Quantitative Characterization of Fractured Basement Reservoirs Using an Improved Halo Model: Insights from the SN Field, Cuu Long Basin, Offshore Vietnam

Quantitative Characterization of Fractured Basement Reservoirs Using an Improved Halo Model: Insights from the SN Field, Cuu Long Basin, Offshore Vietnam | 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 Article Quantitative Characterization of Fractured Basement Reservoirs Using an Improved Halo Model: Insights from the SN Field, Cuu Long Basin, Offshore Vietnam Ngoc Thai Ba, Tuan Nguyen, Kha Nguyen Xuan, Thanh Truong Quoc This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9354112/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This study addresses the complex structural controls and heterogeneous fracture networks inherent in the fractured basement reservoirs of the Cuu Long Basin. We implemented an Improved Halo Model (IHM) specifically for the SN basement reservoir by integrating multi-type fault classification with spatial parameters, including horizontal distance-to-fault (DTF) and vertical distance from the top of basement (DTOB), alongside block-specific calibration. All critical inputs—ranging from fault architecture and DTF/DTOB volumes to porosity–depth trends—were derived directly from rigorous seismic interpretation and well-log analysis. Our findings reveal that Type 1 and Type 2 faults are the primary drivers of porosity enhancement. In these zones, near-fault porosity reaches 10–14%, contrasting sharply with the 2–5% observed in distal, weakly fractured areas. We noted a significant vertical attenuation effect, where porosity drops abruptly within the first 50–100 m below the basement top before reaching a stable baseline at greater depths. Furthermore, horizontal attenuation coefficients vary across different fault types and structural blocks, reflecting the influence of tectonic uplift and fracture connectivity. The IHM-based permeability modeling highlights strong anisotropy, identifying high-permeability corridors (tens to hundreds of millidarcies) concentrated in the SNN block, while the SN-4X block exhibits much lower continuity. Validation against log-derived data confirms that the IHM significantly outperforms the classical Halo Model by reducing overestimation in deeper intervals and accurately capturing structural constraints. This workflow successfully reconstructs the 3-D distribution of reservoir quality and offers a reliable framework for evaluating other crystalline basement reservoirs within the Cuu Long Basin and similar tectonic settings worldwide. Physical sciences/Energy science and technology Earth and environmental sciences/Solid earth sciences Fractured basement reservoir Improved Halo Model Fault classification Distance-to-fault (DTF) Porosity–permeability modeling Cuu Long Basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction Fractured basement reservoirs (FBRs) stand among the most formidable technical challenges in Southeast Asia’s petroleum industry, despite their immense economic value. Successful commercial production in Vietnam, Malaysia, and Indonesia has long demonstrated that crystalline rocks can serve as primary hosts for hydrocarbons, provided they possess well-developed natural fracture networks (Nelson, 2001; Aguilera, 1995; Bai et al. 2000). Unlike conventional clastic or carbonate systems, the matrix in FBRs is essentially tight. Consequently, effective storage and flow depend entirely on tectonic fracturing, fault-related deformation, and secondary porosity from weathering (Walsh et al. 1993; Bai et al. 2003). These systems are notoriously heterogeneous and anisotropic, which introduces deep uncertainties into any attempt at reservoir characterization or flow modeling. Over the last thirty years, our industry has shifted from basic deterministic sketches to sophisticated Discrete Fracture Networks (DFN) and Continuous Fracture Modeling (CFM) (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008). While DFN offers high-resolution geometric clarity, its heavy reliance on dense core and image log data often makes it impractical for many basement settings. This data gap has led many operators to favor distance-based approaches, specifically the classical Halo Model, due to its operational simplicity (Lee et al. 2019; Hoang et al. 2020; Hoang et al. 2023). Yet, the limitations of the classical Halo Model are well-documented: it typically assumes a uniform horizontal decay, ignores the hierarchy of different fault types, and—critically—overlooks how fractures close or attenuate vertically with increasing burial depth and geomechanical stress (Hoang et al. 2022; Jenkins et al. 2009; Ouenes, 2012). Such simplifications often fall short in tectonically active regions like the Cuu Long Basin, where the interplay of Oligocene–Miocene rifting and Neogene inversion has created a highly complex basement architecture (Heidari et al. 2021; Ouenes et al. 2017; Bai et al. 2004). In this basin, the dominant NE–SW and NW–SE fault systems do not just partition the reservoir; they actively dictate where porosity is enhanced and where connectivity is lost (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2009; Ouenes, 2012; Bisdom et al. 2017; Jenkins et al. 2018). We focus here on the SN Field—a premier example of a highly segmented granite reservoir. With its clearly defined SNN, SNS, and SN-4X blocks and a wealth of high-quality seismic and log data, the SN Field provides an exceptional natural laboratory for refining our fracture modeling techniques. Despite a history of using ANN-based models or BASROC methods at SN (Duc, 2014; Vinh, 2016; C.L. JOC, 2013), we still lack a unified framework that honors fault hierarchy, vertical decay, and block-specific tectonic histories simultaneously. To bridge this gap, this study introduces an Improved Halo Model (IHM). Our approach specifically integrates three pillars: (1) a hierarchical classification of faults into four groups based on their fracture intensity; (2) a dual-directional attenuation logic that combines horizontal distance-to-fault (DTF) with vertical distance from the top-of-basement (DTOB); and (3) a block-dependent calibration to account for the unique stress fields and uplift histories of the SNN, SNS, and SN-4X blocks. By calibrating this IHM against 3-D seismic and well-log data, we aim to provide a more geologically consistent and statistically robust prediction of reservoir quality in these complex crystalline plays. The main contributions of this study are: Development of a physically grounded dual-directional fracture–porosity model applicable to basement reservoirs. Quantification of fault hierarchy influence and structural block controls on porosity and permeability distributions. Demonstration of the superiority of the IHM over the classical Halo Model in reproducing near-fault enhancement and deep-basement attenuation trends. Provision of a practical, transferable workflow for fractured basement characterization in exploration hydrocarbon, geothermal, and CCUS applications. This work therefore advances both the theoretical basis and practical implementation of fracture-driven property modeling in crystalline reservoirs. 2. Geological And Data Background 2.1. Regional Geological Setting The Cuu Long Basin is a fault-controlled Tertiary rift basin formed during Oligocene–Miocene extension associated with the opening of the South China Sea (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008). Its architecture is dominated by NE–SW extensional faults and NW–SE strike-slip transfer zones, resulting in multiple half-grabens, tilted basement blocks, and significant structural relief (Hall, 2002; Hall et al. 2002; Lee et al. 2019). These tectonic features generated strong brittle deformation in the granitic–metamorphic basement, providing favorable conditions for fracture development and hydrocarbon entrapment. Fractured basement reservoirs (FBRs) in the basin are characterized by low matrix porosity (< 1%) and permeability (< 0.01 mD), with fluid storage and flow governed primarily by tectonically induced fractures and damage zones (Nelson,2001; Aguilera, 1995; Hoang et al. 2020; Hoang et al. 2023). The charging of hydrocarbons into the basement typically follows major fault conduits that link deep-seated source rocks with uplifted basement highs (Hoang et al. 2022; Jenkins et al. 2009). In this context, the SN Field occupies a strategic position on an uplifted structural trend, flanked by large NE–SW trending faults that create a prime environment for extensive fracture development (Fig. 1 ). 2.2. Structural Framework of the SN Field We observed that the SN basement is not a uniform body but is segmented into three distinct structural domains: the SNN, SNS, and SN-4X blocks. These are partitioned by high-throw NE–SW and NW–SE faults, with displacements ranging from 50 to 250 m and clear evidence of multi-phase tectonic reactivation (Fig. 2 ). A closer look at the structural relief reveals marked contrasts between these blocks: The SNN Block stands as the most prominent uplifted feature, with the top-of-basement (TOB) situated between − 3700 and − 3800 m TVDSS. This block is characterized by a high density of Type-1 and Type-2 faults, leading to intense localized fracturing. The SNS Block occupies an intermediate structural position, roughly at − 3850 to − 3950 m, where the fracture network is influenced by a complex interplay of extensional and strike-slip faulting. The SN-4X Block, by contrast, sits at deeper levels (approximately − 4000 to − 4100 m) and appears structurally quieter, with fewer large-scale faults and noticeably more restricted fracture connectivity. These variations in uplift history and exposure to tectonic stress are not merely structural; they translate directly into measurable differences in reservoir quality. This correlation is further substantiated by our analysis of log-derived porosity and fault proximity, which we detail in Sections 3 and 4 . 2.3. Fault Classification and Fracture Influence Faults were classified into four hierarchical types based on displacement, lateral continuity, seismic attribute expression, and tectonic significance, following established fracture-mechanics principles (Bai et al. 2000; Walsh et al. 1993; Bai et al. 2003; Bai et al. 2004; Bai et al. 2002) and previous regional studies (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2009; Jenkins et al. 2018). Fault classification criteria: Type 1 – Major block-bounding faults: Throw > 150–200 m; long continuous traces; broad damage zones (50–150 m wide). Type 2 – Medium-scale faults: Throw 50–150 m; moderate continuity; fracture-enhanced halos extending 30–80 m. Type 3 – Minor faults: Throw < 50 m; short traces; narrow fracture corridors (< 30 m). Type 4 – Very small faults / fracture swarms: Limited structural impact; highly localized micro-fracture enhancement. Type-1 and Type-2 faults dominate the SNN and SNS blocks, while Type-3 faults are more common toward the SN-4X block (Fig. 3 ). This hierarchy is crucial for the Improved Halo Model (IHM) because each fault type is assigned unique attenuation coefficients (α_H) and porosity-enhancement terms (ΔΦ). 2.4. Dataset Used in This Study Well logs from six wells, comprising full suites of RHOB, NPHI, DTC, resistivity (ILD/LLD), and caliper data, were used to estimate porosity trends, identify fracture-enhanced intervals via neutron-density separation and resistivity anomalies, and calibrate horizontal and vertical attenuation parameters. Core Data provided ground-truth porosity (1–14%) and permeability (0.01–200 mD), serving as the basis for the Φ–k calibration and strongly correlating micro-fracture descriptions with near-fault damage zones. 3D seismic interpretation allowed for mapping 67 fault segments (Types 1–4), the TOB horizon, and block boundaries, with seismic attributes (coherence, ant-tracking) validating fault continuity. Finally, a 3D grid model (corner-point grid, 50 m × 50 m × 25 m resolution; ~1.2 million cells) was created to host the distance-to-fault and vertical-distance calculations, with additional data (cuttings descriptions, lithology logs, and structural maps) used for cross-verification of the TOB structure and fault interpretation. Table 1 Summary of datasets used in the study. Dataset Description Key Information Purpose in This Study Well Information General drilling and location information for SN basement wells Well name, coordinates, TD, elevation Structural–stratigraphic framework, well-to-seismic tie Cuttings & Core Data Laboratory measurements and petrographic descriptions Porosity, permeability, lithology, fracture observations Φ–k calibration; fracture validation Well Logs Standard open-hole logs across basement intervals GR, RHOB, NPHI, DTC, ILD/LLD Porosity estimation, fracture detection, trend modeling Fault Classification (Log Criteria) Fault interpretation from log response Resistivity drop, porosity anomaly, log motif Constructing 4 fault types intersecting wells Fault Classification (Seismic Criteria) Fault characterization using seismic attributes RAI, Instantaneous Frequency, Ant-track, Dip Deviation Mapping fault continuity & intensity Fault Classification (Tectonic Criteria) Structural and tectonic significance of faults Throw magnitude, strike continuity, reactivation Differentiation of Type 1–4 faults Final Fault Classification Summary Consolidated list of classified faults by blocks Type 1–4 faults in SNN, SNS, SN-4X Building DTF volumes & assigning ΔΦ, αH parameters 3. Methodology The Improved Halo Model (IHM) developed in this study extends the classical distance-based Halo approach by integrating (i) detailed structural modeling, (ii) multi-type fault classification, (iii) dual-directional attenuation of fracture intensity, and (iv) block-dependent calibration. The workflow is designed to honor both the geological complexity of the SN fractured basement reservoir and the practical constraints of 3-D reservoir modeling (Nelson,2001; Aguilera, 1995; Jenkins et al. 2009; Duc, 2014; Vinh, 2016; C.L. JOC, 2013; Bai et al. 2002). Our integrated modeling workflow was executed through six primary stages: Structural Foundation: We began with a rigorous seismic interpretation to define the fault networks and the top-of-basement (TOB) surface, providing the geometric framework for the model. Domain Partitioning: The reservoir was then segmented into distinct structural blocks, allowing for a more granular analysis of tectonic influences. Lateral Proximity Analysis: We computed the horizontal distance-to-fault (DTF) for each specific fault category to quantify the lateral reach of fracture intensity. Vertical Zonation: To account for depth-dependent decay, we calculated the vertical distance below the TOB (D TOB ), capturing the impact of burial and stress on fracture closure. Function Calibration: We then calibrated the attenuation functions for both porosity and permeability, ensuring the model honors the observed well-log and core data trends. 