Geologic CO2 Storage Optimization under Geomechanical Risk Using Coupled-Physics Models
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Energy & Fuels↗
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Deep Learning–Assisted Multiobjective Optimization of Geological CO 2 Storage Performance under Geomechanical Risks
In geological CO 2 storage, designing the optimal well control strategy for CO 2 injection to maximize CO 2 storage while minimizing the associated geomechanical risks is not trivial. This challenge arises due to pressure buildup, CO 2 plume migration, the highly nonlinear nature of geomechanical responses to rock-fluid interaction, and the high computational cost associated with coupled flow and geomechanics simulations. In this paper, we introduce a novel optimization framework to address these challenges. The optimization problem is formulated as follows: maximize total CO 2 storage while minimizing geomechanical risks by adjusting the injection schedules within bounded constraints. The geomechanical risks are primarily driven by injection-induced pressure build-up, which is characterized by ground displacement and the induced microseismicity. We used the Fourier neural operator (FNO)-based deep learning model to construct surrogate models, replacing the time-consuming coupled flow and geomechanics simulations for evaluating the aforementioned objective functions. The developed surrogate models have been incorporated into a multiobjective optimization framework through a genetic algorithm to reduce the computational burden. The proposed optimization framework reduces the computational cost from approximately 2,400 hours, when using objective function evaluations based on physics-based simulations, to around 20 minutes. A set of Pareto-optimal solutions of the proposed workflow yields nontrivial optimal decisions, reducing the microseismicity potential and the vertical displacement. This Pareto front highlights the optimal trade-offs between CO 2 storage amount, safety, and ground displacement, emphasizing the need for careful optimization and management of injection strategies to achieve a balanced outcome. The novelty of this work is twofold. First, we demonstrate the importance of incorporating the minimization of the geomechanical risks as objective functions into the CO 2 storage optimization workflow to mitigate the potential risk of induced microseismicity and ground displacement. Second, we leverage the FNO-based surrogate models to optimize a real-field CO 2 storage operation.
42 ENGINEERING↗
Evaluation of Containment and Geomechanical Risks at Integrated Mid-Content Stacked Carbon Storage Hub Sites
The U.S. Department of Energy is leading the development of CCS through collaborative projects and programs to not only develop and demonstrate the technology but to also quantify the risks and provide tools that site operators can use to develop a site-specific understanding of their risk to enable successful project operations. This report summarizes results and findings from the application of risk-assessment tools to an industrial-scale Geologic Carbon Storage (GCS) project. Specifically, we applied the National Risk Assessment Partnership’s (NRAP) Open Integrated Assessment Model (NRAP-Open-IAM) and State of Stress Assessment Tool (SOSAT) to two candidate sites being considered for storage by the Integrated Midcontinent Stacked Carbon Storage Hub (IMSCS-Hub) project team. These sites, Sleepy Hollow Field in Nebraska and Patterson Field in Kansas, have historical oil and gas production and thus are attractive candidates to store the 50 million metric tons (Mt), which is the CarbonSAFE objective. Because these sites have historical operations, they have a significant number of existing wells that pose a risk for well leakage. Additionally, the storage formations will undergo significant pore pressure perturbation (i.e., increase due to CO 2 injection). Hence, we have selected the two NRAP tools best suited to study the risks associated with well leakage and geomechanical risks. The objective of the study was not only to assess the risk at the site, but to also improve the NRAP tools through application using real site data on an ongoing project.
54 ENVIRONMENTAL SCIENCES↗
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↗
State of Stress Uncertainty Quantification and Geomechanical Risk Analysis for Subsurface Engineering
All subsurface stress measurement methods available for deep subsurface applications are indirect and involve a significant amount of uncertainty. Quantifying these uncertainties is critical for applications where the state of stress is a component of a risk analysis with significant safety, environmental, and economic considerations. A publicly available and open source tool called the State of Stress Analysis Tool (SOSAT) has been developed to aid in performing a Bayesian uncertainty quantification and geomechanical risk analysis using many types of commonly available data. A new feature of this tool is using data from the FutureGen 2.0 site. The new feature of the tool was uses information about the absence or presence of borehole breakouts to constrain the state of stress. This new feature allows the user to specify probability distributions for the relevant parameters such as the drilling mud pressure, temperature, and the formation strength properties. The tool computes the posterior joint probability distribution of the horizontal principal stresses. Using random samples from this posterior distribution the tool then computes the probability of activating a critically oriented fault at a range of pore pressures. This latter feature can be used to aid in the assessment of the risk of induced seismicity and leakage in geologic carbon sequestration and enhanced geothermal systems.
