Machine Learning Applications in Subsurface Flow Characterization
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H: Geo-Energy".
Deadline for manuscript submissions: closed (10 November 2022) | Viewed by 3042
Special Issue Editor
Special Issue Information
Dear Colleagues,
Prediction of subsurface flow and transport is essential in many energy and environmental applications such as enhanced hydrocarbon recovery, CO2 geo-sequestration, groundwater flow, and contaminant transport. Given the intrinsic spatial heterogeneity of the subsurface environment and the nonlinearity of governing equations of fluid flow, the prediction of subsurface flow using high-fidelity computational fluid dynamics techniques becomes challenging in terms of computational complexity and cost. Data-driven and machine learning tools can potentially tackle these challenges by offering computationally efficient alternatives to physics-based models.
This Special Issue aims to bring together papers demonstrating the advancement of machine learning-based proxy models with the focus on forward and inverse problems related to subsurface flow and transport. We highly encourage studies on scientific machine learning frameworks such as physics-constrained deep learning algorithms, which incorporate scientific computing and data-driven models in subsurface flow problems.
Dr. Sahar Bakhshian
Guest Editor
Manuscript Submission Information
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Keywords
- porous media
- subsurface flow
- computational fluid dynamics
- multiphase fluid flow
- machine learning
- deep learning
- reduced order models
- physics-informed deep learning
