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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Fracture Network Prediction Using Physics-based Machine Learning Algorithms

In recent years, systematic CO2 injection into geological reservoirs across the U.S. has gained traction as a strategy to mitigate greenhouse gas emissions. This approach necessitates precise monitoring to ensure secure containment, minimize risks, and optimize storage management. Our study leverages machine learning (ML) techniques to advance the understanding of CO2 injection processes, focusing on the Illinois Basin. Over a three-year injection period, we analyzed microseismic data, identifying 19 temporal intervals with significant bottom-hole pressure changes. By partitioning microseismic events into these intervals and estimating b-values, we revealed over 100 clusters of events related to fracture initiation or reactivation. Advanced spatial analysis highlighted horizontally-oriented fractures along the NNW-SSE axis. This quantification of fracture networks informs dynamic injection scheduling, work-over strategies, and risk assessments, enhancing carbon capture, utilization, and storage (CCUS) operations. Additionally, our methodology offers valuable insights for oil and gas operations and geothermal development, supporting fracture-based monitoring and risk mitigation.

Kumar, Abhash↗
DOE OSTI2024

CCS Opportunity Along the Gulf Coast Corridor

The Gulf Coast corridor, onshore and offshore, from Corpus Christ to the Mississippi River presents a high concentration of CO2 from industrial sources and excellent storage reservoirs in the underlying Cretaceous and Tertiary sands. Analysis of the technical, geologic, economic and social aspects of this CCS opportunity is necessary for successful deployment of CCS technology and eventual attainment of NetZero goals. Presentation at the Offshore Technology Conference (OTC) held in Houston, Texas, May 6-9, 2024.

Grant, Timothy↗
DOE OSTI2024

CCS Opportunity Along the Gulf Coast Corridor

The Gulf Coast corridor, onshore and offshore, from Corpus Christ to the Mississippi River presents a high concentration of CO2 from industrial sources and excellent storage reservoirs in the underlying Cretaceous and Tertiary sands. Analysis of the technical, geologic, economic and social aspects of this CCS opportunity is necessary for successful deployment of CCS technology and eventual attainment of NetZero goals. This is the conference paper accompanying an oral presentation made at the Offshore Technology Conference (OTC) held in Houston, Texas, May 6-9, 2024.

Grant, Timothy↗
DOE OSTI2023

SALSA_python (SALSython) v1

SALSA_python (Semi-Analytical Leakage Solutions for Aquifers) is a software that computed semi-analytical solutions for hydraulic head and brine leakage in multilayered aquifer–aquitard systems with geologic pressure forcing. It can simulate brine leakage into aquifers in a multi-aquifer-aquitard system with multiple injection and leaky wells. This situation is encountered in underground CO2 storage wherein brine leakage from pressurized reservoirs into aquifers is of concern. SALSA_python calls the original SALSA[1] subroutines in python by using the salsa2.so library. This enables the incorporation and coupling of SALSA computations into existing python-based codes and tools. SALSA_python is available for Linux and Mac operating systems. [1] Cihan, A., Oldenburg, C. M., & Birkholzer, J. T. (2022). Leakage from coexisting geologic forcing and injection-induced pressurization: A semi-analytical solution for multilayered aquifers with multiple wells. Water Resources Research, 58, e2022WR032343.

Bhuvankar, Pramod↗
DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗
DOE OSTI2024

A Review of Coupled Geochemical–Geomechanical Impacts in Subsurface CO 2 , H 2 , and Air Storage Systems

Increased demand for decarbonization and renewable energy has led to increasing interest in engineered subsurface storage systems for large-scale carbon reduction and energy storage. In these applications, a working fluid (CO 2 , H 2 , air, etc.) is injected into a deep formation for permanent sequestration or seasonal energy storage. The heterogeneous nature of the porous formation and the fluid–rock interactions introduce complexity and uncertainty in the fate of the injected component and host formations in these applications. Interactions between the working gas, native brine, and formation mineralogy must be adequately assessed to evaluate the efficiency, risk, and viability of a particular storage site and operational regime. This study reviews the current state of knowledge about coupled geochemical–geomechanical impacts in geologic carbon sequestration (GCS), underground hydrogen storage (UHS), and compressed air energy storage (CAES) systems involving the injection of CO 2 , H 2 , and air. Specific review topics include (1) existing injection induced geochemical reactions in these systems; (2) the impact of these reactions on the porosity and permeability of host formation; (3) the impact of these reactions on the mechanical properties of host formation; and (4) the investigation of geochemical-geomechanical process in pilot scale GCS. This study helps to facilitate an understanding of the potential geochemical–geomechanical risks involved in different subsurface energy storage systems and highlights future research needs.

