop sections, careful selection of immobile elements can overcome the challenges posed by tropical alteration processes (Nesbitt and Young, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe geomechanical characterization of reservoir and seal rocks has gained increasing importance as the industry moves toward more complex extraction technologies and enhanced recovery methods (Fjaer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Understanding the mechanical properties of rock formations not only influences drilling and completion strategies but also affects long-term reservoir performance and the feasibility of hydraulic fracturing operations (Zoback, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In frontier basins where subsurface data are sparse, outcrop-based geomechanical studies provide essential baseline information for future development activities.\u003c/p\u003e\u003cp\u003ePetroleum geochemistry continues to evolve as a predictive science, with surface geo-chemical techniques offering cost-effective exploration tools in early-stage basin evaluation (Schumacher and Abrams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The integration of traditional source rock analysis with advanced biomarker studies and surface geochemical surveys can provide detailed insights into petroleum system maturity and migration efficiency (Peters, et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In tropical environments like the Bida Basin, where deep weathering profiles can mask or enhance hydrocarbon micro seepage signals, careful interpretation of surface geochemical data becomes particularly critical.\u003c/p\u003e\u003cp\u003eDespite the recognized potential of the Bida Basin, significant knowledge gaps persist regarding the detailed petroleum system characteristics of its southern sector. Previous geochemical studies have been limited in scope and geographic coverage (Obaje, et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), while geomechanical properties remain largely uncharacterized. The absence of integrated datasets has hindered the development of detailed petroleum system models and exploration strategies tailored to this unique geological setting.\u003c/p\u003e\u003cp\u003eThis study aims to address these limitations through a systematic investigation of southern Bida Basin outcrops, with four primary objectives: (Abrams, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) establish a chemostratigraphic framework for regional correlation and sequence boundary identification; (Aadn\u0026oslash;y, 2010) characterize the geomechanical properties of exposed formations and their relationships to lithological and structural variables; (Adeleye, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) evaluate petroleum generation potential and migration indicators through comprehensive organic and surface geochemical analysis; and (Adeleye, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) integrate these multidisciplinary datasets to develop a holistic understanding of petroleum system elements and their spatial relationships.\u003c/p\u003e\u003cp\u003eTo accomplish these objectives, this paper is organized into several complementary sections that progressively build toward an integrated interpretation. Following this introduction, a theoretical framework section establishes the conceptual foundations for chemostratigraphic correlation, geomechanical analysis, and petroleum system evaluation. The methodology section details field sampling strategies and laboratory analytical procedures designed to ensure data quality and comparability. Results are presented in three thematic subsections corresponding to the main analytical approaches, followed by an integrated discussion that synthesizes findings and explores their implications for petroleum exploration. The study concludes with recommendations for future research directions and practical applications in frontier basin evaluation.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Tectonic and Geological Settings of the Bida Basin\u003c/h2\u003e\u003cp\u003eThe Bida Basin is an intracratonic, NW\u0026ndash;SE trending inland sedimentary basin stretching from Shegwa in the northwest to Dekina in the southeast, with a length of about 350 km and a width of 75 to 150 km (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It lies roughly elliptical in plan and is oriented perpendicular to the western margin of the NE\u0026ndash;SW trending Benue Trough (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Unlike many other Nigerian basins, the Bida Basin lack volcanic rocks, carbonate buildups, or Tertiary units; its fill consists largely of continental sediments with only minor marginal to shallow marine and freshwater flood-plain deposits in some intervals (Obaje, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSeveral models have been proposed to explain the tectonic evolution of the basin. An extensional or rift-bounded model suggests that the basin developed under NW-SE fault-bounded structures, with fanglomerates and coarse alluvial fan deposits forming the basal fill as a direct response to basement uplift (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Earlier studies have also proposed a cratonic sag or downwarp model, where the basin originated from subsidence of the crust in an intracratonic setting, thus accommodating widespread continental sedimentation without major volcanic input (Whiteman, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Nwajide, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Other interpretations emphasize the role of wrench or transform tectonics, in which strike-slip faulting and linear basement reactivation controlled basin geometry and sediment dispersal (Ojo \u0026amp; Ajakaiye, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Udensi \u0026amp; Osazuwa, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Rahaman et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), however, integrated these ideas into a hybrid model, proposing that the Bida Basin evolved as a fault-bounded extensional depression influenced by reactivated Pan-African basement structures, with episodic subsidence and sedimentary infill dominated by fluvial and deltaic systems.\u003c/p\u003e\u003cp\u003eThe lithostratigraphy of the Bida Basin varies between the northern and southern sectors but is broadly Campanian to Maastrichtian in age (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the northern Bida Basin, the basal Bida Formation is subdivided into the Doko and Jima members, consisting of very poorly sorted pebbly arkosic sandstones, sub-arkosic sandstones, and quartzose sandstones (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Overlying this are the Sakpe Ironstone Formation, characterized by oolitic and pisolitic ironstones with sandy claystone interbeds, the Enagi Formation, composed of siltstones, sandstones, and claystones, and the Batati Formation, which contains argillaceous oolitic and goethite ironstones interbedded with claystones and siltstones and minor nearshore faunal remains (Obaje, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Akande et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the southern Bida Basin, the sequence begins with the Lokoja Formation, which consists of basal conglomerates and coarse to fine sandstones, sometimes pebbly, directly overlying the Basement Complex (Nwajide, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is followed by the Ahoko Formation, formerly referred to as the Patti Formation, which includes grey to white sandstones, grey clays, carbonaceous silts, shales, and concretionary ironstone bands. The uppermost unit in the southern sector is the Agbaja Formation, composed predominantly of oolitic and pisolitic ironstones interbedded with claystones and sandstones (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLithostratigraphy of the Bida Basin, Nigeria\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSector\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormations / Members (from oldest / basal to youngest / upper)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGeneral Lithology / Characteristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorthern Bida Basin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBida Formation (with Doko Member at base, Jima Member above) followed by Sakpe Ironstone Formation and followed by Enagi Formation and capped by Batati Formation (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBida Formation: very poorly sorted pebbly arkose, sub-arkose, quartzose sandstones; Sakpe: oolitic \u0026amp; pisolitic ironstones, sandy claystones; Enagi: siltstone-sandstone admixture, claystone; Batati: argillaceous, oolitic \u0026amp; goethitic ironstones with claystone/siltstone intercalations\u0026thinsp;+\u0026thinsp;minor shales, nearshore fauna in places (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthern Bida Basin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLokoja Formation (oldest in the basin) and overlie by Ahoko Formation (formerly Patti Formation) and capped by Agbaja Formation (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLokoja: basal conglomerates and sandstones (fine to coarse), sometimes pebbly; Ahoko: (formerly Patti) grey/white sandstones, grey clays, carbonaceous silts \u0026amp; shales, concretionary ironstone bands; Agbaja: oolitic/pisolitic ironstone, ironstone interbeds, ironstone-claystone-sandstone interlayers (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn conclusion, the Bida Basin represents an intracratonic depression that developed during the Late Cretaceous through the interaction of extensional faulting, crustal sagging, and basement reactivation. Its stratigraphy records the transition from basal coarse clastics to ironstone-rich intervals and fine-grained sediments, with clear distinctions between the northern and southern sectors, thereby reflecting both local tectonic control and broader regional subsidence patterns (Rahaman et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Obaje, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nwajide, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"2 Theoretical Framework","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Chemostratigraphic