Meguerdijian, Saro | Search NASA

DOE OSTI2023

A review of risk and uncertainty assessment for geologic carbon storage

Carbon capture, utilization, and storage (CCUS) in geological formations play a key role in mitigating anthropogenic CO 2 emissions and achieving the aggressive goal of net-zero greenhouse gas emissions. Risk and uncertainty assessment is crucial for ensuring the safety and reliability of geologic carbon storage (GCS) by evaluating CO 2 migration in subsurface, forecasting potential leakage and induced seismicity risks, and optimizing operational and monitoring plans. In this review, the use and progress of risk assessment for GCS over the last few decades are examined. Here, we use the Southwest Regional Partnership on Carbon Sequestration (SWP), which is one of the seven regional partnerships supported by the United States Department of Energy (U.S. DOE), as an example of large-scale CCUS projects in North America. Additionally, future trends and requirements for risk assessment in GCS are discussed. The information provided in this review can help readers understand the significance of risk and uncertainty assessment and apply it effectively in large-scale GCS projects.

58 GEOSCIENCES↗

DOE OSTI2023

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