Illinois Basin Research Articles

The St. Meinrad Coal Member (Pennsylvanian)—Reference Sections from Spencer County, Indiana

The purpose of this study is to (a) specify locations in Indiana that can serve as reference sections for the St. Meinrad Coal Member (Pennsylvanian) and (b) acquire more data to better correlate this coal with its equivalents in the Illinois Basin. The work focused on 1) a core from Indiana Geological and Water Survey drill hole (SDH) 537, latitude 38.180721 N, longitude 86.83895 W); and 2) an outcrop of the St. Meinrad Coal Member (latitude 38.147652 N, longitude 86.808903 W), both in Spencer County, Indiana. The borehole was drilled in November 2023 and the core is archived at the Indiana Geological and Water Survey Core Research and Teaching Repository, Bloomington, Indiana, USA. The St. Meinrad Coal Member was encountered at a depth of 44.25 to 48.45 ft (13.49 to 14.77 m), with a total coal thickness of 4.20 ft (1.28 m). On a whole-seam basis, the coal has an ash yield of 1.27 wt % and is 3.92 wt % in total sulfur. Petrographically, the coal consists of moderate amounts of vitrinite (77.12 vol %), liptinite (15.84 vol %) and inertinite (7.04 vol %). Palynologically, the coal is dominated by lycopsid tree spores (74.12 vol %), with reduced amounts of small lycopsid (13.4 vol %) and tree fern (6.14 vol %) spores. Small fern (0.9 vol %) and calamite (4.17 vol %) spores are uncommon to rare, as is cordaite pollen (0.96 vol %). The outcrop location has a coal thickness of 3.15 ft (96 cm). In outcrop, the coal is relatively clean in the middle part and is enriched in mineral matter at the top and bottom parts of the seam. The coal is underlain by claystone. Palynologically, the coal has similar spore assemblage to that documented from the core. The common occurrence of Endosporites globiformis and other stratigraphically diagnostic taxa in both locations warrant assignment of the St. Meinrad Coal Member to the early Atokan NG (Microreticulatisporites nobilis - Endosporites globiformis) miospore zone of the Illinois Basin, which correlates with the early Duckmantian strata of western Europe and Middle-Late Bashkirian strata worldwide. The palynologic data also indicate that, within the Illinois Basin, the St. Meinrad Coal Member is coeval with the Hawesville and Bell coal beds in western Kentucky.

Coal ash resources and potential for rare earth element production in the United States

The renewable energy industry is heavily reliant on rare earth elements, underscoring the need to develop resources and production. The objective of this work was to estimate coal ash resources and potential for extraction of rare earth elements using data for the US. Data on spatiotemporal variability in coal ash resources and disposition were compiled from various federal databases and rare earth elements levels in ash were compiled from the literature. Results show that ~ 52 gigatons (Gt) of coal were produced in the US (1950–2021). Power plants account for most of the coal use, particularly since 1980. Coal ash (5.3 Gt) represents a mean of 10% of coal by weight, ranging from 6% for subbituminous to 14% for lignite. About 70% of coal ash is potentially accessible for rare earth element extraction (1985–2021) and was disposed in landfills and ponds with the remaining coal ash used onsite or sold. Median values of total rare earth elements are much higher in ashes derived from the Appalachian Basin (median 431 mg/kg) than in the Illinois (282 mg/kg) or Powder River basins (264 mg/kg). Considering the market value of rare earth oxides, potentially accessible ash volumes, and percent rare earth element extraction (30% Appalachian and Illinois Basins; 70% Powder River Basin) results in an estimated $8.4 billion value. This study provides fundamental information on accessible coal ash resources in the US, linkages to coal sources, and preliminary estimates of rare earth element levels for future development within the US.