Principles\u003c/h2\u003e\u003cp\u003eChemostratigraphy relies on the systematic documentation and interpretation of chemical variations within sedimentary sequences, exploiting the fact that elemental compositions reflect a complex interplay of provenance, weathering, transport, and depositional processes (Ramkumar, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The theoretical foundation rests on the concept that while individual samples may show considerable scatter due to local factors, systematic trends in elemental ratios can reveal regional patterns related to changes in source area characteristics, tectonic setting, or paleoclimatic conditions (McLennan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn outcrop studies, particularly those conducted in tropical environments, the effects of chemical weathering present both challenges and opportunities for chemostratigraphic analysis. Tropical weathering processes selectively mobilize certain elements while concentrating others, potentially obscuring primary depositional signals (Nesbitt, and Young, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). However, the systematic nature of these processes means that careful selection of element ratios can minimize weathering effects while preserving stratigraphically significant information. Elements such as Ti, Al, and Zr typically exhibit limited mobility during weathering and can serve as effective normalizing factors for more mobile components (Young and Nesbitt, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe application of rare earth element (REE) geochemistry in chemostratigraphic studies has proven particularly valuable due to the coherent behavior of these elements during sedimentary processes and their limited susceptibility to weathering alteration (McLennan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). REE patterns preserve information about source rock characteristics and can distinguish between different provenance types, making them powerful tools for correlation and paleotectonic reconstruction (Cullers, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The Ce anomaly, calculated as Ce\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e/((La\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e + Pr\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e) /2), provides insights into redox conditions during deposition, while Eu anomalies reflect the presence or absence of feldspar in source regions (Murray et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eProvenance discrimination using geochemical data relies on well-established relationships between tectonic setting and sediment composition (Bhatia,1983.). Triangular plots involving elements such as Ti, Zr, and La can effectively distinguish between active continental margin, passive margin, and oceanic island arc settings (Bhatia and Crook, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). These discriminant diagrams, while developed for unaltered rocks, can still provide valuable insights when applied judiciously to weathered outcrop samples, particularly when multiple elemental systems are considered together.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Geomechanical Fundamentals\u003c/h2\u003e\u003cp\u003eThe mechanical behavior of sedimentary rocks reflects a complex integration of compositional, textural, and structural factors that operate across multiple scales (Fjaer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). At the micro scale, grain contacts, cement types, and pore structure exert primary controls on mechanical properties, while larger-scale features such as bedding, fractures, and compositional heterogeneities introduce additional complexity (Zoback, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Understanding these multiscale controls is essential for predicting mechanical behavior and optimizing drilling and completion strategies.\u003c/p\u003e\u003cp\u003eUniaxial compressive strength (UCS) represents one of the most fundamental mechanical properties, providing a measure of a rock\u0026rsquo;s ability to withstand axial loading under unconfined conditions (Hoek and Brown, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). For sedimentary rocks, UCS values typically range from less than 10 MPa for poorly consolidated sediments to over 200 MPa for well-cemented, fine grained rocks. The relationship between UCS and other mechanical properties, such as tensile strength and elastic moduli, follows empirical correlations that can be exploited for property prediction when direct measurements are unavailable (Kahraman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eElastic properties, including Young\u0026rsquo;s modulus and Poisson\u0026rsquo;s ratio, control the deformation response of rocks under applied stress and are critical parameters for geomechanical modeling (Mavko et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Young\u0026rsquo;s modulus quantifies the stiffness of a material under axial loading, while Poisson\u0026rsquo;s ratio describes the relationship between axial and lateral strain. For sedimentary rocks, these properties show systematic variations with porosity, mineralogy, and pore fluid characteristics that can be described using theoretical models such as the Hashin-Shtrikman bounds or differential effective medium theory (Berryman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe anisotropy of mechanical properties in layered sedimentary sequences introduces additional complexity that must be considered in geomechanical analysis (Sone and Zoback, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Bedding parallel weaknesses, compositional variations, and preferred orientation of clay minerals can create significant directional variations in strength and elastic properties. These anisotropic effects become particularly important when designing hydraulic fracturing treatments or predicting wellbore stability in deviated wells (Aadnoy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Petroleum System Elements\u003c/h2\u003e\u003cp\u003ePetroleum systems represent integrated networks of source rocks, migration pathways, reservoir rocks, and trapping mechanisms that operate within specific temporal and spatial frameworks (Magoon and Dow, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). The evaluation of petroleum system elements requires systematic assessment of each component, with particular attention to their timing relationships and spatial associations. In outcrop studies, these evaluations must account for the effects of surface exposure and weathering on organic matter preservation and hydrocarbon retention.\u003c/p\u003e\u003cp\u003eSource rock evaluation encompasses both quantitative and qualitative assessments of organic matter content, type, and thermal maturity (Peters et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Total organic carbon (TOC) content provides a fundamental measure of source rock richness, with values exceeding 1 wt% generally considered sufficient for hydrocarbon generation in marine shales (Tissot and Welte, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). However, TOC values in outcrop samples may be reduced relative to subsurface equivalents due to oxidation and biodegradation processes. Rock-Eval pyrolysis offers insights into both the quantity and quality of organic matter through parameters such as S\u003csub\u003e1\u003c/sub\u003e (free hydrocarbons), S\u003csub\u003e2\u003c/sub\u003e (hydrocarbons generated during pyrolysis), and T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e (temperature of maximum S\u003csub\u003e2\u003c/sub\u003e generation) (Espitali\u0026acute;e et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eKerogen type determination relies on the integration of Rock-Eval parameters with petrographic observations and biomarker analysis (Tyson, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Type I kerogen, derived primarily from algal sources, exhibits high hydrogen indices and excellent oil generation potential. Type II kerogen, with mixed marine and terrestrial inputs, shows intermediate hydrogen indices and good oil generation capacity. Type III kerogen, dominated by terrestrial plant material, typically displays low hydrogen indices and primarily generates gas rather than oil (Vandenbroucke and Largeau, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThermal maturity assessment provides crucial information about the degree of organic matter evolution and hydrocarbon generation potential (Burnham and Sweeney, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Vitrinite reflectance (%Ro) remains the most widely used maturity parameter, with values of 0.5\u0026ndash;0.7% indicating the onset of oil generation, 0.7\u0026ndash;1.3% representing the main oil generation window, and values above 1.3% suggesting gas generation (Dow, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1977\u003c/span\u003e.). In outcrop studies, vitrinite reflectance measurements can be complicated by oxidation effects, requiring careful sample selection and interpretation.\u003c/p\u003e\u003cp\u003eSurface geochemical techniques exploit the migration of light hydrocarbons from subsurface accumulations to the surface through various transport mechanisms (Schumacher and Abrams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). These techniques include soil gas analysis, surface geochemical surveys, and remote sensing applications designed to detect hydrocarbon micro-seepage (Saunders et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The interpretation of surface geochemical data requires consideration of near-surface processes such as biodegradation, oxidation, and lateral migration that can modify or redistribute hydrocarbon signals (Abrams, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Field Sampling Strategy\u003c/h2\u003e\u003cp\u003eField investigations were conducted during the dry season (November-February) to optimize outcrop accessibility and sample quality. Five detailed stratigraphic sections were measured and systematically sampled across the southern Bida Basin, with locations selected to provide maximum geographic coverage while ensuring adequate outcrop quality for reliable sampling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). GPS coordinates were recorded using a Garmin eTrex 30x unit with sub-meter accuracy, and all sample locations were photographed for documentation and future reference.