Burghardt, Jeffrey A.↗
pnnl/SOSAT
State of Stress Analysis Tool (for carbon sequestration). The SOSAT tool provides value during GCS site screening and characterization phases to target collection of specific additional data to constrain uncertainties in geomechanical risk and to help operators to make informed decisions during the operational phase (Appriou et al., 2019). The capability represented in SOSAT, therefore, helps build stakeholder confidence that geomechanical risks are understood and manageable
Central, PNNL Developer↗
A Bayesian Approach for In-Situ Stress Prediction and Uncertainty Quantification for Subsurface Engineering
Many subsurface engineering applications require accurate knowledge of the in-situ state of stress for their safe design and operation. Existing methods to meet this need primarily include field measurements for estimating one or more of the principal stresses from a borehole, or optimization methods for constructing a 3D geomechanical model in terms of geophysical measurements. These methods, however, often contain considerable uncertainty in estimating the state of stress. Here, in this paper, we build on a Bayesian approach to quantify uncertainty in stress estimations for subsurface engineering applications. This approach can provide an estimate of the 3D distribution of stress throughout the volume of interest and provide an estimate of the uncertainty arising from the stress measurement, the rheology parameters, and a paucity of measurements. The value of this approach is demonstrated using stress measurements from the In Salah carbon storage site, which was one of the world’s first industrial carbon capture and storage projects. This demonstration shows the application of this Bayesian approach for estimating the initial state of stress for In Salah and quantifying the uncertainty in the estimated stress. Also, an assessment of a maximum injection pressure to prevent geomechanical risks from CO 2 injection pressures is provided in terms of the probability distribution of the minimum principal stress quantified by the approach. With the In Salah case study, this paper demonstrates that using the Bayesian approach can provide additional insights for site explorations and/or project operations to make informed-site decisions for subsurface engineering applications.
58 GEOSCIENCES↗
Assessing mechanical response of CO 2 storage into a depleted carbonate reef using a site-scale geomechanical model calibrated with field tests and InSAR monitoring data
Geomechanical risks of injection have raised concerns regarding secure CO 2 storage. In this work, a combined monitoring and modeling approach is used to assess the stress changes and surface uplift associated with CO 2 injection into a depleted carbonate reef of the Michigan basin. A site-scale geomechanical model is built by assigning mechanical properties of formations using well-log and experimental data. Gravity load is applied to the model to estimate the vertical component of stress as well as different lateral boundary displacement scenarios to estimate horizontal stresses. We used a poroelastic pressure-dependent model (instead of a linear elastic mechanical earth model) to calibrate initial stresses using hydraulic fracture test data measured at depleted reservoir status. Multi-phase fluid flow-geomechanical simulations are performed to estimate the poroelastic response during (1) primary depletion (2) field-scale CO 2 injection phase (3) a hypothetical forecast scenario in which well bottom hole pressure (BHP) reach 45000 KPa. The predicted surface uplift is less than 1 mm at the end of the field-scale CO 2 injection phase which is in good agreement with Interferometric Synthetic Aperture Radar (InSAR) uplift measurement. Although the InSAR data shows an insignificant uplift, hydromechanical modeling of injection shows that CO 2 injection still causes reservoir deformation emphasizing the role of carbonate overburden and reservoir formation mechanical properties and limited size of reef on diminishing the surface deformation. Modeling indicates poroelastic response of caprock matters to estimate uplift. The lower permeability of the top two layers provides additional barrier to large uplift. Also, history of subsidence due to production should be accounted to predict uplift due to a follow up injection correctly. This report shows the significance of combining a calibrated geomechanical model with field measured stresses and monitoring data to be used as a tool to ensure the safety of CO 2 storage.
42 ENGINEERING↗
Application of quantitative risk assessment to address stakeholder questions in geologic carbon storage
Ambitious international greenhouse gas emissions reduction targets demand a rapid transformation to a low-carbon economy. This transformation includes the accelerated adoption of carbon dioxide (CO2) capture and storage (CCS) technology. However, as with any large-scale engineering enterprise, the widespread commercial-scale deployment of geologic carbon storage (GCS) raises important questions about technology and cost-effectiveness, safety, environmental risk, and long-term liability. Effectively assessing and managing risks and liability associated with GCS projects is a key technical need throughout the project life cycle-from site selection and permitting to monitoring design, operational risk management, and post-operational site closure. This presentation highlights recent advancements in tools for quantitative risk assessment, being developed by the National Risk Assessment Partnership (NRAP). NRAP is a multi-year, multinational laboratory research collaboration sponsored by the U.S. Department of Energy's Office of Fossil Energy and Carbon Management. Our focus will be on these tools' applications in addressing critical stakeholder questions related to supporting permitting to ensure secure and environmentally protective storage; designing effective and efficient monitoring plans; evaluating the effectiveness of remedial actions and risk management alternatives; and informing liability assessment and investment decisions. This paper will detail the key functionality of NRAP’s Open-Source Integrated Assessment Model (NRAP-Open-IAM), a computational framework for assessing leakage risk and containment assurance. This model features streamlined workflows for calculating leakage risk profiles, delineating risk-based area of review, and assessing contingency plans and post-injection site care requirements. ORION is an open-source, observation-based ensemble forecasting toolkit to help operators assess the seismic hazard at a carbon storage site. The State of Stress Analysis Tool (SOSAT), designed to assess subsurface stress conditions and evaluate geomechanical risk resulting from CO2 injection in an area of interest will also be presented. We will also introduce a prototype model to evaluate storage project costs and liability associated with risk management. The Technoeconomic and Liability Evaluation for Storage (TALES) model uses results from forecasts of leakage and induced seismicity risk to estimate the lifecycle cost of managing risk. Finally, a preliminary example of how the NRAP Risk-based Adaptive Monitoring Plan (RAMP) tool can be used to design efficient and effective site monitoring plans and estimate the detectability of fluid leakage will be provided. The relevance of these tools for addressing key stakeholder questions amidst uncertainty will be emphasized.
