3D implicit modeling applied to the evaluation of CO2 geological storage in the shales of the Irati Formation, Paraná Basin, Southeastern Brazil

3D implicit modeling applied to the evaluation of CO2 geological storage in the shales of the Irati Formation, Paraná Basin, Southeastern Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 3D implicit modeling applied to the evaluation of CO 2 geological storage in the shales of the Irati Formation, Paraná Basin, Southeastern Brazil Saulo B. de Oliveira, Colombo C. G. Tassinari, Richardson M. A-A., and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-420789/v2 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Aug, 2021 Read the published version in Greenhouse Gases: Science and Technology → Version 2 posted You are reading this latest preprint version Show more versions Abstract The Paris Agreement established global ambitious targets for reducing carbon dioxide (CO 2 ) emissions, requiring the rapid and extensive development of low carbon technologies, and one of the most efficient is CO 2 geological storage. Among the deep geological formations used for CO 2 storage, the shale layers have been a new emerging topic showing to be efficient because they are abundant and have a high content of organic matter, being favorable for CO 2 retention. However, one of the challenges in evaluating a location for possible reservoirs is the adequate geological characterization and storage volume estimates. This research evaluated the Irati Formation of the Paraná Basin, through the information from hydrocarbon exploration wells in Southeastern Brazil, where most stationary sources of carbon emissions are located. Three-dimensional (3D) implicit modeling techniques were applied not only for the volume calculation purpose, but also in the site selection stage, generating thematic 3D models of thickness, depth, structures, and distance to aquifer systems. The limestones, shales, and black shales of the Irati Formation were locally divided into six units according to geological composition and spatial continuity. The E black shale unit was considered for CO 2 geological storage indicating a theoretical capacity of 1.85 Gt of CO 2 . The potential of the achieved capacity is promising not only for been greater than the total of CO 2 locally produced but also for supporting the implantation of new projects in this region. Geology CO2 geological storage 3D geological modeling carbon capture and storage Irati Formation Paraná 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 Introduction In a scenario of growth in the generation and emission of CO 2 , the global emissions reached a historical record of about 33.1 billion tons (Gt) of CO 2 in 2018 according to the International Energy Agency. 1 Brazil is among the 20 countries with the highest CO 2 emissions in recent years. 1 The data provided by the Sistema de Estimativas de Emissões e Remoções de Gases de Efeito Estufa (SEEG) on CO 2 emissions by the Brazilian energy sector, based on the methodology of Azevedo et al , 2 indicates a significant increase from 173 million tons (Mt) of CO 2 in 1990 to 380 Mt of CO 2 in 2018. 3 Brazil has also set goals to contribute to the reduction of greenhouse gas emissions, aiming at 37% by 2025, and 43% by 2030, based on 2005 emissions, as stated in its Nationally Determined Contribution (NDC) ratification. This worldwide decision requires the rapid and extensive development of low carbon technologies, and one of the most efficient approaches is Carbon Capture and Storage (CCS). In the Brazilian context, CCS technologies are becoming relevant due to the CO 2 emission capacity of the energy sector, considering the development of technologies that respond positively to global trends. Deep geological formations provide suitable repositories for CO 2 storage, however, some requirements must be met to allow large amounts of CO 2 to be injected and that the gas remains trapped in the rock for a long time. 4, 5 Usually depleted oil and gas fields, saline aquifers, deep salt formations (salt caves and abandoned mines), coal seams, basalt, and black shale layers are considered viable CO 2 reservoirs. 6, 7 Each of these types of CO 2 geological reservoirs has distinct trapping mechanisms, including structural/stratigraphic trapping, residual trapping, dissolution trapping, and mineral trapping. 8-10 The efficiency of shale layers for CO 2 storage has gained popularity because they are abundant and have a high content of organic matter and clay minerals, which are favorable materials for CO 2 retention by adsorption. Besides, the shale layers may contain unconventional hydrocarbons (shale gas and shale oil). If shale oil and gas production becomes viable within the Irati Formation, the co-development of hydrocarbon wells and CO 2 storage will reduce cost. 11 Shale gas is an extensive global energy resource, and its development can integrate the CO 2 geological storage as a feasible technological alternative to reduce carbon emissions impact on the climate. 12, 13 The research focuses on generating a three-dimensional (3D) geological model based on a pre-selected area measuring about 185,000 km² (Fig. 1) with potential for CO 2 storage in the São Paulo State, southeastern Brazil. The implicit geological modeling in a three-dimensional (3D) virtual environment 14-17 has been widely used in the mineral industry for at least 15 years. Recent examples of 3D implicit geological modeling include iron deposits (e.g. Sishen Mine, South Africa), 18 base metal deposits (e.g. Falunin Sweden, Flin Flon in Canada, and Shalipayco in Peru), 19-21 gold deposits (e.g. Navachab in Namibia, La Colossa in Colombia, and Sigma-Lamaque in Canada), 22-24 and geothermal reservoirs, 25 amongst others. The 3D implicit modeling expands the analysis of conventional geological data by visualizing continuities, groupings, and spatial trends, as well as geometries of geological bodies or units, structural geological framework, and variation of geochemical contents or any other numerical or categorical parameters in geosciences. Examples of 3D geological modeling focusing on CO 2 storage are scarce. 26-34 Evaluations and studies focusing on CO 2 geological storage in the Paraná Basin include the study based on the Santa Terezinha coalfield, 35 and those considering saline aquifers. 36-40 The objectives in this research include: 1) Site selection on a regional scale for CO 2 storage focusing on the shale reservoirs within the Paraná Basin, and 2) Estimation of the theoretical storage capacity of the selected site. The 3D geological model of Paraná Basin will aid the assessment of the geological feasibility of implementing CCS technology in Southeastern Brazil to reduce CO 2 in this region where most of the country's carbon emission stationary sources are concentrated. 37, 39, 41 Therefore, the innovations of this work are the first application of 3D implicit modeling to evaluate the CO 2 storage potential of the shale reservoirs in the study location, comprising the initial steps of Site Selection and Initial Characterization. 42 The study considered the hypothesis that the Irati Formation organic-rich shales present reservoir units with enough capacity to permanently store the volume of the waste CO 2 released to the atmosphere considering the emitting sources within the region. Main Text The dataset provided by ANP in December 2019 is composed of hydrocarbon exploration well data. The dataset contains data of collar, survey, lithology, stratigraphy, and hydrocarbon occurrence, as well as, wireline logs consisting of gamma-ray, resistivity, density, neutrons, and sonic log, besides some organic geochemistry data acquired by Petrobras Company during the 1950s until the 2000s. A filter was applied in a total of 123 wells in the Paraná Basin resulting in 32 wells in the study area (Fig. 1). From these wells, all have stratigraphy data, 29 of the wells have some lithology data, and 15 have some hydrocarbon indication data. In Fig. 1 and subsequent figures, in the location indicated by the 3-CB-3-SP well label, there are also located other four wells (2-CB-1-SP, 2-CB-1DA-SP, 3-CB-2-SP, and 3-CB-4-SP) lying 3 km from each other on average. Therefore, their labels were omitted in the figures for a scale issue, to avoid overlaps and visual pollution. Almost all filtered wells are vertical with only one having survey data. Then, all available data were loaded in the Leapfrog Geothermal® software for performing three-dimensional (3D) geological modeling. The Leapfrog software uses radial basis functions (RBFs) to perform surfaces by the implicit modeling method. 14 It involves the generation of a regional surface topographic model based on the GTOPO30 data ( https://earthexplorer.usgs.gov/ ). Surface geology data (lithology and structures) were obtained from Companhia de Pesquisa de Recursos Minerais (CPRM), 43 and regional geological structures are from Zalán, 44 and then treated in the free software QGIS Desktop before importing into Leapfrog platform. THE IRATI FORMATION IN THE STUDY AREA The Irati Formation occurs in almost the entire extension of the Paraná Basin. 45-47 In the study area, outcrops of the Irati Formation are restricted to the southeast portion only (Fig. 2). For this research, the classification of the lithologic units depends mainly on well logs data. Almost all the wells in the area penetrate the Irati Formation horizon, except for the 1-QT-1-PR well, which is very shallow, and the 1-TI-1-SP well, which presents an extensive diabase dike at intervals between the Teresina and Palermo Formations (Fig. 3). In the study area, 8 wells (1-J-1-PR, 1-MA-1-SP, 1-PA-1-SP, 1-SA-1-SP, 2-AA-1-SP, 2-AP-1-PR, 2-RI-1-PR, 2-TB-1-SP) present some punctual oil indication, 5 wells present gas indication (2-CB-1-SP, 2-CB-1DA-SP, 3-CB-2-SP, 3-CB-3-SP, 3-CB-4-SP), referring to the Cuiabá Paulista sub-commercial gas accumulation, and 4 wells show bitumen (1-AB-1-SP, 1-GU-4-SP, 2-PN-1-SP, and 2-PP-1-SP), all within the Irati Formation (Fig. 2). A 3D interpretation of the lithology data with some TOC data enabled a subdivision of the Irati Formation into units following the subdivision of literature. 48-50 Due to the erratic availability of samples with TOC analysis, the three A, B, and C base units were undifferentiated and referred to as a single shale unit named Unit A/B/C (Fig. 3). This work considers six units informally named Units A/B/C to H from the bottom to the top (Fig. 3), to conform to the existing divisions. 48-50 The lithologic unit interpretation incorporates the thickness of units less than 1 m into the larger adjacent units. Therefore, larger shaly units may contain thin and discontinuous levels of limestone, and delineated carbonate units may also have incorporated thin-bedded shale levels. Unit A/B/C is a shale layer with incorporated carbonate layers having a thickness measuring up to 30 m at the middle of the study location and about 3 m at the eastern portion. The sequence from Units D to G is laterally continuous and occurs in the entire E-W section (Fig. 3) for more than 300 km across the study area. Unit D consists of limestones with incorporated dolostones, calcilutite to calcarenite having a thickness range of 1 to 16 m. The thickness of the organic-rich shale Unit E varies from 2 to 20 m. Both Units E and F consist of interbedded diabase sill in the eastern portion. Unit F is composed of shales with a thickness range of 1 to 14 m throughout the E-W section. Unit G is composed of limestones and dolostones with grain size varying from calcilutite to calcarenite, and thickness in the wells ranges from 2 to 11 m. The topmost Unit H is an organic-rich shale layer that occurs only at the west of the study location. The presented Irati Formation subdivisions are similar to those showcased in Figure 4 and presented in the literature, 48-50 although it is not exactly equivalent. INITIAL CRITERIA FOR SELECTING THE CO 2 STORAGE LOCATION CO 2 stationary sources and some legal aspects The study area (Fig. 1) already accounted for some of the CO 2 site selection criteria considering the region with the highest CO 2 stationary emitting sources and legal aspects. The land use, land-use change, and forestry (LULUCF) sector is the major net emitter of CO 2 in Brazil, followed by the energy sector. The São Paulo State is the state with the highest CO 2 emissions in the energy sector considering the concentration of thermoelectric power plants (Fig. 1). 