08 HYDROGEN↗
DOE OSTI2020

CO2 injection with fracturing in geomechanically protected caprock: Task 6 of LLNL's Research Activities to Support DOE's Carbon Storage Program (FWP-FEW0191) (Final Technical Report)

Conventional principles of the design and operation of geologic carbon storage (GCS) require injecting CO 2 below the caprock fracturing pressure to ensure the integrity of the storage complex. In non-ideal storage reservoirs with relatively low permeability, modest injection rates can lead to pressure buildup and hydraulic fracturing of the reservoir and caprock. While the GCS community has generally viewed hydraulic fractures as a key risk to storage integrity, the actual behavior of a caprock hydraulic fracture, particularly from a geomechanical perspective, has not been thoroughly studied. The prevailing method of treating a hydraulic fracture as a high-permeability wing of the storage reservoir might have resulted in erroneous understanding of caprock hydraulic fracture behavior. Comprehensive analyses of monitoring data from the In Salah project suggested that one or more hydraulic fractures may have been created in the reservoir and lower caprock system during injection operations, but did not cause detectable leakage of CO 2 out of the storage complex. This observation coincides with a well-known phenomenon in the oil and gas industry: sedimentary rock formations have many inherent features that naturally protect the formations from unbounded vertical growth of hydraulic fractures.

58 GEOSCIENCES↗
DOE OSTI2024

Investigation of Scale Deposition and Wellbore Corrosion in Carbonated Brine Injection: A Simulation Study

Carbonated brine injection (CBI), which involves dissolving CO2 in produced water to be injected into deep geologic reservoirs, is a method to manage produced brine, carbon dioxide (CO2) sequestration, and reduce anthropogenic greenhouse gas emissions. This low-risk CO2 storage strategy can leverage the existing saltwater disposal infrastructure and well resources. However, the potential for corrosion and scale deposition are a major challenge for carbonated brine which may result in subsidence, wellbore failure, casing failure, and cement failure. The main objective of this research was to evaluate the wellbore materials compatibility with carbonated brine water during the injection process, for repurposing Class II saltwater disposal wells for CBI.

Belarbi, Zineb↗
DOE OSTI2022

Automatic Calibration of a Geomechanical Model from Sparse Data for Estimating Stress in Deep Geological Formations

Summary In this study, we demonstrate geomechanical modeling with fully automatic parameter calibration to estimate the full geomechanical stress fields of a prospective US carbon dioxide (CO2) storage site, based on sparse measurement data. The goal is to compute full stress tensor field estimates (principal stresses and orientations) that are maximally compatible with observations within the constraints of the model assumptions, thereby extending pointwise, incomplete partial stress measurement to a simulated full formation stress field, as well as a rough assessment of the associated error. We use the Perch site, located in Otsego County, Michigan, USA, as our case study. The input data consist of partial stress tensor information inferred from in-situ borehole tests, geophysical well logs, and processing of seismic data. A static earth model (SEM) of the site was developed, and geomechanical simulation functionality of the open-source MATLAB Reservoir Simulation Toolbox (MRST) was used to model the stress field. Adjoint-based nonlinear optimization was used to adjust boundary conditions and material properties to calibrate simulated results of observations. Results were interpreted through a Bayesian framework. The focus of this paper is to demonstrate how the fully automatic calibration procedure works and discuss the results obtained; it does not attempt a detailed analysis of the stress field in the context of the proposed CO2 storage initiatives. Our work is part of a larger effort to noninvasively determine in-situ stresses in deep formations considered for CO2 storage. Guided by previously published research on geomechanical model calibration, our work presents a novel calibration approach supporting a potentially large number of linear or nonlinear calibration parameters to produce results optimally agreeing with available measurements and thus extend partial pointwise estimates to full tensor fields compatible with the physics of the site.

