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This is the conference paper accompanying an oral presentation at the 58th US Rock Mechanics/Geomechanics Symposium (ARMA) June 23 to 26 in Golden, CO. The paper highlights using machine learning to quantify fracture network for carbon storage and is part of the SMART Initiative project applications.
This report presents a stakeholder mapping analysis conducted as part of the Play-Based Exploration of CO 2 Storage in the Illinois Basin project (DOE Cooperative Agreement DE-FE0032366). The analysis was undertaken to systematically identify, categorize, and characterize stakeholder groups and community conditions relevant to the planning and deployment of carbon capture and storage (CCS) infrastructure across the Illinois Basin. Its purpose is to establish a contextual baseline that informs targeted engagement strategies, supports equitable project planning, and strengthens the social dimensions of CCS site screening.
The Illinois Basin Decatur Project (IBDP) successfully demonstrated the safe geologic storage of carbon dioxide at a commercial scale. Within the IBDP project three deep wells (injection (CCS1), monitoring (VW1), geophysical (GM1)) were competed and geophysical logs were recorded. During injection and post-injection periods microseismic monitoring was conducted to create a miscoseismic catalog. The correlations between microseimic attributes and geomechanical well logs define major geomechanical drivers of microseismic expression to understand a reservoir response to CO2 injection in geological context. Utilizing standard sonic and density well logs, the dynamic elastic moduli were calculated and employed to correlate with microseismic pseudo-logs. A multi-dimensional Mu-rho and Lambda-rho (MRLR) hyperdimensional plots display of meaningful data and uncovered subtle relationships between elastic properties of sandstones and the seismological attributes of recorded microseismicity.
Data described of the Illinois Basin Coal Wells from the Technical Report: Paronish, T.; Crandall, D.; Jarvis, K.; Workman, S.; Pohl, M.; Drosche, J.; Mckisic, T.; McLaughlin P.; Freidberg, J.; Delpomdor F. Computed Tomography Scanning and Petrophysical Measurements of Illinois Basin Coal Wells; DOE/NETL-2024/XXX; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2024; p 59.
The purpose of this paper is to analyze publicly available geomechanical data from two carbon storage sites in the Illinois Basin: the Illinois Basin-Decatur Project (IBDP) in Macon County, IL and the FutureGen2.0 project in Morgan County, IL, cancelled before injection began. This study estimates the magnitudes and directions of the least compressive principal stress gradient using hydraulic fracture-based stress measurement tests, borehole breakouts, and sonic logs in the Eau Claire Formation (primary confining zone) and the Mount Simon Sandstone (targeted reservoir). The range of stresses expected in the underlying Precambrian basement is also investigated. The evaluation of the state of stress is carried out in a probabilistic manner using the State of Stress Analysis Tool (SOSAT) developed under the National Risk Assessment Partnership (NRAP) program. Finally, the paper also provides a discussion on distinctions between fracture initiation pressure, propagation pressure, and fracture closure pressure, given that hydraulic fracture testing results show a significant difference between these three values. The discussion also addresses why the fracture closure pressure should be preferred in injection pressure determinations, a critical point in geological carbon storage projects.
Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 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 CO 2 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. In conclusion, 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.
Progress, challenges, and next steps for the Illinois Basin Geomodel for NRAP and EDX4CCS.
Abstract Subsurface fluid injection stimulates complex hydromechanical interaction, necessitating the integration of geomechanical data across spatial and temporal scales to consider the sophisticated behavior. Induced seismic response is usually associated with the complex reservoir architecture and pre‐existing features that are three‐dimensional, such as local stratigraphy, fractures, faults, and other discontinuities. This study encompasses laboratory characterization of the coupled hydromechanical response of cores extracted from rock formations in Illinois Basin: reservoir ‐ Mt. Simon sandstone, basal seal ‐ Argenta sandstone, and crystalline basement ‐ Precambrian rhyolite. High‐resolution numerical modeling allows considering the three‐dimensional complexity of the Illinois Basin Decatur Project with spatial resolution comparable to one of the active seismic surveys. A detailed reconstruction of the evolving state of stress in formations lacking direct stress measurements is achieved by numerical modeling that integrated laboratory‐derived hydromechanical properties, a porosity‐permeability relationship, active seismic data, and an inverted three‐dimensional porosity distribution. It appears that the microseismic clusters, mainly observed in the crystalline basement during the injection, are linked to zones experiencing more critically stressed conditions prior to injection. These zones have a potential for reactivation during the injection and are attributed to the specific local stratigraphy of the injection site, as well as transfer of triggering perturbations during the injection.
