FTCN: A Reservoir Parameter Prediction Method Based on a Fusional Temporal Convolutional Network

Hongxia Zhang (), Kaijie Fu, Zhihao Lv, Zhe Wang, Jiqiang Shi, Huawei Yu and Xinmin Ge
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Hongxia Zhang: Qingdao Institute of Software, College of Computer Science and Technology, Qingdao 266580, China
Kaijie Fu: Qingdao Institute of Software, College of Computer Science and Technology, Qingdao 266580, China
Zhihao Lv: Qingdao Institute of Software, College of Computer Science and Technology, Qingdao 266580, China
Zhe Wang: School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Jiqiang Shi: Geophysical Research Institute of Shengli Oilfeld Branch, Sinopec, Dongying 257022, China
Huawei Yu: School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China
Xinmin Ge: School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China

Energies, 2022, vol. 15, issue 15, 1-19

Abstract: Predicting reservoir parameters accurately is of great significance in petroleum exploration and development. In this paper, we propose a reservoir parameter prediction method named a fusional temporal convolutional network (FTCN). Specifically, we first analyze the relationship between logging curves and reservoir parameters. Then, we build a temporal convolutional network and design a fusion module to improve the prediction results in curve inflection points, which integrates characteristics of the shallow convolution layer and the deep temporal convolution network. Finally, we conduct experiments on real logging datasets. The results indicate that compared with the baseline method, the mean square errors of FTCN are reduced by 0.23, 0.24 and 0.25 in predicting porosity, permeability, and water saturation, respectively, which shows that our method is more consistent with the actual reservoir geological conditions. Our innovation is that we propose a new reservoir parameter prediction method and introduce the fusion module in the model innovatively. Our main contribution is that this method can well predict reservoir parameters even when there are great changes in formation properties. Our research work can provide a reference for reservoir analysis, which is conducive to logging interpreters’ efforts to analyze rock strata and identify oil and gas resources.

Keywords: reservoir parameter prediction; temporal convolutional network; porosity; permeability; water saturation (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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