A comprehensive control policy structure utilizing Artificial Intelligence (AI) is presented for closed-loop management in subsurface models. Conventional Closed-Loop Optimisation (CLO) approaches entail the iterative implementation of information assimilation, past synchronization details, and effective optimizing procedures. Information assimilation is more difficult when there is uncertainty in the geological approach and the specific model conclusions. Closed-Loop Reservoir Monitoring (CLRM) offers a control strategy that promptly correlates flow information obtained from wells, as typically accessible, to appropriate well stress configurations. The rule is characterized by time-based compression and gate-based converter sections. Learning is conducted during a preprocessing phase utilizing geological modeling derived from various geological settings. Illustrative instances of oil extraction using water insertion, utilizing both 2 and 3-dimensional geological designs, are shown. The AI-oriented technique demonstrates a 17.2% increase in Net Present Value (NPV) for 2D instances, an additional 31.5% for 3D cases compared to the effective optimization of previous models, and a 1-6.5% enhancement in NPV relative to standard CLRM. Based on the methods and variable configurations examined in this study, the controlling policy method yields a 71.34% reduction in processing expenses compared to conventional CLRM.
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