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Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network

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Abstract

Effective use of the difference between longitudinal and converted shear waves in reservoir sensitivity analysis is a key component to reduce the multi-solution problem of using longitudinal waves only in reservoir characterization. Deep neural network (DNN) is proven to be a powerful tool to solve an end-to-end task in a purely data-driven way, which brings a promising potential in reservoir characterization applications. In this paper, we design a reservoir prediction method using cluster analysis and DNN method. Seismic attributes sensitive to oil and gas responses are first optimized by cluster analysis, and then, a multi-component composite operation is carried out on the optimized attributes to extract oil and gas characteristics. Finally, the DNN is tested and trained to determine the best network model. The final DNN model is further examined using multi-component data in the Fenggu structural area (Sichuan, China) for seismic gas reservoir prediction. The results show that the seismic gas reservoir distribution predicted using this scheme is generally consistent with actual drilling information. Compared to single-component data, the multi-component composite seismic attributes trained network provides prediction results with higher accuracy and reduces the uncertainty of inversion results.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 41174098). We thank Sinopec Petroleum Exploration and Production Research Institute for providing data for this study and Professor Xiucheng Wei and senior engineers Yuxin Ji, Tiansheng Chen, Chunyuan Liu, and Tao Liu for their helpful suggestions. We are grateful to Elsevier for editing and improving the readability of this article. We would like to thank Dong Zhang, Bo Wen, Jianbin Zhang, Qianqian Wei, Chuanwei Zhao, Xiuchao Yang, Xiangchao Liu, Jie Peng, and Jian Sun for their contributions to this study.

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Authors and Affiliations

  1. College of Geological Sciences and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Jiuqiang Yang, Niantian Lin, Kai Zhang, Gaopeng Tian & Cuiyu Song

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

    Niantian Lin

  3. Nuclear Industry Huzhou Engineering Survey Institute Co., Ltd., Huzhou, 313000, China

    Chong Zhang

  4. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan, 250061, China

    Chao Fu

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  1. Jiuqiang Yang
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Correspondence to Niantian Lin.

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Yang, J., Lin, N., Zhang, K. et al. Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network. Nat Resour Res 30, 3429–3454 (2021). https://doi.org/10.1007/s11053-021-09863-z

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