37, 39, 51 Based on 2018 data, from the 380 Mt of CO 2 emitted in the Brazilian energy sector, 80 Mt (21%) is from the São Paulo State. 3 The Paraná State also has a high concentration of thermoelectric plants. However, the Paraná State Law 19.878 of 2019 prohibits the production of unconventional natural gas production through hydraulic fracking, 52 although the state has no oil and gas production activities. The Barra Bonita conventional gas field within the region is not producing. The legal context of shale gas in the Paraná Basin is better and deeply discussed in the literature. 52, 53 Brazil has no federal or states legal restrictions or specific legislation to prevent CCS. 54, 55 Therefore, areas inside the São Paulo State were prioritized in this study, mainly because of the high concentration of energy sector and biomass industries related to sugar-cane ethanol plants. 56 Regional geological aspects The Paraná Basin is tectonically related to a more than 10,000 km-long divergent margin originated by the Gondwana paleocontinent break-up and the separation of the African and South American plates, 57 with no relation with fold belts, therefore, it is a tectonically favorable location for CO 2 storage considering IEA-GHG recommendations. 58 The Paraná Basin in the southeast of Brazil presents a low level of seismic activity because it is a typical intra-plate region. Only five earthquakes with m b magnitude above 5.0 (with two being of large-magnitude: 6.3 and 6.8) have occurred in the studied region for the past 220 years according to Berrocal, 59 and the bulletins of Centro de Sismologia da Universidade de São Paulo (USP), Brazil ( http://moho.iag.usp.br/eq/bulletin ). This scenario also indicates a favorable location for CO 2 reservoir based on IEA-GHG recommendations. 58 Depth, thickness and distance to groundwater aquifers To evaluate the subsurface depth, thickness, and distance to groundwater aquifers 58 , 3D thematic models were built in the Leapfrog platform using lithology data from wells. These models were generated using the Vein type software tool. For the depth of the Irati Formation (Fig. 5), all intervals immediately above the formation were selected, and then the model was generated. The thickness model (Fig. 6) was generated considering only the Irati Formation vertical intervals of the selected wells. For a model of the distance of the CO 2 reservoir until the aquifer systems in the area: Bauru, Serra Geral, and Guarani, 60 it was considered the deepest of them: the Guarani Aquifer, 61, 62 using the lithology intervals from the top of Irati Formation to the Botucatu and Piramboia Formations base (Fig. 7). The depth of the Irati Formation increases in the west towards the depocenters of the basin exceeding 2,800 m (Fig. 5). The Irati Formation thickness varies from 22 to 64 m within the captured area (Fig. 6). In nine wells the Irati Formation thickness has shown outlier values from 94 to 265 m. The respective Irati Formation intervals in the database include some diabase intervals. To eliminate these outliers, the data were filtered to consider only the portions above the diabase. The distance between the top of the Irati Formation and the base of the Guarani Aquifer increases from the west exceeding 1,400 m outside the state of São Paulo in the east of the Mato Grosso do Sul state (Fig. 7). 3D IMPLICIT GEOLOGICAL AND STRUCTURAL MODELING Regional-scale 3D geological and structural model The 3D geological implicit modeling was built initially on a regional scale using the already interpreted stratigraphy data consisting of gamma-ray log with outlined geological formations and groups representing the study location (Fig. 2). 12 geological units were considered in the stratigraphic model from base to top: Paraná Group, Itararé Group, Rio Bonito Formation, Palermo Formation, Irati Formation, Serra Alta Formation, Teresina Formation, Rio do Rasto Formation, Piramboia Formation, Botucatu Formation, Serra Geral Formation, and Bauru Group. The study grouped the Corumbataí Formation data with Teresina Formation data. Descriptions of these units are according to literature. 46, 63 The contacts from the CPRM geological map were involved together with the wireline logs data from the eastern region of the study area where the geological formations are exposed. 43 It includes wells intervals selection and interpretation of the contact surfaces between each geological formation using the Deposit type software tool. Detailed descriptions of the cited software tools are available on the Seequent website ( https://help.leapfrog3d.com/Geothermal/ ). The lithological model was based on wireline log data alone, with no use of trends or other software artifices to avoid bias or subjective interpretation. This approach aided to identify possible abrupt changes in the stratigraphic sequence that could indicate fault displacements. The structural model was generated from the surface fault traces, 43, 44 which have been transformed into fault surfaces assuming a vertical dip for all. This simplified assumption is based on a characteristic of an extensional regime consisting of dominant steeply deep normal faults, 64 following the evolution of the Paraná Basin. 57, 63 The initial analysis for the site selection looked through regions with a low incidence of fractures or faults to avoid possible CO 2 leakages. The approach involves delineating geological formation contacts and activating the fault system in the software to generate fault-assisted blocks. It also involves an empirical approach to active some faults and analyzes the displacement of blocks case by case considering the number of faults and lineaments. The goal was to locate possible structural traps for CO 2 storage. The final configuration of the structural model aided to generate seven fault assisted blocks (Fig. 8), considering six faults: 1) The Guaxupé Fault with an approximate N60E direction; 2) The Mogi Lineament with an E-W direction; 3) The São Jerônimo Fault with an approximate N40W direction; 4-5) The Santo Anastácio and Guapiara Faults both with a N60W direction, and 6) A local fault with N10E direction limited by the previous two faults. A structural and stratigraphic trap was identified in Block 4 based on the structural high and contact between the Irati Formation rocks and the overlaying mudstone-dominated Serra Alta and Teresina Formations (Figs. 9 and 10). In the Block 4 domain, there is no incidence of any other faults that could allow some possible CO 2 leakage. Within Block 4, the Irati Formation covers an area above 1,800 km² with an average thickness of 38 m, an average depth of 2,640 m, and an average distance of 920 m from the Botucatu Formation (Guarani Aquifer) base. Davies et al consider 588 m as the maximum distance for the propagation of fractures in hydraulic fracking processes, considering the thousands of fracture operations performed in the shales of Marcellus, Barnett, Eagle Ford, and other fields. 65 Therefore, the average distance of 920 m from the aquifer is safe. The immediate sequence of about 65 m in thickness overlying the Irati Formation is the Serra Alta Formation consisting of shales and mudstones. 47 The Serra Alta Formation has the Teresina Formation of about 685 m in thickness consisting of mudstones interlaminated with fine sandstones on its top. Therefore, the two (Serra Alta and Teresina) formations will provide a combined caprock with an average thickness of about 750 m. Local 3D geological model Two wells (1-TI-1-SP and the 2-TB-1-SP) are inside Block 4. The model indicates a CO 2 reservoir within Block 4 refining the Irati Formations domain (meshes), considering the previously defined Units A/B/C to H (Fig. 12), and a diabase dike that was intercepted by the well 1-TI-1-SP (from 2,854 to 3,140 m). The dike is interpreted as subvertical, trending N55W in the same direction of magnetic lineaments within the study location, based on the airborne magnetic survey images, 43 and also because it is the preferred direction for intrusions. 44 Therefore, the diabase dike divides Block 4 in two (Fig. 11). The site considered for CO 2 storage is on the eastern side of the diabase dike (Fig. 11). Thus, the study presents a potential CO 2 reservoir covering approximately 1,200 km², bounded by the Mogi Lineament to the north, the Guapiara Fault to the northeast, the diabase dike to the southwest, and a local N10E trending fault to the southeast. LOCAL ASPECTS OF THE CO 2 RESERVOIR Porosity and permeability Table 1 shows the porosity (Ф) and permeability (K) calculated from well 2-TB-1-SP for the Irati Formation shale units. Porosity is estimated using sonic-derived values based on equations from literature. 66 The values computed for porosity were used to predict the permeability of the layers based on the redefined equations for shale units. 66 The porosity of the shale Units A/B/C and F varies from 8.0 to 16.7%, and the permeability from 0.542 to 75.11 mD. While the porosity of the black shale Units E and H range from 6.1 to 9.2%, and permeability values range from 0.088 to 1.383 mD. Geothermal gradient and temperature Gomes 67 presented the geothermal gradient of 20.4 ºC/km with a standard deviation of 1.02 calculated through the bottom-hole temperature (BHT) method, 68 for the Irati Formation in the well 2-TB-1-SP. The geothermal gradient is suitable for CO 2 storage considering recommended range (below 30 ºC/km) based on existing atlases. 5, 58 Therefore, a reservoir at 2,640 m depth with a geothermal gradient of 20.4 ºC/km should have an average temperature of 53.8 ºC, which is also within the recommended (≥ 35 ºC) range. 58 CAPACITY ESTIMATION AND CLASSIFICATION Among the current CO 2 storage reservoir choices, the shale formations seem to have the best potential, especially the black shales with total organic content (TOC) above 2%. 69 The available TOC data of the Irati Formation shale at intervals within well 2-TB-1-SP varies from 0.52 to 9.62% (Table 2). Although the black shale Unit H presents higher TOC values (8.45 and 9.62%) compared to Unit E (0.52 to 7.36%), the study considered reservoir CO 2 storage capacity estimation of Unit E due to its higher thickness (20.00 m for Unit E against 2.00 m for Unit H) (Table 1 and Fig. 3). The study considered the DOE NETL equation for shales to estimate the CO 2 capacity of the reservoir. 69 According to Azenkeng et al , there are several challenges regarding storage capacity estimation based on shale reservoirs. 70 Factors that control the CO 2 storage in organic-rich shales are matrix pore spaces, and natural and induced fractures, which are not easily differentiated. 70 Table 3 summarizes the variables engaged for the storage capacity estimation in the organic-rich shale Unit E of Irati Formation within Block 4. The calculated porosity of 6.1% based on well 2-TB-1-SP within the Unit E (Table 1) connotes the natural fractures and pore spaces. The approach assumed for this initial assessment of the Irati Formation shale reservoir was quite conservative. The study engaged the CO 2 density of 842.3 kg/m 3 at supercritical conditions considering the geothermal gradient of 20.4 ºC/km, 67 an average depth of 2.640 m from the well 2-TB-1-SP, and an assumed hydrostatic gradient of 10.7 kPa/m for capacity estimation. Weniger et al performed high-pressure experimental sorption isotherms with CO 2 at 45 °C on shale samples from the Irati Formation. 35 The Langmuir sorption model is a limiting model for low pressures, Weniger et al applied a modified Langmuir model with an average Langmuir pressure of 15,81 Mpa for the Irati Formation shale samples. 35 These authors also recognize a linear correlation between TOC (wt.%) data and the sorption capacities of CO 2 , in agreement with literature results achieved for the Kentucky and the Marcellus Shale. 71, 72 The CO 2 sorbed mass was calculated using the gradient of 10.7 kPa/m at average depth of 2.640 m, and weighted average TOC of 3.2%, both from well 2-TB-1-SP, using the correlation equations from Weniger et al . 