Correction: Generating H2 during the CO2 sequestration in basalt formations
In this article [1], the author’s name Carl Steefel was incorrectly written as Carl Stefeel. The original article has been corrected.
Huang, Lizhi↗
Engineering topics
Hu, Mengsu
Publications and source records attributed to Hu, Mengsu.
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.
Chung, Jaehong↗
Modeling injection-induced fault slip using long short-term memory networks
Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.
58 GEOSCIENCES↗
Brine Availability Test in Salt (BATS) FY24 Update
This report summarizes fiscal year 2024 (FY24) activities centered around a series of field tests in bedded salt at the Waste Isolation Pilot Plant (WIPP) funded by the Office of Spent Fuel and Waste Science and Technology in the Spent Fuel and Waste Disposition (SFWD) program of the US Department of Energy’s Office of Nuclear Energy (DOE-NE). High-level Purpose of Experiments: The Brine Availability Test in Salt (BATS) field tests are revealing both brine occurrence (i.e., where, and how much) and brine migration (i.e., how easily it moves) in the excavation damaged zone (EDZ). This understanding is foundational to develop a safety case for a future heat-generating waste repository in salt, and to starting up a generic repository program in salt to buy down risk. BATS seeks to predict how much brine can flow into both ambient and heated excavations (e.g., boreholes or rooms) in salt. This work is educating and empowering new repository scientists on two fronts: “design and execution of field tests” and “prediction and modeling of coupled processes.” DOE-NE capabilities in salt have grown and been tested through international modeling and benchmarking exercises (e.g., DECOVALEX, RANGERS, KOMPASS, and MEASURES; see Mills et al., 2024). The hands-on expertise we are building is a necessary step towards large-scale disposal demonstrations and eventual implementation.
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Pore‐Scale Modeling of Reactive Transport with Coupled Mineral Dissolution and Precipitation
Abstract We present a new pore‐scale model for multicomponent advective‐diffusive transport with coupled mineral dissolution and precipitation. Both dissolution and precipitation are captured simultaneously by introducing a phase transformation vector field representing the direction and magnitude of the overall phase change. An effective viscosity model is adopted in simulating fluid flow during mineral dissolution‐precipitation that can accurately capture the velocity field without introducing any empirical parameters. The proposed approach is validated against analytical solutions and interface tracking simulations in simplified structures. After validation, the proposed approach is employed in modeling realistic rocks where mineral dissolution and precipitation are dominant at different locations. We have identified three regimes for mineral dissolution‐precipitation coupling: (a) compact dissolution‐precipitation where dissolution is dominant near the inlet and precipitation is dominant near the outlet, (b) wormhole dissolution with clustered precipitation where dissolution generates wormholes in the main flow paths and precipitation clogs the secondary flow paths, and (c) dissolution dominant where all solid grains are gradually dissolved. In the three regimes, the proposed approach provides reliable porosity‐permeability relationships that cannot be described well by traditional macroscale models. We find that the permeability can increase while the overall porosity decreases when the main flow paths are expanded by dissolution and adjacent pore spaces are clogged by precipitation.
58 GEOSCIENCES↗
DECOVALEX-2023: Task G, SAFENET Final Report
DECOVALEX Task G deals with fracture mechanics at several scales using a combined approach of experimental work, related model development, benchmarking and experimental analysis. Fig. 0.1 provides a graphical abstract for Task G. The experimental basis for the related mechanical (M), hydro-mechanical (HM), and thermo-mechanical (TM) processes comes from the rock mechanics laboratories of Universities of Freiberg (TUBAF) and Edinburgh, as well as the Korea Institute of Civil Engineering and Building Technology (KICT). The experimental work in the rock laboratories is closely linked to the underground research laboratories (URLs) Reiche Zeche (Germany), KURT (Korea), and Mont Terri (Switzerland). The scientific key questions are related to fracture permeability evolution under THM conditions, anisotropy effects on fracturing processes, and thermal fracture slip, which are being addressed in specific steps of the task. A large variety of numerical methods have been developed and applied for experimental analysis, ranging from continuum to discontinuum approaches. A detailed comparison of mechanical and hydro-mechanical processes is given in the section 3 via the benchmarking exercises. As a result of DECOVALEX-2023 Task G we further improved our understanding and predictability of fracturing processes under THM conditions. This was based on robust numerical simulation methods and in-depth experimental analysis.
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