Reservoir modeling and simulation is a powerful tool to interpret, visualize and analyze oil and gas reservoirs. In recent years, artificial intelligence (AI) has shown a strong potential to improve the performance of reservoir modeling and simulation due to a significant enhancement in computation speed and accuracy. Data-driven reservoir modeling enables to bridge measured geo-data and geological model to explore various uncertainties and minimize exploration risks. AI-based reservoir simulation such as proxy model further integrates the measurement into the physics of fluid flow in porous media. Instead of simply relying on governing equations, AI-based reservoir modeling and simulation builds relationships between geological information, fluids flow and field constraints.
AI-Based reservoir modeling and simulation is focused on successful combination of geology, drilling and completion, reservoir engineering and production engineering driven by data. However, due to the subsurface uncertainty and algorithm limitation, there are still a lot of challenges to apply AI in reservoir modeling and simulation, which thus cannot replace conventional modeling and simulation methods completely.
This Research Topic welcomes original research or review articles on the theory and application of artificial intelligence in reservoir modeling and simulation. Potential topics include but are not limited to the following:
• AI-assisted formation evaluation;
• AI-assisted seismic interpretation and analysis;
• AI-assisted well log/core interpretation;
• AI-assisted geological modeling;
• AI-assisted history match;
• AI-assisted forecast and optimization;
• Application of AI in production analysis;
• Development and optimization of AI algorithms in reservoir modeling and simulation;
• Proxy models;
• Case studies of AI-based reservoir simulation and modeling.
Reservoir modeling and simulation is a powerful tool to interpret, visualize and analyze oil and gas reservoirs. In recent years, artificial intelligence (AI) has shown a strong potential to improve the performance of reservoir modeling and simulation due to a significant enhancement in computation speed and accuracy. Data-driven reservoir modeling enables to bridge measured geo-data and geological model to explore various uncertainties and minimize exploration risks. AI-based reservoir simulation such as proxy model further integrates the measurement into the physics of fluid flow in porous media. Instead of simply relying on governing equations, AI-based reservoir modeling and simulation builds relationships between geological information, fluids flow and field constraints.
AI-Based reservoir modeling and simulation is focused on successful combination of geology, drilling and completion, reservoir engineering and production engineering driven by data. However, due to the subsurface uncertainty and algorithm limitation, there are still a lot of challenges to apply AI in reservoir modeling and simulation, which thus cannot replace conventional modeling and simulation methods completely.
This Research Topic welcomes original research or review articles on the theory and application of artificial intelligence in reservoir modeling and simulation. Potential topics include but are not limited to the following:
• AI-assisted formation evaluation;
• AI-assisted seismic interpretation and analysis;
• AI-assisted well log/core interpretation;
• AI-assisted geological modeling;
• AI-assisted history match;
• AI-assisted forecast and optimization;
• Application of AI in production analysis;
• Development and optimization of AI algorithms in reservoir modeling and simulation;
• Proxy models;
• Case studies of AI-based reservoir simulation and modeling.