35 The volume was directly obtained from the 3D solid of the black shale Unit E from the 3D implicit geological model (Table 3). The volume estimation did not directly involve the area efficiency factor ( E A ) and thickness efficiency factor ( E h ) because of some previously considered legal surface and structural (limiting faults) constraints in the site selection process. Although almost all geological parameters (porosity, temperature, thickness, and TOC) come from a single exploration well (2-TB-1-SP), the volume has a considerable level of reliability based on the constructed 3D geological model, which also considers the lithology data of all other 31 wells, geological mapping, topography, and local and regional structures. The two efficiency factors from DOE NETL’s equation ( E Ф and E s ) were assumed from the simulations of Myshakin et al , 73 contemplating 60 years of CO 2 injection. Considering the most conservative parameters of P 10 probability values of 0.15 for E Ф , and 0.11 for E s , Unit E shows a CO 2 storage capacity of 1.85 billion tons (Table 3). Considering the P 90 probability values of 0.36 for , and of 0.24 for from Myshakin et al , 73 the total capacity would be 4.44 billion tons. For comparison with other organic-rich shale formations, in the Devonian shale in Kentucky, a capacity of 27.7 Gt was estimated at least 304 m deep and 15 m thick, 71 while in the Marcellus Shale a total theoretical capacity of 171.2 Gt was estimated for a depth greater than 915 m. 72 Bachu et al proposed a classification considering technical and economic aspects in four categories: theoretical, effective, practical, and matched capacity, according to a gradual level of uncertainty in storage potential. 74 The present study is the initial assessment of the shales of Irati Formation as a CO 2 reservoir considering the basic geological aspects while aiming to provide the basis for future studies addressing technical parameters (e.g. injection, wettability, capillary pressure, relative permeability, geochemical reactions) and involving numerical models. We assume a Theoretical classification even if a few associated parameters, such as seal, depth, geothermal gradient, temperature, and distance to aquifers are well defined. Although these parameters may not vary with the progress of the research, there is a need for an explicitly detailed definition to reduce the associated uncertainties involving CO 2 storage within the Irati Formation. Discussion Site selection Table 4 presents the parameters considered for site selection and evaluation of potential CO 2 reservoirs. Within the study location, the conditions (low seismicity, absence of fold belts, uniform stratigraphy, high seal/overburden integrity, and the presence of reservoir-seal pairs) considered for CO 2 storage conform to the required standards based on existing atlases. 5, 58, 75 Therefore, the study location presents good CO 2 storage potentials within the black shale repositories. Although the research considered limitations involving the Paraná state legislation that prevents hydraulic fracturing within the region, it is pertinent to address the associated social and environmental factors. 76 The 3D implicit modeling is more robust and has better representativeness of the structure and geology than other surface-based methods, improving the modeling accuracy. 14, 77 The methodology of implicit modeling aided to build thematic models based on the depth and thickness of the Irati Formation sequence. The models enhance quick interpretations, and the evaluation involving the distance to protect groundwater (Guarani Aquifer system) proves satisfactory and with fast results of easy interpretation (Figs. 5, 6, and 7). The presented technique applies to other regions especially at the CO 2 storage Site Selection stage. The 3D structural model divided the study area into seven fault blocks and defined a structural high (based on Block 4) to indicate a combined structural and stratigraphic trap (Figs. 8, 9, and 10). These geological aspects of the selected site are favorable for CO 2 storage. The siltstones of Serra Alta and Teresina formations occur on the vertical and horizontal scales to serve as seals and traps for the Irati Formation shale units, preventing post-CO 2 storage leakages. The study incorporated data from wellbores, surface, regional geological and structural mapping to construct the 3D geological model. The evaluation (e.g. seismic interpretation) involving fault plane orientation and distribution with other associated structures not visible in the current work scale is pertinent to describe the detailed structural framework. Reservoir potential Considering the CO 2 reservoir located in the black shale Unit E, there are 15 power plants within a radius of 75 km with a total installed capacity of 562 MW. 13 of these power plants depend on biomass (mainly sugarcane bagasse), and the other two power plants use diesel fuel. 78 There are 81 power plants total capacity of 1,625 MW within a radius of 150 km around the shale Unit E. A biomass power plant with 600 MW capacity emits on average 3.5 million tons of CO 2 per year, according to 2018 base year data from the United States Environmental Protection Agency. 79 The theoretical capacity of 1.85 Gt of CO 2 storage in the Irati Formation organic-rich shale would account for the equivalent production of 500 years of only the 75 km radius power plants. Alternatively, applying a simple regression, the CO 2 site location could support up to five times more than the current capacity considering a regional industrial park installation in a long-term horizon of about 68 years, taking into account also the location and the privileged infrastructure of the region. The CO 2 geological storage in organic-rich shales has been considered a promissory way to mitigate greenhouse gas emissions. The study includes the advantages commonly pointed out in CO 2 storage sites around the world as the volumetric spatial continuity and geological extension, adequate depth, and potential for methane recovery in the Irati shales. The research presents the theoretical CO 2 storage capacity of the reservoir considering free pore spaces and stratigraphic traps. Furthermore, limited petrophysical data is a challenge. The study also estimates de CO 2 trapping by adsorption based on the limited available literature concerning the Irati Formation. Therefore, future studies should account for associated challenges such as low permeability, matrix porosity, and geological heterogeneity within the Irati Formation shales. Concluding Remarks 1. The Paraná Basin in Southeastern Brazil meets most of the requirements for CO 2 storage base on international best practices. These conditions include the low seismicity, the absence of fold belt, uniform stratigraphy, the absence of complex lateral variations, and the presence of reservoir-seal pairs in multi-layered systems. 2. The application of 3D implicit modeling provided a fast approach via the thematic models to delimitate potential CO 2 reservoirs based on the reservoir depth, thickness, structural geology, and distance to protect groundwater (aquifer) proves satisfactory. 3. The Irati Formation was locally subdivided into two shale units (Units A/B/C and F), two limestone units (Units D and G), and two black shale units (Units E and H) in a three-dimensional ambient. The black shale Unit E was evaluated for CO 2 geological storage. 4. The organic-rich shale Unit E within Block 4 of the Irati Formation has a theoretical CO 2 reservoir capacity of 1.85 Gt of CO 2 , assuming a CCS project with CO 2 injection at supercritical conditions through hydraulic fracturing. 5. The results provide the basis of subsequent studies involving mineral characterization of each geological unit of Irati Formation and numerical simulations. Declarations ACKNOWLEDGMENTS We gratefully acknowledge the support of the Research Centre for Gas Innovation (RCGI), hosted by the Universidade de São Paulo (USP), Shell Brasil, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), and the strategic importance of the support given by Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (ANP) through the R&D levy regulation, and for providing exploration well data. 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Data base of the Brazilian Power Sector-SIGEL Rio de Janeiro2020 [Available from: https://sigel.aneel.gov.br/Down/ . 79. EPA. Emissions & generation resource integrated database (eGRID) Washington, DC: United States Environmental Protection Agency; 2020 [Available from: https://www.epa.gov/energy/egrid ,. Tables Table 1. Rock type, thickness, porosity (Ф), and permeability (K) data for shale units of Irati Formation in the 2-TB-1-SP well. Unit Rock type Thickness (m) Ф K (mD) H Black shale 2.00 0.092 1.383 F Shale 1.00 0.080 0.542 E Black shale 20.0 0.061 0.088 A/B/C Shale 7.00 0.167 75.11 Table 2. Total organic carbon (TOC wt.%) data on the Irati Formation intervals in the well 2-TB-1-SP, with the geological unit subdivision of this study. Top Bottom Rock type Unit TOC wt.% 2,618.94 2,619.94 Black shale H 8.45 2,620.00 2,621.00 Black shale H 9.62 2,622.94 2,623.94 Limestone G 2.02 2,623.94 2,624.94 Shale F 1.44 2,624.94 2,625.95 Black shale E 7.36 2,628.32 2,629.32 Black shale E 7.26 2,631.32 2,632.32 Black shale E 4.53 2,632.32 2,633.42 Black shale E 2.88 2,633.42 2,633.82 Black shale E 5.03 2,633.82 2,635.32 Black shale E 2.45 2,635.32 2,636.32 Black shale E 0.52 2,637.32 2,637.90 Black shale E 1.15 2,640.00 2,646.00 Black shale E 2.25 2,649.00 2,652.00 Limestone D 1.51 2,653.40 2,654.40 Shale A/B/C 0.19 2,656.28 2,657.28 Shale A/B/C 0.28 Table 3. Theoretical CO 2 storage for the black shale Unit E of the Irati Formation. Symbol Unit Parameters Value v m 3 volume 23,914,000,000 Φ % porosity 6.1% ρ CO2 kg/m 3 CO 2 density at reservoir conditions 842.3 ρs CO2 kg/m 3 mass of CO 2 sorbed per unit volume of solid rock 0.31 E Φ % free phase storage efficiency factor 0.15% E s % sorption efficiency factor 0.11% M CO2 Gt mass of CO 2 1.85 Table 4. Site selection criteria for geological CO 2 storage and our study results. Criterion Eliminatory or unfavourable Preferred or Favourable Reference This study Reservoir-seal pairs; extensive and competent barrier to vertical flow Poor, discontinuous, faulted and/or breached Intermediate and excellent; many pairs (multi-layered system) IEA-GHG, 2009 Vertically sealing faults, multi-layered systems - Vertically sealing faults, multi-layered systems Miocic et al., 2016 Stratigraphy Complex lateral variation and complex connectivity Uniform Smith et al., 2011 Uniform Located within fold belts Yes No IEA-GHG, 2009 No Seismicity High Moderate and less IEA-GHG, 2009 Low Depth 2,500 m Between 1,000 and 2,500 m Chadwick et al. 2008 Average depth 2,640 m 800 m IEA-GHG, 2009 2,500m > 800 m 1,200 m Miocic et al., 2016 Thickness 50 m Chadwick et al. 2008 Average thickness 20 m < 20 m ≥ 20 m IEA-GHG, 2009 Affecting protected groundwater quality Yes No IEA-GHG, 2009 Distance of 920m to aquifer system Faulting and fracturing intensity Small or no faults Chadwick et al. 2008 Minimal faulting, with trapping structure Extensive Limited to moderate IEA-GHG, 2009 Minimal faulting, with trapping structure Smith et al., 2011 Caprock t hickness 100 m Chadwick et al. 2008 > 750 m (Serra Alta 64 m thick + Teresina Formations 690 m thick) < 10 m ≥ 10 m IEA-GHG, 2009 100 m thick Smith et al., 2011 - > 150m Miocic et al., 2016 Lateral continuity of caprock Lateral variations, faulted Unfaulted (Uniform) Chadwick et al. 2008 Unfaulted Porosity 20% Chadwick et al. 2008 6.1% < 10% ≥ 10% IEA-GHG, 2009 20% Smith et al., 2011 Geothermal regime Gradients ≥ 35 ºC/km and/or high surface temperature Gradients < 35 ºC/km and low surface temperature IEA-GHG, 2009 20.4 ºC/km - Geo-thermal gradient of max. 30 ºC/km Miocic et al., 2016 Temperature < 35 ºC ≥ 35 ºC IEA-GHG, 2009 54 ºC Total organic organic carbon 100 km < 75 km Smith et al., 2011 15 powerplants (562 MW) within a radius of 75 km Total storage capacity Total capacity estimated to be similar to or less than the total amount produced from the CO source Total capacity estimated to be much larger than the total amount produced from the CO 2 Chadwick et al. 2008 Total capacity estimated to be much larger than the total amount produced from the CO 2 Cite Share Download PDF Status: Published Journal Publication published 10 Aug, 2021 Read the published version in Greenhouse Gases: Science and Technology → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Power plants' locations are from ANEEL-SIGEL 78. MG = Minas Gerais State, MS = Mato Grosso do Sul State, PR = Paraná State, SC = Santa Catarina State, SP = São Paulo State.