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSampling focused on fresh rock faces exposed through recent road cuts, quarry operations, and natural erosional features to minimize weathering effects. Where possible, samples were collected from at least 30 cm behind weathered surfaces to ensure representative compositions. A systematic sampling strategy was employed with sample spacing ranging from 2\u0026ndash;5 m in homogeneous units to 0.5-1 m across lithological transitions and potential sequence boundaries. Each sample weighed approximately 2\u0026ndash;3 kg to provide sufficient material for the comprehensive analytical program.\u003c/p\u003e\u003cp\u003eStructural measurements were recorded at each sampling location using a Brunton compass, including bedding orientations, fracture systems, and any evidence of tectonic deformation. Particular attention was paid to identifying structural controls on outcrop exposure and potential influences on hydrocarbon migration pathways. Field sketches and measured sections were prepared for each locality to document stratigraphic relationships and lateral facies variations.\u003c/p\u003e\u003cp\u003eRegional geological mapping was conducted at 1:25,000 scale to establish the structural and stratigraphic context for detailed sampling. This mapping integrated existing geological surveys with new field observations, paying particular attention to unconformity relationships, fault systems, and igneous intrusions that might influence petroleum system development. The integrated field program resulted in collection of 68 samples suitable for geochemical analysis and 45 samples appropriate for geomechanical testing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Laboratory Analytical Procedures\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Chemostratigraphic Analysis\u003c/h2\u003e\u003cp\u003eSample preparation for geochemical analysis followed established protocols designed to minimize contamination while ensuring representative compositions (Potts, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Fresh rock samples were crushed using a tungsten carbide mill to avoid metallic contamination, with particle sizes reduced to less than 200 mesh for optimal analytical precision. Loss on ignition (LOI) was determined by heating powdered samples to 1000\u0026deg;C for 2 hours to quantify volatile content and degree of alteration.\u003c/p\u003e\u003cp\u003eMajor element analysis was performed using X-ray fluorescence (XRF) spectrometry on a Rigaku Primus II wavelength dispersive system following the methods of (Norrish and Hutton, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Fused glass discs were prepared using lithium metaborate flux in a 1:10 sample to-flux ratio to ensure homogeneous analytical surfaces. Analytical precision, based on replicate analyses of international reference materials, was better than 2% relative standard deviation for major elements and 5% for trace elements above 10 ppm.\u003c/p\u003e\u003cp\u003eTrace element and rare earth element (REE) concentrations were determined using inductively coupled plasma mass spectrometry (ICP-MS) on an Agilent 7700x instrument following acid digestion procedures modified from (Longerich et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Sample digestion utilized a four-acid attack (HF-HNO\u003csub\u003e3\u003c/sub\u003e-HClO\u003csub\u003e4\u003c/sub\u003e-HCl) in Teflon beakers to ensure complete dissolution of resistant phases. Internal standards (Be, In, Re) were employed to correct for instrumental drift and matrix effects. Detection limits ranged from 0.01 ppm for REE to 0.1 ppm for transition metals.\u003c/p\u003e\u003cp\u003eMineralogical compositions were determined using X-ray diffraction (XRD) analysis on a Bruker D8 Advance diffractometer equipped with a LynxEye position-sensitive detector. Random powder mounts were scanned from 5\u0026ndash;70\u0026deg; 2 using Cu K radiation at 40 kV and 40 mA. Clay mineral identification utilized oriented aggregates prepared on glass slides, with treatments including air drying, ethylene glycol solvation, and heating to 550\u0026deg;C to distinguish between expandable and non-expandable phases (Moore and Reynolds, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Geomechanical Testing\u003c/h2\u003e\u003cp\u003eMechanical property determination required preparation of cylindrical test specimens with length-to-diameter ratios of 2.5:1 following ASTM D4543 specifications (ASTM International, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Core drilling was performed using diamond-tipped bits with water cooling to minimize thermal damage, and specimen ends were ground flat and parallel to within 0.05 mm tolerance. Only samples free of visible fractures or weathering were selected for testing to ensure representative mechanical properties.\u003c/p\u003e\u003cp\u003eUniaxial compressive strength (UCS) testing was conducted using a servo-controlled MTS universal testing machine with a loading rate of 0.5 MPa/s until failure. Axial and circumferential strains were monitored using electrical resistance strain gauges to determine elastic constants. Young\u0026rsquo;s modulus was calculated from the linear portion of the stress-strain curve, typically between 50\u0026ndash;80% of peak strength, while Poisson\u0026rsquo;s ratio was determined from the ratio of lateral to axial strain in the elastic regime (Fairhurst and Hudson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBrazilian tensile strength tests were performed on disc-shaped specimens with diameter-to-thickness ratios of 2:1, following ASTM D3967 procedures (ASTM International, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Loading was applied across the diameter at a rate of 200 N/s until failure, with tensile strength calculated using the standard formula accounting for specimen geometry. Point load index testing provided additional strength classification data using irregular rock fragments, with results corrected to a standard 50 mm diameter equivalent (Brook, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePorosity and permeability measurements were conducted using helium porosimetry and steady-state flow methods respectively. Porosity determinations utilized a Micromeritics AccuPyc 1340 helium pycnometer with precision of \u0026plusmn;\u0026thinsp;0.1% at the 95% confidence level. Permeability measurements employed nitrogen gas flow through cylindrical plugs under controlled confining pressure, with Klinkenberg corrections applied to obtain absolute permeability values (Klinkenberg, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1941\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Petroleum Indicators Assessment\u003c/h2\u003e\u003cp\u003eOrganic geochemical analysis required careful sample selection to identify intervals with the highest preservation potential for organic matter. Samples were collected from fine grained units, particularly dark-colored shales and mudstones, which typically exhibit superior source rock characteristics. Surface weathering effects were minimized by collecting samples from fresh exposures and avoiding obviously oxidized intervals.\u003c/p\u003e\u003cp\u003eTotal organic carbon (TOC) content was determined using a LECO CS-244 carbon analyzer following acid treatment to remove carbonate carbon (Dean, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Sample powders were treated with 10% HCl to dissolve carbonates, washed with distilled water, and dried before analysis. Organic carbon content was measured by combustion at 1350\u0026deg;C in an oxygen atmosphere, with CO\u003csub\u003e2\u003c/sub\u003e detection by infrared spectrometry. Analytical precision was \u0026plusmn;\u0026thinsp;0.05 wt% based on duplicate analyses.\u003c/p\u003e\u003cp\u003eRock-Eval pyrolysis was performed using a Weatherford Source Rock Analyzer to assess hydrocarbon generation potential and thermal maturity (Espitali\u0026acute;e et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). Approximately 100 mg of powdered sample was heated from 300\u0026ndash;600\u0026deg;C at 25\u0026deg;C/min under helium atmosphere to determine free hydrocarbons (S\u003csub\u003e1\u003c/sub\u003e), pyrolytic hydrocarbons (S\u003csub\u003e2\u003c/sub\u003e), temperature of maximum S\u003csub\u003e2\u003c/sub\u003e generation (T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e), and organic carbon content. Hydrogen index (HI\u0026thinsp;=\u0026thinsp;S\u003csub\u003e2\u003c/sub\u003e/TOC \u0026times; 100) and oxygen index (OI\u0026thinsp;=\u0026thinsp;S\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e/TOC\u003c/sup\u003e \u0026times; 100) were calculated to characterize kerogen type and quality.\u003c/p\u003e\u003cp\u003eVitrinite reflectance measurements were conducted on polished particulate mounts using a Zeiss Axioplan microscope equipped with a photomultiplier system and monochromatic light at 546 nm wavelength (Taylor et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Measurements were made on vitrinite particles larger than 5 m in diameter, with at least 50 readings per sample when sufficient vitrinite was present. Random reflectance values were corrected to equivalent Ro values using standard procedures, and mean values were calculated with 95% confidence intervals.\u003c/p\u003e\u003cp\u003eSurface geochemical analysis involved collection of soil samples at 1 m depth across systematic grid patterns covering approximately 10 km\u003csup\u003e2\u003c/sup\u003e around major outcrop exposures. Soil gas compositions were analyzed using gas chromatography to determine C\u003csub\u003e1\u003c/sub\u003e-C\u003csub\u003e4\u003c/sub\u003e hydrocarbon concentrations, with particular attention to ethane/methane and propane/ethane ratios as indicators of thermogenic versus biogenic origins (Schumacher and Abrams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Carbon isotope ratios of soil carbonate and organic matter were determined using mass spectrometry to identify potential migration signatures.