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[HTML][HTML] CO2 sequestration in subsurface geological formations: A review of trap** mechanisms and monitoring techniques
Carbon capture and storage (CCS) in subsurface formations has emerged as a promising
strategy to address global warming. In light of this, this review aims to provide a …
strategy to address global warming. In light of this, this review aims to provide a …
Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience
P Yao, Z Yu, Y Zhang, T Xu - Fuel, 2023 - Elsevier
Greenhouse gas emissions cause serious global climate change, and it is urgent to curb CO
2 emissions. As the last-guaranteed technology to reduce carbon emissions, carbon capture …
2 emissions. As the last-guaranteed technology to reduce carbon emissions, carbon capture …
Subsurface sedimentary structure identification using deep learning: A review
The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …
Data‐worth analysis for heterogeneous subsurface structure identification with a stochastic deep learning framework
Reliable characterization of subsurface structures is essential for earth sciences and related
applications. Data assimilation‐based identification frameworks can reasonably estimate …
applications. Data assimilation‐based identification frameworks can reasonably estimate …
Selecting Geological Formations for CO2 Storage: A Comparative Rating System
Underground storage of carbon dioxide (CO2) in geological formations plays a vital role in
carbon capture and storage (CCS) technology. It involves capturing CO2 emissions from …
carbon capture and storage (CCS) technology. It involves capturing CO2 emissions from …
Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration
A deep-learning-based surrogate model capable of predicting flow and geomechanical
responses in CO 2 storage operations is presented and applied. The 3D recurrent RU-Net …
responses in CO 2 storage operations is presented and applied. The 3D recurrent RU-Net …
CCSNet: a deep learning modeling suite for CO2 storage
Numerical simulation is an essential tool for many applications involving subsurface flow
and transport, yet often suffers from computational challenges due to the multi-physics …
and transport, yet often suffers from computational challenges due to the multi-physics …
Surrogate model for geological CO2 storage and its use in hierarchical MCMC history matching
Deep-learning-based surrogate models show great promise for use in geological carbon
storage operations. In this work we target an important application—the history matching of …
storage operations. In this work we target an important application—the history matching of …
A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon
dioxide (CO 2) plume migration under geologic uncertainties is a challenging problem in …
dioxide (CO 2) plume migration under geologic uncertainties is a challenging problem in …
Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data
Underground hydrogen storage (UHS) is considered as a scalable approach for massive
storage and seasonal extraction of hydrogen (H 2). Although conventional leakage detection …
storage and seasonal extraction of hydrogen (H 2). Although conventional leakage detection …