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/f0b038c4fa01fb7cb6651a10.jpg"},{"id":11676555,"identity":"32fd520b-1358-47f8-b428-d350575c6137","added_by":"auto","created_at":"2021-07-21 16:08:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109105,"visible":true,"origin":"","legend":"Geological map of the study area with local structures,43 and main regional structures.44 Location of the schematic geological section (A’-A”) of Fig. 3. 1 - Cândido Fault, 2 - Curitiba Fault Zone, 3 - São Jerônimo Fault, 4 - Santo Anastácio Fault, 5 – Guapiara Fault Zone, 6 - Mogi Lineament, 7 - São Sebastião Lineament, 8 - Jacutinga Fault, 9 - Guaxupé Fault, 10 - Araçatuba Lineament.","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/33f11cfd07ea34d542b6fafe.jpg"},{"id":11676451,"identity":"e760baa7-e602-4f21-9123-30b3f0d14fe2","added_by":"auto","created_at":"2021-07-21 16:05:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53203,"visible":true,"origin":"","legend":"Schematic geological section of the Irati Formation with a local subdivision in six geological units: shale Unit A/B/C, limestone Unit D, black shale Unit E, shale Unit F, limestone Unit G, and black shale Unit H. ","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/3c2eb6eff34603dbc611002e.jpg"},{"id":11676556,"identity":"b7918213-881a-459b-902e-7bab809c2b2a","added_by":"auto","created_at":"2021-07-21 16:08:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82740,"visible":true,"origin":"","legend":"Intercalations of shale and limestone layers of Irati Formation in the Elba Quarry, Northwest of Paraná Basin.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/b4bbe0b59778bfe0a0a011d2.jpg"},{"id":11676462,"identity":"d3fb6668-ea59-4866-bd0c-a40e61a16d27","added_by":"auto","created_at":"2021-07-21 16:05:58","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55287,"visible":true,"origin":"","legend":"Plan view of the Irati Formation depth model in the study area.","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/b9eb0269ca16dba160e6cc05.jpg"},{"id":11676553,"identity":"a4d1f1c1-8494-4c42-9957-9b25d3045701","added_by":"auto","created_at":"2021-07-21 16:08:57","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58110,"visible":true,"origin":"","legend":"Plan view of the Irati Formation thickness model in the study area.","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/cd3030780946eb0babeb14ca.jpg"},{"id":11676452,"identity":"bdca5138-3eaf-40b1-847f-a68bac2fff17","added_by":"auto","created_at":"2021-07-21 16:05:57","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":52346,"visible":true,"origin":"","legend":"Plan view of the model of the distance from the top of the Irati Formation to the base of the Guarani Aquifer in the study area.","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/18a1d101900414fcdd68a480.jpg"},{"id":11676453,"identity":"bb3536b2-e8a6-4218-bad8-d1a692585040","added_by":"auto","created_at":"2021-07-21 16:05:57","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81081,"visible":true,"origin":"","legend":"Plan view of the structural model showing the seven fault blocks defined by the main regional structures in the study area. ","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/2a8b7e332083d651808597fc.jpg"},{"id":11676461,"identity":"e9fe80da-b378-4a99-8b12-b9df8e3673b3","added_by":"auto","created_at":"2021-07-21 16:05:58","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":51137,"visible":true,"origin":"","legend":"3D view with an E-W slice. A) Structural fault blocks. B) Structural geological model. The legend color is the same as in Fig. 2. Vertical scale = 10x.","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/1fa3af5406bcacb41ad78fa6.jpg"},{"id":11676457,"identity":"43708ec8-9900-443e-a978-e3c04a7f3f54","added_by":"auto","created_at":"2021-07-21 16:05:57","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":27686,"visible":true,"origin":"","legend":"NW-SE section view of the 3D structural and geological modeling highlighting the Fault Block 4 as a structural high. Vertical scale = 10x.","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/2df2c8e18fd3dbb90c63af38.jpg"},{"id":11676582,"identity":"49d35792-5500-4711-8662-881926bad326","added_by":"auto","created_at":"2021-07-21 16:11:57","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":31579,"visible":true,"origin":"","legend":"3D view with an E-W slice of the site location for CO2 storage used for calculation of theoretical capacity. Vertical scale = 10x.","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/99ebdf59f79712d8884789c4.jpg"},{"id":11676458,"identity":"b85434cb-239f-4451-b1d3-795b3d81305e","added_by":"auto","created_at":"2021-07-21 16:05:57","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":22332,"visible":true,"origin":"","legend":"3D view of the local geological model of the CO2 reservoir with the well 2-TB-1-SP and geological units according to Fig. 3. Vertical scale = 30x.","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/05e9759f543a946a002cdcf0.jpg"},{"id":15674118,"identity":"2153fc45-3ef8-447b-be84-ce95389892ab","added_by":"auto","created_at":"2021-11-18 14:21:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":927648,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-420789/v2/95d6d336-7600-4196-85a5-1b839aa43663.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e3D implicit modeling applied to the evaluation of CO\u003csub\u003e2\u003c/sub\u003e geological storage in the shales of the Irati Formation, Paraná Basin, Southeastern Brazil\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn a scenario of growth in the generation and emission of CO\u003csub\u003e2\u003c/sub\u003e, the global emissions reached a historical record of about 33.1 billion tons (Gt) of CO\u003csub\u003e2\u003c/sub\u003e in 2018 according to the International Energy Agency.\u003csup\u003e1\u003c/sup\u003e Brazil is among the 20 countries with the highest CO\u003csub\u003e2\u003c/sub\u003e emissions in recent years.\u003csup\u003e1\u003c/sup\u003e The data provided by the \u0026nbsp;\u003cem\u003eSistema de Estimativas de Emiss\u0026otilde;es e Remo\u0026ccedil;\u0026otilde;es de Gases de Efeito Estufa (SEEG)\u003c/em\u003e on CO\u003csub\u003e2\u003c/sub\u003e emissions by the Brazilian energy sector, based on the methodology of Azevedo \u003cem\u003eet al\u003c/em\u003e,\u003csup\u003e2\u003c/sup\u003e indicates a significant increase from 173 million tons (Mt) of CO\u003csub\u003e2\u003c/sub\u003e in 1990 to 380 Mt of CO\u003csub\u003e2\u003c/sub\u003e in 2018.\u003csup\u003e3\u003c/sup\u003e Brazil has also set goals to contribute to the reduction of greenhouse gas emissions, aiming at 37% by 2025, and 43% by 2030, based on 2005 emissions, as stated in its Nationally Determined Contribution (NDC) ratification. This worldwide decision requires the rapid and extensive development of low carbon technologies, and one of the most efficient approaches is Carbon Capture and Storage (CCS). In the Brazilian context, CCS technologies are becoming relevant due to the CO\u003csub\u003e2\u003c/sub\u003e emission capacity of the energy sector, considering the development of technologies that respond positively to global trends.\u003c/p\u003e\n\u003cp\u003eDeep geological formations provide suitable repositories for CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003estorage, however, some requirements must be met to allow large amounts of CO\u003csub\u003e2\u003c/sub\u003e to be injected and that the gas remains trapped in the rock for a long time.\u003csup\u003e4, 5\u003c/sup\u003e Usually depleted oil and gas fields, saline aquifers, deep salt formations (salt caves and abandoned mines), coal seams, basalt, and black shale layers are considered viable CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003ereservoirs.\u003csup\u003e6, 7\u003c/sup\u003e Each of these types of CO\u003csub\u003e2\u003c/sub\u003e geological reservoirs has distinct trapping mechanisms, including structural/stratigraphic trapping, residual trapping, dissolution trapping, and mineral trapping.\u003csup\u003e8-10\u003c/sup\u003e The efficiency of shale layers for CO\u003csub\u003e2\u003c/sub\u003e storage has gained popularity because they are abundant and have a high content of organic matter and clay minerals, which are favorable materials for CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eretention by adsorption. Besides, the shale layers may contain unconventional hydrocarbons (shale gas and shale oil). If shale oil and gas production becomes viable within the Irati Formation, the co-development of hydrocarbon wells and CO\u003csub\u003e2\u003c/sub\u003e storage will reduce cost.\u003csup\u003e11\u003c/sup\u003e Shale gas is an extensive global energy resource, and its development can integrate the CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003egeological storage as a feasible technological alternative to reduce carbon emissions impact on the climate.\u003csup\u003e12, 13\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe research focuses on generating a three-dimensional (3D) geological model based on a pre-selected area\u0026nbsp;measuring about 185,000 km\u0026sup2;\u0026nbsp;(Fig. 1) with potential for CO\u003csub\u003e2\u003c/sub\u003e storage in the S\u0026atilde;o Paulo State, southeastern Brazil. The implicit geological modeling in a three-dimensional (3D) virtual environment\u003csup\u003e14-17\u003c/sup\u003e has been widely used in the mineral industry for at least 15 years. Recent examples of 3D implicit geological modeling include iron deposits (e.g. Sishen Mine, South Africa),\u003csup\u003e18\u003c/sup\u003e base metal deposits (e.g. Falunin Sweden, Flin Flon in Canada, and Shalipayco in Peru),\u003csup\u003e19-21\u003c/sup\u003e gold deposits (e.g. Navachab in Namibia, La Colossa in Colombia, and Sigma-Lamaque in Canada),\u003csup\u003e22-24\u003c/sup\u003e and geothermal reservoirs,\u003csup\u003e25\u003c/sup\u003e amongst others. The 3D implicit modeling expands the analysis of conventional geological data by visualizing continuities, groupings, and spatial trends, as well as geometries of geological bodies or units, structural geological framework, and variation of geochemical contents or any other numerical or categorical parameters in geosciences. Examples of 3D geological modeling focusing on CO\u003csub\u003e2\u003c/sub\u003e storage are scarce.\u003csup\u003e26-34\u003c/sup\u003e Evaluations and studies focusing on CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003egeological storage in the Paran\u0026aacute; Basin include the study based on the Santa Terezinha coalfield,\u003csup\u003e35\u003c/sup\u003e and those considering saline aquifers.\u003csup\u003e36-40\u003c/sup\u003e The objectives in this research include: 1) Site selection on a regional scale for CO\u003csub\u003e2\u003c/sub\u003e storage focusing on the shale reservoirs within the Paran\u0026aacute; Basin, and 2) Estimation of the theoretical storage capacity of the selected site. The 3D geological model of Paran\u0026aacute; Basin will aid the assessment of the geological feasibility of implementing CCS technology in Southeastern Brazil to reduce CO\u003csub\u003e2\u003c/sub\u003e in this region where most of the country\u0026apos;s carbon emission stationary sources are concentrated.\u003csup\u003e37, 39, 41\u003c/sup\u003e Therefore, the innovations of this work are the first application of 3D implicit modeling to evaluate the CO\u003csub\u003e2\u003c/sub\u003e storage potential of the shale reservoirs in the study location, comprising the initial steps of Site Selection and Initial Characterization.\u003csup\u003e42\u003c/sup\u003e The study considered the hypothesis that the Irati Formation organic-rich shales present reservoir units with enough capacity to permanently store the volume of the waste CO\u003csub\u003e2\u003c/sub\u003e released to the atmosphere considering the emitting sources within the region.