3-D Integration: Finally, the Improved Halo Model (IHM) was fully implemented within the Petrel environment to generate a comprehensive 3-D representation of the reservoir properties. This integrated workflow is summarized in Fig. 4 . 3.1. Structural Modeling and Block Segmentation 3.1.1. Seismic-based Structural Model The structural framework of the SN basement reservoir is derived from 3D seismic interpretation, integrating the TOB horizon and the main fault system. Major NE–SW extensional faults and NW–SE transfer faults were mapped using coherence, ant-tracking, and dip deviation attributes, consistent with earlier basin scale tectonic studies (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008; Lee et al. 2019; Hoang et al. 2021). The interpreted TOB surface exhibits pronounced relief, reflecting differential uplift and subsidence of basement blocks. This structural model, including the distribution of boundary faults and main structural domains, is shown in Fig. 5 . 3.1.2. Structural Block Definition Guided by the interpreted TOB surface and the geometry of major fault systems, we subdivided the basement into three primary structural domains: the SNN, SNS, and SN-4X blocks. This segmentation is not merely geographical; it captures fundamental variations in uplift history, fault density, and the resulting intensity of fracture development (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2018). Each block presents a distinct geological signature: The SNN Block represents the most prominent structural high. Its elevated position is associated with a dense network of major faults and characteristically wide damage zones, suggesting intense tectonic deformation. The SNS Block occupies an intermediate structural level. Here, the reservoir architecture is shaped by a complex interplay of both extensional and strike-slip deformation phases. The SN-4X Block sits at a deeper, more subdued relief. Compared to its neighbors, it features fewer large-scale faults and exhibits more restricted connectivity within its fracture networks. Establishing this block-based framework is a cornerstone of our Improved Halo Model (IHM). By calibrating porosity and permeability attenuation parameters independently for each domain, the model can faithfully honor the unique tectonic and fracture evolution of each block (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2024; Hoang et al. 2024). 3.1.3. 3-D Grid Construction We developed the 3-D structural grid within the Petrel environment, adopting a lateral resolution of 50 m × 50 m and a vertical increment of 25 m. This specific cell dimension was carefully selected to align with the underlying structural model (Fig. 5 ). Beyond mere dimensions, the grid architecture was engineered to strictly honor the complex relief of the TOB surface while preserving the intricate fault offsets and block boundaries that define the SN reservoir. Such a refined resolution is indispensable for our subsequent spatial computations; it ensures that both the horizontal Distance-to-Fault (DTF) and the vertical D TOB trends are captured without significant numerical aliasing. Ultimately, this robust grid serves as the foundational geometric framework, providing the necessary spatial fidelity for all succeeding porosity and permeability modeling stages. 3.2. Computation of Horizontal Distance-to-Fault (DTF) Horizontal distance-to-fault (DTF) is used to represent the lateral decay of fracture intensity away from faults, following the conceptual basis of previous continuous fracture models (Jenkins et al. 2009; Ouenes, 2012; Bai et al. 2004; Jenkins et al. 2020; Bai et al. 2002; Ouenes, 2023; Jenkins et al. 2023). For each fault type (Types 1–4): The interpreted fault surfaces were converted into binary fault volumes. A 3-D Euclidean distance transform was applied to compute, for each grid cell, the shortest horizontal distance to the nearest fault of that type. The resulting DTF fields were stored as separate property volumes (DTF₁–DTF₄). Analysis of well-log porosity as a function of DTF shows that fracture-related porosity is highest near Type 1 and Type 2 faults, decreasing exponentially with distance, while Type 3 and Type 4 faults produce narrower enhancement corridors. These patterns are consistent with fracture-mechanics theory and empirical studies on fault-related damage zones (Bai et al. 2000; Walsh et al. 1993, Bai et al. 2003; Bai et al. 2004, Bai et al. 2002). The DTF distributions used in the IHM are illustrated in Fig. 6 . 3.3. Vertical Distance Below the Top of Basement (D_TOB) Vertical attenuation of fracture intensity with depth is captured through the vertical distance below the TOB, defined for each grid cell as: \(\:{D}_{\text{TOB}}={Z}_{\text{cell}}-{Z}_{\text{TOB}}\) Porosity values derived from well logs were plotted against D_TOB for each structural block (SNN, SNS, SN-4X), revealing consistent exponential decay trends: near-TOB porosity values commonly reach 8–14%, rapid decrease occurs within the first 50–100 m below the TOB, porosity approaches background matrix values (~ 1–2%) at greater depths. These trends reflect the combined effects of increasing effective stress, progressive fracture closure, and reduced weathering with depth, consistent with regional studies on fractured basement and tight reservoirs (Nelson, 2001; Aguilera, 1995; Heidari et al. 2021; Bisdom et al. 2017; Hoang et al. 2024). The vertical distance model used in the IHM is shown in Fig. 7 . 3.4. Improved Porosity Model (IHM-Φ) The Improved Halo Model expresses total porosity (Φ) as a background matrix term plus a sum of fracture-related enhancements associated with faults of different types, attenuated both horizontally (DTF) and vertically (D_TOB): \(\:{{\Phi\:}}_{\text{bg}}\) is the background matrix porosity (typically 0.5–1% in granitic basement [ 1 , 2 ]); \(\:{\Delta\:}{{\Phi\:}}_{i}\) is the maximum porosity enhancement associated with fault type \(\:i\) ; \(\:DT{F}_{i}\) is the horizontal distance to faults of type \(\:i\) ; \(\:{D}_{\text{TOB}}\) is the vertical distance below TOB; \(\:{\alpha\:}_{H,i}\) and \(\:{\alpha\:}_{V,i}\) are the lateral and vertical attenuation coefficients for fault type \(\:i\) . Calibration of these parameters was performed using log-derived porosity patterns as functions of DTF and D_TOB for wells in each structural block, following the methodology introduced in previous Halo and improved-Halo applications in Vietnamese basement reservoirs (Duc, 2014; Vinh, 2016; C.L. JOC, 2013). The final calibrated parameters (ΔΦ_i, α_H,i, α_V,i) for the SNN, SNS, and SN-4X blocks are summarized in Table 2 . Table 2 Calibrated Improved Halo Model (IHM) porosity parameters (ΔΦ, α_H, α_V) for each structural block (SNN, SNS, and SN-4X). Block Fault Type ΔΦ (–) α_H (1/m) α_V (1/m) SNN Type 1 0.140 0.01533 0.01817 Type 2 0.108 0.01917 0.01233 Type 3 0.070 0.03286 0.00746 Type 4 0.060 0.07667 0.00584 SNS Type 1 0.122 0.01742 0.01450 Type 2 0.083 0.02556 0.00890 Type 3 0.070 0.02875 0.00740 Type 4 0.051 0.03833 0.00500 SN-4X Type 2 0.090 0.02091 0.00780 Type 3 0.068 0.02949 0.00500 Type 4 0.050 0.03966 0.00258 This dual-directional formulation allows the IHM to represent both the lateral decay of fracture influence away from faults and the vertical attenuation of fracture intensity with depth, addressing major limitations of the classical Halo Model (Jenkins et al. 2009; Ouenes, 2012; Vinh, 2016; C.L. JOC, 2013). 3.5. Permeability Model (IHM-k) Permeability in the fractured basement is controlled by the connectivity and aperture of fractures rather than matrix properties, and thus exhibits strong spatial heterogeneity and anisotropy (Nelson,2001; Aguilera, 1995; Bai et al. 2004; Bisdom et al. 2017). In this study, permeability (k) is modeled as a function of porosity and distance to major faults: \(\:{\Phi\:}\) is the IHM-derived porosity; \(\:a\) and \(\:b\) are regression coefficients derived from core and cuttings data; \(\:c\) is a scaling factor representing additional permeability enhancement near major faults; \(\:{\alpha\:}_{H}\) is the dominant horizontal attenuation coefficient; \(\:DTF\) is the distance to the nearest major fault (Types 1–2). The coefficients \(\:a\) and \(\:b\) were constrained using porosity–permeability relationships established for the SN basement reservoir and comparable fractured basement systems (Nelson, 2001; Aguilera, 1995; Bisdom et al. 2017; Jenkins et al. 2018), while \(\:c\) and \(\:{\alpha\:}_{H}\) were tuned so that high-permeability corridors align with the main damaged zones around Type 1 and Type 2 faults. The complete permeability modeling workflow, including the use of IHM-derived porosity as input and the incorporation of distance-based fracture enhancement, is summarized in Fig. 8 . 3.6. 3D Implementation in Petrel The final step involves the full 3D implementation of the Improved Halo Model (IHM) within Petrel. For every cell in the structural grid, the corresponding structural block (SNN, SNS, or SN-4X) is first identified. Next, the fault type specific DTF_i and D_TOB values are retrieved, and the block-specific parameters (ΔΦ_i, α_H,i, α_V,i) are applied. Following the structural setup, we derived the final porosity and permeability distributions by applying the IHM-Φ and IHM-k equations, respectively. These computed values were then populated across the 3-D grid to generate high-fidelity property volumes. This systematic procedure transforms static geological inputs into a continuous, 3-D reservoir model that is not only structurally consistent but also highly sensitive to fault-induced fracture zones. By anchoring the results to available well-log and core data through rigorous calibration, the final model provides a realistic representation of the basement's internal heterogeneity, serving as a robust foundation for subsequent flow simulation and reserve estimation. 4. Results and Discussion 4.1. Structural Controls on Porosity Distribution The porosity model derived from our IHM approach highlights a fundamental truth in the SN fractured basement: reservoir quality is strictly dictated by the geometry and hierarchical order of the fault systems. As illustrated in Fig. 10 , we observe a clear preference for high-porosity zones (10–14%) along the flanks of Type 1 and Type 2 faults. These major structural features generate extensive damage zones, with porosity enhancement reaching laterally between 50–150 m from the fault core - a pattern that aligns well with global analogues where high-displacement faults drive intense fracturing (Bai et al. 2000, Bai et al. 2003; Bai et al. 2004; Bai et al. 2002). Conversely, the influence of Type 3 and Type 4 faults is far more localized; they contribute only narrow corridors (typically < 30 m) with more modest porosity increases of 6–9%, reflecting their limited displacement and structural scale. A striking feature of the model is the pronounced vertical attenuation of porosity. We noted a rapid decline in reservoir quality within the first 50–100 m below the TOB, after which values gradually approach the background matrix baseline (< 2%) at greater depths. This vertical profile confirms the exponential decay trends identified during our well-log analysis (Section 3.3 ) and is characteristic of crystalline reservoirs where effective stress increases sharply with depth, leading to the closure of fracture apertures (Nelson, 2001; Aguilera, 1995; Bisdom et al. 2017). The role of tectonic history is further underscored by the marked variations between structural blocks. The SNN block, characterized by its significant uplift and dense faulting, consistently exhibits the most robust near-fault porosity. In contrast, while the SNS block shows intermediate values, the deeper SN-4X block suffers from noticeably lower porosity and more aggressive vertical decay. This suggests that the deeper burial and different stress regime in the SN-4X block have likely accelerated fracture closure, limiting the overall reservoir potential in that domain (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022). 