Review of artificial intelligence applications in geothermal energy extraction from hot dry rock DOI Creative Commons

Kun Ji,

Li Hong, Yu Zhao

et al.

Deep Underground Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Abstract The geothermal resources in hot dry rock (HDR) are considered the future trend energy extraction due to their abundant reserves. However, exploitation of is fraught with complexity and technical challenges arising from unique characteristics high temperature, strength, low permeability. With continuous advancement artificial intelligence (AI) technology, intelligent algorithms such as machine learning evolutionary gradually replacing or assisting traditional research methods, providing new solutions for HDR resource exploitation. This study first provides an overview technologies AI methods. Then, latest progress systematically reviewed applications reservoir characterization, deep drilling, heat production, operational parameter optimization. Additionally, this discusses potential limitations methods highlights corresponding opportunities AI's application. Notably, proposes framework system, offering a valuable reference practice.

Language: Английский

Overview and outlook of thermal processes in geothermal energy extraction DOI
Biao Shu, Y. Bao, Christos N. Markides

et al.

Applied Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 126329 - 126329

Published: March 1, 2025

Language: Английский

Citations

2
Current status and construction scheme of smart geothermal field technology DOI Creative Commons
Gensheng Li, Xianzhi Song, Yu Shi

et al.

Petroleum Exploration and Development, Journal Year: 2024, Volume and Issue: 51(4), P. 1035 - 1048

Published: Aug. 1, 2024

Language: Английский

Citations

4
A theoretical model and experimental investigation of fluid flow in granite rough fracture DOI
Yunsheng Dong,

Baoping Xi,

Shuixin He

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(1)

Published: Jan. 1, 2025

The fissure serves as the primary flow channel within a rock mass and plays crucial role in behavior of fractures. geometric features fracture, combined with nonlinear phenomena, complicate process significantly. To investigate fluid characteristics fractures rough granite, this study presents an improved mathematical model that correlates rock's true surfaces pressure variations during flow. effectively describes relationship between drop velocity. fluids fractures, proposes based on Forchheimer's law to describe rate. accounts for two conditions: linear low Reynolds number region higher region. Hydraulic tests were conducted three granites varying fracture geometries, validating model's accuracy. Subsequently, granite are quantitatively described, underlying mechanisms illustrated through analysis experimental data. Finally, empirical formula was established critical geometrical characterization parameters clear physical significance. These results enhance understanding contribute numerical simulation processes.

Language: Английский

Citations

0
Optimization of hot dry rock heat extraction performance considering the interaction of multi-mineral component water-rock reactions and fracture roughness DOI

Jiayan Ji,

Jialin Zhao, Junlin Yi

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135089 - 135089

Published: Feb. 1, 2025

Language: Английский

Citations

0
Review of artificial intelligence applications in geothermal energy extraction from hot dry rock DOI Creative Commons

Kun Ji,

Li Hong, Yu Zhao

et al.

Deep Underground Science and Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 9, 2025

Abstract The geothermal resources in hot dry rock (HDR) are considered the future trend energy extraction due to their abundant reserves. However, exploitation of is fraught with complexity and technical challenges arising from unique characteristics high temperature, strength, low permeability. With continuous advancement artificial intelligence (AI) technology, intelligent algorithms such as machine learning evolutionary gradually replacing or assisting traditional research methods, providing new solutions for HDR resource exploitation. This study first provides an overview technologies AI methods. Then, latest progress systematically reviewed applications reservoir characterization, deep drilling, heat production, operational parameter optimization. Additionally, this discusses potential limitations methods highlights corresponding opportunities AI's application. Notably, proposes framework system, offering a valuable reference practice.

Language: Английский

Citations

0