\u0026nbsp;\u003c/p\u003e"},{"header":"Main Text","content":"\u003cp\u003eThe dataset provided by ANP in December 2019 is composed of hydrocarbon exploration well data. The dataset contains data of collar, survey, lithology, stratigraphy, and hydrocarbon occurrence, as well as, wireline logs consisting of gamma-ray, resistivity, density, neutrons, and sonic log, besides some organic geochemistry data acquired by Petrobras Company during the 1950s until the 2000s. A filter was applied in a total of 123 wells in the Paran\u0026aacute; Basin resulting in 32 wells in the study area (Fig. 1). From these wells, all have stratigraphy data, 29 of the wells have some lithology data, and 15 have some hydrocarbon indication data. In Fig. 1 and subsequent figures, in the location indicated by the 3-CB-3-SP well label, there are also located other four wells (2-CB-1-SP, 2-CB-1DA-SP, 3-CB-2-SP, and 3-CB-4-SP) lying 3 km from each other on average. Therefore, their labels were omitted in the figures for a scale issue, to avoid overlaps and visual pollution. Almost all filtered wells are vertical with only one having survey data. Then, all available data were loaded in the Leapfrog Geothermal\u0026reg; software for performing three-dimensional (3D) geological modeling. The Leapfrog software uses radial basis functions (RBFs) to perform surfaces by the implicit modeling method.\u003csup\u003e14\u003c/sup\u003e It involves the generation of a regional surface topographic model based on the GTOPO30 data (\u003ca href=\"https://earthexplorer.usgs.gov/\"\u003ehttps://earthexplorer.usgs.gov/\u003c/a\u003e). Surface geology data (lithology and structures) were obtained from Companhia de Pesquisa de Recursos Minerais (CPRM),\u003csup\u003e43\u003c/sup\u003e and regional geological structures are from Zal\u0026aacute;n,\u003csup\u003e44\u003c/sup\u003e and then treated in the free software QGIS Desktop before importing into Leapfrog platform.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTHE IRATI FORMATION IN THE STUDY AREA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Irati Formation occurs in almost the entire extension of the Paran\u0026aacute; Basin.\u003csup\u003e45-47\u003c/sup\u003e In the study area, outcrops of the Irati Formation are restricted to the southeast portion only (Fig. 2). For this research, the classification of the lithologic units depends mainly on well logs data. Almost all the wells in the area penetrate the Irati Formation horizon, except for the 1-QT-1-PR well, which is very shallow, and the 1-TI-1-SP well, which presents an extensive diabase dike at intervals between the Teresina and Palermo Formations (Fig. 3). In the study area, 8 wells (1-J-1-PR, 1-MA-1-SP, 1-PA-1-SP, 1-SA-1-SP, 2-AA-1-SP, 2-AP-1-PR, 2-RI-1-PR, 2-TB-1-SP) present some punctual oil indication, 5 wells present gas indication (2-CB-1-SP, 2-CB-1DA-SP, 3-CB-2-SP, 3-CB-3-SP, 3-CB-4-SP), referring to the Cuiab\u0026aacute; Paulista sub-commercial gas accumulation, and 4 wells show bitumen (1-AB-1-SP, 1-GU-4-SP, 2-PN-1-SP, and 2-PP-1-SP), all within the Irati Formation (Fig. 2).\u003c/p\u003e\n\u003cp\u003eA 3D interpretation of the lithology data with some TOC data enabled a subdivision of the Irati Formation into units following the subdivision of literature.\u003csup\u003e48-50\u003c/sup\u003e Due to the erratic availability of samples with TOC analysis, the three A, B, and C base units were undifferentiated and referred to as a single shale unit named Unit A/B/C (Fig. 3). This work considers six units informally named Units A/B/C to H from the bottom to the top (Fig. 3), to conform to the existing divisions.\u003csup\u003e48-50\u003c/sup\u003e The lithologic unit interpretation incorporates the thickness of units less than 1 m into the larger adjacent units. Therefore, larger shaly units may contain thin and discontinuous levels of limestone, and delineated carbonate units may also have incorporated thin-bedded shale levels. Unit A/B/C is a shale layer with incorporated carbonate layers having a thickness measuring up to 30 m at the middle of the study location and about 3 m at the eastern portion. The sequence from Units D to G is laterally continuous and occurs in the entire E-W section (Fig. 3) for more than 300 km across the study area. Unit D consists of limestones with incorporated dolostones, calcilutite to calcarenite having a thickness range of 1 to 16 m. The thickness of the organic-rich shale Unit E varies from 2 to 20 m. Both Units E and F consist of interbedded diabase sill in the eastern portion. Unit F is composed of shales with a thickness range of 1 to 14 m throughout the E-W section. Unit G is composed of limestones and dolostones with grain size varying from calcilutite to calcarenite, and thickness in the wells ranges from 2 to 11 m. The topmost Unit H is an organic-rich shale layer that occurs only at the west of the study location. The presented Irati Formation subdivisions are similar to those showcased in Figure 4 and presented in the literature,\u003csup\u003e48-50\u003c/sup\u003e although it is not exactly equivalent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eINITIAL CRITERIA FOR SELECTING THE CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eSTORAGE LOCATION\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003estationary sources and some legal aspects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area (Fig. 1) already accounted for some of the CO\u003csub\u003e2\u003c/sub\u003e site selection criteria considering the region with the highest CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003estationary emitting sources and legal aspects. The land use, land-use change, and forestry (LULUCF) sector is the major net emitter of CO\u003csub\u003e2\u003c/sub\u003e in Brazil, followed by the energy sector. The S\u0026atilde;o Paulo State is the state with the highest CO\u003csub\u003e2\u003c/sub\u003e emissions in the energy sector considering the concentration of thermoelectric power plants (Fig. 1).\u003csup\u003e37, 39, 51\u003c/sup\u003e Based on 2018 data, from the 380 Mt of CO\u003csub\u003e2\u003c/sub\u003e emitted in the Brazilian energy sector, 80 Mt (21%) is from the S\u0026atilde;o Paulo State.\u003csup\u003e3\u003c/sup\u003e The Paran\u0026aacute; State also has a high concentration of thermoelectric plants. However, the Paran\u0026aacute; State Law 19.878 of 2019 prohibits the production of unconventional natural gas production through hydraulic fracking,\u003csup\u003e52\u003c/sup\u003e although the state has no oil and gas production activities. The Barra Bonita conventional gas field within the region is not producing. The legal context of shale gas in the Paran\u0026aacute; Basin is better and deeply discussed in the literature.\u003csup\u003e52, 53\u003c/sup\u003e Brazil has no federal or states legal restrictions or specific legislation to prevent CCS.\u003csup\u003e54, 55\u003c/sup\u003e Therefore, areas inside the S\u0026atilde;o Paulo State were prioritized in this study, mainly because of the high concentration of energy sector and biomass industries related to sugar-cane ethanol plants.\u003csup\u003e56\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional geological aspects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Paran\u0026aacute; Basin is tectonically related to a more than 10,000 km-long divergent margin originated by the Gondwana paleocontinent break-up and the separation of the African and South American plates,\u003csup\u003e57\u003c/sup\u003e with no relation with fold belts, therefore, it is a tectonically favorable location for CO\u003csub\u003e2\u003c/sub\u003e storage considering IEA-GHG recommendations.\u003csup\u003e58\u003c/sup\u003e The Paran\u0026aacute; Basin in the southeast of Brazil presents a low level of seismic activity because it is a typical intra-plate region. Only five earthquakes with m\u003csub\u003eb\u0026nbsp;\u003c/sub\u003emagnitude above 5.0 (with two being of large-magnitude: 6.3 and 6.8) have occurred in the studied region for the past 220 years according to Berrocal,\u003csup\u003e59\u003c/sup\u003e and the bulletins of Centro de Sismologia da Universidade de S\u0026atilde;o Paulo (USP), Brazil (\u003ca href=\"http://moho.iag.usp.br/eq/bulletin\"\u003ehttp://moho.iag.usp.br/eq/bulletin\u003c/a\u003e). This scenario also indicates a favorable location for CO\u003csub\u003e2\u003c/sub\u003e reservoir based on IEA-GHG recommendations.\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepth, thickness and distance to groundwater aquifers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the subsurface depth, thickness, and distance to groundwater aquifers \u003csup\u003e58\u003c/sup\u003e, 3D thematic models were built in the Leapfrog platform using lithology data from wells. These models were generated using the \u003cem\u003eVein type\u0026nbsp;\u003c/em\u003esoftware tool. For the depth of the Irati Formation (Fig. 5), all intervals immediately above the formation were selected, and then the model was generated. The thickness model (Fig. 6) was generated considering only the Irati Formation vertical intervals of the selected wells. For a model of the distance of the CO\u003csub\u003e2\u003c/sub\u003e reservoir until the aquifer systems in the area: Bauru, Serra Geral, and Guarani,\u003csup\u003e60\u003c/sup\u003e it was considered the deepest of them: the Guarani Aquifer,\u003csup\u003e61, 62\u003c/sup\u003e using the lithology intervals from the top of Irati Formation to the Botucatu and Piramboia Formations base (Fig. 7).\u003c/p\u003e\n\u003cp\u003eThe depth of the Irati Formation increases in the west towards the depocenters of the basin exceeding 2,800 m (Fig. 5). The Irati Formation thickness varies from 22 to 64 m within the captured area (Fig. 6). In nine wells the Irati Formation thickness has shown outlier values from 94 to 265 m. The respective Irati Formation intervals in the database include some diabase intervals. To eliminate these outliers, the data were filtered to consider only the portions above the diabase. The distance between the top of the Irati Formation and the base of the Guarani Aquifer increases from the west exceeding 1,400 m outside the state of S\u0026atilde;o Paulo in the east of the Mato Grosso do Sul state (Fig. 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3D IMPLICIT GEOLOGICAL AND STRUCTURAL MODELING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional-scale 3D geological and structural model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 3D geological implicit modeling was built initially on a regional scale using the already interpreted stratigraphy data consisting of gamma-ray log with outlined geological formations and groups representing the study location (Fig. 2). 12 geological units were considered in the stratigraphic model from base to top: Paran\u0026aacute; Group, Itarar\u0026eacute; Group, Rio Bonito Formation, Palermo Formation, Irati Formation, Serra Alta Formation, Teresina Formation, Rio do Rasto Formation, Piramboia Formation, Botucatu Formation, Serra Geral Formation, and Bauru Group. The study grouped the Corumbata\u0026iacute; Formation data with Teresina Formation data. Descriptions of these units are according to literature.\u003csup\u003e46, 63\u003c/sup\u003e The contacts from the CPRM geological map were involved together with the wireline logs data from the eastern region of the study area where the geological formations are exposed.\u003csup\u003e43\u003c/sup\u003e It includes wells intervals selection and interpretation of the contact surfaces between each geological formation using the \u003cem\u003eDeposit type\u003c/em\u003e software tool. Detailed descriptions of the cited software tools are available on the Seequent website (\u003ca href=\"https://help.leapfrog3d.com/Geothermal/\"\u003ehttps://help.leapfrog3d.com/Geothermal/\u003c/a\u003e). The lithological model was based on wireline log data alone, with no use of trends or other software artifices to avoid bias or subjective interpretation. This approach aided to identify possible abrupt changes in the stratigraphic sequence that could indicate fault displacements.\u003c/p\u003e\n\u003cp\u003eThe structural model was generated from the surface fault traces,\u003csup\u003e43, 44\u003c/sup\u003e which have been transformed into fault surfaces assuming a vertical dip for all. This simplified assumption is based on a characteristic of an extensional regime consisting of dominant steeply deep normal faults,\u003csup\u003e64\u003c/sup\u003e following the evolution of the Paran\u0026aacute; Basin.