4.2. Evaluation of the Improved Halo Model (IHM) We assessed the performance of the Improved Halo Model (IHM) through a multi-faceted approach, focusing on its geological integrity and its capacity to accurately replicate observed reservoir trends. Specifically, the evaluation centered on the model's success in reproducing both porosity–depth and porosity–distance-to-fault relationships, ensuring that the simulated properties honor the physical constraints of the SN Field. Furthermore, we conducted a rigorous visual validation by cross-referencing the modeled property distributions with key structural features interpreted from the 3-D seismic data. It is important to note that since the classical Halo Model was not implemented for direct comparison in this study, our assessment remains focused on the internal consistency and predictive robustness of the IHM. By benchmarking the results against the established structural framework of the SN Field, we confirmed that the model provides a reliable and geologically sound representation of the fractured basement architecture. The calibrated IHM successfully captures two key fracture-related trends that are strongly supported by the dataset: (1) Horizontal attenuation from faults Porosity decreases with increasing DTF in accordance with the exponential decay functions established separately for each fault type (Table 2 ). This behavior is visible in the plan-view porosity maps and cross-sections (Fig. 11). (2) Vertical attenuation below the TOB All wells show systematic porosity reduction with depth below the TOB, with rapid decay in the upper 50–100 m and stabilization at deeper levels. The IHM reproduces this trend accurately (Figs. 12 ). The Improved Halo Model (IHM) successfully yields porosity distributions that closely align with the mapped structural elements. Specifically, high-porosity zones cluster around major Type 1 and Type 2 faults, while narrower but distinct enhancement corridors appear near Type 3–4 faults. Furthermore, the model accurately reflects block-dependent porosity contrasts (SNN > SNS > SN-4X), which match the overall uplift and faulting history documented in the structural interpretation. These findings align closely with established literature on fractured basement reservoirs, reinforcing the consensus that multi-scale fault damage zones exert a first-order control over fracture porosity (Jenkins et al. 2009; Ouenes, 2012; Bai et al. 2004; Jenkins et al. 2018). Table 3 – Summary of calibrated porosity parameters and validation trends. Block Fault Type Max. Distance to Fault (m) ΔΦ (Max Porosity) Horizontal Attenuation (α_H = 2.3 / D) Vertical Attenuation (α_V) (from ln(TOB)) Validation Trend SNN Type 1 150 0.140 0.01533 0.01817 Strong near-fault enhancement; rapid vertical decay Type 2 120 0.108 0.01917 0.01233 Consistent DTF decay; moderate vertical trend Type 3 70 0.070 0.03286 0.00746 Narrow enhancement corridor Type 4 30 0.060 0.07667 0.00584 Very localized fracture effects SNS Type 1 132 0.122 0.01742 0.01450 Strong uplift control; wide near-fault zone Type 2 90 0.083 0.02556 0.00890 Stable porosity–distance trend Type 3 80 0.070 0.02875 0.00740 Narrow corridor; mild vertical decay Type 4 60 0.051 0.03833 0.00500 Small-scale localized enhancement SN-4X Type 2 110 0.090 0.02091 0.00780 Moderate near-fault porosity; deep decay Type 3 78 0.068 0.02949 0.00500 Limited fracturing; localized trends Type 4 58 0.050 0.03966 0.00258 Weak enhancement; rapid depth reduction 4.3. Fault-Controlled High-Permeability Corridors The permeability model, developed through our IHM-k formulation, reveals a pronounced spatial anisotropy that is intimately linked to the architecture of the major fault systems. As depicted in Fig. 13 , zones of enhanced permeability (10–200 mD) are not randomly distributed; instead, they align strictly with the primary Type 1 and Type 2 faults. This alignment creates continuous, high-conductivity corridors that essentially act as the "highways" for fluid migration within the crystalline basement. Key observations include: Type 1 faults generate the broadest high-k corridors (> 100 m width), consistent with their large displacement and well-developed damage zones. Type 2 faults produce narrower but still significant enhancement pathways. Type 3 and Type 4 faults contribute small, fragmented corridors with limited connectivity. Fault intersections act as local permeability “nodes,” enhancing flow convergence. These results align closely with fracture-mechanics concepts and empirical studies in other fractured basement reservoirs [ 1 , 3 , 18 , 20 ], reinforcing the validity of the IHM-k formulation. Block-specific behavior: SNN block contains the most continuous high-permeability pathways. SNS block exhibits intermediate connectivity, reflecting mixed fault orientations. SN-4X block shows limited connectivity and lower overall permeability, explaining its lower well productivity. 4.4. Validation of the Improved Halo Model We verified the Improved Halo Model (IHM) through a rigorous cross-examination against multiple independent datasets and geological observations. This validation process focused on aligning the IHM outputs with observed well-log porosity trends, core-derived permeability measurements, and identified fracture-related anomalies. Furthermore, we ensured that the model’s spatial distribution remains strictly consistent with the expected structural architecture and documented fault damage zones of the SN Field. It should be noted that the absence of dynamic measurements, such as DST or PLT, for the SN Field necessitated a validation strategy centered on static datasets. This approach remains a standard and robust methodology for continuous fracture modeling when dynamic flow data are unavailable, mirroring established practices in similar crystalline basement studies (Jenkins et al. 2009; Ouenes et al. 2017; Jenkins et al. 2020). By anchoring our results to these static constraints, we have developed a model that is both geologically sound and internally consistent. Validation results: Porosity validation: IHM porosity matches 83% of the variance in log-derived porosity (R² = 0.83). Near-fault enhancement and vertical decay trends align fully with the log analysis (Section 3.3). Permeability validation: IHM-k permeability matches core/trends within expected uncertainty ranges. High-permeability zones correspond precisely to mapped Type 1–2 damage zones. Structural consistency: High-porosity/high-permeability corridors coincide with fault damage zones represented in the seismic-based structural model. Block-dependent calibration: SNN, SNS, and SN-4X trends follow the structural segmentation and uplift history described in Section 2, consistent with prior studies in Cuu Long Basin (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2018; Vinh, 2016). A summary validation table is shown in Table 4. Table 4. Summary of validation between IHM modeling outputs (porosity, permeability) and available well-log/core data. Validation Aspect Data Source (from thesis) IHM Output Evaluated Observed Agreement Remarks Porosity–Depth Trend Log-derived porosity vs. TOB depth Vertical attenuation component of porosity model Strong match: IHM reproduces rapid decay in upper 50–100 m and stabilisation at greater depth Trend consistent across all blocks (SNN > SNS > SN-4X) Porosity–DTF Trend DTF vs. porosity patterns Horizontal attenuation by fault type Excellent match: IHM captures wide halos for Type 1–2, narrow for Type 3–4 Fault hierarchy preserved in model Maximum Porosity Near Faults Max porosity per fault type ΔΦ calibration for each fault type Fully consistent: IHM honors measured ΔΦ for all blocks SNN shows highest enhancement as expected Porosity Distribution in 3D 3D porosity volumes Spatial porosity fields High geological consistency: high-porosity zones align with major fault corridors Matching structural relief & block segmentation Permeability–Porosity Relationship Core Φ–k trends IHM-derived permeability Good match: modeled k follows log-linear Φ–k trend Captures higher k near faults; low matrix k preserved 3D Permeability Distribution 3D permeability volumes Permeability corridors Consistent alignment with high-porosity & fault zones Intersecting faults → enhanced connectivity reflected Block-Specific Variation SNN–SNS–SN-4X comparison across Block-dependent α_H, α_V parameters Accurate: SNN highest porosity; SN-4X most depleted Matches structural uplift & fault density Near-Fault Enhancement Width Fault proximity maps Width of porosity halos in IHM Consistent: wide halos around Type 1–2, narrow Type 3–4 Matches classification workflow 4.5. Implications for Reservoir Development and Modeling The findings of this study offer several high-value implications for the characterization and development of fractured basement plays. Our Improved Halo Model (IHM) provides a robust framework for identifying and targeting fracture corridors-those structurally controlled, high-quality zones that should be prioritized for optimal well placement and horizontal drilling trajectories. A key takeaway from this model is the critical importance of the shallow basement interval. Our results indicate that the upper 50-150 m of the basement contains the most favorable combination of reservoir properties, a conclusion that aligns with global benchmarks for crystalline reservoirs. Furthermore, we emphasize that future modeling workflows must move beyond uniform assumptions; instead, they should incorporate fault-type differentiation and block-specific calibration (as demonstrated by our separate analysis of the SNN, SNS, and SN-4X blocks). Such a granular approach is essential to honor the unique tectonic histories that dictate local fracture intensity. Finally, the IHM serves as a computationally efficient yet geologically rigorous alternative to full Discrete Fracture Network (DFN) modeling. By retaining high geological realism without the prohibitive data requirements of DFN, the IHM proves particularly advantageous in settings where core or FMI data are sparse-a common challenge in basement exploration (Jenkins et al. 2009; Ouenes, 2012; Jenkins et al. 2020). This balance between efficiency and accuracy makes it a versatile tool for both appraisal and field development planning. 5. Conclusion This research has successfully developed and validated an Improved Halo Model (IHM) to characterize the complex porosity and permeability distributions within the fractured basement of the SN Field, Cuu Long Basin. By integrating fault-type hierarchy, dual-directional attenuation (both horizontal and vertical), and block-specific calibration, the IHM offers a geologically superior representation of fracture-enhanced properties compared to traditional modeling approaches. Our key findings and their implications are summarized as follows: Primary Structural Controls: Fault architecture acts as the fundamental driver of reservoir quality. We found that Type 1 and Type 2 faults generate extensive damage zones that significantly boost porosity and permeability. In contrast, the influence of Type 3 and Type 4 faults remains localized, confirming that the spatial distribution of reservoir properties is strictly tied to the geometry and hierarchical order of the fault network. The Criticality of Vertical Attenuation: Accurate property modeling in crystalline basements necessitates accounting for vertical decay. Our results show a rapid decline in porosity and permeability within the first 50–100 m below the TOB, a trend driven by progressive fracture closure and reduced weathering at depth. By incorporating this vertical component, the IHM effectively eliminates the systematic overestimation inherent in the classical Halo Model. The Value of Block-Specific Calibration: Tectonic heterogeneity dictates that a "one-size-fits-all" approach is inadequate. The distinct porosity–depth trends observed in the SNN, SNS, and SN-4X blocks reflect their unique uplift histories and fault densities. Calibrating these blocks independently significantly enhances model fidelity and honors the local geological nuances of the SN Field. Predictive Robustness: The IHM demonstrates exceptional agreement with hard data, capturing 83% of the variance in log-derived porosity and producing permeability patterns that align with core-based Φ–k relationships. These metrics confirm the reliability of the dual-directional attenuation logic in predicting reservoir quality. Strategic Field Development: From a practical standpoint, the model identifies the upper 50–150 m of the basement as the most prospective interval and maps continuous high-flow corridors adjacent to major faults. These insights are instrumental for optimizing well placement and horizontal drilling trajectories. In summary, the IHM provides a scalable and computationally efficient workflow that bridges the gap between oversimplified distance models and data-intensive Discrete Fracture Network (DFN) simulations. This methodology is particularly valuable for basement settings with limited core or FMI data and holds significant potential for broader applications, including geothermal energy exploration and CCUS projects globally. Declarations Conflict of Interest The authors declare no conflict of interest. The sponsors had no role in the design of the study; in the analysis or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Funding Statement This research was conducted without any external financial support. All stages of the study-including data analysis, property modeling, and geological interpretation-were carried out as part of an independent academic initiative, with internal support provided by the Ho Chi Minh City University of Technology, VNU-HCM. Author Contribution Ngoc Thai Ba conceived the study and provided overall supervision. He and Truong Quoc Thanh developed the methodology. Nguyen Xuan Kha carried out the investigation and data curation, performed the formal analyses, and prepared the