Email this article Machine Learning Applications for Predictive Modeling of Petroleum Reservoir Behavior and Production Dynamics
Abstract
This research paper investigates the application of machine learning (ML) in enhancing reservoir management and production dynamics within the petroleum engineering domain. Focusing on two key aspects, the study explores the use of ML algorithms for reservoir characterization, utilizing seismic data analysis and rock typing based on core data. The second facet involves predictive modeling for reservoir production dynamics, employing regression models and neural networks to analyze historical data and forecast future reservoir behavior. The integration of real-time data further refines these predictions. The paper also delves into the potential of ML-based algorithms in optimizing production strategies, enabling data-driven decisions to maximize recovery and minimize operational costs. This research contributes to the evolving landscape of petroleum reservoir management by leveraging ML for improved efficiency, sustainability, and economic viability in resource extraction.
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