\u003csup\u003e57, 63\u003c/sup\u003e The initial analysis for the site selection looked through regions with a low incidence of fractures or faults to avoid possible CO\u003csub\u003e2\u003c/sub\u003e leakages. The approach involves delineating geological formation contacts and activating the fault system in the software to generate fault-assisted blocks. It also involves an empirical approach to active some faults and analyzes the displacement of blocks case by case considering the number of faults and lineaments. The goal was to locate possible structural traps for CO\u003csub\u003e2\u003c/sub\u003e storage. The final configuration of the structural model aided to generate seven fault assisted blocks (Fig. 8), considering six faults: 1) The Guaxup\u0026eacute; Fault with an approximate N60E direction; 2) The Mogi Lineament with an E-W direction; 3) The S\u0026atilde;o Jer\u0026ocirc;nimo Fault with an approximate N40W direction; 4-5) The Santo Anast\u0026aacute;cio and Guapiara Faults both with a N60W direction, and 6) A local fault with N10E direction limited by the previous two faults.\u003c/p\u003e\n\u003cp\u003eA structural and stratigraphic trap was identified in Block 4 based on the structural high and contact between the Irati Formation rocks and the overlaying mudstone-dominated Serra Alta and Teresina Formations (Figs. 9 and 10). In the Block 4 domain, there is no incidence of any other faults that could allow some possible CO\u003csub\u003e2\u003c/sub\u003e leakage. Within Block 4, the Irati Formation covers an area above 1,800 km\u0026sup2; with an average thickness of 38 m, an average depth of 2,640 m, and an average distance of 920 m from the Botucatu Formation (Guarani Aquifer) base. Davies \u003cem\u003eet al\u003c/em\u003e consider 588 m as the maximum distance for the propagation of fractures in hydraulic fracking processes, considering the thousands of fracture operations performed in the shales of Marcellus, Barnett, Eagle Ford, and other fields. \u003csup\u003e65\u003c/sup\u003e Therefore, the average distance of 920 m from the aquifer is safe. The immediate sequence of about 65 m in thickness overlying the Irati Formation is the Serra Alta Formation consisting of shales and mudstones.\u003csup\u003e47\u003c/sup\u003e The Serra Alta Formation has the Teresina Formation of about 685 m in thickness consisting of mudstones interlaminated with fine sandstones on its top. Therefore, the two (Serra Alta and Teresina) formations will provide a combined caprock with an average thickness of about 750 m.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLocal 3D geological model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo wells (1-TI-1-SP and the 2-TB-1-SP) are inside Block 4. The model indicates a CO\u003csub\u003e2\u003c/sub\u003e reservoir within Block 4 refining the Irati Formations domain (meshes), considering the previously defined Units A/B/C to H (Fig. 12), and a diabase dike that was intercepted by the well 1-TI-1-SP (from 2,854 to 3,140 m). The dike is interpreted as subvertical, trending N55W in the same direction of magnetic lineaments within the study location, based on the airborne magnetic survey images,\u003csup\u003e43\u003c/sup\u003e and also because it is the preferred direction for intrusions.\u003csup\u003e44\u003c/sup\u003e Therefore, the diabase dike divides Block 4 in two (Fig. 11). The site considered for CO\u003csub\u003e2\u003c/sub\u003e storage is on the eastern side of the diabase dike (Fig. 11). Thus, the study presents a potential CO\u003csub\u003e2\u003c/sub\u003e reservoir covering approximately 1,200 km\u0026sup2;, bounded by the Mogi Lineament to the north, the Guapiara Fault to the northeast, the diabase dike to the southwest, and a local N10E trending fault to the southeast.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOCAL ASPECTS OF THE CO\u003csub\u003e2\u003c/sub\u003e RESERVOIR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePorosity and permeability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 shows the porosity (Ф) and permeability (K) calculated from well 2-TB-1-SP for the Irati Formation shale units. Porosity is estimated using sonic-derived values based on equations from literature.\u003csup\u003e66\u003c/sup\u003e The values computed for porosity were used to predict the permeability of the layers based on the redefined equations for shale units.\u003csup\u003e66\u003c/sup\u003e The porosity of the shale Units A/B/C and F varies from 8.0 to 16.7%, and the permeability from 0.542 to 75.11 mD. While the porosity of the black shale Units E and H range from 6.1 to 9.2%, and permeability values range from 0.088 to 1.383 mD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeothermal gradient and temperature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGomes \u003csup\u003e67\u003c/sup\u003e presented the geothermal gradient of 20.4 \u0026ordm;C/km with a standard deviation of 1.02 calculated through the bottom-hole temperature (BHT) method,\u003csup\u003e68\u003c/sup\u003e for the Irati Formation in the well 2-TB-1-SP. The geothermal gradient is suitable for CO\u003csub\u003e2\u003c/sub\u003e storage considering recommended range (below 30 \u0026ordm;C/km) based on existing atlases.\u003csup\u003e5, 58\u003c/sup\u003e Therefore, a reservoir at \u0026nbsp;2,640 m depth with a geothermal gradient of 20.4 \u0026ordm;C/km should have an average temperature of 53.8 \u0026ordm;C, which is also within the recommended (\u0026ge; 35 \u0026ordm;C) range.\u003csup\u003e58\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAPACITY ESTIMATION AND CLASSIFICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the current CO\u003csub\u003e2\u003c/sub\u003e storage reservoir choices, the shale formations seem to have the best potential, especially the black shales with total organic content (TOC) above 2%.\u003csup\u003e69\u003c/sup\u003e\u0026nbsp;The available TOC data of the Irati Formation shale at intervals within well\u0026nbsp;2-TB-1-SP varies from 0.52 to 9.62% (Table 2). Although the black shale Unit H presents higher TOC values (8.45 and 9.62%) compared to Unit E (0.52\u0026nbsp;to\u0026nbsp;7.36%), the study considered reservoir CO\u003csub\u003e2\u003c/sub\u003e storage capacity estimation of Unit E due to its higher thickness (20.00 m for Unit E against 2.00 m for Unit H) (Table 1 and Fig. 3).\u003c/p\u003e\n\u003cp\u003eThe study considered the DOE NETL equation for shales to estimate the CO\u003csub\u003e2\u003c/sub\u003e capacity of the reservoir.\u003csup\u003e69\u003c/sup\u003e\u0026nbsp;According to Azenkeng \u003cem\u003eet al\u003c/em\u003e, there are several challenges regarding storage capacity estimation based on shale reservoirs.\u003csup\u003e70\u003c/sup\u003e\u0026nbsp;Factors that control the CO\u003csub\u003e2\u003c/sub\u003e storage in organic-rich shales are matrix pore spaces, and natural and induced fractures, which are not easily differentiated.\u003csup\u003e70\u003c/sup\u003e\u0026nbsp;Table 3 summarizes the variables engaged for the storage capacity estimation in the organic-rich shale Unit E of Irati Formation within Block 4. The calculated porosity of 6.1%\u0026nbsp;based on well 2-TB-1-SP within the Unit E\u0026nbsp;(Table 1) connotes the natural fractures and pore spaces. The approach assumed for this initial assessment of the Irati Formation shale reservoir was quite conservative. The study engaged the CO\u003csub\u003e2\u003c/sub\u003e density\u0026nbsp; \u0026nbsp;of 842.3 kg/m\u003csup\u003e3\u003c/sup\u003e at supercritical conditions considering the geothermal gradient of\u0026nbsp;20.4 \u0026ordm;C/km,\u003csup\u003e67\u003c/sup\u003e an average depth of 2.640 m from the well 2-TB-1-SP, and an assumed hydrostatic gradient of 10.7 kPa/m for capacity estimation.\u003c/p\u003e\n\u003cp\u003eWeniger \u003cem\u003eet al\u003c/em\u003e performed high-pressure experimental sorption isotherms with CO\u003csub\u003e2\u003c/sub\u003e at 45 \u0026deg;C on shale samples from the Irati Formation.\u003csup\u003e35\u003c/sup\u003e\u0026nbsp;The Langmuir sorption model is a limiting model for low pressures, Weniger \u003cem\u003eet al\u003c/em\u003e applied a modified Langmuir model with an average Langmuir pressure of 15,81 Mpa for the Irati Formation shale samples.\u003csup\u003e35\u003c/sup\u003e\u0026nbsp;These authors also recognize a linear correlation between TOC\u0026nbsp;(wt.%) data and the sorption capacities of CO\u003csub\u003e2\u003c/sub\u003e, in agreement with literature results achieved for the Kentucky and the Marcellus Shale.\u003csup\u003e71, 72\u003c/sup\u003e\u0026nbsp;The CO\u003csub\u003e2\u003c/sub\u003e sorbed mass\u0026nbsp; \u0026nbsp;was calculated using the\u0026nbsp;gradient of 10.7 kPa/m\u0026nbsp;at average depth of\u0026nbsp;2.640 m, and weighted average TOC of 3.2%, both from well 2-TB-1-SP,\u0026nbsp;using the correlation equations from\u0026nbsp;Weniger \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe volume was directly obtained from the 3D solid of the black shale Unit E from the 3D implicit geological model (Table 3). The volume estimation did not directly involve the area efficiency factor (\u003cem\u003eE\u003csub\u003eA\u003c/sub\u003e\u003c/em\u003e)\u0026nbsp;and\u0026nbsp;thickness\u0026nbsp;efficiency factor (\u003cem\u003eE\u003csub\u003eh\u003c/sub\u003e\u003c/em\u003e)\u0026nbsp;because of some previously considered legal surface and structural (limiting faults) constraints in the site selection process. Although almost all geological parameters (porosity, temperature, thickness, and TOC) come from a single exploration well (2-TB-1-SP), the volume has a considerable level of reliability based on the constructed 3D geological model, which also considers the lithology data of all other 31 wells, geological mapping, topography, and local and regional structures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe two efficiency factors from\u0026nbsp;DOE NETL\u0026rsquo;s\u0026nbsp;equation (\u003cem\u003eE\u003c/em\u003e\u003cem\u003e\u003csub\u003eФ\u003c/sub\u003e\u003c/em\u003e\u0026nbsp;and \u003cem\u003eE\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e) were assumed from the simulations of Myshakin \u003cem\u003eet al\u003c/em\u003e,\u003csup\u003e73\u003c/sup\u003e\u0026nbsp;contemplating 60 years of CO\u003csub\u003e2\u003c/sub\u003e injection. Considering the most conservative parameters of P\u003csub\u003e10\u003c/sub\u003e probability values of 0.15 for \u003cem\u003eE\u003c/em\u003e\u003cem\u003e\u003csub\u003eФ\u003c/sub\u003e\u003c/em\u003e, and 0.11 for \u003cem\u003eE\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e, Unit E shows a CO\u003csub\u003e2\u003c/sub\u003e storage capacity of 1.85 billion tons (Table 3). Considering the P\u003csub\u003e90\u003c/sub\u003e probability values of 0.36 for\u0026nbsp; , and of 0.24 for\u0026nbsp; \u0026nbsp;from Myshakin \u003cem\u003eet al\u003c/em\u003e,\u003csup\u003e73\u003c/sup\u003e\u0026nbsp;the total capacity would be 4.44 billion tons.\u0026nbsp;For comparison with other organic-rich shale formations, in the Devonian shale in Kentucky, a capacity of 27.7 Gt was estimated at least 304 m deep and 15 m thick,\u003csup\u003e71\u003c/sup\u003e\u0026nbsp;while in the Marcellus Shale a total theoretical capacity of 171.2 Gt was estimated for a depth greater than 915 m.\u003csup\u003e72\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eBachu \u003cem\u003eet al\u003c/em\u003e proposed a classification considering technical and economic aspects in four categories: theoretical, effective, practical, and matched capacity, according to a gradual level of uncertainty in storage potential.\u003csup\u003e74\u003c/sup\u003e\u0026nbsp;The present study is the initial assessment of the shales of Irati Formation as a CO\u003csub\u003e2\u003c/sub\u003e reservoir considering the basic geological aspects while aiming to provide the basis for future studies addressing technical parameters (e.g. injection, wettability, capillary pressure, relative permeability, geochemical reactions) and involving numerical models. We assume a Theoretical classification even if a few associated parameters, such as seal, depth, geothermal gradient, temperature, and distance to aquifers are well defined. Although these parameters may not vary with the progress of the research, there is a need for an explicitly detailed definition to reduce the associated uncertainties involving CO\u003csub\u003e2\u003c/sub\u003e storage within the Irati Formation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSite selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 presents the parameters considered for site selection and evaluation of potential CO\u003csub\u003e2\u003c/sub\u003e reservoirs. Within the study location, the conditions (low seismicity, absence of fold belts, uniform stratigraphy, high seal/overburden integrity, and the presence of reservoir-seal pairs) considered for CO\u003csub\u003e2\u003c/sub\u003e storage conform to the required standards based on existing atlases.