This report documents the results of "A Play-Based Exploration of CO₂ Storage in the Illinois Basin" (DE-FE0032366), a project funded by the U.S. Department of Energy Office of Fossil Energy and Carbon Management and conducted by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in partnership with Visage Energy. The project adapted play-based exploration (PBE), a systematic basin-scale evaluation methodology from the petroleum industry, to screen areas of Illinois for commercial geologic carbon storage (GCS) in Cambro-Ordovician strata. The traditional play concept was expanded to encompass three play element groups, subsurface geologic factors, surface features, and societal factors, yielding 24 play elements with defensible suitability criteria applied through a five-tier classification scheme. An integrated geospatial database was assembled from ISGS, MGSC, MRCI, NATCARB, and public data sources, supported by significant data-improvement work including correction of legacy well locations, digitization of more than 1,300 well construction records using the DOE CATALOG team's OGRRE tool, compilation of a statewide 2D seismic database, and production of a refined fault and fold geodatabase.
The U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) researchers in Morgantown, West Virginia, utilized computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) to evaluate the integrity of three cores (Geophysical Monitor #2 (GM2), Verification Well #1 (VW #1), and Wabash #1 for the Illinois Basin Carbon Storage Assurance Facility Enterprise (CarbonSAFE) efforts. This work was carried out in collaboration with the Illinois State Geological Survey (ISGS) as part of their efforts to analyze cores from two field locations, including the Illinois Basin-Decatur Project (IBDP) and second stage Illinois Industrial Carbon Capture and Storage (IL-ICCS) sites at Decatur, Illinois, and the Wabash CarbonSAFE project at the Wabash Valley Resources Integrated Gasification Combined Cycle plant in Terre Haut, Indiana. The results of this study are presented in several formats and are available online on the Energy Data eXchange (EDX). The rock characterization was conducted using non-destructive techniques, allowing for future analysis of the cores. While the equipment used did not provide direct visualization of shale pore space, it allowed for detection of fractures and discontinuities. Low resolution CT imagery with the NETL medical CT scanner was performed on the entire core. Qualitative analysis of the medical CT images, coupled with x-ray fluorescence (XRF), P-wave, and magnetic susceptibility measurements from the MSCL were utilized to identify areas of interest for further study as well as fractured zones. The ability to quickly identify key areas for more detailed study with higher resolution will save time and resources in future studies. The combination of methods used provided a multi-scale analysis of this core and provided both a macro and micro description of the core that is relevant for many subsurface energy-related examinations traditionally performed at NETL.
The overall objective of this project was to complete a feasibility study for a Regional Direct Air Capture (DAC) Hub that encompasses Illinois, Indiana, and Kentucky. The geographic construct of this hub was based on the Illinois Basin – a geological area where at least seven (7) geological storage studies were conducted. The Hub was designed to assure a capacity to capture, store, and utilize at least 1,000,000 metric tons (tonnes) of CO2 from the atmosphere annually 1 metric tonnes per annum (MTA), starting from an initial capacity of 200,000 tonnes of CO2 per annum (KTA). The Hub also included:(1) clean energy providers to supply renewable energy to the DAC units; (2) geological storage facilities to inject and sequestrate the captured CO2; (3) CO2 utilization facilities that would uptake the captured CO2 and convert it into products; and (4) small business organizations that would assist in workforce development efforts for the region. This closeout report consolidates the tasks undertaken and accomplished as deliverables for the project. The project team had successfully completed all required tasks of phase 0a, confirming that the project is feasible based on the preliminary conceptual designs and operational requirements of the DAC systems. Of particular interest, the hub ownership structure was identified and was implemented in follow on projects that would transition the hub infrastructure to a build/operate phase.
Final Report; Illinois Basin - Carbon Ore, Rare Earth and Critical Mineral (IB-CORE-CM) project team has conducted a comprehensive basin-wide assessment of CORE-CM presence in coal, coal-based, and waste stream resources, as well as a thorough examination of mining practices, separation technologies, and the local infrastructure necessary for producing and providing the needed CORE-CM resources for U.S. industry. This final report details the research, methods, and results of the project.