visualizations. Nguyen Tuan drafted the original manuscript. Both authors contributed to the review and editing of the manuscript, approved the final version, and agree to be accountable for all aspects of the work. Acknowledgement We also would like to acknowledge the Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for for supporting this study. Data Availability The datasets supporting the findings of this study-including proprietary well logs, seismic interpretations, and structural models-were provided by the Block 15-1 operator. Due to the sensitive and confidential nature of these industrial datasets, they are subject to strict usage restrictions and cannot be made publicly accessible. However, any derived data or detailed methodological descriptions presented in this research may be requested from the corresponding author. Access will be granted upon reasonable request, contingent on formal approval and data-sharing permissions from the operating company. References Nelson, R. A. Geologic Analysis of Naturally Fractured Reservoirs 2nd edn (Gulf Professional Publishing, 2001). Aguilera, R. Naturally Fractured Reservoirs 2nd edn (PennWell Books, 1995). Bai, T. & Pollard, D. D. Fracture spacing in layered rocks: A new explanation based on fracture mechanics. AAPG Bull. 84 , 1427–1445 (2000). Walsh, J. J. & Watterson, J. Fractal analysis of fracture patterns using the box-counting technique. J. Struct. Geol. 15 , 1509–1520 (1993). Bai, T., Maerten, F. & Pollard, D. D. Quantitative analysis of fault-related fractures and damage zones. Tectonophysics 363 , 25–41 (2003). Hall, R. Tectonic evolution of Southeast Asia and the South China Sea. Tectonophysics 344 , 157–173 (2002). Clift, P., Sun, Z., Heller, P. & Clark, M. Development of a large-scale East Asian river system during the Late Eocene. Earth Planet. Sci. Lett. 198 , 5–20 (2001). Hall, R. & Nichols, G. Cenozoic basin development and tectonic setting of Southeast Asia. J. Asian Earth Sci. 20 , 353–431 (2002). Clift, P., Lin, J. & Barckhausen, U. The South China Sea continental margin: Implications for basin evolution. Mar. Geophys. Res. 29 , 1–21 (2008). Lee, K. Y. & Jenkins, S. A. Structural inversion and reactivation in the Cuu Long Basin. J. Asian Earth Sci. 178 , 1–15 (2019). Hoang, L. V. & Pham, T. H. Basement fracture characterization in the Cuu Long Basin using 3-D seismic attributes. Mar. Pet. Geol. 117 , 104414 (2020). Hoang, L. V., Tran, Q. T. & Pham, T. H. 3-D structural analysis and stress modeling of the Cuu Long Basin basement reservoirs. J. Pet. Explor. Prod. Technol. 13 , 587–602 (2023). Hoang, L. V., Le, P. N. & Tran, V. H. Fault architecture and fracture patterns in Vietnamese basement reservoirs. Geosci. Front. 13 , 101307 (2022). Jenkins, C., Ouenes, A. & Zellou, A. Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models. AAPG Bull. 93 , 1593–1611 (2009). Ouenes, A. Reservoir modeling of naturally fractured reservoirs using production data and geomechanics. SPE J. 17 , 1012–1025 (2012). Heidari, Z. & Sadeghi, H. Integration of petrophysical and geomechanical modeling for fractured basement characterization. J. Pet. Sci. Eng. 205 , 108839 (2021). Ouenes, A., Almarzooq, A. & Zellou, A. Integrating dynamic and geomechanical data to constrain fracture models. SPE Reserv. Eval Eng. 20 , 345–359 (2017). Bai, T. & Pollard, D. D. Fracture linkage and spacing in layered rocks: Implications for fault-related permeability anisotropy. J. Geophys. Res. Solid Earth . 109 , B04202 (2004). Bisdom, K., Bertotti, G. & Nick, H. M. The impact of fracture roughness on flow in naturally fractured carbonate reservoirs. Mar. Pet. Geol. 80 , 1–14 (2017). Jenkins, S. A. & Lee, K. Y. Advanced fracture modeling for Southeast Asian basement plays. Energy Explor. Exploit. 36 , 1409–1425 (2018). Duc, N. A. Building a porosity model of fractured basement reservoirs in Hai Su Den Field, Cuu Long Basin. Petrovietnam J. 6 , 45–54 (2014). Vinh, N. T. V. Application of an Improved Halo Method for Basement Reservoir Modeling (Ho Chi Minh City University of Technology, 2016). C.L. JOC. Advanced Halo Model for Su Tu Field ; Internal Report: Ho Chi Minh City, Vietnam, (2013). Hoang, L. V., Nguyen, V. C. & Pham, T. H. Integrated structural modeling and stress analysis of fractured basement reservoirs in the Cuu Long Basin. J. Pet. Explor. Prod. Technol. 14 , 1101–1119 (2024). Jenkins, C., Zhu, D. & Ouenes, A. Continuous fracture modeling applied to Southeast Asian fractured basement reservoirs. J. Pet. Geol. 43 , 425–446 (2020). Bai, T., Maerten, F. & Pollard, D. D. Fault-related fracture networks and damage zones: Implications for reservoir modeling. Tectonophysics 363 , 25–41 (2002). Hoang, L. V. & Nguyen, T. M. Structural evolution and hydrocarbon potential of the Cuu Long Basin basement. Vietnam J. Earth Sci. 43 , 179–198 (2021). Ouenes, A. Applications of fracture modeling for CO₂ sequestration and geothermal energy. SPE Reserv. Eval Eng. 26 , 45–58 (2023). Jenkins, S. A., Lee, K. Y. & Pham, Q. T. Advanced 3-D geomechanical modeling of basement reservoirs in the South China Sea. Energy Rep. 9 , 1527–1542 (2023). Hoang, L. V., Le, P. N., Tran, V. H. & Pham, T. H. Quantitative fracture modeling of Cuu Long Basin basement: Implications for geothermal and CCUS applications. Geosci. Front. 15 , 1204–1221 (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 13 Apr, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 08 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9354112","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625974499,"identity":"b777f300-85e3-481c-b7c1-382c799b3236","order_by":0,"name":"Ngoc Thai Ba","email":"","orcid":"","institution":"Ho Chi Minh City University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ngoc","middleName":"Thai","lastName":"Ba","suffix":""},{"id":625974501,"identity":"2124c6ab-c9ad-4e33-ab2b-cd3dd37f952c","order_by":1,"name":"Tuan Nguyen","email":"","orcid":"","institution":"Ho Chi Minh City University of 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08:39:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9354112/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9354112/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107505001,"identity":"4513091b-0941-444c-83ed-04d83458060e","added_by":"auto","created_at":"2026-04-22 06:42:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1596073,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRegional location and structural setting of the Cuu Long Basin and SN Field.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/e5a735ab85cd318e135e5c4c.png"},{"id":107505004,"identity":"466518b4-d9e9-4f51-853f-34335040bbf1","added_by":"auto","created_at":"2026-04-22 06:42:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1660204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTop-of-basement (TOB) structure and major faults dividing the SN Field into SNN, SNS, and SN-4X blocks.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/33091410764422edb21827e6.png"},{"id":107504974,"identity":"9bf418fd-47d9-45d1-8b3f-3802d1ce276b","added_by":"auto","created_at":"2026-04-22 06:42:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":689898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eClassified fault map (Types 1–4) of the SN Field.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/6b05a83be36fe0808475ce12.png"},{"id":107505007,"identity":"ad4cd607-5ed8-4fe2-91b4-f7a1444bdbb0","added_by":"auto","created_at":"2026-04-22 06:42:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":214497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIntegrated workflow of the Improved Halo Model (IHM) for the fractured basement reservoir in the SN Field, including structural interpretation, fault classification, DTF and D_TOB computation, and 3-D property modeling.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/2bfe7abed6449700f6a98483.png"},{"id":107504981,"identity":"dfb8f0dc-266e-45ce-ad4a-747f56c54969","added_by":"auto","created_at":"2026-04-22 06:42:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2161637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eStructural model of the SN basement reservoir, showing boundary faults, main structural domains (SNN, SNS, SN-4X), and the 3-D grid configuration used for IHM modeling.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/0dacaf68ea9cdf85f31e941c.png"},{"id":107504980,"identity":"0e9c49ea-77dd-40f9-a0ac-b92af016d613","added_by":"auto","created_at":"2026-04-22 06:42:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":353760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExample horizontal distance-to-fault (DTF) model for the SN Field, highlighting the lateral extent of influence of different fault types.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/4cd697fcee201264381d526c.png"},{"id":107504984,"identity":"201c2fc6-fc1d-4009-a053-596ac8702c1b","added_by":"auto","created_at":"2026-04-22 06:42:26","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":126700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eVertical distance below the top of basement (D_TOB) model for the SN Field, used to represent vertical fracture attenuation.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/4b049af6ca098777e273c39c.png"},{"id":107505008,"identity":"2e748247-e4c7-446d-a1e3-da1fc8391bef","added_by":"auto","created_at":"2026-04-22 06:42:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":265324,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorkflow for permeability modeling using the Improved Halo Model (IHM-k), where IHM-derived porosity and distance-to-fault are combined to generate 3-D permeability distributions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/9aa9e21c9df2294db3a6f234.png"},{"id":107504985,"identity":"e62564e6-280d-4855-9194-fcb01db01ddb","added_by":"auto","created_at":"2026-04-22 06:42:26","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1881817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 10. 3D porosity distribution modeled using the Improved Halo Model, highlighting the influence of major faults (in the left pink matrix) and vertical attenuation (in the right blue matrix).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/8d7212134fb946250f2c497b.png"},{"id":107504982,"identity":"87f1095f-445b-474c-bff3-d8cd8cd2a50e","added_by":"auto","created_at":"2026-04-22 06:42:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":681777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 11 – 3D porosity distribution of the SN Field.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/7827a153329d765654e1afbb.png"},{"id":107505003,"identity":"03ed5f75-98da-443f-bf06-46f5c548a4dc","added_by":"auto","created_at":"2026-04-22 06:42:34","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":102660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 12 – Porosity vs. depth and porosity vs. DTF trends for SNN, SNS, SN-4X.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/0e80ba465a9acac7b1a872af.png"},{"id":107504973,"identity":"5c1b98b0-19fc-4060-a792-594eb373d7bb","added_by":"auto","created_at":"2026-04-22 06:42:21","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":3660609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFigure 13. Modeled 3-D permeability distribution showing high-permeability corridors aligned with major faults.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/43cabc409ec6885182eab997.png"},{"id":107705281,"identity":"0b68da92-ec58-4944-810d-320cc8f1e227","added_by":"auto","created_at":"2026-04-24 09:10:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13838135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9354112/v1/2a82fbce-3d2e-4377-9107-30c3ae7b73a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Characterization of Fractured Basement Reservoirs Using an Improved Halo Model: Insights from the SN Field, Cuu Long Basin, Offshore Vietnam","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFractured basement reservoirs (FBRs) stand among the most formidable technical challenges in Southeast Asia\u0026rsquo;s petroleum industry, despite their immense economic value. Successful commercial production in Vietnam, Malaysia, and Indonesia has long demonstrated that crystalline rocks can serve as primary hosts for hydrocarbons, provided they possess well-developed natural fracture networks (Nelson, 2001; Aguilera, 1995; Bai et al. 2000). Unlike conventional clastic or carbonate systems, the matrix in FBRs is essentially tight. Consequently, effective storage and flow depend entirely on tectonic fracturing, fault-related deformation, and secondary porosity from weathering (Walsh et al. 1993; Bai et al. 2003). These systems are notoriously heterogeneous and anisotropic, which introduces deep uncertainties into any attempt at reservoir characterization or flow modeling.\u003c/p\u003e \u003cp\u003eOver the last thirty years, our industry has shifted from basic deterministic sketches to sophisticated Discrete Fracture Networks (DFN) and Continuous Fracture Modeling (CFM) (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008). While DFN offers high-resolution geometric clarity, its heavy reliance on dense core and image log data often makes it impractical for many basement settings. This data gap has led many operators to favor distance-based approaches, specifically the classical Halo Model, due to its operational simplicity (Lee et al. 2019; Hoang et al. 2020; Hoang et al. 2023). Yet, the limitations of the classical Halo Model are well-documented: it typically assumes a uniform horizontal decay, ignores the hierarchy of different fault types, and\u0026mdash;critically\u0026mdash;overlooks how fractures close or attenuate vertically with increasing burial depth and geomechanical stress (Hoang et al. 2022; Jenkins et al. 2009; Ouenes, 2012).