A Physics-Informed Machine Learning Framework for Real-Time Predictive Diagnostics and Conformance Control in Produced Water Management

Abstract

The global hydrocarbon exploration and production (E&P) industry is severely impacted by increasing produced water management challenges, evidenced by average water-oil ratios (WOR) frequently approaching 9:1. This excessive aqueous phase volume significantly escalates operational expenditure (OPEX) through costly handling, separation, and treatment processes, simultaneously diminishing the net efficiency of hydrocarbon recovery. Current conventional water conformance control strategies exhibit an estimated success rate of approximately 40%, primarily due to the latency in intervention deployment and the diagnostic ambiguity in precisely characterizing the spatial origin and mechanism of water influx within the reservoir-wellbore system. This study introduces a novel methodology predicated on integrating artificial intelligence (AI) and machine learning (ML) algorithms for advanced, real-time diagnostic analytics of produced fluid dynamics. The core ML models are trained on a globalized, multivariate production dataset comprising production profiles and sensor telemetry from over 25 distinct operational wells. The algorithms are specifically engineered to perform spatiotemporal pattern recognition within the WOR signature. This enables the system to differentiate diagnostically between near-wellbore conformance issues (e.g., inadequate cement bond, channeling) and deeper reservoir-scale heterogeneity effects (e.g., thief zones). The trained ML models yield high-fidelity predictive analytics, capable of forecasting future water production trends and accurately predicting the imminent onset of significant water breakthrough events. These proactive capability transitions produced water management from a reactive framework to a data-driven decision support system (DSS). The real-time integration of high-frequency sensor data enhances the system's responsiveness and adaptive capability. The resulting output guides operators toward the timely deployment of optimized, targeted chemical or mechanical water shutoff treatments, moving beyond reliance on conventional, time-averaged flow measurements. The proposed AI-enabled workflow represents a significant innovation in reservoir management. By achieving early and precise identification of conformance breaches, the system empowers operators to implement remediation strategies that are both mechanistically specific and spatially optimized. This approach is crucial for: (1) maximizing hydrocarbon recovery efficiency, (2) substantially reducing OPEX by minimizing the volume of treated water, and (3) sustaining optimal reservoir sweep performance. Selected case studies will be presented to quantitatively demonstrate the potential efficacy of this methodology in delivering superior water conformance control and optimizing overall field economics.