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data Integration and Interpretation\u003c/h2\u003e\u003cp\u003eStatistical analysis of geochemical data employed multivariate techniques to identify correlation patterns and discriminate between different sample populations. Principal component analysis (PCA) was used to reduce dimensionality and highlight major sources of compositional variation, while cluster analysis helped identify geochemically similar sample groups. Correlation matrices were calculated to identify significant element associations and guide interpretation of geochemical processes.\u003c/p\u003e\u003cp\u003eQuality control procedures included analysis of certified reference materials (CRM) with each analytical batch to monitor accuracy and precision. Duplicate analyses were performed on 10% of samples to assess analytical reproducibility, and blank samples were included to monitor contamination. All analytical data were stored in a normalized database structure to facilitate statistical analysis and visualization.\u003c/p\u003e\u003cp\u003eThe analytical workflow is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which illustrates the integration of field and laboratory procedures designed to address the multidisciplinary objectives of this study. This comprehensive approach ensures that data quality is maintained across all analytical techniques while providing the basis for meaningful integration and interpretation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Chemostratigraphic Characterization\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.1.1 Major Element Geochemistry\u003c/h2\u003e\u003cp\u003eThe major element compositions of southern Bida Basin samples exhibit systematic variations that reflect both primary depositional processes and post-depositional modification (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Silica contents range from 47.2 to 84.6 wt%, with the highest values occurring in quartzose sandstones of the Lokoja Formation and the lowest in Fe-rich ironstones of the Agbaja Formation. Alumina concentrations vary from 8.1 to 19.7 wt%, generally showing inverse relationships with silica content that reflect the balance between quartz and feldspar/clay mineral components.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage major element compositions (wt %) of southern Bida Basin formations with standard deviations in parentheses. n\u0026thinsp;=\u0026thinsp;number of samples analyzed.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLokoja Formation (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatti Formation (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgbaja Formation (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.4 (8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.3 (11.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.8 (12.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAl2O3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.2 (3.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.8 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.9 (4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.8 (2.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.9 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23.7 (8.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMgO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8 (1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCaO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.4 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.2 (0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.1 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.9 (0.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eK\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.4 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.2 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.8 (1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62 (0.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.89 (0.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.71 (0.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP2O5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08 (0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.12 (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.18 (0.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.3 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.8 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.9 (2.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: Average major element compositions (wt %) of southern Bida Basin formations with standard deviations in parentheses. n\u0026thinsp;=\u0026thinsp;number of samples analyzed.\u003c/p\u003e\u003cp\u003eIron oxide concentrations show the most dramatic variations, ranging from 1.8 wt% in clean quartz sandstones to 41.2 wt% in lateritic ironstones. This variation reflects both primary depositional controls and subsequent weathering processes that have concentrated iron oxides through lateritization. The elevated loss on ignition (LOI) values in the Patti and Agbaja formations (3.8\u0026ndash;4.9 wt %) compared to the Lokoja Formation (2.3 wt%) indicate greater degrees of alteration and hydrous mineral formation in the younger units. Alkali element distributions provide insights into provenance characteristics and weathering intensity. The K\u003csub\u003e2\u003c/sub\u003eO/Na\u003csub\u003e2\u003c/sub\u003eO ratios increase systematically from 1.6 in the Lokoja Formation to 2.3 in the Patti Formation and 3.1 in the Agbaja Formation, suggesting either increasing contributions from K-feldspar-rich sources or preferential leaching of sodium during weathering. The Chemical Index of Alteration (CIA), calculated as Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e/(Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CaO* + Na\u003csub\u003e2\u003c/sub\u003eO\u0026thinsp;+\u0026thinsp;K\u003csub\u003e2\u003c/sub\u003e\u003csup\u003eO)\u003c/sup\u003e \u0026times; 100, ranges from 58 to 89, with mean values of 68, 74, and 81 for the Lokoja, Patti, and Agbaja formations respectively. These values indicate moderate to intense chemical weathering, consistent with tropical climatic conditions during and after deposition.\u003c/p\u003e\u003cp\u003eThe vertical distribution of major elements reveals several distinct chemostratigraphic intervals that can be correlated across the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Most notable are systematic variations in Ti/Al ratios that range from 0.042 in the lower Lokoja Formation to 0.078 in the upper Patti Formation. These variations reflect changes in heavy mineral content and possibly provenance characteristics, providing excellent correlation markers across distances of up to 15 km.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePhosphorus concentrations, while generally low (0.03\u0026ndash;0.34 wt% P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e), show systematic increases toward the top of the succession that may reflect increasing marine influence or enhanced preservation of organic matter. The correlation between P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e and TOC ( r\u0026thinsp;=\u0026thinsp;0.67, p \u0026iexcl; 0.01) suggests that phosphorus enrichment is linked to organic productivity or preservation processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.1.2 Trace Element Signatures\u003c/h2\u003e\u003cp\u003eTrace element compositions provide additional constraints on provenance characteristics and depositional processes while offering alternative correlation parameters less susceptible to weathering effects. High field strength elements (HFSE) such as Zr, Nb, and Ta show limited mobility during weathering and preserve primary signatures related to source rock compositions and transport processes.\u003c/p\u003e\u003cp\u003eZirconium concentrations range from 89 to 487 ppm, with the highest values occurring in coarse-grained sandstones where heavy mineral concentrations are enhanced through hydraulic sorting processes. The Zr/TiO\u003csub\u003e2\u003c/sub\u003e ratio varies systematically between formations, with mean values of 0.082, 0.095, and 0.071 ppm/wt% for the Lokoja, Patti, and Agbaja formations respectively. These variations likely reflect changes in provenance characteristics, with higher ratios indicating greater contributions from evolved igneous sources rich in zircon.\u003c/p\u003e\u003cp\u003eRare earth element (REE) patterns provide powerful insights into provenance characteristics and depositional processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). All samples exhibit light REE (LREE) enrichment relative to heavy REE (HREE), with (La/Yb)\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e ratios ranging from 7.8 to 18.3. Total REE concentrations vary from 98 to 267 ppm, generally correlating with clay mineral content and degree of weathering. The upper continental crust-normalized patterns show relatively flat HREE distributions and variable LREE slopes, consistent with derivation from mixed felsic and intermediate igneous sources.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCerium anomalies, calculated as Ce\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e/((La\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e + Pr\u003csub\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sub\u003e)/2), range from 0.89 to 1.12, with most samples showing slight negative anomalies (Ce/Ce* \u0026iexcl; 1.0) that suggest oxic depositional conditions or subaerial weathering effects (Wright et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Europium anomalies vary more systematically, with Eu/Eu* values of 0.82\u0026ndash;0.95 in sandstones and 0.65\u0026ndash;0.78 in mudstones. These negative Eu anomalies reflect feldspar fractionation in source regions and are consistent with derivation from evolved continental crust rather than mafic volcanic sources. The transition metal signature provides additional provenance constraints, with Cr/Ni ratios ranging from 1.8 to 4.2 across the study area. Lower ratios typically occur in the Lokoja Formation, suggesting contributions from more mafic sources, while higher ratios in younger formations indicate increasing input from felsic sources. Vanadium concentrations (47\u0026ndash;189 ppm) correlate positively with organic carbon content (r\u0026thinsp;=\u0026thinsp;0.58, p \u0026iexcl; 0.01), consistent with preferential concentration in reducing depositional environments. Tectonic discrimination diagrams based on immobile trace elements suggest deposition in a passive continental margin setting (Bhatia and Crook, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The Ti-Zr-La plot positions most samples within the passive margin field, while Th/Sc versus Zr/Sc ratios indicate mixed provenance from both recycled sedimentary sources and felsic igneous rocks. These interpretations are consistent with the inferred depositional setting of the Bida Basin as an intracratonic sag basin receiving sediment from surrounding Precambrian basement terrains.