Saturation and Pressure Prediction for Multi-Layer Irregular Reservoirs with Variable Well Patterns

Haochen Wang, Yafeng Ju, Kai Zhang (), Chengcheng Liu, Hongwei Yin, Zhongzheng Wang, Zhigang Yu, Ji Qi, Yanzhong Wang and Wenzheng Zhou
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Haochen Wang: School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Yafeng Ju: Petroleum Technology Research Institute of PetroChina Changqing Oilfield Company, Xi’an 712042, China
Kai Zhang: School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Chengcheng Liu: Qingdao Ocean Engineering and Subsea Equipment Inspection & Testing Co., Ltd., Qingdao 266237, China
Hongwei Yin: ZePu Oil and Gas Development Department of PetroChina, Tarim Oilfield Company, Korla 841000, China
Zhongzheng Wang: School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Zhigang Yu: National Engineering Laboratory for Exploration and Development of Low-Permeability Oil & Gas Fields, Xi’an 710018, China
Ji Qi: School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Yanzhong Wang: School of Petroleum Engineering, China University of Petroleum, Qingdao 266580, China
Wenzheng Zhou: State Key Laboratory of Offshore Oil Exploitation, Beijing 100028, China

Energies, 2023, vol. 16, issue 6, 1-25

Abstract: The well pattern and boundary shape of reservoirs determine the distribution of the remaining oil distribution to a large extent, especially for small-scale reservoir blocks. However, it is difficult to replicate experiences from other reservoirs directly to predict the remaining oil distribution because of the variety of irregular boundary shapes and corresponding well patterns. Meanwhile, the regular well pattern can hardly suit irregular boundary shapes. In this paper, we propose a well placement method for undeveloped irregular reservoirs and a multi-step prediction framework to predict both oil saturation and pressure fields for any reservoir shape and well pattern. To boost the physical information of input characteristics, a feature amplification approach based on physical formulae is initially presented. Then, 3D convolution technology is employed for the first time in 3D reservoir prediction to increase the spatial information in the vertical direction of the reservoir in the input. Moreover, to complete the two-field prediction, the concept of multi-task learning is adopted for the first time, improving the rationality of the forecast. Through the loss-based ablation test, we found that the operation we adopt will increase the accuracy of prediction to some extent. By testing on both manually designed and real irregular-shape reservoirs, our method is proven to be an accurate and fast oil saturation prediction method with its prediction loss less than 0.01 and calculation time less than 10 s in the future one year.

Keywords: convolutional network; multi-task learning; oil saturation prediction; pressure 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: 2023
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