Data-Driven Time-Series Modeling for Intelligent Extraction of Reservoir Development Indicators

To address the challenges of large-scale production data, complex temporal dynamics, and the difficulty in extracting key reservoir performance indicators, this study proposes an intelligent time-series analytics approach, validated using an offshore oilfield case. The methodology integrates a cascaded outlier detection framework combining the 3-Sigma rule and the One-Class Support Vector Machine (OC-SVM). The 3-Sigma rule is first used for rapid statistical screening of extreme outliers, followed by OC-SVM for nonlinear anomaly detection, enhancing the accuracy of dynamic production data preprocessing. Key indicators—including initial production capacity, decline rate, water-cut trend, and recoverable reserves—are automatically extracted through hybrid modeling combining production decline analysis and waterflood characteristic curves. Algorithm reliability is rigorously evaluated using error metrics (SSE: Sum of Squared Errors, MSE: Mean Squared Error, MAE: Mean Absolute Error, RMSE: Root Mean Squared Error) and goodness-of-fit (R2). Experimental results demonstrate that the proposed method outperforms manual extraction, achieving <10% error in daily oil production and waterflood performance curve fitting, while significantly enhancing accuracy and automation. This framework provides a robust data−driven foundation for intelligent reservoir management.