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Geomechanical Properties\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Rock Strength Characteristics\u003c/h2\u003e\u003cp\u003eMechanical property measurements reveal significant variations related to lithology, mineralogical composition, and degree of cementation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Uniaxial compressive strength (UCS) values range from 28.4 MPa in poorly cemented sandstones to 165.7 MPa in silicified ironstone horizons. The wide range of strength values reflects the diverse lithological assemblage present in the southern Bida Basin and highlights the importance of detailed characterization for drilling and completion planning.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of geomechanical properties by lithology with statistical parameters. Values represent means with standard deviations in parentheses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLithology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUCS (MPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTensile Strength (MPa)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYoung\u0026rsquo;s Modulus ( GPa )\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartz Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.3 (23.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.2 (2.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45.8 (12.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArkosic Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.1 (18.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.8 (1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38.9 (9.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiltstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.4 (15.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.1 (1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.7 (8.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMudstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.7 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.9 (1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIronstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134.6 (31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.7 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.3 (18.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaterite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.7 (28.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.9 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.2 (15.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eQuartz sandstones exhibit the most consistent mechanical properties, with UCS values typically ranging from 65\u0026ndash;115 MPa and coefficient of variation around 26\u003c/p\u003e\u003cp\u003eFine-grained lithologies generally display lower strength values but also show systematic relationships between composition and mechanical properties. Mudstones with higher illite content tend to exhibit greater strength than those dominated by smectitic clays, reflecting the superior bonding characteristics of non-expandable clay minerals. The presence of organic matter generally reduces strength, with TOC contents above 2 wt%\u003c/p\u003e\u003cp\u003eIronstone horizons represent mechanical anomalies within the stratigraphic succession, exhibiting UCS values that exceed 150 MPa in well-cemented intervals. These rocks are characterized by pervasive hematite and goethite cements that create exceptionally strong intergranular bonds. However, the brittle nature of iron oxide cements results in relatively low ratios of tensile to compressive strength (typically 0.07\u0026ndash;0.09 compared to 0.10\u0026ndash;0.12 for sandstones).\u003c/p\u003e\u003cp\u003eThe relationship between compressive and tensile strength follows expected empirical correlations, with tensile strength averaging approximately 9% of UCS across all lithologies tested (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This ratio is slightly lower than typical values for fresh sedimentary rocks (10\u0026ndash;15%), possibly reflecting micro-crack development during weathering and stress relief processes. The correlation coefficient between UCS and tensile strength is 0.87 (p \u0026iexcl; 0.001), indicating strong predictive relationships that can be exploited for property estimation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePoint load strength index values range from 2.1 to 8.7 MPa, with systematic relationships to UCS that follow established empirical correlations. The conversion factor between point load index and UCS averages 11.2 for sandstones and 9.8 for fine-grained rocks, consistent with published values for sedimentary rocks (Brook, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). These relationships provide valuable tools for rapid strength assessment in the field using portable testing equipment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Elastic Properties\u003c/h2\u003e\u003cp\u003eYoung\u0026rsquo;s modulus values exhibit a wide range from 12.8 GPa in weathered mudstones to 89.3 GPa in well-cemented quartz sandstones. The elastic modulus correlates strongly with UCS (r\u0026thinsp;=\u0026thinsp;0.89) and shows systematic relationships to porosity and mineralogical composition. Quartz-rich rocks generally exhibit higher modulus values than feldspar bearing or clay-rich lithologies, reflecting the superior elastic properties of quartz relative to other common rock-forming minerals.\u003c/p\u003e\u003cp\u003ePoisson\u0026rsquo;s ratio values range from 0.18 to 0.34, with an overall mean of 0.26 across all lithologies tested. Fine-grained rocks typically exhibit higher Poisson\u0026rsquo;s ratios (0.28\u0026ndash;0.32) than coarse-grained sandstones (0.20\u0026ndash;0.26), consistent with the higher Poisson\u0026rsquo;s ratios of clay minerals compared to quartz and feldspar. These values fall within the expected range for sedimentary rocks and provide essential input parameters for geomechanical modeling applications.\u003c/p\u003e\u003cp\u003eThe relationship between elastic modulus and porosity follows theoretical predictions based on effective medium theory, with higher porosity samples showing systematically lower modulus values. A power-law relationship of the form E\u0026thinsp;=\u0026thinsp;E\u003csub\u003e0\u003c/sub\u003e(1-)\u003csup\u003e\u003cem\u003en\u003c/em\u003e\u003c/sup\u003e provides an excellent fit to the data, with E\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;78.2 GPa and n\u0026thinsp;=\u0026thinsp;2.34 for sandstone lithologies. This relationship enables porosity-based prediction of elastic properties for reservoir modeling applications.\u003c/p\u003e\u003cp\u003eBulk modulus and shear modulus were calculated from Young\u0026rsquo;s modulus and Poisson\u0026rsquo;s ratio measurements using standard elastic relationships. Bulk modulus values range from 8.9 to 52.4 GPa, while shear modulus varies from 4.8 to 34.7 GPa. These derived parameters are essential for advanced geomechanical modeling and provide insights into the volumetric versus shear deformation characteristics of different lithologies.\u003c/p\u003e\u003cp\u003eDynamic elastic properties, estimated from empirical correlations with static measurements, suggest that in-situ values may be 10\u0026ndash;30\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Reservoir Quality\u003c/h2\u003e\u003cp\u003eThe sandstone quality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) of the Patti and Lokoja Formation show that clean quartz sandstone has the highest general reservoir potential, with a rather high porosity (12.4%), middle and good permeability (up to 284.7 mD), and the highest net-to-gross ratio (0.85). The arkosic sandstone has also fair reservoir quality but it is a little less due to feldspar content which might have resulted in lower porosity and permeability than the quartz sandstone. Interestingly, the conglomeratic sandstone has a relatively low porosity (8.2 percent) but very high permeability (mean 156.2 mD), implying that the large grain size and network distribution of pores should increase fluid flow even though the volume of pores is small. Silty sandstone and cemented sandstone in particular are poor-quality reservoirs, which have low porosity, permeability, and net-to-gross ratios, and are indicative of the influence of fine-grained matrix and diagenetic cementation respectively. In general, the findings indicate that clean quartz and conglomeratic sandstones are the most promising intervals in the reservoir in the southern Bida Basin.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReservoir quality parameters for potential reservoir intervals in the southern Bida Basin. Values represent arithmetic means with ranges in parentheses.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLithofacies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePorosity (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003ePermeability (mD)Pore Throat (m) Net:Gross\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClean Quartz Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.4 (8.7\u0026ndash;18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89.3 (12.1\u0026ndash;284.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.6 (8.4\u0026ndash;31.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArkosic Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.8 (5.2\u0026ndash;15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.7 (2.8\u0026ndash;127.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.3 (4.1\u0026ndash;24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConglomeratic Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.2 (6.1\u0026ndash;11.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156.2 (45.8\u0026ndash;389.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.4 (15.2\u0026ndash;42.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSilty Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.4 (3.8\u0026ndash;9.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.9 (0.4\u0026ndash;28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.7 (2.1\u0026ndash;12.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCemented Sandstone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.1 (1.2\u0026ndash;6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.8 (0.01\u0026ndash;4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.4 (0.8\u0026ndash;5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Petroleum Indicators\u003c/h2\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.3.1 Source Rock Evaluation\u003c/h2\u003e\u003cp\u003eOrganic geochemical analysis reveals heterogeneous but locally promising source rock potential within the southern Bida Basin succession (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Total organic carbon (TOC) contents range from 0.12 to 2.84 wt%, with the highest values occurring in dark colored mudstones and carbonaceous shales of the Patti Formation. While most samples fall below the 1 wt% threshold typically considered minimum for effective source rocks, several intervals exceed 2 wt% and warrant further evaluation for unconventional resource potential\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSource rock evaluation parameters for organic-rich intervals in the southern Bida Basin. HI\u0026thinsp;=\u0026thinsp;Hydrogen Index, OI\u0026thinsp;=\u0026thinsp;Oxygen Index, PI\u0026thinsp;=\u0026thinsp;Production Index.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFormation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTOC (wt%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eS\u003csub\u003e1\u003c/sub\u003e (mg/g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS\u003csub\u003e2\u003c/sub\u003e (mg/g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHI (mg/g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOI (mg/g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eT\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e (\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e429\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e435\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgbaja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e447\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e438\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgbaja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD-59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRock-Eval pyrolysis data indicate predominantly Type II/III mixed kerogen assemblages, with hydrogen indices ranging from 89 to 234 mg HC/g TOC. The higher hydrogen indices (\u0026iquest;200 mg/g) occur in Patti Formation samples, suggesting better preservation of hydrogen-rich organic matter, possibly including algal or bacterial components. Oxygen indices vary from 67 to 156 mg CO\u003csub\u003e2\u003c/sub\u003e/g TOC, with lower values generally corresponding to higher hydrogen indices in a pattern consistent with variable oxidation during deposition or early diagenesis.\u003c/p\u003e\u003cp\u003eThe van Krevelen diagram constructed from Rock-Eval data shows most samples plotting along a Type II/III evolutionary pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), indicating mixed marine and terrestrial organic matter inputs. Several samples from the Patti Formation plot closer to the Type II field, suggesting episodic marine influence or enhanced preservation of marine-derived organic matter. The evolutionary trend suggests oil and gas generation potential, though thermal maturity levels will ultimately control the nature and timing of hydrocarbon generation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eS\u003csub\u003e1\u003c/sub\u003e values (free hydrocarbons) range from 0.21 to 0.89 mg HC/g rock, with the highest concentrations occurring in samples with elevated TOC contents. The S\u003csub\u003e1\u003c/sub\u003e/TOC ratio averages 0.34 mg/g, indicating moderate hydrocarbon retention that could reflect either indigenous generation or minor migration from deeper sources. Production Index (PI\u0026thinsp;=\u0026thinsp;S\u003csub\u003e1\u003c/sub\u003e/(S\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;S\u003csub\u003e2\u003c/sub\u003e)) values range from 0.12 to 0.21, suggesting early mature to mature organic matter with limited hydrocarbon expulsion.\u003c/p\u003e\u003cp\u003eTemperature of maximum S\u003csub\u003e2\u003c/sub\u003e generation (T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e) varies from 429 to 452\u0026deg;C, indicating thermal maturity levels ranging from early mature to peak oil generation. The systematic increase in T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e values from the Patti Formation (mean 435\u0026deg;C) to the Agbaja Formation (mean 449\u0026deg;C) suggests either deeper burial of older units or differential thermal effects related to igneous activity. These maturity levels are consistent with active hydrocarbon generation and suggest that deeper basin areas may have achieved full maturity for oil and gas generation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e4.3.2 Thermal Maturity Assessment\u003c/h2\u003e\u003cp\u003eVitrinite reflectance measurements provide the most reliable assessment of thermal maturity in the southern Bida Basin samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Measured Ro values range from 0.52 to 0.89% across the study area, with systematic variations related to stratigraphic position and proximity to igneous intrusions. The overall maturity trend indicates that most of the exposed succession has entered the early oil generation window (Ro \u0026iquest; 0.5%), with several locations approaching peak oil generation conditions (Ro\u0026thinsp;=\u0026thinsp;0.7-1.0%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpatial variations in thermal maturity reveal important patterns related to basin structure and thermal history. Samples collected within 2 km of documented igneous intrusions consistently show elevated reflectance values, with Ro measurements 0.1\u0026ndash;0.3% higher than regional trends. This thermal aureole effect suggests that Tertiary magmatic activity has significantly influenced the thermal evolution of petroleum source rocks in the basin. The relationship between T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e and vitrinite reflectance follows established calibrations for Type II/III kerogen, with the regression equation T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e = 409\u0026thinsp;+\u0026thinsp;17.4 \u0026times; Ro (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.78) closely matching published correlations (Peters et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This agreement provides confidence in both maturity datasets and suggests that Rock-Eval T\u003csub\u003e\u003cem\u003emax\u003c/em\u003e\u003c/sub\u003e can be used for maturity assessment in samples where vitrinite is absent or poorly preserved.\u003c/p\u003e\u003cp\u003eCalculated transformation ratios based on kinetic modeling suggest that 15\u0026ndash;35% of the original hydrocarbon generation potential has been realized in the most mature samples. This indicates significant remaining potential for continued generation with additional burial or thermal input. The timing of hydrocarbon generation, estimated from basin modeling constraints, suggests active generation beginning in the Paleocene and continuing through the present day.\u003c/p\u003e\u003cp\u003eBiomarker maturity parameters, determined from gas chromatography-mass spectrometry analysis of extractable organic matter, generally support the vitrinite reflectance interpretations. The 20S/(20S\u0026thinsp;+\u0026thinsp;20R) sterane ratio averages 0.41 in mature samples, approaching equilibrium values of 0.55 that characterize peak oil generation. Moretane/hopane ratios (0.18\u0026ndash;0.26) are consistent with early to peak mature conditions, while the methylphenanthrene index (MPI-1) ranges from 0.68 to 1.12, corresponding to calculated vitrinite reflectance equivalents of 0.55\u0026ndash;0.95%.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e4.3.3 Surface Geochemical Anomalies\u003c/h2\u003e\u003cp\u003eSurface geochemical surveys conducted across a 10 km\u003csup\u003e2\u003c/sup\u003e area surrounding major outcrop exposures reveal several discrete hydrocarbon anomalies that provide evidence for active petroleum migration (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Soil gas concentrations of C\u003csub\u003e2\u003c/sub\u003e-C\u003csub\u003e4\u003c/sub\u003e hydrocarbons show coherent spatial patterns with peak values occurring over structural highs and along mapped fault trends. Methane concentrations range from background levels of 2\u0026ndash;5 ppm to anomalous values exceeding 50 ppm in areas interpreted as active seepage zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of samples collected with stratigraphic positions and analytical methods applied. XRF\u0026thinsp;=\u0026thinsp;X-ray fluorescence, ICP-MS\u0026thinsp;=\u0026thinsp;inductively coupled plasma mass spectrometry, XRD\u0026thinsp;=\u0026thinsp;X-ray diffraction, GM\u0026thinsp;=\u0026thinsp;geomechanical testing, OG\u0026thinsp;=\u0026thinsp;organic geochemistry.