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A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field

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Abstract

The most crucial elements in the oil and gas sector are predicting subsurface lithofacies utilizing geophysical logs for reservoir characterization and sweet spot assessment procedures. Nevertheless, accurately predicting payable lithofacies in a complex heterogeneous geological setting, such as the lower goru formation, poses considerable difficulty because conventional methods fall short in delivering highly accurate outcomes. Hence, this research proposes an advanced cost and time-saving data intelligence strategy using multiple classifiers to predict lithofacies with maximum accuracy that will aid in sweet spot evaluation in oil and gas fields globally. Geophysical log data of five wells from a mature gas field were used. The targeted reservoir formation was classified into seven facies types. We evaluated the performance of seven different models: support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), decision tree (DTr), naive Bayes (NB), adaptive boosting (AB), and ensemble (an integrated SVM, KNN, RF, and DTr classifier). RF and ensemble classifiers predicted the lithofacies with accuracies of 97.5 and 97.3%, respectively. Their efficacy in lithofacies prediction with high accuracy renders them as valuable tools in the domain of sweet spot evaluation. The proposed digital intelligence strategy could help operators identify drilling sites based on in-depth reservoir characterizations.

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Acknowledgments

The authors would like to thank the Directorate General of Petroleum Concessions (DGPC), Ministry of Petroleum, Pakistan, for the release of well data.

Funding

This research was supported by the National Natural Science Foundation of China (Grant No. U2202207), and the Special Project for Social Development of Yunnan Province (Grant No. 202103AC100001).

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Authors and Affiliations

  1. Institute of International Rivers and Eco-Security, Yunnan University, Kunming, 650500, China

    Umar Ashraf & Aqsa Anees

  2. Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming, 650500, China

    Umar Ashraf, Hucai Zhang, Aqsa Anees & Xiaonan Zhang

  3. Faculty of Science and Engineering, Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan

    Hung Vo Thanh

  4. Waseda Center for a Carbon Neutral Society, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan

    Hung Vo Thanh

  5. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430074, China

    Muhammad Ali

  6. China National Petroleum Company, Bohai Drilling Engineering Company Limited, Tianjin, 300280, China

    Zhenhua Duan

  7. School of Mines, China University of Mining and Technology, Xuzhou, 221116, China

    Hassan Nasir Mangi

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Ashraf, U., Zhang, H., Thanh, H.V. et al. A Robust Strategy of Geophysical Logging for Predicting Payable Lithofacies to Forecast Sweet Spots Using Digital Intelligence Paradigms in a Heterogeneous Gas Field. Nat Resour Res (2024). https://doi.org/10.1007/s11053-024-10350-4

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