A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network

Fuquan Song (), Heying Ding, Yongzheng Wang, Shiming Zhang and Jinbiao Yu
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Fuquan Song: School of Petroleum and Natural Gas Engineering, Changzhou University, Changzhou 213164, China
Heying Ding: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China
Yongzheng Wang: School of Petrochemical Engineering and Environment, Zhejiang Ocean University, Zhoushan 316022, China
Shiming Zhang: Exploration and Development Scientific Research Institute of Shengli Oil Field Branch of Sinopec, Dongying 257015, China
Jinbiao Yu: Exploration and Development Scientific Research Institute of Shengli Oil Field Branch of Sinopec, Dongying 257015, China

Energies, 2023, vol. 16, issue 6, 1-22

Abstract: Tight reservoirs have poor physical properties: low permeability and strong heterogeneity, which makes it difficult to predict productivity. Accurate prediction of oil well production plays a very important role in the exploration and development of oil and gas reservoirs, and improving the accuracy of production prediction has always been a key issue in reservoir characterization. With the development of artificial intelligence, high-performance algorithms make reliable production prediction possible from the perspective of data. Due to the high cost and large error of traditional seepage theory formulas in predicting oil well production, this paper establishes a horizontal well productivity prediction model based on a hybrid neural network method (CNN-LSTM), which solves the limitations of traditional methods and produces accurate predictions of horizontal wells’ daily oil production. In order to prove the effectiveness of the model, compared with the prediction results of BPNN, RBF, RNN and LSTM, it is concluded that the error results of the CNN-LSTM prediction model are 67%, 60%, 51.3% and 28% less than those of the four models, respectively, and the determination coefficient exceeds 0.95. The results show that the prediction model based on a hybrid neural network can accurately reflect the dynamic change law of production, which marks this study as a preliminary attempt of the application of this neural network method in petroleum engineering, and also provides a new method for the application of artificial intelligence in oil and gas field development.

Keywords: tight oil reservoir; CNN-LSTM neural network; production prediction; fracturing horizontal wells (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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