Residual oil zones (ROZs) are economically viable targets for carbon dioxide enhanced oil recovery (CO 2 -EOR); thick, carbonate ROZs in the San Andres Formation in the Wasson Field of the Permian Basin are an example. Incremental oil produced from CO 2 -EOR can be carbon negative via associated storage of injected CO 2 . Given the regional extent of ROZ fairways and generally higher net utilization of CO 2 (compared to conventional CO 2 -EOR), ROZs provide the opportunity for significant carbon neutral to carbon negative oil production with associated CO 2 storage, and therefore should be a priority target for exploration. However, ROZs have not been widely recognized or identified due to poor or no conventional oil production. Hence, in places like the Illinois Basin (ILB), exploratory analyses are required to recognize the existence of an ROZ. As part of this study, four formations were selected for detailed regional characterization and analysis: Carper Sandstone (part of the Borden Siltstone), Tar Springs Sandstone, Cypress Sandstone, and Middle Devonian (Geneva Dolomite and associated Dutch Creek Sandstone). The four formations underwent regional geological characterization to develop a regional geologic framework for the ROZs and their overlying seals. Findings of this study are supported by data from two field laboratory sites, one with stacked greenfield ROZs and the other with a stacked complex that includes a brownfield ROZ and depleted conventional reservoirs. The field laboratory sites were used to collect data and conduct tests to validate ROZ detection methodologies and identify economic field-deployable strategies to co-optimize CO 2 -EOR and associated storage in stacked ROZs. Findings from these field sites were extrapolated to characterize the basin-wide stacked ROZ fairway resource.
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.
This report documents the workflow developed and applied for a play-based exploration (PBE) of carbon capture and storage (CCS) potential in the Illinois Basin, conducted under U.S. Department of Energy Cooperative Agreement DE-FE0032366. The project was led by the Illinois State Geological Survey (ISGS) at the University of Illinois Urbana-Champaign in collaboration with Visage Energy. The goal of this workflow is to identify locations where subsurface, surface, and societal conditions align to support safe and effective geologic CO 2 storage. To achieve this, the project team developed a systematic, seven-step workflow for conducting play-based exploration: (1) defining play elements, (2) collecting data, (3) constructing a geodatabase, (4) defining suitability criteria, (5) conducting suitability mapping, (6) generating Common Risk Segment (CRS) maps, and (7) creating composite play maps. The workflow is designed to be repeatable and adaptable for CCS assessments in other regions or for alternative subsurface energy applications such as hydrogen or natural gas storage.
The computed tomography (CT) facilities and the Multi-Sensor Core Logger (MSCL) at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) in Morgantown, West Virginia, were used to characterize core from four wells that represent coal resources across Illinois. The primary impetus of this work was to capture a detailed digital representation of the core from the Brush Creek Quarry, E. Miller/Hanna City, Morris, and Weatherford Wells. The collaboration between the NETL and the Illinois State Geological Survey (ISGS) enables other research entities to access information about this potential carbon ore, rare earth, and critical mineral resource play in the Illinois Basin.
Funded in 2023 by the U.S. Department of Energy’s Phase II Carbon Storage Assurance Facility Enterprise (CarbonSAFE) initiative, a Heidelberg Materials cement plant in Mitchell, Indiana, is currently being evaluated as a potential Carbon Capture and Storage (CCS) subsurface injection site. The Heidelberg CCS project targets the middle to upper Prairie du Chien Group (Early Ordovician) in southwestern Indiana. Assessment of reservoir feasibility requires collection of field data, seismic surveys, well-log correlation, geologic modeling, characterization well drilling, well testing, and reservoir simulation. However, the proposed Heidelberg CCS site is in a data-limited region, lacking both outcrop analogs and deep wells penetrating the target interval, which makes geologic modelling difficult prior to drilling a characterization well. To directly address this problem, the present study was undertaken to understand the sedimentologic composition and stratigraphic architecture of the Prairie du Chien Group from analogous outcrops and cores in the Illinois Basin and adjacent regions.
This paper presents a deep learning-based approach for optimizing CO2 injection in carbon capture and storage (CCS) operations. We developed a multi-resolution machine learning model to significantly reduce data generation costs. Utilizing this proxy model, we implemented a multi-objective genetic algorithm to optimize well control during the CO2 injection process. The proposed approach was applied to the Illinois Basin Decatur Project (IBDP), successfully optimizing the CO2 injection schedule based on three key objectives: maximizing the amount of CO2 stored, maximizing sweep efficiency, and minimizing pressure increase. The use of the proxy model accelerated the optimization workflow by two orders of magnitude, while the cost of data generation for the proxy model was reduced by 90% by utilizing a coarse-scale model.