Engineering↗
DOE OSTI2020

Impacts on mechanical strength of chemical reactions induced by hydrous supercritical CO2 in Boise Sandstone

Geomechanics experiments were used to assess mechanical alteration of Boise Sandstone promoted by reactions with supercritical carbon dioxide (scCO 2 ) and water vapor. During geologic carbon storage, scCO 2 is injected into subsurface reservoirs, forming buoyant plumes. At brine-plume interfaces, scCO 2 can dissolve into native brines, and water from brines can partition into scCO 2 , forming hydrous scCO 2 . This study investigates the effect of hydrous scCO 2 on the strength of Boise Sandstone. Samples are first exposed to recirculating hydrous scCO 2 for 24 h at 70 °C and 13.8 MPa scCO 2 pressure. Samples are reacted with scCO 2 with added water contents up to 500 mL. After scCO 2 exposure, samples are deformed at room temperature under confining pressures of 3.4, 6.9, and 10.3 MPa. The results demonstrate that hydrous scCO 2 induces chemical reactions in Boise Sandstone, with ions migrating from the solid into the hydrous scCO 2 phase. At the longer time-scales, these reactions could lead to mechanical weakening in the samples; however, on the scale of our experiments, the strength changes are within sample variability. Because the solubility of water in scCO 2 is extremely low (0.008 mol H 2 O per 1 mol CO 2 ), the mineral dissolution of Boise Sandstone was under 0.002 wt.%. Additionally, mineral grains and pore throats in Boise Sandstone are cemented with quartz, which is not susceptible to dissolution at these conditions. Our results indicate that humidity in scCO 2 plumes is unlikely to sustain chemical reactions and induce long term strength changes in quartz cemented sandstones due to resistant mineralogies and low water solubility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

CO2 Capture from Biofuels Production and Storage into the Mt Simon Sandstone

Advanced carbon capture and storage (CCS) technologies offer significant potential for reducing anthropogenic carbon dioxide (CO2) emissions, while minimizing the cost of employing these technologies. Under the Industrial Carbon Capture and Storage (ICCS) Program, the U.S. Department of Energy (DOE) collaborated with industry in cost-sharing arrangements to demonstrate technologies that captured CO2 emissions from industrial sources and either stored or beneficially re-use them. The technologies included in the ICCS program progressed beyond the research and development stage to a scale that can be deployed into commercial practice within the industry. The Illinois Industrial Carbon Capture and Storage (IL-ICCS) project sought to demonstrate the ability of the Mt. Simon Sandstone to accept and retain industrial-scale volumes of carbon dioxide (CO2) from an anthropogenic source for permanent geologic sequestration. The project was a collaboration of Archer Daniels Midland (ADM) Company, the Illinois State Geological Survey (ISGS), Schlumberger Carbon Services (SCS), and Richland Community College (RCC), and had average annual injection rate of between 1,500 and 2,400 metric tons per day (MTPD) or 0.5 to 0.7 million metric tons (MMT) annually. The project site is in Decatur, Illinois on the property of ADM and RCC (Fig 1) and is directly adjacent to the Illinois Basin – Decatur Project (IBDP), a large scale pilot project of the Midwest Geological Sequestration Consortium (MGSC), which collected and injected CO2 from the ADM fuel ethanol production unit, where high purity biogenic CO2 is produced during the anaerobic fermentation of sugars to alcohol. The IL-ICCS project had an operational period of approximately six (6) years, in which 3.5 MMT of CO2 was captured, compressed, injected, and permanently stored in the Mt Simon Sandstone.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Probabilistic Evaluation of Geomechanical Risks in CO2 Storage: An Exploration of Caprock Integrity Metrics Using a Multilaminate Model

The probabilistic uncertainty assessment of geomechanical risk—specifically, caprock failure—attributable to CO2 injection, as presented in a simplified hypothetical geological model, was the focus of this study. Our approach amalgamates the implementation of a multilaminate model, the creation of a response surface model in conjunction with the Box–Behnken sampling design, the execution of associated numerical modeling experiments, and the utilization of Monte Carlo simulations. Probability distributions to encapsulate the inherent variability (elastic and mechanical properties of the caprock and reservoir) and uncertainty in prediction estimates (vertical displacement, total strain, and F value) were employed. Our findings reveal that the Young modulus of the caprock is a key factor controlling equivalent total strain but is insufficient as a stand-alone indicator of caprock integrity. It is confirmed that the caprock can accommodate significant deformation without failure, if it possesses a low Young’s modulus and high mechanical strength properties, such as the friction angle and uniaxial compressive strength. Similarly, vertical displacement was found to be an unreliable indicator for caprock integrity, as caprock failure can occur across a broad spectrum of vertical displacements, particularly when both the Young modulus and mechanical strength properties have wide ranges. This study introduces the F value as the most dependable indicator for caprock failure, although it is a theoretical attribute (the shortest distance between the Mohr circle and the nearest failure envelope used to measure the sensitivity to failure) and not physically measurable in the field. Deviatoric stress levels were found to vary based on stress regimes, with the maximum levels observed under extensive and compressive stress regimes. In conjunction with the use of the response surface method, this study demonstrates the efficacy of the multilaminate framework and the Mohr–Coulomb constitutive model in providing a simplified, yet effective, probabilistic model of the mechanical behavior of caprock failure, reducing mathematical and computational complexities.