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

A workflow for estimating the CO2 injection rate of a vertical well in a notional storage project

A workflow that considers regulatory and technical constraints applicable to subsurface CO2 injection projects was developed to determine fluid injection rates accurately. The constraints considered include, but are not limited to, maximum injection bottomhole pressure, maximum injection pressure at the surface or wellhead pressure, and threshold vibration velocity. The workflow was developed and tested using a reservoir model developed from site characterization data of an Illinois Basin CarbonSAFE Phase II notional storage project. The Nexus® reservoir simulation software suite and the Peng-Robinson equation-of-state were used to perform compositional dynamic simulations. Reservoir modeling results indicated that (1) the regulated and technically feasible CO2 injection rate of a vertical well is predetermined by the most stringent parameter amongst maximum bottomhole pressure, maximum wellhead (surface injection) pressure, and threshold vibration velocity constraints; (2) the threshold vibrational velocity constraint predetermines CO2 injection rate for high-permeability injection zones; and (3) the most stringent constraint for low-permeability injection zones could be either the maximum bottomhole pressure or the maximum wellhead pressure. However, the injection rate may be further reduced if faults or hydraulically conductive fractures are present within the injection zone and adjacent formations because the pressure required to reactivate the faults may be lower than maximum injection bottomhole pressure, maximum wellhead pressure, and vibrational velocity constraints.

Constrained simultaneous recovery of the depth to basement and lateral susceptibility variation

AbstractThe basin environment is a widely studied subject in both geology and geophysics for its economic significance in energy and mineral explorations. However, the estimation of the basement depth is often a challenging task given the complexity of the basement relief and lateral physical property change. Previous works simplify the problem by only inverting for the depth to the basement, and more recent studies have suggested the need to incorporate the variation of physical properties to improve basement structure imaging. In this study, we develop an inversion method with the associated workflow to simultaneously recover both the depth to a magnetic basement and a laterally varying magnetic susceptibility in the basement rock. To achieve this, we employ a set of constraints on the inverse problem. Particularly, both the recovered susceptibility and basement depth models are bounded below a possible maximum value, and the depth model is guided by a few depth points obtained from the resistivity models that are obtained from the one‐dimensional blocky inversions of magnetotelluric (MT) data. In addition, we apply the fuzzy C‐means (FCM) clustering to the susceptibility model during the inversion and use the inverted cluster centers to differentiate for different geological units in the basement. To show the effectiveness of our work, we compare the existing approaches and our method using two test inversions on one synthetic model resembling the basin–basement environment before demonstrating our method on a field data example with magnetic data collected by the U.S. Geological Survey (USGS) over the Illinois Basin. Our results show improved recovery in both basement relief and susceptibility in the basement rock, and inversion with field data is able to identify three different susceptibility zones in basement rock below the Illinois Basin.

Geostatistical Inversion for Subsurface Characterization Using Stein Variational Gradient Descent With Autoencoder Neural Network: An Application to Geologic Carbon Sequestration

AbstractGeophysical subsurface characterization plays a key role in the success of geologic carbon sequestration (GCS). While deterministic inversion methods are commonly used due to their computational efficiency, they often fail to adequately quantify the model uncertainty, which is essential for informed decision‐making and risk mitigation in GCS projects. In this study, we propose the SVGD‐AE method, a novel geostatistical inversion approach that integrates geophysical data with prior geological knowledge to estimate subsurface properties. SVGD‐AE combines Stein Variational Gradient Descent (SVGD) for sampling high‐dimensional distributions with an autoencoder (AE) neural network for re‐parameterizing reservoir models, aiming to accurately preserve geologic characteristics of reservoir models derived from prior knowledge. Through a synthetic example of pre‐stack seismic inversion, we demonstrate that the SVGD‐AE method outperforms traditional probabilistic methods, particularly in inverse problems with complex posterior distributions. Then, we apply the SVGD‐AE method to the Illinois Basin—Decatur Project (IBDP), a large‐scale CO2 storage initiative in Decatur, Illinois, USA. The resulting petrophysical models with quantified uncertainty enhance our understanding of subsurface properties and have broad implications for the feasibility, decision making, and long‐term safety of CO2 storage at the IBDP.

