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A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
computer vision, natural language processing, and speech recognition. It can unravel …
A gradient-based deep neural network model for simulating multiphase flow in porous media
Simulation of multiphase flow in porous media is crucial for the effective management of
subsurface energy and environment-related activities. The numerical simulators used for …
subsurface energy and environment-related activities. The numerical simulators used for …
An efficient deep learning-based workflow for CO2 plume imaging considering model uncertainties with distributed pressure and temperature measurements
Monitoring CO 2 plumes throughout the operation of geologic CO 2 sequestration projects is
essential to environmental safety. The evolution of underground CO 2 saturation can be …
essential to environmental safety. The evolution of underground CO 2 saturation can be …
Physics-informed machine learning for noniterative optimization in geothermal energy recovery
Geothermal energy is clean, renewable, and cost-effective and its efficient recovery
management mandates optimizing engineering parameters while considering the …
management mandates optimizing engineering parameters while considering the …
[HTML][HTML] Shale gas production evaluation framework based on data-driven models
YW He, ZY He, Y Tang, YJ Xu, JC Long… - Petroleum Science, 2023 - Elsevier
Increasing the production and utilization of shale gas is of great significance for building a
clean and low-carbon energy system. Sharp decline of gas production has been widely …
clean and low-carbon energy system. Sharp decline of gas production has been widely …
An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls
Abstract In Geological Carbon Sequestration (GCS), effectively managing the project
requires predicting state variables such as pressure and saturation. However, numerical …
requires predicting state variables such as pressure and saturation. However, numerical …
Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration
Abstract In Geological Carbon Sequestration (GCS), mineralization is a secure carbon
dioxide (CO 2) trap** mechanism to prevent possible leakage at a later stage of the GCS …
dioxide (CO 2) trap** mechanism to prevent possible leakage at a later stage of the GCS …
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 …
Flow prediction of heterogeneous nanoporous media based on physical information neural network
The simulation and prediction of fluid flow in porous media play a profoundly significant role
in today's scientific and engineering domains, particularly in gaining a deeper …
in today's scientific and engineering domains, particularly in gaining a deeper …
An efficient deep learning-based workflow for real-time CO2 plume visualization in saline aquifer using distributed pressure and temperature measurements
Underground carbon dioxide (CO 2) sequestration is widely accepted as a proven and
established technology to respond to global warming from greenhouse gas emissions. It is …
established technology to respond to global warming from greenhouse gas emissions. It is …