Energy & Fuels↗
DOE OSTI2024

Investigation of acoustic waves under subsurface conditions to improve the predictions of rock mechanical properties and natural fracture characteristics

Mechanical properties and natural fracture characteristics are critical to investigate for subsurface engineering applications, including carbon storage, well drilling, and stimulation, as they govern rock stability, fluid flow, and mechanical behavior under stress. This dissertation integrates experimental and machine learning approaches to enhance the prediction and understanding of these properties by analyzing acoustic wave behavior under varied subsurface conditions. First, the influence of temperature, pore pressure, and supercritical CO2 (scCO2) saturation on poroelastic properties is examined using Gray Berea sandstone samples. The results show that temperature and pore pressure significantly affect the bulk modulus and Biot’s coefficient, while scCO2 saturation impacts rock compressibility, informing strategies for effective geological carbon storage. The study extends this understanding by experimentally evaluating the impact of reservoir depletion on the dynamic mechanical properties of the emerging Caney shale in South Oklahoma with the employment of unsupervised machine learning to predict static mechanical properties across the Caney shale. Integrating petrophysical data and chemostratigraphy, the workflow—featuring K-means clustering, principal component analysis (PCA), and inverse distance weighting (IDW)—improves stratigraphic characterization and the estimation of static-to-dynamic modulus ratios, which is vital for optimizing drilling and stimulation strategies. Finally, the work explores how natural fracture characteristics in shale influence acoustic waveforms and shear wave splitting (SWS) analysis. Experimental data on fractured samples under different stress and temperature conditions, combined with machine learning models such as K-nearest neighbors (KNN) and extreme gradient boosting (XGBoost), reveal key fracture properties impacting SWS and wave propagation. Together, these studies provide a comprehensive framework for linking acoustic wave behavior with rock properties, advancing the methods for monitoring and predicting geomechanical changes. The insights offered valuable implications for safer, more efficient CO2 injection, hydrocarbon extraction, and subsurface management.

Elkholy, Sherif↗
DOE OSTI2022

Quantitative Evaluation of CO 2 Storage Potential in the Offshore Atlantic Lower Cretaceous Strata, Southeastern United States

The geological storage of CO 2 in the Earth’s subsurface has the potential to significantly offset greenhouse gas emissions for safe, economical, and acceptable public use. Due to legal advantages and vast resource capacity, offshore CO 2 storage provides an attractive alternative to onshore options. Although offshore Lower Cretaceous reservoirs have a vast expected storage capacity, there is a limited quantitative assessment of the offshore storage resource in the southeastern United States. This work is part of the Southeast Offshore Storage Resource Assessment (SOSRA) project, which presents a high-quality potential geological repository for CO 2 in the Mid- and South Atlantic Planning Areas. This is the first comprehensive investigation and quantitative assessment of CO 2 storage potential for the Lower Cretaceous section of the outer continental shelf that includes the Southeast Georgia Embayment and most of the Blake Plateau. An interpretation of 200,000 km of legacy industrial 2D seismic reflection profiles and geophysical well logs (i.e., TRANSCO 1005-1-1, COST GE-1, and EXXON 564-1) were utilized to create structure and thickness maps for the potential reservoirs and seals. We identified and assessed three target reservoirs isolated by seals based on their effective porosity values. The CO 2 storage capacity of these reservoirs was theoretically calculated using the DOE-NETL equation for saline formations. The prospective storage resources are estimated between 450 and 4700 Mt of CO 2 , with an offshore geological efficiency factor of dolomite between 2% and 3.6% at the formation scale.

03 NATURAL GAS↗
DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗
DOE OSTI2021

Monitoring, Reporting, and Verification Plan (MRV)- Meeting EPA Guidelines for GHGRP and Subpart RR

This report summarizes the process and work completed to develop and successfully submit an MRV plan to EPA. This work used the lessons learned from various MRCSP tasks to demonstrate the geologic understanding of the Niagaran reefs, successful monitoring and accounting methodologies, understanding of potential risks and leakage pathways, and mass balance of CO2 baseline and net storage. The plan currently encompasses 10 reefs and also provides a reporting plan for future reef development.

accounting↗