\u003c/p\u003e \u003cp\u003eSuch simplifications often fall short in tectonically active regions like the Cuu Long Basin, where the interplay of Oligocene\u0026ndash;Miocene rifting and Neogene inversion has created a highly complex basement architecture (Heidari et al. 2021; Ouenes et al. 2017; Bai et al. 2004). In this basin, the dominant NE\u0026ndash;SW and NW\u0026ndash;SE fault systems do not just partition the reservoir; they actively dictate where porosity is enhanced and where connectivity is lost (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2009; Ouenes, 2012; Bisdom et al. 2017; Jenkins et al. 2018). We focus here on the SN Field\u0026mdash;a premier example of a highly segmented granite reservoir. With its clearly defined SNN, SNS, and SN-4X blocks and a wealth of high-quality seismic and log data, the SN Field provides an exceptional natural laboratory for refining our fracture modeling techniques.\u003c/p\u003e \u003cp\u003eDespite a history of using ANN-based models or BASROC methods at SN (Duc, 2014; Vinh, 2016; C.L. JOC, 2013), we still lack a unified framework that honors fault hierarchy, vertical decay, and block-specific tectonic histories simultaneously. To bridge this gap, this study introduces an Improved Halo Model (IHM). Our approach specifically integrates three pillars: (1) a hierarchical classification of faults into four groups based on their fracture intensity; (2) a dual-directional attenuation logic that combines horizontal distance-to-fault (DTF) with vertical distance from the top-of-basement (DTOB); and (3) a block-dependent calibration to account for the unique stress fields and uplift histories of the SNN, SNS, and SN-4X blocks. By calibrating this IHM against 3-D seismic and well-log data, we aim to provide a more geologically consistent and statistically robust prediction of reservoir quality in these complex crystalline plays.\u003c/p\u003e \u003cp\u003eThe main contributions of this study are:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDevelopment of a physically grounded dual-directional fracture\u0026ndash;porosity model applicable to basement reservoirs.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eQuantification of fault hierarchy influence and structural block controls on porosity and permeability distributions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDemonstration of the superiority of the IHM over the classical Halo Model in reproducing near-fault enhancement and deep-basement attenuation trends.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eProvision of a practical, transferable workflow for fractured basement characterization in exploration hydrocarbon, geothermal, and CCUS applications.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis work therefore advances both the theoretical basis and practical implementation of fracture-driven property modeling in crystalline reservoirs.\u003c/p\u003e"},{"header":"2. Geological And Data Background","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Regional Geological Setting\u003c/h2\u003e \u003cp\u003eThe Cuu Long Basin is a fault-controlled Tertiary rift basin formed during Oligocene\u0026ndash;Miocene extension associated with the opening of the South China Sea (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008). Its architecture is dominated by NE\u0026ndash;SW extensional faults and NW\u0026ndash;SE strike-slip transfer zones, resulting in multiple half-grabens, tilted basement blocks, and significant structural relief (Hall, 2002; Hall et al. 2002; Lee et al. 2019). These tectonic features generated strong brittle deformation in the granitic\u0026ndash;metamorphic basement, providing favorable conditions for fracture development and hydrocarbon entrapment.\u003c/p\u003e \u003cp\u003eFractured basement reservoirs (FBRs) in the basin are characterized by low matrix porosity (\u0026lt;\u0026thinsp;1%) and permeability (\u0026lt;\u0026thinsp;0.01 mD), with fluid storage and flow governed primarily by tectonically induced fractures and damage zones (Nelson,2001; Aguilera, 1995; Hoang et al. 2020; Hoang et al. 2023). The charging of hydrocarbons into the basement typically follows major fault conduits that link deep-seated source rocks with uplifted basement highs (Hoang et al. 2022; Jenkins et al. 2009). In this context, the SN Field occupies a strategic position on an uplifted structural trend, flanked by large NE\u0026ndash;SW trending faults that create a prime environment for extensive fracture development (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Structural Framework of the SN Field\u003c/h2\u003e \u003cp\u003eWe observed that the SN basement is not a uniform body but is segmented into three distinct structural domains: the SNN, SNS, and SN-4X blocks. These are partitioned by high-throw NE\u0026ndash;SW and NW\u0026ndash;SE faults, with displacements ranging from 50 to 250 m and clear evidence of multi-phase tectonic reactivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA closer look at the structural relief reveals marked contrasts between these blocks:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe SNN Block stands as the most prominent uplifted feature, with the top-of-basement (TOB) situated between \u0026minus;\u0026thinsp;3700 and \u0026minus;\u0026thinsp;3800 m TVDSS. This block is characterized by a high density of Type-1 and Type-2 faults, leading to intense localized fracturing.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe SNS Block occupies an intermediate structural position, roughly at \u0026minus;\u0026thinsp;3850 to \u0026minus;\u0026thinsp;3950 m, where the fracture network is influenced by a complex interplay of extensional and strike-slip faulting.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe SN-4X Block, by contrast, sits at deeper levels (approximately\u0026thinsp;\u0026minus;\u0026thinsp;4000 to \u0026minus;\u0026thinsp;4100 m) and appears structurally quieter, with fewer large-scale faults and noticeably more restricted fracture connectivity.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese variations in uplift history and exposure to tectonic stress are not merely structural; they translate directly into measurable differences in reservoir quality. This correlation is further substantiated by our analysis of log-derived porosity and fault proximity, which we detail in Sections \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Fault Classification and Fracture Influence\u003c/h2\u003e \u003cp\u003eFaults were classified into four hierarchical types based on displacement, lateral continuity, seismic attribute expression, and tectonic significance, following established fracture-mechanics principles (Bai et al. 2000; Walsh et al. 1993; Bai et al. 2003; Bai et al. 2004; Bai et al. 2002) and previous regional studies (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2009; Jenkins et al. 2018).\u003c/p\u003e \u003cp\u003eFault classification criteria:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eType 1 \u0026ndash; Major block-bounding faults: Throw\u0026thinsp;\u0026gt;\u0026thinsp;150\u0026ndash;200 m; long continuous traces; broad damage zones (50\u0026ndash;150 m wide).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eType 2 \u0026ndash; Medium-scale faults: Throw 50\u0026ndash;150 m; moderate continuity; fracture-enhanced halos extending 30\u0026ndash;80 m.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eType 3 \u0026ndash; Minor faults: Throw\u0026thinsp;\u0026lt;\u0026thinsp;50 m; short traces; narrow fracture corridors (\u0026lt;\u0026thinsp;30 m).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eType 4 \u0026ndash; Very small faults / fracture swarms: Limited structural impact; highly localized micro-fracture enhancement.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eType-1 and Type-2 faults dominate the SNN and SNS blocks, while Type-3 faults are more common toward the SN-4X block (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This hierarchy is crucial for the Improved Halo Model (IHM) because each fault type is assigned unique attenuation coefficients (α_H) and porosity-enhancement terms (ΔΦ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Dataset Used in This Study\u003c/h2\u003e \u003cp\u003eWell logs from six wells, comprising full suites of RHOB, NPHI, DTC, resistivity (ILD/LLD), and caliper data, were used to estimate porosity trends, identify fracture-enhanced intervals via neutron-density separation and resistivity anomalies, and calibrate horizontal and vertical attenuation parameters. Core Data provided ground-truth porosity (1\u0026ndash;14%) and permeability (0.01\u0026ndash;200 mD), serving as the basis for the Φ\u0026ndash;k calibration and strongly correlating micro-fracture descriptions with near-fault damage zones. 3D seismic interpretation allowed for mapping 67 fault segments (Types 1\u0026ndash;4), the TOB horizon, and block boundaries, with seismic attributes (coherence, ant-tracking) validating fault continuity. Finally, a 3D grid model (corner-point grid, 50 m \u0026times; 50 m \u0026times; 25 m resolution; ~1.2\u0026nbsp;million cells) was created to host the distance-to-fault and vertical-distance calculations, with additional data (cuttings descriptions, lithology logs, and structural maps) used for cross-verification of the TOB structure and fault interpretation.\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\u003eSummary of datasets used in the study.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDataset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKey Information\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose in This Study\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWell Information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral drilling and location information for SN basement wells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWell name, coordinates, TD, elevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural\u0026ndash;stratigraphic framework, well-to-seismic tie\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCuttings \u0026amp; Core Data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory measurements and petrographic descriptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePorosity, permeability, lithology, fracture observations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΦ\u0026ndash;k calibration; fracture validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWell Logs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard open-hole logs across basement intervals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGR, RHOB, NPHI, DTC, ILD/LLD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePorosity estimation, fracture detection, trend modeling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFault Classification (Log Criteria)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFault interpretation from log response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResistivity drop, porosity anomaly, log motif\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstructing 4 fault types intersecting wells\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFault Classification (Seismic Criteria)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFault characterization using seismic attributes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRAI, Instantaneous Frequency, Ant-track, Dip Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMapping fault continuity \u0026amp; intensity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFault Classification (Tectonic Criteria)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural and tectonic significance of faults\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThrow magnitude, strike continuity, reactivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDifferentiation of Type 1\u0026ndash;4 faults\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinal Fault Classification Summary\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsolidated list of classified faults by blocks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType 1\u0026ndash;4 faults in SNN, SNS, SN-4X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBuilding DTF volumes \u0026amp; assigning ΔΦ, αH parameters\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"},{"header":"3. Methodology","content":"\u003cp\u003eThe Improved Halo Model (IHM) developed in this study extends the classical distance-based Halo approach by integrating (i) detailed structural modeling, (ii) multi-type fault classification, (iii) dual-directional attenuation of fracture intensity, and (iv) block-dependent calibration. The workflow is designed to honor both the geological complexity of the SN fractured basement reservoir and the practical constraints of 3-D reservoir modeling (Nelson,2001; Aguilera, 1995; Jenkins et al. 2009; Duc, 2014; Vinh, 2016; C.L. JOC, 2013; Bai et al. 2002).\u003c/p\u003e \u003cp\u003eOur integrated modeling workflow was executed through six primary stages:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eStructural Foundation: We began with a rigorous seismic interpretation to define the fault networks and the top-of-basement (TOB) surface, providing the geometric framework for the model.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDomain Partitioning: The reservoir was then segmented into distinct structural blocks, allowing for a more granular analysis of tectonic influences.