decision support↗
Carbon Storage in Fold‐and‐Thrust Belts: An Overlooked Gigatonne Storage Opportunity
This study presents numerical investigations of the trapping characteristics of fold-and-thrust belt structures, defining three carbon capture and storage (CCS) play types that could be used to store commercial volumes (millions of tonnes) of CO 2 . Specifically, we present simulations of CO 2 storage in three fold-and-thrust belt models comprising a thrust-ramp, duplex, and thrust-fold geometry. To constrain these play types in realistic geology, each model is based on a study site, including a novel investigation of a greenfield saline reservoir in Virginia, USA, being considered for commercial carbon storage and two well-characterized petroleum fields: the Wilburton field in Oklahoma, USA, and the Incahuasi field in Bolivia. Our results provide insight into several key parameters, such as the long-term security of injected CO 2 in these geologies and injection strategies for maximizing storage efficiency while reducing pressure-related risk. These results improve the understanding of CCS in fold-and-thrust belt storage sites globally by describing general storage parameters that may be applied to site-specific projects. We find that thrust-ramp geometries may securely trap CO 2 through solubility and hydrodynamic trapping under suitable reservoir conditions, duplex structures may store some quantities of CO 2 but are pressure-constrained, and that thrust-ramp structures may store large quantities of CO 2 by maximizing fetch volume, which simultaneously lowers geomechanical risk by reducing pressure buildup along zones of weakness.
42 ENGINEERING↗
Hydromechanical Modeling of Fault Rupture in Geologic CO 2 Sequestration: A Comparison of Two Failure Criteria
Abstract The large‐scale implementation of geological carbon sequestration has raised concerns about potential fault activation and induced seismicity, which could compromise storage integrity and pose seismic risks. We theoretically compared two failure criteria, Mohr‐Coulomb (MC) and Modified Cam‐Clay criteria (MCC), to assess fault rupture during CO 2 storage. Both criteria characterize fault behavior in a specific stress regime but differ in reducing the complexity of fault rupture to a few key mechanisms. Using a coupled hydromechanical model, we demonstrate that the choice of a failure criterion and the physics of fault weakening associated with these criteria strongly condition fault response to a given fluid injection. MC mainly relates rupture to friction, while MCC relates rupture to fault poro‐plasticity. Our findings highlight that the selection of a failure criterion, being inherently subjective, can significantly alter the predicted fault behavior during CO 2 storage, thereby impacting the reliability of geomechanical risk assessments.
Cao, M. [Energy Geosciences Division Lawrence Berk↗
Pore-scale visualization of natural hydrate-bearing sediments
Accurate modeling of gas hydrate reservoir productivity and geomechanical risks associated with subsurface dissociation of natural gas hydrates (NGH) requires the determination of model parameters through physical testing on natural hydrate-bearing sediments (HBS). This involves investigating the hydro-mechanical behavior of undisturbed hydrate samples from nature under in situ conditions using pressure core characterization and analysis, which provides a unique opportunity for research. By employing state-of-the-art micro computed tomography imagery on cryogenically preserved, hydrate-bearing sediment samples, we can determine hydrate saturation as well as permeability with and without the presence of hydrates in the sediment. Furthermore, utilizing a machine learning based image segmentation technique, it is possible to extract pore space and grain information. Subsections of the entire image volume were used to determine anisotropic permeabilities using a finite-difference method Stokes solver (FDMSS). Additionally, permeability measurements on whole pressure and temperature preserved hydrate-bearing core were analyzed by utilizing the National Energy Technology Laboratory’s (NETL) Pressure Core Characterization and X-ray CT Visualization Tool (PCXT) to manipulate, cut, and analyze pressure preserved sediment. Permeabilities were measured under a broad range of vertical stress states to simulate expected pressure changes during production scenarios, and the results show that permeabilities derived from images are in agreement with those from traditional core derived experiments. The collected stress-dependent permeability, permeability anisotropy, and corresponding gas hydrate saturations provide valuable input into numerical simulations of reservoir productivity. These properties have been proven to be key parameters determining a long-term reservoir response under depressurization.