Mechanisms for Microseismicity Occurrence Due to CO2 Injection at Decatur, Illinois: A Coupled Multiphase Flow and Geomechanics Perspective

ABSTRACT We numerically investigate the mechanisms that resulted in induced seismicity occurrence associated with CO2 injection at the Illinois Basin–Decatur Project (IBDP). We build a geologically consistent model that honors key stratigraphic horizons and 3D fault surfaces interpreted using surface seismic data and microseismicity locations. We populate our model with reservoir and geomechanical properties estimated using well-log and core data. We then performed coupled multiphase flow and geomechanics modeling to investigate the impact of CO2 injection on fault stability using the Coulomb failure criteria. We calibrate our flow model using measured reservoir pressure during the CO2 injection phase. Our model results show that pore-pressure diffusion along faults connecting the injection interval to the basement is essential to explain the destabilization of the regions where microseismicity occurred, and that poroelastic stresses alone would result in stabilization of those regions. Slip tendency analysis indicates that, due to their orientations with respect to the maximum horizontal stress direction, the faults where the microseismicity occurred were very close to failure prior to injection. These model results highlight the importance of accurate subsurface fault characterization for CO2 sequestration operations.

Origin, distribution, and CO2 EOR and CO2 storage resource assessment of selected residual oil zone fairways in the Illinois Basin

Residual Oil Zones (ROZs) are volumes of rock with low, largely immobile, oil saturations that provide opportunities for low carbon intensity oil production via carbon dioxide enhanced oil recovery (CO2-EOR) and associated CO2 storage. Oil shows of varying degree have been observed throughout the Illinois Basin (ILB). This work is the first to assess these shows as ROZs.In the ILB, the distribution of ROZ fairways and prospects is controlled by the geologic setting and oil migration history of the basin. ILB ROZ fairways that are stratigraphically proximal to the source rock are generally widespread whereas fairways developed in formations with a greater thickness of intervening strata are more discrete, an indication of influence from fault systems and fracturing associated with major structures. Within these fairways, 41 ROZ prospects were defined across three formations (Tar Springs Sandstone, Cypress Sandstone, Carper Sandstone) containing a best estimate (P50) of five billion barrels (bbls) of original oil in place (OOIP). For context, this volume is 35% of the estimated 14.1 billion bbls of OOIP in the conventional reservoirs in the ILB. In comparison to the Permian Basin, the ILB ROZ OOIP volume is 20% of the estimated 25 billion bbl of ROZ OOIP within a nine-county area of the San Andres Formation.The CO2-EOR resource of the ILB ROZ prospects exceeds 518 million bbls of oil using CO2 injection via pattern flooding. Associated CO2 storage is estimated to be 411 million tonnes of CO2. This study builds on past efforts to establish the CO2-EOR and associated CO2 storage resource potential of the ILB and presents a methodology that can be used to assess ROZ resources in other parts of the world.

Vitrinite Reflectance Suppression Revisited

Vitrinite Reflectance Suppression Revisited

Strategic Qualitative Risk Assessment of Thousands of Legacy Wells within the Area of Review (AoR) of a Potential CO2 Storage Site

The subsurface confinement of anthropogenic carbon dioxide (CO2) demands robust risk assessment methodologies to identify potential leakage pathways. Legacy wells within the Area of Review (AoR) represent one potential leakage pathway. Robust methodologies require enormous amounts of data, which are not available for many old legacy wells. This study strategically categorizes 4386 legacy wells within the AoR of a potential CO2 storage site in the Illinois basin and identifies the high-risk wells by leveraging publicly available data—reports and well logs submitted to state regulatory agencies. Wells were categorized based on their proximity to the injection well location, depth, the mechanical integrity of well barriers, and the accessibility to these wells throughout the project lifecycle. Wells posing immediate risks were identified, guiding prioritized corrective actions and monitoring plans. Out of 4386 wells, 54 have high priority for corrective action, 10 have medium priority, and the remainder are of low priority. Case study results from the Illinois basin demonstrate the effectiveness and applicability of this approach, to assess the risk associated with legacy wells within the AoR of potential CO2 storage site, strategically categorizing over 4000 such wells despite data limitations.

