Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …

This review focuses on recent advancements in data-driven methods for analyzing flow and
transport in porous media, which are showing promising potential for applications in energy …

Deep-learning-based surrogate models show great promise for use in geological carbon
storage operations. In this work we target an important application—the history matching of …

Geothermal energy is clean, renewable, and cost-effective and its efficient recovery
management mandates optimizing engineering parameters while considering the …

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 …

Artificial neural networks have been widely applied in reservoir engineering. As a powerful
tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning …

Production forecast based on historical data provides essential value for develo**
hydrocarbon resources. Classic history matching workflow is often computationally intense …

Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO 2
sequestration and wastewater injection. Managing the pressures by controlling …

Geothermal reservoir simulation often considers the coupled thermo-hydro-mechanical
physics, so the computational cost is remarkably expensive, which brings challenges for …

Geological CO2 sequestration (GCS) has been proposed as an effective approach to
mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate …