[HTML][HTML] Uncertainty quantification and optimization method applied to time-continuous geothermal energy extraction
Uncertainties in static and dynamic subsurface parameters are involved in geothermal field
modeling. The quantification of such uncertainties is important to guide field-development …
modeling. The quantification of such uncertainties is important to guide field-development …
A dual-porosity flow-net model for simulating water-flooding in low-permeability fractured reservoirs
The physics-based data-driven flow-network models with high computational efficiency have
received great attention as the promising surrogate models for reservoir numerical …
received great attention as the promising surrogate models for reservoir numerical …
Flow Patterns and Pore Structure Effects on Residual Oil during Water and CO2 Flooding: In Situ CT Scanning
Y Zhang, C Lin, L Ren - Energy & Fuels, 2023 - ACS Publications
Carbon dioxide (CO2) enhanced oil recovery (EOR) is an important technology to achieve
carbon neutrality by sequestering CO2 underground while simultaneously recovering crude …
carbon neutrality by sequestering CO2 underground while simultaneously recovering crude …
Cell-level deep learning as proxy model for reservoir simulation and production forecasting
RM Magalhães, TJ Machado, MD Santos… - Journal of Petroleum …, 2025 - Springer
Optimizing strategies in the Oil and Gas Industry, particularly within reservoir engineering
and management, remains a significant challenge due to the prohibitive computational time …
and management, remains a significant challenge due to the prohibitive computational time …
[HTML][HTML] Deep learning-assisted Bayesian framework for real-time CO2 leakage locating at geologic sequestration sites
Accurate and efficient localization of CO 2 leakage if occurred in subsurface formations, is of
significant importance in achieving secure geological carbon sequestration (GCS) projects …
significant importance in achieving secure geological carbon sequestration (GCS) projects …
Deep learning framework for history matching CO2 storage with 4D seismic and monitoring well data
Geological carbon storage entails the injection of megatonnes of supercritical CO 2 into
subsurface formations. The properties of these formations are usually highly uncertain …
subsurface formations. The properties of these formations are usually highly uncertain …
An Interpretable Recurrent Neural Network for Waterflooding Reservoir Flow Disequilibrium Analysis
Waterflooding is one of the most common reservoir development programs, driving the oil in
porous media to the production wells by injecting high-pressure water into the reservoir. In …
porous media to the production wells by injecting high-pressure water into the reservoir. In …
Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning
Geologic CO2 sequestration (GCS) is a promising engineering measure to reduce global
greenhouse emissions. However, accurate detection of CO2 leakage locations from …
greenhouse emissions. However, accurate detection of CO2 leakage locations from …
Production Forecast of Deep-Coalbed-Methane Wells Based on Long Short-Term Memory and Bayesian Optimization
D Wang, Z Li, Y Fu - SPE Journal, 2024 - onepetro.org
This study analyzes the production behaviors of six deep coalbed-methane (CBM) wells (>
1980 m) completed in the Ordos Basin and presents a machine-learning method to predict …
1980 m) completed in the Ordos Basin and presents a machine-learning method to predict …
Prophet modeling for oil production forecasting in an enhanced oil recovery field
HK Chavan, RK Sinharay - Physics of Fluids, 2024 - pubs.aip.org
Accurate daily oil production forecasting is essential for efficient reservoir management and
investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and …
investment strategy. Forecasting oil production in enhanced oil recovery (EOR) and …