Machine Learning-Enabled Reservoir Dynamics Prediction and Recovery Factor Optimization

Dear Colleagues,

Machine learning, deep learning, and artificial intelligence-enabled reservoir dynamics prediction and recovery factor optimization are crucial for maximizing and sustainable hydrocarbon production with environmentally friendly impacts and nominal costs. In addition, data-driven models using the above techniques lead to more efficient and profitable operations, reservoir forecasting, as well as a more sustainable approach to oil and gas resource management.

This Special Issue, entitled “Machine Learning-Enabled Reservoir Dynamics Prediction and Recovery Factor Optimization”, requests high-quality and novel research contribution and innovative review works focusing on dynamic model development, optimization processes, and software applications in the fields of conventional and unconventional reservoir systems, production optimization, and enhanced oil and gas recovery prediction by coupling real-time data and ML/DL/explainable AI techniques.

Prospective research topics include, but are not limited to, the following areas:

• Physics-informed machine learning-based predictive models, feature selection and dimensionality reduction techniques for reservoir behavior analysis and near-wellbore geomecahnics.
• Efficacy of hybrid/metaheuristic optimization techniques to reservoir dynamics and oil recovery prediction.
• Genetic algorithm and advance novel approach-based improved model for predicting oil recovery.
• Validation and field case studies of ML/DL/XAI guided robust models to hydrocarbon reservoir description and forcasting.
• Application of commercial simulation tools for reservoir dynamics model and oil recovery performance.
• Innovative guidelines and workflows for integrating ML/DL/XAI into digital oilfield and reservoir management practices. 

Dr. Mohammad Islam Miah
Dr. Murtada A. Elhaj
Dr. Arifur Rahman
Guest Editors

Manuscript Submission Information

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