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

Ling Qiu, Chuan Lu, Zupeng Ding, Zhaoyv Wang, Long Chen, Yintao Dong, Qinwan Chong, Wenlong Xia and Fankun Meng ()
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Ling Qiu: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Chuan Lu: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Zupeng Ding: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Zhaoyv Wang: College of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Long Chen: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Yintao Dong: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Qinwan Chong: State Key Laboratory of Offshore Oil and Gas Exploitation, Beijing 100028, China
Wenlong Xia: College of Petroleum Engineering, Yangtze University, Wuhan 430100, China
Fankun Meng: College of Petroleum Engineering, Yangtze University, Wuhan 430100, China

Energies, 2025, vol. 18, issue 21, 1-18

Abstract: 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 (R 2 ). 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.

Keywords: time−series analysis; outlier detection; reservoir performance indicators; waterflood characteristic curves; production decline analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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