A Carbonate Reservoir Prediction Method Based on Deep Learning and Multiparameter Joint Inversion

Xingda Tian, Handong Huang, Suo Cheng, Chao Wang, Pengfei Li and Yaju Hao
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Xingda Tian: State Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum-Beijing, Beijing 102249, China
Handong Huang: State Key Laboratory of Petroleum Resources and Prospecting, College of Geophysics, China University of Petroleum-Beijing, Beijing 102249, China
Suo Cheng: PetroChina Tarim Oil Field Company, Korla 841000, China
Chao Wang: China National Petroleum Corporation, Exploration and Development Institution of Tarim Oilfield, Korla 841000, China
Pengfei Li: PetroChina Tarim Oil Field Company, Korla 841000, China
Yaju Hao: School of Geophysics and Measurement-Control Technology, East China University of Technology, Nanchang 330013, China

Energies, 2022, vol. 15, issue 7, 1-20

Abstract: Deep-water carbonate reservoirs are currently the focus of global oil and gas production activities. The characterization of strongly heterogeneous carbonate reservoirs, especially the prediction of fluids in deep-water presalt carbonate reservoirs, exposes difficulties in reservoir inversion due to their complex structures and weak seismic signals. Therefore, a multiparameter joint inversion method is proposed to comprehensively utilize the information of different seismic angle gathers and the simultaneous inversion of multiple seismic parameters. Compared with the commonly used simultaneous constrained sparse-pulse inversion method, the multiparameter joint inversion method can characterize thinner layers that are consistent with data and can obtain higher-resolution presalt reservoir results. Based on the results of multiparameter joint inversion, in this paper, we further integrate the long short-term memory network algorithm to predict the porosity of presalt reef reservoirs. Compared with a fully connected neural network based on the backpropagation algorithm, the porosity results are in better agreement with the new log porosity curves, with the average porosity of the four wells increasing from 89.48% to 97.76%. The results show that the method, which is based on deep learning and multiparameter joint inversion, can more accurately identify porosity and has good application prospects in the prediction of carbonate reservoirs with complex structures.

Keywords: carbonate reservoirs; LSTM neural network; prestack inversion; parameter prediction (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: 2022
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