\u003csup\u003e5, 58, 75\u003c/sup\u003e Therefore, the study location presents good CO\u003csub\u003e2\u003c/sub\u003e storage potentials within the black shale repositories. Although the research considered limitations involving the Paran\u0026aacute; state legislation that prevents hydraulic fracturing within the region, it is pertinent to address the associated social and environmental factors.\u003csup\u003e76\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 3D implicit modeling is more robust and has better representativeness of the structure and geology than other surface-based methods, improving the modeling accuracy.\u003csup\u003e14, 77\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe methodology of implicit modeling aided to build thematic models based on the depth and thickness of the Irati Formation sequence. The models enhance quick interpretations, and the evaluation involving the distance to protect groundwater (Guarani Aquifer system) proves satisfactory and with fast results of easy interpretation (Figs. 5, 6, and 7). The presented technique applies to other regions especially at the CO\u003csub\u003e2\u003c/sub\u003e storage Site Selection stage. The 3D structural model divided the study area into seven fault blocks and defined a structural high (based on Block 4) to indicate a combined structural and stratigraphic trap (Figs. 8, 9, and 10). These geological aspects of the selected site are favorable for CO\u003csub\u003e2\u003c/sub\u003e storage. The siltstones of Serra Alta and Teresina formations occur on the vertical and horizontal scales to serve as seals and traps for the Irati Formation shale units, preventing post-CO\u003csub\u003e2\u003c/sub\u003e storage leakages. The study incorporated data from wellbores, surface, regional geological and structural mapping to construct the 3D geological model. The evaluation (e.g. seismic interpretation) involving fault plane orientation and distribution with other associated structures not visible in the current work scale is pertinent to describe the detailed structural framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReservoir potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the CO\u003csub\u003e2\u003c/sub\u003e reservoir located in the black shale Unit E, there are 15 power plants within a radius of 75 km with a total installed capacity of 562 MW. 13 of these power plants depend on biomass (mainly sugarcane bagasse), and the other two power plants use diesel fuel.\u003csup\u003e78\u003c/sup\u003e There are 81 power plants total capacity of 1,625 MW within a radius of 150 km around the shale Unit E. A biomass power plant with 600 MW capacity emits on average 3.5 million tons of CO\u003csub\u003e2\u003c/sub\u003e per year, according to 2018 base year data from the United States Environmental Protection Agency.\u003csup\u003e79\u003c/sup\u003e The theoretical capacity of 1.85 Gt of CO\u003csub\u003e2\u003c/sub\u003e storage in the Irati Formation organic-rich shale would account for the equivalent production of 500 years of only the 75 km radius power plants. Alternatively, applying a simple regression, the CO\u003csub\u003e2\u003c/sub\u003e site location could support up to five times more than the current capacity considering a regional industrial park installation in a long-term horizon of about 68 years, taking into account also the location and the privileged infrastructure of the region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e geological storage in organic-rich shales has been considered a promissory way to mitigate greenhouse gas emissions. The study includes the advantages commonly pointed out in CO\u003csub\u003e2\u003c/sub\u003e storage sites around the world as the volumetric spatial continuity and geological extension, adequate depth, and potential for methane recovery in the Irati shales. The research presents the theoretical CO\u003csub\u003e2\u003c/sub\u003e storage capacity of the reservoir considering free pore spaces and stratigraphic traps. Furthermore, limited petrophysical data is a challenge. The study also estimates de CO\u003csub\u003e2\u003c/sub\u003e trapping by adsorption based on the limited available literature concerning the Irati Formation. Therefore, future studies should account for associated challenges such as low permeability, matrix porosity, and geological heterogeneity within the Irati Formation shales.\u003c/p\u003e"},{"header":"Concluding Remarks","content":"\u003cp\u003e1. The Paran\u0026aacute; Basin in Southeastern Brazil meets most of the requirements for CO\u003csub\u003e2\u003c/sub\u003e storage base on international best practices. These conditions include the low seismicity, the absence of fold belt, uniform stratigraphy, the absence of complex lateral variations, and the presence of reservoir-seal pairs in multi-layered systems.\u003c/p\u003e\n\u003cp\u003e2. The application of 3D implicit modeling provided a fast approach via the thematic models to delimitate potential CO\u003csub\u003e2\u003c/sub\u003e reservoirs based on the reservoir depth, thickness, structural geology, and distance to protect groundwater (aquifer) proves satisfactory.\u003c/p\u003e\n\u003cp\u003e3. The Irati Formation was locally subdivided into two shale units (Units A/B/C and F), two limestone units (Units D and G), and two black shale units (Units E and H) in a three-dimensional ambient. The black shale Unit E was evaluated for CO\u003csub\u003e2\u003c/sub\u003e geological storage.\u003c/p\u003e\n\u003cp\u003e4. \u0026nbsp;The organic-rich shale Unit E within Block 4 of the Irati Formation has a theoretical CO\u003csub\u003e2\u003c/sub\u003e reservoir capacity of 1.85 Gt of CO\u003csub\u003e2\u003c/sub\u003e, assuming a CCS project with CO\u003csub\u003e2\u003c/sub\u003e injection at supercritical conditions through hydraulic fracturing.\u003c/p\u003e\n\u003cp\u003e5. The results provide the basis of subsequent studies involving mineral characterization of each geological unit of Irati Formation and numerical simulations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the support of the Research Centre for Gas Innovation (RCGI), hosted by the Universidade de S\u0026atilde;o Paulo (USP), Shell Brasil, Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo (FAPESP), and the strategic importance of the support given by Ag\u0026ecirc;ncia Nacional do Petr\u0026oacute;leo, G\u0026aacute;s Natural e Biocombust\u0026iacute;veis (ANP) through the R\u0026amp;D levy regulation, and for providing exploration well data. The authors are also grateful to Seequent Limited for providing the Leapfrog Geothermal academic license.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; IEA. Global energy \u0026amp; CO\u003csub\u003e2\u003c/sub\u003e status report 2019. Paris. (2019).\u003c/p\u003e\n\u003cp\u003e2. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Azevedo TR, Costa Junior C, Brand\u0026atilde;o Junior A, Cremer MdS, Piatto M, Tsai DS, et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Scientific Data 5:180045 (2018).\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; SEEG. 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International Journal of Greenhouse Gas Control 1:430-443 (2007).\u003c/p\u003e\n\u003cp\u003e75. \u0026nbsp; \u0026nbsp; \u0026nbsp; Smith M, Campbell D, Mackay E, Polson D. CO\u003csub\u003e2\u003c/sub\u003e aquifer storage site evaluation and monitoring: Understanding the challenges of CO\u003csub\u003e2\u003c/sub\u003e storage: results of the CASSEM project. Heriot-Watt University, Edimburgh: Scottish Carbon Capture and Storage (SCCS); 2011.\u003c/p\u003e\n\u003cp\u003e76. \u0026nbsp; \u0026nbsp; \u0026nbsp; Ciotta M, Peyerl D, Barrozo L, Sant Anna L, Moutinho dos Santos E, Bermann C, et al. An overview of carbon capture and storage atlases around the world. Environ Geosci 27:1-8 (2020).\u003c/p\u003e\n\u003cp\u003e77. \u0026nbsp; \u0026nbsp; \u0026nbsp; Wellmann F, Caumon G. Chapter One - 3-D Structural geological models: Concepts, methods, and uncertainties. In: Schmelzbach C, editor. Adv Geophys. 59: Elsevier; \u0026nbsp;p. 1-121.(2018).\u003c/p\u003e\n\u003cp\u003e78. \u0026nbsp; \u0026nbsp; \u0026nbsp; ANEEL-SIGEL. Data base of the Brazilian Power Sector-SIGEL Rio de Janeiro2020 [Available from: \u003ca href=\"https://sigel.aneel.gov.br/Down/\"\u003ehttps://sigel.aneel.gov.br/Down/\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e79. \u0026nbsp; \u0026nbsp; \u0026nbsp; EPA. Emissions \u0026amp; generation resource integrated database (eGRID) Washington, DC: United States Environmental Protection Agency; 2020 [Available from: \u003ca href=\"https://www.epa.gov/energy/egrid\"\u003ehttps://www.epa.gov/energy/egrid\u003c/a\u003e,.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Rock type, thickness, porosity (Ф), and permeability (K) data for shale units of Irati Formation in the 2-TB-1-SP well.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"87%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRock type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThickness (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eФ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eK (mD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.224489795918368%\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.224489795918368%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.224489795918368%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.224489795918368%\"\u003e\n \u003cp\u003eA/B/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"26.53061224489796%\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e75.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Total organic carbon (TOC wt.%) data on the Irati Formation intervals in the well 2-TB-1-SP, with the geological unit subdivision of this study.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTop\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBottom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRock type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTOC wt.%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,618.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,619.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,620.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,621.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,622.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,623.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,623.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,624.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,624.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,625.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,628.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,629.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e7.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,631.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,632.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e4.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,632.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,633.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,633.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,633.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,633.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,635.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,635.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,636.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,637.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,637.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,640.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,646.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eBlack shale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,649.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,652.