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFormation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThickness (m)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSamples\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eXRF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eICP-MS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eXRD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eGM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSurface Geochem\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgbaja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45\u0026ndash;78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatti\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120\u0026ndash;180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLokoja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200\u0026ndash;350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurface samples\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEthane/methane ratios provide critical information about the origin of detected hydrocarbons, with values ranging from 0.001 to 0.047 across the survey area. Background areas typically show ratios below 0.005, consistent with predominantly biogenic methane generation in soils. Anomalous zones exhibit ratios between 0.015\u0026ndash;0.047, approaching values expected for thermogenic hydrocarbons and suggesting contribution from subsurface petroleum sources (Schumacher and Abrams, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePropane and butane concentrations are generally low but show systematic spatial associations with ethane anomalies. The presence of C\u003csub\u003e3\u003c/sub\u003e-C\u003csub\u003e4\u003c/sub\u003e hydrocarbons in soil gas provides strong evidence for thermogenic origins, as these compounds are rarely generated by near-surface biological processes. The propane/ethane ratio averages 0.31 in anomalous areas, consistent with oil-associated gas compositions rather than dry gas systems.\u003c/p\u003e\u003cp\u003eCarbon isotope analysis of soil carbonate reveals systematic variations that may reflect hydrocarbon micro seepage processes. \u003csup\u003e13\u003c/sup\u003eC values range from \u0026minus;\u0026thinsp;8.7\u0026permil; to -15.2\u0026permil; PDB, with the most negative values occurring in areas of elevated hydrocarbon concentrations. This isotopic depletion is consistent with incorporation of light carbon derived from oxidized hydrocarbons, providing additional evidence for active seepage processes (Abrams, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMagnetic surveys conducted concurrent with geochemical sampling reveal subtle magnetic lows (10\u0026ndash;25 nT below regional background) that spatially coincide with hydrocarbon anomalies. These magnetic depressions likely reflect pyrite formation and magnetite destruction in reducing zones created by hydrocarbon oxidation, providing independent evidence for seepage-related alteration processes.\u003c/p\u003e\u003cp\u003eThe integration of surface geochemical data with structural and stratigraphic information suggests three primary migration pathways: (Abrams, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) fault-controlled vertical migration along reactivated Precambrian structures, (Aadnoy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) lateral migration along permeable sandstone beds, and (Adeleye, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1974\u003c/span\u003e) focused migration through fracture networks associated with igneous intrusions. These pathways connect potential source rocks at depth with surface expression, providing important constraints on petroleum system architecture.\u003c/p\u003e\u003cp\u003eThe data (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) gives a good representation of the key stratigraphic units of the southern Bida Basin, with 68 core/outcrop samples and 247 surface samples tested using various techniques. Geochemical and geomechanical testing of the Agbaja Formation was a factor of the relatively thin development and localized distribution of the Agbaja Formation relative to the Patti and Lokoja Formations. The thicker successions of the Patti and Lokoja Formations provided the most samples, as well as the most analytical results, hence providing the most detailed information regarding reservoir, source and seal properties. The combination of supplementary analytical techniques (XRF, ICP-MS, XRD, GM, and OG) gave a combined analysis of the lithology, mineralogy, mechanical behavior and the hydrocarbon potential. The extensive sample of surface geochemical samples (247) also enhances the petroleum system evaluation by giving information on hydrocarbon leakage on a basin wide basis. The combination of the sampling strategy and the analysis methodology lays a good foundation to reconstruct the depositional environments, diagenesis history as well as petroleum prospectivity of the area being studied.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Integrated Petroleum System Analysis\u003c/h2\u003e\u003cp\u003eThe integration of chemostratigraphic, geomechanical, and petroleum geochemical datasets provides a comprehensive framework for understanding petroleum system development in the southern Bida Basin. This multidisciplinary approach reveals systematic relationships between geological processes and petroleum system elements that would not be apparent from single-parameter studies.\u003c/p\u003e\u003cp\u003eChemostratigraphic correlation demonstrates lateral continuity of potential source rock intervals across distances exceeding 15 km, suggesting basin-wide distribution of organic-rich facies. The Ti/Al and Zr/Al ratios provide particularly robust correlation parameters that remain stable despite surface weathering effects. These correlations enable confident extrapolation of petroleum potential assessments from outcrop locations to broader basin areas.\u003c/p\u003e\u003cp\u003eSource rock distribution shows strong correlation with specific chemostratigraphic intervals, particularly those characterized by elevated V/Al ratios and negative Ce anomalies that indicate reducing depositional conditions. The systematic relationship between redox-sensitive trace elements and organic carbon preservation suggests that chemostratigraphic parameters can serve as predictive tools for source rock occurrence in areas where direct sampling is not feasible.\u003c/p\u003e\u003cp\u003eGeomechanical properties show systematic variations that correlate with both lithology and hydrocarbon potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Organic-rich intervals typically exhibit reduced mechanical strength compared to adjacent rocks, creating zones of mechanical weakness that may enhance hydraulic fracturing effectiveness. The negative correlation between TOC content and Young\u0026rsquo;s modulus (r = -0.63) suggests that organic matter content can be estimated from mechanical property measurements, providing an indirect exploration tool.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of surface geochemical anomalies correlates strongly with zones of reduced mechanical strength and elevated fracture density, supporting interpreted migration pathways through structurally compromised zones. Areas showing the strongest hydrocarbon anomalies typically correspond to locations where fault-controlled fracture networks intersect organic-rich stratigraphic intervals.\u003c/p\u003e\u003cp\u003eThermal maturity patterns reveal the critical role of igneous activity in advancing hydrocarbon generation beyond levels expected from burial history alone. The thermal aureoles surrounding intrusions create zones of enhanced maturity that extend 1\u0026ndash;3 km beyond intrusion contacts, significantly expanding the areas of active generation. This relationship suggests that the timing and distribution of igneous activity may be a controlling factor in petroleum system effectiveness.\u003c/p\u003e\u003cp\u003eThe integrated analysis supports a petroleum system model in which organic-rich intervals of the Patti Formation serve as primary source rocks, with hydrocarbon generation enhanced by Tertiary igneous heating (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Migration occurs along fault-controlled pathways and permeable sandstone beds, with surface expression providing evidence for system activity. Reservoir potential exists in quartz sandstone intervals of the Lokoja Formation, while fine-grained units provide effective sealing capacity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis detailed multidisciplinary study of southern Bida Basin outcrops has established new frameworks for understanding petroleum system development in Nigeria\u0026rsquo;s frontier sedimentary basins. The integration of chemostratigraphic, geomechanical, and petroleum geochemical analyses provides insights that extend far beyond those achievable through single-parameter investigations.\u003c/p\u003e\u003cp\u003eThe chemostratigraphic framework demonstrates that immobile element ratios, particularly Ti/Al and Zr/Al, provide robust correlation tools that remain effective despite tropical weathering effects. These correlations enable basin-wide extrapolation of petroleum system characteristics and support systematic exploration strategies based on predictable stratigraphic relationships. The rare earth element signatures indicate mixed provenance from basement terrains and recycled sedimentary sources, consistent with the intracratonic setting of the Bida Basin.\u003c/p\u003e\u003cp\u003eGeomechanical characterization reveals systematic relationships between lithology, organic content, and mechanical properties that have important implications for unconventional resource development (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The inverse correlation between organic carbon content and rock strength suggests that organic-rich intervals may be preferentially targeted for hydraulic fracturing operations (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Young\u0026rsquo;s modulus values ranging from 12.8 to 89.3 GPa provide essential input parameters for completion design and production optimization.\u003c/p\u003e\u003cp\u003eSource rock evaluation identifies significant petroleum generation potential within organic-rich intervals of the Patti Formation, with TOC contents reaching 2.84 wt% and hydrogen indices up to 234 mg HC/g TOC. The Type II/III kerogen assemblages indicate oil and gas generation potential, while thermal maturity levels (Ro\u0026thinsp;=\u0026thinsp;0.52\u0026ndash;0.89%) suggest active generation processes. The thermal influence of Tertiary igneous intrusions has significantly enhanced maturation beyond levels expected from burial history alone.