Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM

Daihong Gu, Rongchen Zheng, Peng Cheng, Shuaiqi Zhou, Gongjie Yan, Haitao Liu, Kexin Yang, Jianguo Wang, Yuan Zhu and Mingwei Liao ()
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Daihong Gu: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Rongchen Zheng: State Key Laboratory of Shale Oil and Gas Enrichment Mechanisms and Effective Development, Sinopec, Beijing 100083, China
Peng Cheng: No. 4 Oil Production Plant of Huabei Oilfield Company, CNPC, Langfang 065000, China
Shuaiqi Zhou: Research Institute of Petroleum Exploration & Development, Huabei Oilfield Company, PetroChina, Renqiu 062552, China
Gongjie Yan: No. 5 Oil Production Plant of Huabei Oilfield Company, CNPC, Xinji 051200, China
Haitao Liu: No. 5 Oil Production Plant of Huabei Oilfield Company, CNPC, Xinji 051200, China
Kexin Yang: No. 5 Oil Production Plant of Huabei Oilfield Company, CNPC, Xinji 051200, China
Jianguo Wang: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Yuan Zhu: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Mingwei Liao: College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Energies, 2024, vol. 17, issue 22, 1-18

Abstract: In the prediction of single-well production in gas reservoirs, the traditional empirical formula of gas reservoirs generally shows poor accuracy. In the process of machine learning training and prediction, the problems of small data volume and dirty data are often encountered. In order to overcome the above problems, a single-well production prediction model of gas reservoirs based on CNN-BILSTM-AM is proposed. The model is built by long-term and short-term memory neural networks, convolutional neural networks and attention modules. The input of the model includes the production of the previous period and its influencing factors. At the same time, the fitting production and error value of the traditional gas reservoir empirical formula are introduced to predict the future production data. The loss function is used to evaluate the deviation between the predicted data and the real data, and the Bayesian hyperparameter optimization algorithm is used to optimize the model structure and comprehensively improve the generalization ability of the model. Three single wells in the Daniudi D28 well area were selected as the database, and the CNN-BILSTM-AM model was used to predict the single-well production. The results show that compared with the prediction results of the convolutional neural network (CNN) model, long short-term memory neural network (LSTM) model and bidirectional long short-term memory neural network (BILSTM) model, the error of the CNN-BILSTM-AM model on the test set of three experimental wells is reduced by 6.2425%, 4.9522% and 3.0750% on average. It shows that on the basis of coupling the empirical formula of traditional gas reservoirs, the CNN-BILSTM-AM model meets the high-precision requirements for the single-well production prediction of gas reservoirs, which is of great significance to guide the efficient development of oil fields and ensure the safety of China’s energy strategy.

Keywords: production forecast; neural network; the empirical formula of gas reservoir; CNN-BILSTM; attention mechanism; Bayesian optimization (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: 2024
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