Boosting Reservoir Prediction Accuracy: A Hybrid Methodology Combining Traditional Reservoir Simulation and Modern Machine Learning Approaches

Mohammed Otmane (), Syed Imtiaz, Adel M. Jaluta and Amer Aborig
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Mohammed Otmane: Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
Syed Imtiaz: Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
Adel M. Jaluta: Petroleum Engineering, University of Tripoli, Tripoli P.O. Box 13932, Libya
Amer Aborig: Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada

Energies, 2025, vol. 18, issue 3, 1-24

Abstract: This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses various data sources to develop robust reservoir static and dynamic models, including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling, well completion, fluid model definition, and rock physics functions. History matching and prediction are performed using simulation cases, and machine learning techniques such as data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through the Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, the machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concludes that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods.

Keywords: reservoir simulation; machine learning; seismic interpretation; history matching; prediction; Petrel; dynamic time warping (DTW); long short-term memory (LSTM); transfer learning (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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