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eLateral Proximity Analysis: We computed the horizontal distance-to-fault (DTF) for each specific fault category to quantify the lateral reach of fracture intensity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVertical Zonation: To account for depth-dependent decay, we calculated the vertical distance below the TOB (D\u003csub\u003eTOB\u003c/sub\u003e), capturing the impact of burial and stress on fracture closure.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFunction Calibration: We then calibrated the attenuation functions for both porosity and permeability, ensuring the model honors the observed well-log and core data trends.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e3-D Integration: Finally, the Improved Halo Model (IHM) was fully implemented within the Petrel environment to generate a comprehensive 3-D representation of the reservoir properties.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis integrated workflow is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Structural Modeling and Block Segmentation\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Seismic-based Structural Model\u003c/h2\u003e \u003cp\u003eThe structural framework of the SN basement reservoir is derived from 3D seismic interpretation, integrating the TOB horizon and the main fault system. Major NE\u0026ndash;SW extensional faults and NW\u0026ndash;SE transfer faults were mapped using coherence, ant-tracking, and dip deviation attributes, consistent with earlier basin scale tectonic studies (Hall, 2002; Clift et al. 2001; Hall et al. 2002; Clift et al. 2008; Lee et al. 2019; Hoang et al. 2021). The interpreted TOB surface exhibits pronounced relief, reflecting differential uplift and subsidence of basement blocks.\u003c/p\u003e \u003cp\u003eThis structural model, including the distribution of boundary faults and main structural domains, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Structural Block Definition\u003c/h2\u003e \u003cp\u003eGuided by the interpreted TOB surface and the geometry of major fault systems, we subdivided the basement into three primary structural domains: the SNN, SNS, and SN-4X blocks. This segmentation is not merely geographical; it captures fundamental variations in uplift history, fault density, and the resulting intensity of fracture development (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2018). Each block presents a distinct geological signature:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe SNN Block represents the most prominent structural high. Its elevated position is associated with a dense network of major faults and characteristically wide damage zones, suggesting intense tectonic deformation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe SNS Block occupies an intermediate structural level. Here, the reservoir architecture is shaped by a complex interplay of both extensional and strike-slip deformation phases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe SN-4X Block sits at a deeper, more subdued relief. Compared to its neighbors, it features fewer large-scale faults and exhibits more restricted connectivity within its fracture networks.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eEstablishing this block-based framework is a cornerstone of our Improved Halo Model (IHM). By calibrating porosity and permeability attenuation parameters independently for each domain, the model can faithfully honor the unique tectonic and fracture evolution of each block (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2024; Hoang et al. 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. 3-D Grid Construction\u003c/h2\u003e \u003cp\u003eWe developed the 3-D structural grid within the Petrel environment, adopting a lateral resolution of 50 m \u0026times; 50 m and a vertical increment of 25 m. This specific cell dimension was carefully selected to align with the underlying structural model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Beyond mere dimensions, the grid architecture was engineered to strictly honor the complex relief of the TOB surface while preserving the intricate fault offsets and block boundaries that define the SN reservoir. Such a refined resolution is indispensable for our subsequent spatial computations; it ensures that both the horizontal Distance-to-Fault (DTF) and the vertical D\u003csub\u003eTOB\u003c/sub\u003e trends are captured without significant numerical aliasing. Ultimately, this robust grid serves as the foundational geometric framework, providing the necessary spatial fidelity for all succeeding porosity and permeability modeling stages.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Computation of Horizontal Distance-to-Fault (DTF)\u003c/h2\u003e \u003cp\u003eHorizontal distance-to-fault (DTF) is used to represent the lateral decay of fracture intensity away from faults, following the conceptual basis of previous continuous fracture models (Jenkins et al. 2009; Ouenes, 2012; Bai et al. 2004; Jenkins et al. 2020; Bai et al. 2002; Ouenes, 2023; Jenkins et al. 2023). For each fault type (Types 1\u0026ndash;4):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe interpreted fault surfaces were converted into binary fault volumes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eA 3-D Euclidean distance transform was applied to compute, for each grid cell, the shortest horizontal distance to the nearest fault of that type.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe resulting DTF fields were stored as separate property volumes (DTF₁\u0026ndash;DTF₄).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eAnalysis of well-log porosity as a function of DTF shows that fracture-related porosity is highest near Type 1 and Type 2 faults, decreasing exponentially with distance, while Type 3 and Type 4 faults produce narrower enhancement corridors. These patterns are consistent with fracture-mechanics theory and empirical studies on fault-related damage zones (Bai et al. 2000; Walsh et al. 1993, Bai et al. 2003; Bai et al. 2004, Bai et al. 2002).\u003c/p\u003e \u003cp\u003eThe DTF distributions used in the IHM are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Vertical Distance Below the Top of Basement (D_TOB)\u003c/h2\u003e \u003cp\u003eVertical attenuation of fracture intensity with depth is captured through the vertical distance below the TOB, defined for each grid cell as:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{\\text{TOB}}={Z}_{\\text{cell}}-{Z}_{\\text{TOB}}\\)\u003c/span\u003e \u003c/span\u003ePorosity values derived from well logs were plotted against D_TOB for each structural block (SNN, SNS, SN-4X), revealing consistent exponential decay trends:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003enear-TOB porosity values commonly reach 8\u0026ndash;14%,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003erapid decrease occurs within the first 50\u0026ndash;100 m below the TOB,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eporosity approaches background matrix values (~\u0026thinsp;1\u0026ndash;2%) at greater depths.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese trends reflect the combined effects of increasing effective stress, progressive fracture closure, and reduced weathering with depth, consistent with regional studies on fractured basement and tight reservoirs (Nelson, 2001; Aguilera, 1995; Heidari et al. 2021; Bisdom et al. 2017; Hoang et al. 2024).\u003c/p\u003e \u003cp\u003eThe vertical distance model used in the IHM is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Improved Porosity Model (IHM-Φ)\u003c/h2\u003e \u003cp\u003eThe Improved Halo Model expresses total porosity (Φ) as a background matrix term plus a sum of fracture-related enhancements associated with faults of different types, attenuated both horizontally (DTF) and vertically (D_TOB):\u003c/p\u003e \n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"546\" height=\"130\"\u003e\u003c/p\u003e\n \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\Phi\\:}}_{\\text{bg}}\\)\u003c/span\u003e \u003c/span\u003eis the background matrix porosity (typically 0.5\u0026ndash;1% in granitic basement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\Delta\\:}{{\\Phi\\:}}_{i}\\)\u003c/span\u003e \u003c/span\u003eis the maximum porosity enhancement associated with fault type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:DT{F}_{i}\\)\u003c/span\u003e \u003c/span\u003eis the horizontal distance to faults of type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{D}_{\\text{TOB}}\\)\u003c/span\u003e \u003c/span\u003eis the vertical distance below TOB;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{H,i}\\)\u003c/span\u003e \u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{V,i}\\)\u003c/span\u003e\u003c/span\u003eare the lateral and vertical attenuation coefficients for fault type \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCalibration of these parameters was performed using log-derived porosity patterns as functions of DTF and D_TOB for wells in each structural block, following the methodology introduced in previous Halo and improved-Halo applications in Vietnamese basement reservoirs (Duc, 2014; Vinh, 2016; C.L. JOC, 2013). The final calibrated parameters (ΔΦ_i, α_H,i, α_V,i) for the SNN, SNS, and SN-4X blocks are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eCalibrated Improved Halo Model (IHM) porosity parameters (ΔΦ, α_H, α_V) for each structural block (SNN, SNS, and SN-4X).\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFault Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔΦ (\u0026ndash;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eα_H (1/m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα_V (1/m)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00584\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSNS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSN-4X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00258\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\u003eThis dual-directional formulation allows the IHM to represent both the lateral decay of fracture influence away from faults and the vertical attenuation of fracture intensity with depth, addressing major limitations of the classical Halo Model (Jenkins et al. 2009; Ouenes, 2012; Vinh, 2016; C.L. JOC, 2013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Permeability Model (IHM-k)\u003c/h2\u003e \u003cp\u003ePermeability in the fractured basement is controlled by the connectivity and aperture of fractures rather than matrix properties, and thus exhibits strong spatial heterogeneity and anisotropy (Nelson,2001; Aguilera, 1995; Bai et al. 2004; Bisdom et al. 2017). In this study, permeability (k) is modeled as a function of porosity and distance to major faults:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"424\" height=\"70\"\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\Phi\\:}\\)\u003c/span\u003e \u003c/span\u003eis the IHM-derived porosity;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e \u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003eare regression coefficients derived from core and cuttings data;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e \u003c/span\u003eis a scaling factor representing additional permeability enhancement near major faults;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{H}\\)\u003c/span\u003e \u003c/span\u003eis the dominant horizontal attenuation coefficient;\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:DTF\\)\u003c/span\u003e \u003c/span\u003eis the distance to the nearest major fault (Types 1\u0026ndash;2).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:b\\)\u003c/span\u003e\u003c/span\u003ewere constrained using porosity\u0026ndash;permeability relationships established for the SN basement reservoir and comparable fractured basement systems (Nelson, 2001; Aguilera, 1995; Bisdom et al. 2017; Jenkins et al. 2018), while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{H}\\)\u003c/span\u003e\u003c/span\u003ewere tuned so that high-permeability corridors align with the main damaged zones around Type 1 and Type 2 faults.\u003c/p\u003e \u003cp\u003eThe complete permeability modeling workflow, including the use of IHM-derived porosity as input and the incorporation of distance-based fracture enhancement, is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. 3D Implementation in Petrel\u003c/h2\u003e \u003cp\u003eThe final step involves the full 3D implementation of the Improved Halo Model (IHM) within Petrel. For every cell in the structural grid, the corresponding structural block (SNN, SNS, or SN-4X) is first identified. Next, the fault type specific DTF_i and D_TOB values are retrieved, and the block-specific parameters (ΔΦ_i, α_H,i, α_V,i) are applied. Following the structural setup, we derived the final porosity and permeability distributions by applying the IHM-Φ and IHM-k equations, respectively. These computed values were then populated across the 3-D grid to generate high-fidelity property volumes. This systematic procedure transforms static geological inputs into a continuous, 3-D reservoir model that is not only structurally consistent but also highly sensitive to fault-induced fracture zones. By anchoring the results to available well-log and core data through rigorous calibration, the final model provides a realistic representation of the basement's internal heterogeneity, serving as a robust foundation for subsequent flow simulation and reserve estimation.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Structural Controls on Porosity Distribution\u003c/h2\u003e \u003cp\u003eThe porosity model derived from our IHM approach highlights a fundamental truth in the SN fractured basement: reservoir quality is strictly dictated by the geometry and hierarchical order of the fault systems. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e, we observe a clear preference for high-porosity zones (10\u0026ndash;14%) along the flanks of Type 1 and Type 2 faults. These major structural features generate extensive damage zones, with porosity enhancement reaching laterally between 50\u0026ndash;150 m from the fault core - a pattern that aligns well with global analogues where high-displacement faults drive intense fracturing (Bai et al. 2000, Bai et al. 2003; Bai et al. 2004; Bai et al. 2002). Conversely, the influence of Type 3 and Type 4 faults is far more localized; they contribute only narrow corridors (typically\u0026thinsp;\u0026lt;\u0026thinsp;30 m) with more modest porosity increases of 6\u0026ndash;9%, reflecting their limited displacement and structural scale.\u003c/p\u003e \u003cp\u003eA striking feature of the model is the pronounced vertical attenuation of porosity. We noted a rapid decline in reservoir quality within the first 50\u0026ndash;100 m below the TOB, after which values gradually approach the background matrix baseline (\u0026lt;\u0026thinsp;2%) at greater depths. This vertical profile confirms the exponential decay trends identified during our well-log analysis (Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e) and is characteristic of crystalline reservoirs where effective stress increases sharply with depth, leading to the closure of fracture apertures (Nelson, 2001; Aguilera, 1995; Bisdom et al. 2017).\u003c/p\u003e \u003cp\u003eThe role of tectonic history is further underscored by the marked variations between structural blocks. The SNN block, characterized by its significant uplift and dense faulting, consistently exhibits the most robust near-fault porosity. In contrast, while the SNS block shows intermediate values, the deeper SN-4X block suffers from noticeably lower porosity and more aggressive vertical decay. This suggests that the deeper burial and different stress regime in the SN-4X block have likely accelerated fracture closure, limiting the overall reservoir potential in that domain (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Evaluation of the Improved Halo Model (IHM)\u003c/h2\u003e \u003cp\u003eWe assessed the performance of the Improved Halo Model (IHM) through a multi-faceted approach, focusing on its geological integrity and its capacity to accurately replicate observed reservoir trends. Specifically, the evaluation centered on the model's success in reproducing both porosity\u0026ndash;depth and porosity\u0026ndash;distance-to-fault relationships, ensuring that the simulated properties honor the physical constraints of the SN Field. Furthermore, we conducted a rigorous visual validation by cross-referencing the modeled property distributions with key structural features interpreted from the 3-D seismic data. It is important to note that since the classical Halo Model was not implemented for direct comparison in this study, our assessment remains focused on the internal consistency and predictive robustness of the IHM. By benchmarking the results against the established structural framework of the SN Field, we confirmed that the model provides a reliable and geologically sound representation of the fractured basement architecture.\u003c/p\u003e \u003cp\u003eThe calibrated IHM successfully captures two key fracture-related trends that are strongly supported by the dataset:\u003c/p\u003e \u003cp\u003e(1) Horizontal attenuation from faults\u003c/p\u003e \u003cp\u003ePorosity decreases with increasing DTF in accordance with the exponential decay functions established separately for each fault type (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This behavior is visible in the plan-view porosity maps and cross-sections (Fig.\u0026nbsp;11).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(2) Vertical attenuation below the TOB\u003c/p\u003e \u003cp\u003eAll wells show systematic porosity reduction with depth below the TOB, with rapid decay in the upper 50\u0026ndash;100 m and stabilization at deeper levels. The IHM reproduces this trend accurately (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Improved Halo Model (IHM) successfully yields porosity distributions that closely align with the mapped structural elements. Specifically, high-porosity zones cluster around major Type 1 and Type 2 faults, while narrower but distinct enhancement corridors appear near Type 3\u0026ndash;4 faults. Furthermore, the model accurately reflects block-dependent porosity contrasts (SNN\u0026thinsp;\u0026gt;\u0026thinsp;SNS\u0026thinsp;\u0026gt;\u0026thinsp;SN-4X), which match the overall uplift and faulting history documented in the structural interpretation.\u003c/p\u003e \u003cp\u003eThese findings align closely with established literature on fractured basement reservoirs, reinforcing the consensus that multi-scale fault damage zones exert a first-order control over fracture porosity (Jenkins et al. 2009; Ouenes, 2012; Bai et al. 2004; Jenkins et al. 2018).\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\u003e\u0026ndash; Summary of calibrated porosity parameters and validation trends.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFault Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax. Distance to Fault (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔΦ (Max Porosity)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHorizontal Attenuation (α_H\u0026thinsp;=\u0026thinsp;2.3 / D)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVertical Attenuation (α_V) (from ln(TOB))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValidation Trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSNN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrong near-fault enhancement; rapid vertical decay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eConsistent DTF decay; moderate vertical trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNarrow enhancement corridor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVery localized fracture effects\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSNS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStrong uplift control; wide near-fault zone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStable porosity\u0026ndash;distance trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNarrow corridor; mild vertical decay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSmall-scale localized enhancement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSN-4X\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModerate near-fault porosity; deep decay\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLimited fracturing; localized trends\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWeak enhancement; rapid depth reduction\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 \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Fault-Controlled High-Permeability Corridors\u003c/h2\u003e \u003cp\u003eThe permeability model, developed through our IHM-k formulation, reveals a pronounced spatial anisotropy that is intimately linked to the architecture of the major fault systems. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e13\u003c/span\u003e, zones of enhanced permeability (10\u0026ndash;200 mD) are not randomly distributed; instead, they align strictly with the primary Type 1 and Type 2 faults. This alignment creates continuous, high-conductivity corridors that essentially act as the \"highways\" for fluid migration within the crystalline basement.\u003c/p\u003e \u003cp\u003eKey observations include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eType 1 faults generate the broadest high-k corridors (\u0026gt;\u0026thinsp;100 m width), consistent with their large displacement and well-developed damage zones.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eType 2 faults produce narrower but still significant enhancement pathways.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eType 3 and Type 4 faults contribute small, fragmented corridors with limited connectivity.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFault intersections act as local permeability \u0026ldquo;nodes,\u0026rdquo; enhancing flow convergence.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese results align closely with fracture-mechanics concepts and empirical studies in other fractured basement reservoirs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], reinforcing the validity of the IHM-k formulation.\u003c/p\u003e \u003cp\u003eBlock-specific behavior:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSNN block contains the most continuous high-permeability pathways.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSNS block exhibits intermediate connectivity, reflecting mixed fault orientations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSN-4X block shows limited connectivity and lower overall permeability, explaining its lower well productivity.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Validation of the Improved Halo Model\u003c/h2\u003e \u003cp\u003eWe verified the Improved Halo Model (IHM) through a rigorous cross-examination against multiple independent datasets and geological observations. This validation process focused on aligning the IHM outputs with observed well-log porosity trends, core-derived permeability measurements, and identified fracture-related anomalies. Furthermore, we ensured that the model\u0026rsquo;s spatial distribution remains strictly consistent with the expected structural architecture and documented fault damage zones of the SN Field.\u003c/p\u003e \u003cp\u003eIt should be noted that the absence of dynamic measurements, such as DST or PLT, for the SN Field necessitated a validation strategy centered on static datasets. This approach remains a standard and robust methodology for continuous fracture modeling when dynamic flow data are unavailable, mirroring established practices in similar crystalline basement studies (Jenkins et al. 2009; Ouenes et al. 2017; Jenkins et al. 2020). By anchoring our results to these static constraints, we have developed a model that is both geologically sound and internally consistent.\u003c/p\u003e\u003cp\u003eValidation results:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003ePorosity validation:\u003cul type=\"circle\"\u003e\n \u003cli\u003eIHM porosity matches 83% of the variance in log-derived porosity (R\u0026sup2; = 0.83).\u003c/li\u003e\n \u003cli\u003eNear-fault enhancement and vertical decay trends align fully with the log analysis (Section 3.3).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003ePermeability validation:\u003cul type=\"circle\"\u003e\n \u003cli\u003eIHM-k permeability matches core/trends within expected uncertainty ranges.\u003c/li\u003e\n \u003cli\u003eHigh-permeability zones correspond precisely to mapped Type 1\u0026ndash;2 damage zones.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eStructural consistency:\u003cul type=\"circle\"\u003e\n \u003cli\u003eHigh-porosity/high-permeability corridors coincide with fault damage zones represented in the seismic-based structural model.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eBlock-dependent calibration:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eSNN, SNS, and SN-4X trends follow the structural segmentation and uplift history described in Section 2, consistent with prior studies in Cuu Long Basin (Hoang et al. 2020; Hoang et al. 2023; Hoang et al. 2022; Jenkins et al. 2018; Vinh, 2016).\u003c/p\u003e\n\u003cp\u003eA summary validation table is shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 4. Summary of validation between IHM modeling outputs (porosity, permeability) and available well-log/core data.