Uncertain storage prospects create a conundrum for carbon capture and storage ambitions
Grand hopes exist that carbon capture and storage can have a major decarbonization role at
global, regional and sectoral scales. Those hopes rest on the narrative that an abundance of …
global, regional and sectoral scales. Those hopes rest on the narrative that an abundance of …
Real-time high-resolution CO 2 geological storage prediction using nested Fourier neural operators
Carbon capture and storage (CCS) plays an essential role in global decarbonization.
Scaling up CCS deployment requires accurate and high-resolution modeling of the storage …
Scaling up CCS deployment requires accurate and high-resolution modeling of the storage …
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 …
Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR
Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS)
by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS …
by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS …
Geologic CO2 sequestration and permeability uncertainty in a highly heterogeneous reservoir
To understand the implications of permeability uncertainty in basalt-hosted CCS reservoirs,
this study investigates the feasibility of industrial-scale CCS operations within the Columbia …
this study investigates the feasibility of industrial-scale CCS operations within the Columbia …
A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach
Deep learning (DL) models are extensively used as surrogate models for high-fidelity
simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts …
simulations of multiphase fluid flow in porous media at large scales, enabling fast forecasts …
Machine-learning-based porosity estimation from multifrequency poststack seismic data
Estimating porosity models via seismic data is challenging due to the low signal-to-noise
ratio and insufficient resolution of the data. Although impedance inversion is often used in …
ratio and insufficient resolution of the data. Although impedance inversion is often used in …
Multi-fidelity Fourier neural operator for fast modeling of large-scale geological carbon storage
Deep learning-based surrogate models have been widely applied in geological carbon
storage (GCS) problems to accelerate the prediction of reservoir pressure and CO 2 plume …
storage (GCS) problems to accelerate the prediction of reservoir pressure and CO 2 plume …
Measured CO2 sorption isotherms with 25 Bakken Petroleum System rock samples from the Lower and Upper Shales, Middle Bakken, and Three Forks formations
SB Hawthorne, DJ Miller, LJ Pekot, NA Azzolina… - International Journal of …, 2023 - Elsevier
Isotherms were measured with 25 Bakken Petroleum System (BPS) rock samples using a
magnetic suspension balance at reservoir conditions of 110° C and pressures up to 345 bar …
magnetic suspension balance at reservoir conditions of 110° C and pressures up to 345 bar …
Quantitative analysis of the numerical simulation uncertainties from geological models in CO2 geological storage: A case study of Shenhua CCS project
H Shi, J Li, H Shen, X Li, N Wei, Y Wang… - International Journal of …, 2024 - Elsevier
The intensifying global climate change has prompted the imperative implementation of CO 2
capture and storage (CCS) projects as a mitigation strategy. Ensuring the safety and …
capture and storage (CCS) projects as a mitigation strategy. Ensuring the safety and …