Machine learning in subsurface geothermal energy: Two decades in review
This paper reviews the trends in applying machine learning to subsurface geothermal
resource development. The review is focused on the machine learning applications over the …
resource development. The review is focused on the machine learning applications over the …
Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO 2
sequestration and wastewater injection. Managing the pressures by controlling …
sequestration and wastewater injection. Managing the pressures by controlling …
Current status and advancement from high yield and oilfield geothermal energy production: A systematic review
This review provides a comprehensive assessment of the current state and advancements in
geothermal energy production from enhanced geothermal systems, and the oilfields. As the …
geothermal energy production from enhanced geothermal systems, and the oilfields. As the …
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
Abstract The Paris Agreement, a landmark international treaty signed in 2016 to limit global
warming to 2° C, has urged researchers to explore various strategies for achieving its …
warming to 2° C, has urged researchers to explore various strategies for achieving its …
Machine learning for geothermal resource exploration in the Tularosa Basin, New Mexico
Geothermal energy is considered an essential renewable resource to generate flexible
electricity. Geothermal resource assessments conducted by the US Geological Survey …
electricity. Geothermal resource assessments conducted by the US Geological Survey …
Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs
Accurate prediction of geothermal reservoir responses to alternative energy production
scenarios is critical for optimizing the development of the underlying resources. While the …
scenarios is critical for optimizing the development of the underlying resources. While the …
Data-driven geothermal reservoir modeling: Estimating permeability distributions by machine learning
Numerical modeling for geothermal reservoir engineering is a crucial process to evaluate
the performance of the reservoir and to develop strategies for the future development. The …
the performance of the reservoir and to develop strategies for the future development. The …
Generating 3D geothermal maps in Catalonia, Spain using a hybrid adaptive multitask deep learning procedure
Map** the subsurface temperatures can efficiently lead to identifying the geothermal
distribution heat flow and potential hot spots at different depths. In this paper, an advanced …
distribution heat flow and potential hot spots at different depths. In this paper, an advanced …
Modeling subsurface performance of a geothermal reservoir using machine learning
Geothermal power plants typically show decreasing heat and power production rates over
time. Mitigation strategies include optimizing the management of existing wells—increasing …
time. Mitigation strategies include optimizing the management of existing wells—increasing …
Probing geothermal heat source based on the fuzzy inference of heat process
C Zhou, C Xu, G Liu, S Liao - Sustainable Energy Technologies and …, 2023 - Elsevier
Reconstructing the geothermal temperature field is the priority of geothermal better
utilization. The inverse heat transfer methodology provides a reliable mentality to explore …
utilization. The inverse heat transfer methodology provides a reliable mentality to explore …