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A Framework for Predicting the Gas-Bearing Distribution of Unconventional Reservoirs by Deep Learning

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Abstract

Multicomponent seismic data can be used to predict unconventional reservoirs; however, this is a challenging task. Although machine learning (ML), particularly deep learning, can be used in this regard, its accuracy in reservoir prediction depends largely on the amount of data available for training and the complexity of the architecture. This study attempted to address this problem using transfer learning (TL) and a compact convolutional neural network with a self-attention mechanism (SACNN). We developed a framework for unconventional reservoir prediction by expanding the data samples and optimizing model performance. First, the synthetic data for both oil and gas reservoirs were used as the source data; their effectiveness was tested using the SACNN model. Subsequently, a real dataset was obtained by optimizing the real multicomponent seismic attributes. The TL dataset was constructed by transferring synthetic gas reservoir data to real dataset. Finally, the constructed SACNN model was used to predict the gas-bearing distribution in tight sandstone gas reservoirs. The results showed the superiority of the proposed model over conventional ML models, with lower error in the unconventional reservoir distribution prediction. Moreover, the proposed model exhibited superior prediction performance (R2 = 0.9731) on the testing dataset compared to models trained solely on synthetic (R2 = 0.9389) and real (R2 = 0.9627) data. Moreover, uncertainty analysis showed that the proposed model is robust and efficient. The proposed framework provides a basis for constructing data-driven models for energy conversion and utilization.

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Notes

  1. 1 mD = 9.869233 × 10-16 m2

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Acknowledgments

The research was funded by the Natural Science Foundation of Shandong Province (ZR2023QD025; ZR2021MD061), China Postdoctoral Science Foundation (2022M721972), National Natural Science Foundation of China (41174098), Natural Science Basic Research Program of Shaanxi (2022JQ-274), Young Talents Foundation of Inner Mongolia University (10000-23112101/055), and Qingdao Postdoctoral Science Foundation (QDBSH20230102094). We would also like to thank Xiucheng Wei, Hong Liu, Jianwen Chen, and Hong Chen for their valuable contributions to this study.

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Authors and Affiliations

  1. College of Earth Sciences and Engineering, College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Jiuqiang Yang, Niantian Lin & Kai Zhang

  2. Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266071, China

    Niantian Lin

  3. Shaanxi Xueqian Normal University, Xian, 710000, China

    Lingyun Jia

  4. Transportation institute of Inner Mongolia University, Hohhot, 010000, China

    Chao Fu

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  1. Jiuqiang Yang
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Correspondence to Kai Zhang.

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Yang, J., Lin, N., Zhang, K. et al. A Framework for Predicting the Gas-Bearing Distribution of Unconventional Reservoirs by Deep Learning. Nat Resour Res (2024). https://doi.org/10.1007/s11053-024-10345-1

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