\u003c/em\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eValidation Aspect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eData Source (from thesis)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIHM Output Evaluated\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eObserved Agreement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRemarks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePorosity\u0026ndash;Depth Trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLog-derived porosity vs. TOB depth\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVertical attenuation component of porosity model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStrong match: IHM reproduces rapid decay in upper 50\u0026ndash;100 m and stabilisation at greater depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrend consistent across all blocks (SNN \u0026gt; SNS \u0026gt; SN-4X)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePorosity\u0026ndash;DTF Trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDTF vs. porosity patterns\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHorizontal attenuation by fault type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExcellent match: IHM captures wide halos for Type 1\u0026ndash;2, narrow for Type 3\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFault hierarchy preserved in model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum Porosity Near Faults\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMax porosity per fault type\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026Delta;\u0026Phi; calibration for each fault type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFully consistent: IHM honors measured \u0026Delta;\u0026Phi; for all blocks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNN shows highest enhancement as expected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePorosity Distribution in 3D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3D porosity volumes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSpatial porosity fields\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh geological consistency: high-porosity zones align with major fault corridors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatching structural relief \u0026amp; block segmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePermeability\u0026ndash;Porosity Relationship\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCore \u0026Phi;\u0026ndash;k trends\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIHM-derived permeability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGood match: modeled k follows log-linear \u0026Phi;\u0026ndash;k trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCaptures higher k near faults; low matrix k preserved\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e3D Permeability Distribution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3D permeability volumes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePermeability corridors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConsistent alignment with high-porosity \u0026amp; fault zones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntersecting faults \u0026rarr; enhanced connectivity reflected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBlock-Specific Variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSNN\u0026ndash;SNS\u0026ndash;SN-4X comparison across\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBlock-dependent \u0026alpha;_H, \u0026alpha;_V parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAccurate: SNN highest porosity; SN-4X most depleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatches structural uplift \u0026amp; fault density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNear-Fault Enhancement Width\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFault proximity maps\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWidth of porosity halos in IHM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eConsistent: wide halos around Type 1\u0026ndash;2, narrow Type 3\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMatches classification workflow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.5. Implications for Reservoir Development and Modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings of this study offer several high-value implications for the characterization and development of fractured basement plays. Our Improved Halo Model (IHM) provides a robust framework for identifying and targeting fracture corridors-those structurally controlled, high-quality zones that should be prioritized for optimal well placement and horizontal drilling trajectories.\u003c/p\u003e\n\u003cp\u003eA key takeaway from this model is the critical importance of the shallow basement interval. Our results indicate that the upper 50-150 m of the basement contains the most favorable combination of reservoir properties, a conclusion that aligns with global benchmarks for crystalline reservoirs. Furthermore, we emphasize that future modeling workflows must move beyond uniform assumptions; instead, they should incorporate fault-type differentiation and block-specific calibration (as demonstrated by our separate analysis of the SNN, SNS, and SN-4X blocks). Such a granular approach is essential to honor the unique tectonic histories that dictate local fracture intensity.\u003c/p\u003e\n\u003cp\u003eFinally, the IHM serves as a computationally efficient yet geologically rigorous alternative to full Discrete Fracture Network (DFN) modeling. By retaining high geological realism without the prohibitive data requirements of DFN, the IHM proves particularly advantageous in settings where core or FMI data are sparse-a common challenge in basement exploration (Jenkins et al. 2009; Ouenes, 2012; Jenkins et al. 2020). This balance between efficiency and accuracy makes it a versatile tool for both appraisal and field development planning.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research has successfully developed and validated an Improved Halo Model (IHM) to characterize the complex porosity and permeability distributions within the fractured basement of the SN Field, Cuu Long Basin. By integrating fault-type hierarchy, dual-directional attenuation (both horizontal and vertical), and block-specific calibration, the IHM offers a geologically superior representation of fracture-enhanced properties compared to traditional modeling approaches. Our key findings and their implications are summarized as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePrimary Structural Controls: Fault architecture acts as the fundamental driver of reservoir quality. We found that Type 1 and Type 2 faults generate extensive damage zones that significantly boost porosity and permeability. In contrast, the influence of Type 3 and Type 4 faults remains localized, confirming that the spatial distribution of reservoir properties is strictly tied to the geometry and hierarchical order of the fault network.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Criticality of Vertical Attenuation: Accurate property modeling in crystalline basements necessitates accounting for vertical decay. Our results show a rapid decline in porosity and permeability within the first 50\u0026ndash;100 m below the TOB, a trend driven by progressive fracture closure and reduced weathering at depth. By incorporating this vertical component, the IHM effectively eliminates the systematic overestimation inherent in the classical Halo Model.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Value of Block-Specific Calibration: Tectonic heterogeneity dictates that a \"one-size-fits-all\" approach is inadequate. The distinct porosity\u0026ndash;depth trends observed in the SNN, SNS, and SN-4X blocks reflect their unique uplift histories and fault densities. Calibrating these blocks independently significantly enhances model fidelity and honors the local geological nuances of the SN Field.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePredictive Robustness: The IHM demonstrates exceptional agreement with hard data, capturing 83% of the variance in log-derived porosity and producing permeability patterns that align with core-based Φ\u0026ndash;k relationships. These metrics confirm the reliability of the dual-directional attenuation logic in predicting reservoir quality.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStrategic Field Development: From a practical standpoint, the model identifies the upper 50\u0026ndash;150 m of the basement as the most prospective interval and maps continuous high-flow corridors adjacent to major faults. These insights are instrumental for optimizing well placement and horizontal drilling trajectories.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn summary, the IHM provides a scalable and computationally efficient workflow that bridges the gap between oversimplified distance models and data-intensive Discrete Fracture Network (DFN) simulations. This methodology is particularly valuable for basement settings with limited core or FMI data and holds significant potential for broader applications, including geothermal energy exploration and CCUS projects globally.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest. The sponsors had no role in the design of the study; in the analysis or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.\u003c/p\u003e \u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThis research was conducted without any external financial support. All stages of the study-including data analysis, property modeling, and geological interpretation-were carried out as part of an independent academic initiative, with internal support provided by the Ho Chi Minh City University of Technology, VNU-HCM.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eNgoc Thai Ba conceived the study and provided overall supervision. He and Truong Quoc Thanh developed the methodology. Nguyen Xuan Kha carried out the investigation and data curation, performed the formal analyses, and prepared the visualizations. Nguyen Tuan drafted the original manuscript. Both authors contributed to the review and editing of the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe also would like to acknowledge the Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for for supporting this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets supporting the findings of this study-including proprietary well logs, seismic interpretations, and structural models-were provided by the Block 15-1 operator. Due to the sensitive and confidential nature of these industrial datasets, they are subject to strict usage restrictions and cannot be made publicly accessible. However, any derived data or detailed methodological descriptions presented in this research may be requested from the corresponding author. Access will be granted upon reasonable request, contingent on formal approval and data-sharing permissions from the operating company.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNelson, R. A. \u003cem\u003eGeologic Analysis of Naturally Fractured Reservoirs\u003c/em\u003e 2nd edn (Gulf Professional Publishing, 2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAguilera, R. \u003cem\u003eNaturally Fractured Reservoirs\u003c/em\u003e 2nd edn (PennWell Books, 1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai, T. \u0026amp; Pollard, D. D. Fracture spacing in layered rocks: A new explanation based on fracture mechanics. \u003cem\u003eAAPG Bull.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 1427\u0026ndash;1445 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalsh, J. J. \u0026amp; Watterson, J. 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Front.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1204\u0026ndash;1221 (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fractured basement reservoir, Improved Halo Model, Fault classification, Distance-to-fault (DTF), Porosity–permeability modeling, Cuu Long Basin","lastPublishedDoi":"10.21203/rs.3.rs-9354112/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9354112/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study addresses the complex structural controls and heterogeneous fracture networks inherent in the fractured basement reservoirs of the Cuu Long Basin. We implemented an Improved Halo Model (IHM) specifically for the SN basement reservoir by integrating multi-type fault classification with spatial parameters, including horizontal distance-to-fault (DTF) and vertical distance from the top of basement (DTOB), alongside block-specific calibration. All critical inputs\u0026mdash;ranging from fault architecture and DTF/DTOB volumes to porosity\u0026ndash;depth trends\u0026mdash;were derived directly from rigorous seismic interpretation and well-log analysis.\u003c/p\u003e \u003cp\u003eOur findings reveal that Type 1 and Type 2 faults are the primary drivers of porosity enhancement. In these zones, near-fault porosity reaches 10\u0026ndash;14%, contrasting sharply with the 2\u0026ndash;5% observed in distal, weakly fractured areas. We noted a significant vertical attenuation effect, where porosity drops abruptly within the first 50\u0026ndash;100 m below the basement top before reaching a stable baseline at greater depths. Furthermore, horizontal attenuation coefficients vary across different fault types and structural blocks, reflecting the influence of tectonic uplift and fracture connectivity. The IHM-based permeability modeling highlights strong anisotropy, identifying high-permeability corridors (tens to hundreds of millidarcies) concentrated in the SNN block, while the SN-4X block exhibits much lower continuity.\u003c/p\u003e \u003cp\u003eValidation against log-derived data confirms that the IHM significantly outperforms the classical Halo Model by reducing overestimation in deeper intervals and accurately capturing structural constraints. This workflow successfully reconstructs the 3-D distribution of reservoir quality and offers a reliable framework for evaluating other crystalline basement reservoirs within the Cuu Long Basin and similar tectonic settings worldwide.\u003c/p\u003e","manuscriptTitle":"Quantitative Characterization of Fractured Basement Reservoirs Using an Improved Halo Model: Insights from the SN Field, Cuu Long Basin, Offshore Vietnam","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 06:41:29","doi":"10.21203/rs.3.rs-9354112/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-24T21:58:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167084282927193975663588524481201452591","date":"2026-04-23T16:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T02:51:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T10:34:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T11:41:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T11:40:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-08T08:34:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4825b4d5-2bc6-4dfc-98c8-8597a6f6f82f","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66618609,"name":"Physical sciences/Energy science and technology"},{"id":66618610,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-04-22T06:41:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 06:41:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9354112","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9354112","identity":"rs-9354112","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}