Liu, Mengwei [Oak Ridge Institute for Science and ↗
Modeling based assessment of salt cavern performance for underground hydrogen storage
This poster is about the recent progress in my LDRD project of underground hydrogen storage in multiple salt caverns. The main progress of my work is to develop modeling-based workflow to evaluate the salt cavern performance and assess the potential of geomechanical risk for hydrogen storage.
Chang, Kyung Won [Sandia National Laboratories (SN↗
The U.S. Department of Energy’s National Risk Assessment Partnership: Delivering Tools to Support Risk-Based Decision Making for Geologic Carbon Storage Deployment
Overview presentation summarizing computational risk assessment/risk management tools and recommended practices developed by the U.S. DOE's National Risk Assessment Partnership.
carbon storage↗
Experimental workflow to estimate model parameters for evaluating long term viscoelastic response of CO2 storage caprocks
Understanding the time-dependent behavior of reservoir and sealing formations is critical to assessing risks associated with geological carbon storage since time-dependent deformation strongly influences mechanical responses of some rock types. Many studies have evaluated the risk of CO2 leakage and induced seismicity by assuming poroelastic rheology in sealing formations. Few have considered viscoelastic or other time-dependent responses, where the existing literature adopts 1D models to represent long-term time-dependent responses. This is primarily because to date, the general form of a reasonable 3D time-dependent model for rocks remains unclear. In this paper, we address this unclear issue by proposing a new workflow to select constitutive modeling parameters to evaluate if a 3D viscoelastic model is reasonable using several-hour-long experimental data and a power-law response to extrapolate to the decades-long time frames of interest in geologic carbon storage. To provide experimental data, we conducted multi-level loading/unloading triaxial relaxation tests with four rock types. The experimental results showed that the maximum load relaxation observed is approximately 49%, with some rock types showing as little as 1.4%. Using a simple linear viscoelastic model, parameters were chosen such that a maximum deviation of 1.5 MPa in axial stress and 7 MPa in radial stress was attained with the extrapolated 30-year data. We found that a reasonable parameter range for the normalized elastic modulus is 0.1~2 for rocks with significant time-dependent responses and 0.01~0.06 for those with small time-dependent responses. No matter how significant time-dependent responses are for rocks considered, our results showed that the relaxation time has a general range of 1~10^10 s, whose time scale can be one or two orders higher than a time frame typically envisioned for CO2 injection projects.
Stress relaxation, 3D time-dependent model, viscoe↗
A quantitative risk assessment framework for fault reactivation in underground hydrogen storage: Coupled simulation and deep learning approach
Underground hydrogen storage (UHS) is emerging as a critical solution for large-scale energy storage. However, like all subsurface fluid injection activities, UHS poses the risk of injection-induced fault reactivation. Accurate risk assessment is essential to ensuring the safety and efficiency of UHS operations. This study presents the development of deep-learning surrogate models for fault reactivation prediction in UHS, trained on a comprehensive database of fully coupled fluid flow-geomechanics simulations. Our findings reveal that analytical models often yield unreliable estimates, with errors up to 54% in the allowable injection pressure, potentially leading to a 40% reduction in UHS operational capacity. The developed surrogate models were incorporated into a quantitative risk assessment (QRA) framework, enabling probabilistic evaluation of fault reactivation risk while accounting for uncertainties in the input variables. Site-specific features, such as horizontal stress gradients, fault’s dip and strike angles, and operational parameters like bottom-hole injection pressure and well-fault distance, were identified as the primary drivers of fault reactivation across various stress regimes. Whereas other hydraulic, geological, and poroelastic reservoir properties were found to have a secondary impact. Notably, we observed that the risk of fault reactivation for a critically oriented fault with a static friction coefficient greater than 0.55 remains below 10% in a normal faulting stress regime. However, the risk significantly increases as the stress regime transitions from normal to strike-slip and ultimately to reverse faulting conditions. These findings underscore the importance of rigorous site characterization and comprehensive QRA evaluations to optimize UHS performance and minimize geomechanical risks.
25 ENERGY STORAGE↗
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↗
Machine Learning Application for CCUS Carbon Storage: Fracture Analysis and Mapping in The Illinois Basin
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.
Liu, Guoxiang↗