4.7 Article

Prediction of Optimal Production Time during Underground CH4 Storage with Cushion CO2 Using Reservoir Simulations and Artificial Neural Networks

Related references

Note: Only part of the references are listed.
Article Energy & Fuels

Deep-Learning-Based Flow Prediction for CO2 Storage in Shale-Sandstone Formations

Andrew K. Chu et al.

Summary: Carbon capture and storage (CCS) is crucial for achieving carbon neutrality. This study proposes a deep learning architecture, RU-FNO, to predict CO2 migration in complex shale-sandstone reservoirs. The RU-FNO model provides rapid and accurate predictions of gas saturation plume and pressure buildup, outperforming traditional numerical models by 8000 times. Case studies show that shale-sandstone reservoirs with moderate heterogeneity and spatial continuity can optimize storage efficiency.

ENERGIES (2023)

Article Chemistry, Physical

Pore-scale Ostwald ripening of gas bubbles in the presence of oil and water in porous media

Deepak Singh et al.

Summary: Through numerical experiments, we investigated the ripening process of nitrogen bubbles in homogeneous porous media containing decane and water, and found that their sizes depend on the surrounding liquid configuration and oil/water capillary pressure.

JOURNAL OF COLLOID AND INTERFACE SCIENCE (2023)

Article Energy & Fuels

Mechanistic simulation of cushion gas and working gas mixing during underground natural gas storage

Sina Sadeghi et al.

Summary: Underground natural gas storage (UGS) is a sustainable energy supply approach that utilizes base gas and working gas. The high cost of supplying base gas can be reduced by using alternative gases like nitrogen and carbon dioxide. Factors such as molecular diffusion, operating conditions, reservoir, and rock properties affect the replacement of cushion gas and can lead to a decrease in the heat value of the produced gas.

JOURNAL OF ENERGY STORAGE (2022)

Article Water Resources

U-FNO-An enhanced Fourier neural operator-based deep-learning model for flow

Gege Wen et al.

Summary: Numerical simulation of multiphase flow plays a vital role in geoscience applications. The U-FNO neural network architecture, based on the Fourier neural operator (FNO), offers a superior and efficient solution for solving multiphase flow problems. It outperforms traditional simulators in accuracy, speed, and data utilization.

ADVANCES IN WATER RESOURCES (2022)

Article Energy & Fuels

Experimental Study on the Density-Driven Convective Mixing of CO2 and Brine at Reservoir Temperature and Pressure Conditions

Lanlan Jiang et al.

Summary: Carbon capture and storage technology is crucial for reducing CO2 emissions worldwide. In this study, convective mixing experiments between CO2 and saline showed that high pressure and low salinity conditions can promote mixing and enhance the dissolution of CO2, leading to higher efficiency.

ENERGY & FUELS (2022)

Article Energy & Fuels

Permeability anisotropy in sandstones from the Soultz-sous-Forets geothermal reservoir (France): implications for large-scale fluid flow modelling

Margaux Goupil et al.

Summary: This laboratory study assessed the permeability anisotropy of Buntsandstein sandstone cores taken from the Soultz-sous-Forets geothermal reservoir in France. The results showed that the permeability anisotropy can be up to four orders of magnitude in Buntsandstein sandstones, and it increases with increasing porosity. The study suggests that permeability anisotropy should be considered in future fluid flow modelling at geothermal sites.

GEOTHERMAL ENERGY (2022)

Article Green & Sustainable Science & Technology

Application of hybrid artificial intelligent models to predict deliverability of underground natural gas storage sites

Hung Vo Thanh et al.

Summary: Underground natural gas storage is a promising solution for reducing greenhouse gas emissions and achieving sustainable development goals. This study proposes hybrid intelligent models to accurately estimate the deliverability of underground natural gas storage in different geological formations. The models were trained and validated using extensive data sets and showed high accuracy in predicting storage deliverability.

RENEWABLE ENERGY (2022)

Article Thermodynamics

Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns

Aliyuda Ali

Summary: This study proposes the application of machine learning algorithms in predicting the deliverability of underground natural gas storage in salt caverns, and examines the capabilities of artificial neural network, support vector machine, and random forest algorithms. Experimental results show that the random forest model outperforms other models in predicting deliverability with different data partitions.

ENERGY (2021)

Article Green & Sustainable Science & Technology

Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers

Youngsoo Song et al.

INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL (2020)

Article Energy & Fuels

Effective Prediction and Management of a CO2 Flooding Process for Enhancing Oil Recovery Using Artificial Neural Networks

Si Le Van et al.

JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME (2018)

Article Engineering, Chemical

An Experimental and Numerical Study of Relative Permeability Estimates Using Spatially Resolved T1-z NMR

Igor Shikhov et al.

TRANSPORT IN POROUS MEDIA (2017)

Article Water Resources

Micro-computed tomography pore-scale study of flow in porous media: Effect of voxel resolution

S. M. Shah et al.

ADVANCES IN WATER RESOURCES (2016)

Article Engineering, Industrial

The generalization of Latin hypercube sampling

Michael D. Shields et al.

RELIABILITY ENGINEERING & SYSTEM SAFETY (2016)

Article Green & Sustainable Science & Technology

Capillary trapping for geologic carbon dioxide storage - From pore scale physics to field scale implications

Samuel Krevor et al.

INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL (2015)

Article Energy & Fuels

Comparison of nonlinear formulations for two-phase multi-component EoS based simulation

Denis V. Voskov et al.

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING (2012)

Article Environmental Sciences

Effective models for CO2 migration in geological systems with varying topography

Sarah E. Gasda et al.

WATER RESOURCES RESEARCH (2012)

Article Energy & Fuels

Carbon dioxide as cushion gas for natural gas storage

CM Oldenburg

ENERGY & FUELS (2003)