Advancing subsurface analysis: Integrating computer vision and deep learning for the near real-time interpretation of borehole image logs in the Illinois Basin-Decatur Project

The accurate quantification and mapping of subsurface natural fracture systems using borehole imaging logs are critical for the success of CO2 sequestration in geologic formations, optimization of engineered geothermal systems, and hydrocarbon production enhancement. However, traditional interpretation processes suffer from time-consuming procedures and human bias. To address these challenges and expedite fracture analysis, we investigated the application of integrated computer vision and DL workflows to automate image log analysis. Specifically, the design of our workflow was crafted to swiftly detect fractures and baffles by using actual electrical resistivity of borehole wall from microresistivity imaging device alongside their binary representation. This novel approach significantly reduces computational time while providing invaluable insights. By incorporating conventional logging and microseismic data, we present a regional subsurface natural fracture mapping technique. Through the minimization of human bias in image log analysis, our automated workflow achieves reduced fracture interpretation time and costs while ensuring robust and reproducible results. We demonstrated the efficacy of our approach by applying the workflow to the Illinois Basin-Decatur Project site. The automated workflow successfully identified major fractured zones, multiple baffles, and an interbedded layer with a high resolution of 0.01 ft or 0.12 in. (0.3 cm) and can be upscaled to any desired resolution. Validation through microseismic and image log interpretations allows for accurate and near-real-time mapping of fractures and baffles, significantly enhancing CO2 pressure forecasting and postinjection site care. Our approach stands out due to its robustness, consistency, and reduced computational cost compared with alternative feature extraction technologies. It presents exciting possibilities for advancing CO2 sequestration and engineered geothermal efforts by offering comprehensive and efficient fracture mapping solutions. This technology can contribute significantly to the optimization of CO2 sequestration projects, facilitating sustainable environmental practices, and combating climate change.

Constraining the provenance of black shales using rare earth elements and neodymium isotopes, Midcontinent United States

We conducted research to determine neodymium model dates (TDM), εNd values, and the relative proportions of rare earth elements (REEs) of Paleozoic black shales from the Midcontinent United States to constrain their sediment sources. Twenty-one Cambrian to Pennsylvanian shale formations of the Illinois, Cherokee, Forest City, and Arkoma basins, the Ozark Dome, and the Ouachita Mountains were examined. Findings reveal that these midcontinental shales consist primarily of felsic detrital minerals that originated from the craton. The Cambrian Mt. Simon, Eau Claire, and Tunnel City shales of the northern Illinois Basin exhibit REE patterns and Nd isotopic signatures similar to those of the Ordovician Mazarn and Womble shales of the Ouachita Mountains, which indicates derivation from a similar sediment source. Sediment was likely derived from the Superior and/or Trans-Hudson cratonic provinces. The dominant sediment source shifted during deposition of the Middle Ordovician Womble Shale due to the uplift of the Appalachian Mountains during the Taconic orogeny, as suggested by the younger TDM dates, less negative εNd values, and similar REE patterns compared to those of the older Cambrian and Early Ordovician shales. The Grenville province and Appalachian Basin remained the primary sediment source into the Pennsylvanian.

A Reservoir Modeling Study for the Evaluation of CO2 Storage Upscaling at the Decatur Site in the Eastern Illinois Basin

The study of geological CO2 sequestration and its long-term implications are crucial for ensuring the safety and sustainability of carbon capture and storage (CCS) projects. This work presents a numerical reservoir modeling study to upscale CO2 injection in the Eastern Illinois Basin to a cumulative value of 27 Mt within the next 20 years, adding one proposed Class VI injector well to the two already existing ones in this field. Along with the reservoir simulations that include the main CO2 trapping mechanisms that ensure a minimum of a 100-year Area-of-Review containment, we describe a step-by-step approach to enhance measurement, monitoring, and verification (MMV) plans, starting from low-cost methods such as repeated 1D VSP in existing boreholes to 2D seismic surveys and higher-cost data acquisition techniques.