\u003c/p\u003e\u003cp\u003eSurface geochemical anomalies provide compelling evidence for active petroleum migration, with thermogenic hydrocarbon signatures occurring along fault-controlled pathways and structural highs. The integration of soil gas compositions, isotopic signatures, and magnetic data supports a model of ongoing hydrocarbon seepage from subsurface accumulations.\u003c/p\u003e\u003cp\u003eThe petroleum system model developed from this integrated analysis suggests significant remaining potential for both conventional and unconventional hydrocarbon resources in the southern Bida Basin. Source rock intervals show lateral continuity and favorable generation characteristics, while reservoir-quality sandstones provide exploration targets. The structural complexity and igneous influence create a unique petroleum system that warrants continued investigation and possible commercial evaluation.\u003c/p\u003e\u003cp\u003eFuture research should focus on extending these integrated analyses to other parts of the Bida Basin and similar frontier basins across West Africa. Basin modeling studies incorporating the thermal effects of igneous activity would provide valuable insights into generation timing and migration efficiency. Advanced organic geochemical studies, including compound-specific isotope analysis, could further refine source-oil correlations and migration pathway interpretations.\u003c/p\u003e\u003cp\u003eThe methodological approaches developed in this study demonstrate the value of integrated outcrop investigations in frontier basin evaluation. The combination of chemostratigraphic correlation, geomechanical characterization, and petroleum geochemical analysis provides a comprehensive framework that can be applied to similar geological settings worldwide. This integrated approach represents a significant advance in outcrop-based petroleum system analysis and provides essential tools for early-stage exploration in frontier sedimentary basins.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp; Acknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the management of Federal University Lokoja, for funding the research through institution-based research fund and was invaluable for project success. \u0026nbsp;Laboratory analyses were conducted at the Federal University Lokoja Geological Lab and University of Benin Civil Engineering Laboratory (Nigeria) and the Geomarkland laboratories (Port-Harcourt), with appreciation for high-quality analytical services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of authors contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.N.O. conceived, designed the study and supervised the field studies. He led the integration of chemostratigraphic, geomechanical, and petroleum geochemical analyses also prepared the initial draft of the manuscript and coordinated revisions.\u003c/p\u003e\n\u003cp\u003eG.E.J. contributed to fieldwork, sample collection, and laboratory analyses, including geochemical and geomechanical testing. She participated in data interpretation, drafting specific sections of the methodology and results, and assisted in preparing figures and tables.\u003c/p\u003e\n\u003cp\u003eBoth authors reviewed and approved the final version of the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Informed Consent\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declared no external source of funding\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAadnoy, B.S. (2010). Modern Well Design: Second Edition (2\u003csup\u003end\u003c/sup\u003e ed.). CRC Press. https://doi.org/10.1201/b10431\u003c/li\u003e\n\u003cli\u003eAbrams, M.A., 2005. Significance of hydrocarbon seepage relative to petroleum generation and entrapment. \u003cem\u003eMarine and Petroleum Geology \u003c/em\u003e22(4), 457-477. https://doi.org/10.1016/j.marpetgeo.2004.08.003\u003c/li\u003e\n\u003cli\u003eAdeleye, D.R., 1974. Stratigraphy and sedimentation of the Upper Cretaceous strata in the Bida Basin of Nigeria. \u003cem\u003eJournal of Mining and Geology \u003c/em\u003e9, 25-43. https://doi.org/10.1016/0037-0738(74)90013-X\u003c/li\u003e\n\u003cli\u003eAdeleye, D.R., 1989. The geology of the Middle Niger Basin. In: Kogbe, C.A. ( Ed.), Geology of Nigeria, 2nd ed. Rock View Nigeria Ltd., Jos, pp. 283-287.\u003c/li\u003e\n\u003cli\u003eAkande, S. O., Ojo, O. J., Erdtmann, B. D., \u0026amp; Hetenyi, M. (2005). Paleoenvironments, source rock potential and thermal maturity of the Upper Cretaceous successions in the southern Bida Basin, Nigeria: Organic geochemical perspective. \u003cem\u003eJournal of African Earth Sciences\u003c/em\u003e, 41(3), 394\u0026ndash;406. https://doi.org/10.1016/j.jafrearsci.2005.04.008\u003c/li\u003e\n\u003cli\u003eASTM International, 2008. Standard Test Methods for Compressive Strength and Elastic Moduli of Intact Rock Core Specimens under Varying States of Stress and Temperatures. ASTM D7012-10, West Conshohocken, PA.\u003c/li\u003e\n\u003cli\u003eBerryman, J.G., 1995. Mixture theories for rock properties. In: Ahrens, T.J. ( Ed.), Rock Physics and Phase Relations: A Handbook of Physical Constants. American Geophysical Union, Washington DC, pp. 205-228. https://doi.org/10.1029/RF003p0205\u003c/li\u003e\n\u003cli\u003eBhatia, M.R., 1983. 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Processes controlling the distribution of Ti and Al in weathering profiles, siliciclastic sediments and sedimentary rocks. \u003cem\u003eJournal of \u003c/em\u003e \u003cem\u003eSedimentary Research \u003c/em\u003e68, 448-455. https://doi.org/10.2110/jsr.68.448\u003c/li\u003e\n\u003cli\u003eZoback, M.D., 2007. Reservoir Geomechanics. Cambridge University Press, Cambridge, 449 p. https://doi.org/10.1017/CBO978051158647\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chemostratigraphy, geomechanical properties, petroleum indicators, Nigeria, outcrop analysis, source rock and, reservoir characterization","lastPublishedDoi":"10.21203/rs.3.rs-7694693/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7694693/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Bida Basin represents one of Nigeria\u0026rsquo;s most promising yet underexplored sedimentary sequences, with outcropping formations in the southern sector pro viding unique opportunities for integrated petroleum system analysis. This study presents a comprehensive evaluation combining chemostratigraphic characterization, geomechanical property assessment, and petroleum indicator analysis of Cretaceous to Tertiary outcrops across a 450 km\u003csup\u003e2\u003c/sup\u003e area in the southern Bida Basin. Sixty-eight samples were systematically collected from five measured sections spanning the Lokoja, Patti, and Agbaja Formations. X-ray fluorescence analysis reveals distinct chemostratigraphic signatures with Ti/Al ratios ranging from 0.042 to 0.078, enabling correlation across 15 km of strike length. Rare earth element patterns indicate mixed provenance from both felsic basement and recycled sedimentary sources. Geomechanical testing demonstrates significant lithological control on rock strength properties, with uniaxial compressive strengths varying from 28.4 MPa in poorly cemented sandstones to 165.7 MPa in ironstone horizons. Young\u0026rsquo;s modulus values (12.8\u0026ndash;89.3 GPa) correlate strongly with quartz content and degree of silicification. Organic geochemical analysis identifies three distinct zones of petroleum potential, with total organic carbon contents reaching 2.8 wt% in organic-rich shales. Rock Eval pyrolysis indicates predominantly Type II/III kerogen with hydrogen indices of 89\u0026ndash;234 mg HC/g TOC. Vitrinite reflectance measurements (0.52\u0026ndash;0.89% Ro) suggest early to peak oil generation windows across the study area. Surface geochemical anomalies, including elevated C2-C4 hydrocarbon concentrations and distinctive isotopic signatures, provide evidence for active petroleum migration. Integration of these multidisciplinary datasets establishes a robust framework for regional correlation and petroleum system understanding, with implications for both conventional and unconventional resource assessment in Nigeria\u0026rsquo;s frontier basins.\u003c/p\u003e","manuscriptTitle":"Chemostratigraphy, geomechanical characteristics, and petroleum indicators from southern Bida Basin outcrops, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 15:28:32","doi":"10.21203/rs.3.rs-7694693/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-17T14:59:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T21:36:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T20:21:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228452880715485193853410452639137816548","date":"2025-11-16T03:27:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267517605685435156602659929126942770695","date":"2025-11-11T09:56:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152583568007385662833420864494578785140","date":"2025-10-26T19:22:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"34876516043277383785392674269640906774","date":"2025-10-26T03:19:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76580677043506280712013320643415388890","date":"2025-10-23T09:41:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325984242549741658968138368830777391810","date":"2025-10-21T04:40:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326815426359077615406830543604812856542","date":"2025-10-21T03:01:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-20T23:08:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T06:53:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T06:48:13+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T13:31:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Geoscience","date":"2025-10-10T13:26:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-geoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Geoscience](https://www.springer.com/journal/44288)","snPcode":"44288","submissionUrl":"https://submission.nature.com/new-submission/44288","title":"Discover Geoscience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6d392833-c9cb-4875-8c52-5213b22f4b08","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-06T04:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 15:28:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7694693","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7694693","identity":"rs-7694693","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}