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eLimestone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,653.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,654.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eA/B/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,656.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e2,657.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eShale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003eA/B/C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Theoretical CO\u003csub\u003e2\u003c/sub\u003e storage for the black shale Unit E of the Irati Formation.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSymbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003ev\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003em\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003evolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e23,914,000,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Phi;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003eporosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;\u003c/em\u003e\u003cem\u003e\u003csub\u003eCO2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003ekg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e density at reservoir conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e842.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026rho;s\u003csub\u003eCO2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003ekg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003emass of CO\u003csub\u003e2\u003c/sub\u003e sorbed per unit volume of solid rock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003csub\u003e\u0026Phi;\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003efree phase storage efficiency factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e0.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003eE\u003csub\u003es\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003esorption efficiency factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e0.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003e\u003cem\u003eM\u003csub\u003eCO2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"13.26530612244898%\"\u003e\n \u003cp\u003eGt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"45.91836734693877%\"\u003e\n \u003cp\u003emass of CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Site selection criteria for geological CO\u003csub\u003e2\u003c/sub\u003e storage and our study results.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCriterion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEliminatory or unfavourable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreferred or Favourable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThis study\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReservoir-seal pairs; extensive and competent barrier to vertical flow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003ePoor, discontinuous, faulted and/or breached\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eIntermediate and excellent; many pairs (multi-layered system)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003eVertically sealing faults, multi-layered systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003eVertically sealing faults, multi-layered systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eMiocic et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStratigraphy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eComplex lateral variation and complex connectivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eUniform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eUniform\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocated within fold belts\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeismicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eModerate and less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 800 m or \u0026gt; 2,500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eBetween 1,000 and 2,500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003eAverage depth 2,640 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 750-800 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 800 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 800 m \u0026gt; 2,500m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 800 m \u0026lt; 2,500 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 1,200 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eMiocic et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eThickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026gt; 50 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003eAverage thickness 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026ge; 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAffecting protected groundwater quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eDistance of 920m to aquifer system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFaulting and fracturing intensity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eSmall or no faults\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003eMinimal faulting, with trapping structure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eExtensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003eLimited to moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003eMinimal faulting, with trapping structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaprock t\u003c/strong\u003e\u003cstrong\u003ehickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 20 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026gt; 100 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003e\u0026gt; 750 m (Serra Alta 64 m thick + Teresina Formations 690 m thick)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026ge; 10 m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 20 m thick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 100 m thick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 150m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eMiocic et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLateral continuity of caprock\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eLateral variations, faulted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eUnfaulted (Uniform)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eUnfaulted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePorosity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026gt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003e6.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026ge; 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u0026lt; 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003e\u0026gt; 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeothermal regime\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eGradients \u0026ge; 35 \u0026ordm;C/km and/or high surface temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eGradients \u0026lt; 35 \u0026ordm;C/km and low surface temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"17.346938775510203%\"\u003e\n \u003cp\u003e20.4 \u0026ordm;C/km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.333333333333336%\"\u003e\n \u003cp\u003eGeo-thermal gradient of max. 30 \u0026ordm;C/km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.333333333333332%\"\u003e\n \u003cp\u003eMiocic et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 35 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026ge; 35 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e54 \u0026ordm;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal organic organic carbon\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026lt; 2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026ge; 2.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eGoodman et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e3.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWell density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eLow to moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eIEA-GHG, 2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProximity to powerplant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026gt; 100 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026lt; 75 km\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eSmith et al., 2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003e15 powerplants (562 MW) within a radius of 75 km\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal storage capacity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003eTotal capacity estimated to be similar to or less than the total amount produced from the CO source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\"\u003e\n \u003cp\u003eTotal capacity estimated to be much larger than the total amount produced from the CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eChadwick et al. 2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\"\u003e\n \u003cp\u003eTotal capacity estimated to be much larger than the total amount produced from the CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CO2 geological storage, 3D geological modeling, carbon capture and storage, Irati Formation, Paraná Basin","lastPublishedDoi":"10.21203/rs.3.rs-420789/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-420789/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Paris Agreement established global ambitious targets for reducing carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions, requiring the rapid and extensive development of low carbon technologies, and one of the most efficient is CO\u003csub\u003e2\u003c/sub\u003e geological storage. Among the deep geological formations used for CO\u003csub\u003e2\u003c/sub\u003e storage, the shale layers have been a new emerging topic showing to be efficient because they are abundant and have a high content of organic matter, being favorable for CO\u003csub\u003e2\u003c/sub\u003e retention. However, one of the challenges in evaluating a location for possible reservoirs is the adequate geological characterization and storage volume estimates. This research evaluated the Irati Formation of the Paran\u0026aacute; Basin, through the information from hydrocarbon exploration wells in Southeastern Brazil, where most stationary sources of carbon emissions are located. Three-dimensional (3D) implicit modeling techniques were applied not only for the volume calculation purpose, but also in the site selection stage, generating thematic 3D models of thickness, depth, structures, and distance to aquifer systems. The limestones, shales, and black shales of the Irati Formation were locally divided into six units according to geological composition and spatial continuity. The E black shale unit was considered for CO\u003csub\u003e2\u003c/sub\u003e geological storage indicating a theoretical capacity of 1.85 Gt of CO\u003csub\u003e2\u003c/sub\u003e. The potential of the achieved capacity is promising not only for been greater than the total of CO\u003csub\u003e2\u003c/sub\u003e locally produced but also for supporting the implantation of new projects in this region.\u003c/p\u003e","manuscriptTitle":"3D implicit modeling applied to the evaluation of CO2 geological storage in the shales of the Irati Formation, Paraná Basin, Southeastern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2021-07-21 16:05:55","doi":"10.21203/rs.3.rs-420789/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2021-04-19 19:47:07","doi":"10.21203/rs.3.rs-420789/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5a471f6-15a1-4cca-a3d3-c5ffd2228aff","owner":[],"postedDate":"July 21st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3754985,"name":"Geology"}],"tags":[],"updatedAt":"2021-08-12T12:41:35+00:00","versionOfRecord":{"articleIdentity":"rs-420789","link":"https://doi.org/10.1002/ghg.2111","journal":{"identity":"greenhouse-gases-science-and-technology","isVorOnly":true,"title":"Greenhouse Gases: Science and Technology"},"publishedOn":"2021-08-11 00:00:00","publishedOnDateReadable":"August 11th, 2021"},"versionCreatedAt":"2021-07-21 16:05:55","video":"","vorDoi":"10.1002/ghg.2111","vorDoiUrl":"https://doi.org/10.1002/ghg.2111","workflowStages":[]},"version":"v2","identity":"rs-420789","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-420789","identity":"rs-420789","version":["v2"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}