What question did this study set out to answer?

This research aims to develop a machine learning framework for assessing and predicting reservoir health in oil fields.

March 26, 2026

Artificial Intelligence–Powered Predictive Maintenance in Smart Reservoirs: Data-Driven Production Health Assessment and Early Warning via CatBoost

Puntos clave

This research aims to develop a machine learning framework for assessing and predicting reservoir health in oil fields.
Developed CatBoost model to predict cumulative oil production from key indicators.
Used data from 42 real blocks and 158 simulated cases.
Applied k-fold cross-validation for accuracy assessment.
Employed SHAP analysis to quantify feature importance.
Created an enhanced radar chart to visualize reservoir health and warning levels.
Achieved high predictive accuracy with R² = 0.964 and RMSE = 0.3096.
Identified remaining recoverable reserves as the most influential factor.
Used a radar chart to categorize health status into green, yellow, and red warning levels.
Detected anomalous trends, allowing for proactive strategy adjustments.

Resumen

Summary Accurate assessment of reservoir health is essential for efficient and sustainable oilfield development. In this study, we develop a data-driven approach to evaluate and classify health status in conventional sandstone reservoirs using machine learning and interpretable analysis. The categorical boosting (CatBoost) ensemble model predicts cumulative oil production based on 15 key development indicators, trained and validated on a data set comprising 42 real blocks and 158 simulated cases from a mature oil field. K-fold cross-validation yields high predictive accuracy coefficient of determination (R2) = 0.964; root mean squared error (RMSE) = 0.3096. Shapely additive explanations (SHAP) analysis quantifies feature importance, identifying remaining recoverable reserves per well as the dominant positive driver and water-cut increase rate as the least influential. An enhanced radar chart, integrated with a quantified risk coefficient, effectively visualizes reservoir health status and categorizes warning levels as green (safe), yellow (light warning), and red (heavy warning) in accordance with established industry standards for conventional sandstone reservoirs. The framework effectively detects anomalous development trends, enabling proactive adjustment of injection/production strategies. This interpretable data-driven framework supports rapid decision-making and near-real-time health assessment based on periodically updated field data.

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