A Physics-Informed Machine Learning Framework for Real-Time Predictive Diagnostics and Conformance Control in Produced Water Management
The study presents a physics-informed machine learning framework that analyzes real-time sensor data from over 25 wells to detect and predict water conformance issues in hydrocarbon production. By distinguishing between near-wellbore and reservoir-scale problems, the system forecasts water breakthrough events and guides targeted shutoff treatments, potentially improving recovery and reducing OPEX. The approach transforms produced water management from reactive to data-driven decision support.
The study presents a physics-informed machine learning framework that analyzes real-time sensor data from over 25 wells to detect and predict water conformance issues in hydrocarbon production. By distinguishing between near-wellbore and reservoir-scale problems, the system forecasts water breakthrough events and guides targeted shutoff treatments, potentially improving recovery and reducing OPEX. The approach transforms produced water management from reactive to data-driven decision support.
This AI-driven diagnostic tool aligns with Swiss AI initiatives focused on industrial digitalization and predictive maintenance, offering a scalable solution for the global oil and gas sector.
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