Multiple Paleokarst Events in the Cambrian Potosi Dolomite, Illinois Basin

The Cambrian (Furongian) Potosi Dolomite (100-183 m) in Illinois is part of the Cambro-Ordovician Knox Group. It is a uniformly dolomitized unit with very low intercrystalline porosity but contains very permeable vug, fracture/cavern porosity intervals. Here, we interpret the characteristics of the widespread porous zones in the Potosi as paleokarst features formed by rising hypogenic basinal/hydrothermal fluids. The conformity bounded Potosi Dolomite is characterized by massive dolomitization, overdolomitization and occlusion of previously generated intercrystalline porosity, void filling mineralization, and extensive dissolution and formation of cavity-conduit systems. The pore spaces are typically lined with drusy quartz or are characterized by partial to complete infilling with chalcedonic silica and/or dolomite cements. Clay minerals may partially fill pore spaces; physical properties and thorium-potassium crossplot suggest chlorite as the main clay mineral present. Dolomite crystals typically are planar-s or nonplanar with open-space filling, inclusion rich saddle dolomite displaying curved and zigzag crystal faces. Void filling cement does not exhibit sign of pressure solution and in places vug porosity is developed along bedding parallel stylolite indicating post burial origin of these features. Cavern reservoirs in the Potosi are laterally extensive and often stacked with intervening very low porosity dolomite; very low bulk density, excursion of caliper log signature from the baseline, and loss of fluid circulation during drilling in these intervals signify anomalously high porosity and permeability interpreted as being the result of cavern forming multiple paleokarst events. Post burial origin of cavities and void filling cements, association of saddle dolomite and chlorite, and occurrence of Mississippi Valley-type (MVT) ore deposits in Missouri suggest karstification by hypogenic warm basinal/hydrothermal fluids. Dissolution and mineralization likely occurred by flow of deep basinal formation waters and hydrothermal fluids (sourced from the crystalline basement underlying the Reelfoot Rift and the Illinois Basin) along numerous basement-rooted normal, reverse, and strike-slip faults, and the associated fold and fractures. Expansion and contraction because of fault-related seismicity likely developed fracture porosity in brittle host dolomite and possibly ruptured any underling impermeable units to enable large-scale upward and outward fluid movement. The Potosi fracture/cavern porosity intervals are confined by thick very low porosity dolomite intervals that could serve as effective seal. There is no report of any show of oil in the Potosi Dolomite, but the unit has an excellent potential to serve as a combined reservoir and seal for storing anthropogenic CO2 and waste material.

Qualitative risk assessment of legacy wells based on publicly available data for class VI well permit applications—Illinois basin case study

Carbon Capture & Storage (CCS) refers to capturing the anthropogenic carbon dioxide (CO2) at a source followed by injecting and storing it safely in the subsurface. The success of the CCS projects therefore heavily relies on how efficiently the confining zones (containment seals) prevent migration of the CO2 to the surface and/or to the underground sources of drinking water (USDW) through leakage pathways. CCS projects requires drilling of Underground Injection Control (UIC) Class VI wells for permanent storage of CO2 in subsurface. Detailed information of all the wells within the Area of Review (AoR) is needed to develop the map and tabulation components of the Class VI well permit application. This paper discusses the generic process that can be used by permit applicants to develop the dataset necessary for preparing the map and tabulation components of the Class VI permit application and perform qualitative risk assessment of all the wells within the AoR. This methodology directs the corrective action plans as well as monitoring plans to some extent. Results of the application of the newly developed methodology for wells in Illinois basin is discussed in detail. This methodology can be applied to all wells including orphaned wells, abandoned wells, active oil and gas wells, stratigraphic wells, disposal wells, etc. This methodology generalized categorization of legacy wells that will help in easier comparison of different CCS projects across the globe. The qualitative risk assessment methodology discussed in this paper is a good starting point for developing crossflow modeling.

Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles

Located in the eastern Illinois Basin, the Princeton 1:100,000-scale quadrangle comprises portions of Knox, Gibson, Pike, Dubois, Posey, Vanderburgh, Warrick, and Spencer Counties in Indiana as well as small portions of Edwards, Gallatin, Wabash, and White Counties in Illinois. The mapped area is situated in the rolling topography of the Wabash Lowland and dominated by Pennsylvanian-age coal-bearing strata. Pennsylvanian siliciclastics in the Princeton quadrangle host economically important coal seams. The most widespread coal beds occur in the Carbondale Group, but the Raccoon Creek Group hosts coal of superior quality (low sulfur, low ash) in Indiana. Surface and underground mining of these coals have been extensive, with expansive impacts on the landscape and subsequent reclamation. Bedrock outcrops are obscured mainly by glacial sediments and extensive fluvial deposits from the Wabash River that formed deep channels (paleo and active) filled with outwash and alluvium. The geologic map of Gray and others (1970), the Compendium of Paleozoic Rock-Unit Stratigraphy in Indiana (1986), and Indiana Geologic Names Information System (IGNIS, https://legacy.igws.indiana.edu/ignis/) are the fundamental compilation of previous geological knowledge from this area. Additional data come from previous 1:24,000-scale mapping through the National Cooperative Mapping Program STATEMAP component. However, most information for the Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles and, more broadly, the western corridor, is derived from Indiana-scale coal seam mapping conducted between 2011 and 2017 based on the National Coal Resources Data System (NCRDS), Indiana Coal Quality Database (ICQD), and geophysical logs from oil and gas wells archived in the Petroleum Database Management System (PDMS). The majority of these data were previously presented in Esri-based Indiana Coal Atlas StoryMaps for the entire western corridor. This geologic mapping project was funded in part through the STATEMAP Program supported by the U.S. Geological Survey under Cooperative Agreement No. G22AC00424. The Bedrock Geologic Database of the Indiana Portions of the Princeton, Evansville, Mount Vernon, and West Frankfort 30- x 60-minute Quadrangles is a composite geodata set that conforms to "GeMS (Geologic Map Schema)--a standard format for the digital publication of geologic maps," available at http://ngmdb.usgs.gov/Info/standards/GeMS/. Metadata records associated with each element within the geodata set contain more detailed descriptions of their purposes, constituent entities, and attributes. An OPEN shapefile version of the data set is also available. It consists of shapefiles, DBF files, and delimited text files and retains all information in the native geodatabase, but some programming will likely be necessary to assemble these components into usable formats.

Machine Learning Provides Fracture Analysis, Mapping for CCUS

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214996, “Machine-Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in the Illinois Basin,” by Guoxiang Liu, SPE, US Department of Energy, and Abhash Kumar and William Harbert, SPE, Contractors, et al. The paper has not been peer reviewed. _ The monitoring of storage reservoirs to ensure safe, long-term storage of CO2 and to derisk operations and storage management is undergoing dynamic shifts, expanding opportunities for implementing innovative techniques and applications, especially for commercial-scale deployment. In the complete paper, a method with multitiered analysis has been developed to leverage advanced machine-learning (ML) techniques to process passive seismic monitoring data acquired during an injection period along with pumping/injection pressure and rate in the Illinois Basin for potential fracture and fault analysis. Introduction This study is part of the Science-Informed ML for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative funded by the US Department of Energy Carbon Storage Program. The outcomes of the SMART Initiative are science-informed, ML-based tools that can be applied at carbon storage sites throughout the nation and the world to achieve the following objectives: - Improve the ability to consolidate technical knowledge, site-specific characterization information, and real-time data in a digestible way - Enable the optimization of carbon-storage reservoirs by creating a capability for real-time forecasting of the behavior of such reservoirs - Improve the ability to understand and communicate subsurface behavior during carbon-storage operations to nonexperts Methodology In the authors’ study, a multitiered, data-driven approach was created by integrating all available measurement data to visualize fracture networks. The data sets included measurements from drilling, log and core testing, injection and downhole pressure measurements, and five vertical-pressure-monitoring gauges, providing a wealth of detail on the fracture networks. A fracture network encompasses natural fractures, induced or hydraulic fractures, and the dynamic response of such fractures, culminating in a comprehensive understanding of this system. Because of the wide range of resolutions and scales of the data sets, the fracture network was mapped into 19 distinct time windows over a 3-year injection period by analyzing pumping data, which included CO2 injection and bottomhole pressure. Passive seismic data (microseismic) were compartmentalized into these time windows, similar to the industry practice of compartmentalizing microseismic data into separate stages for high-pressure fluid- injection activities (i.e., induced fracturing). In each of these time windows, the b-value of the associated microseismic population was estimated. Furthermore, event-occurrence time and distance from the treatment well were used to identify the distinct triggering front of microseismicity likely associated with pre-existing fractures and faults within the reservoir. Seismogenic b-value and diffusivity analyses were applied to identify triggered fronts for further clustering and fracture-plane and 3D fracture-distribution analysis. The potential of several unsupervised ML algorithms was leveraged to identify the spatial clusters of microseismic events within each triggering front of individual time windows by following the work flow described in Fig. 1 of the complete paper. Furthermore, these ML techniques were implemented to determine the best-fitting surface for each spatial cluster of microseismicity to infer the directional and spatial distributions of fractures.

Quantitative seismic fracture characterization of a sandstone reservoir — Decatur, Illinois Basin

The Illinois Basin Decatur Project, a carbon capture and sequestration task, was undertaken to sequester 1 million tonnes of CO2 into a sandstone reservoir. A 3D seismic survey was conducted to characterize the reservoir. A geomodel was developed from seismic data, inversion results, and well data to geostatistically map the storage potential of the reservoir. However, no fracture model was created or utilized in this exercise. Fractures inherently influence the porosity and permeability of a reservoir. Ignoring them in reservoir characterization is not an optimal reservoir management practice. The image-log interpretation from a few vertical wells drilled in the area shows the bedding plane dips, but no fracture has been identified. However, the lack of fracture crossings in a few vertical wells does not imply that a formation is devoid of fractures altogether. Hence, seismic fracture characterization (leveraging the dense 3D seismic data) is necessary for a reservoir characterization exercise. We utilized the publicly available Decatur 3D seismic data set to run a seismic fracture characterization workflow to delineate potential fracture corridors present in the reservoir. We calculated three edge detection attributes (structural tensor, structure-oriented semblance, and structural dip) in combination to delineate the fracture lineaments. Our workflow extracts several quantitative measures of the seismic lineaments such as dip, azimuth, area, and length, which can be analyzed statistically. The principal focus of this work is to find a way forward to integrate the fractures from seismic data in a geologic model that can be utilized in simulations. Based on our interpretation of seismic fractures, we created a discrete fracture network that can be a building block for creating a finer-resolution fracture model. We also explored the fractal characteristics of seismic-derived fracture lineaments as a way forward for generating discrete fracture networks.

Machine learning and deep learning for mineralogy interpretation and CO2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO2) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. The study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

Identifying Detrital and Diagenetic Minerals in Paleosols of the Illinois Basin

Phyllosilicates are hypothesized to be primarily of pedogenic origin in shallowly buried paleosols (≤3 km depth), regardless of the age of the paleosol. To test this hypothesis, this work evaluates the possible presence of detrital and diagenetic phyllosilicates in middle and upper Pennsylvanian paleosols, collected from three drill cores along a north–south transect in the Illinois Basin. The abundances of 2M1 muscovite, quartz, and K-feldspar are greater in a morphologically immature Protosol from the southernmost core; 1Md illite and interstratified illite-smectite with R1 and R0 stacking orders are more abundant in the more mature Vertisols of the central and northern cores. K-Ar age values of multiple clay-size fractions from each paleosol averaged ~260 Ma in the northern core, 270 Ma in the central core, and 295 Ma in the southern core. While considering the complex tectonic and thermal history of the Illinois Basin, detrital minerals are more abundant in immature paleosols that experienced relatively greater maximum burial depths and thus greater sediment supply whereas illitization in more mature paleosols was probably initiated primarily during protracted burial diagenesis. As the present study found evidence for diagenetic and detrital minerals in clay-size fractions of shallowly buried, deep-time paleosols, caution is advised when using paleosol minerals for ancient climate and environment reconstructions.