Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Xuechen Li, Xinfang Ma, Fengchao Xiao, Fei Wang and Shicheng Zhang
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Xuechen Li: State Key Laboratory of Petroleum Resources and Prospecting & MOE Key Laboratory of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Xinfang Ma: State Key Laboratory of Petroleum Resources and Prospecting & MOE Key Laboratory of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Fengchao Xiao: State Key Laboratory of Petroleum Resources and Prospecting & MOE Key Laboratory of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Fei Wang: State Key Laboratory of Petroleum Resources and Prospecting & MOE Key Laboratory of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Shicheng Zhang: State Key Laboratory of Petroleum Resources and Prospecting & MOE Key Laboratory of Petroleum Engineering, China University of Petroleum (Beijing), Beijing 102249, China

Energies, 2020, vol. 13, issue 22, 1-22

Abstract: Production prediction plays an important role in decision making, development planning, and economic evaluation during the exploration and development period. However, applying traditional methods for production forecasting of newly developed wells in the conglomerate reservoir is restricted by limited historical data, complex fracture propagation, and frequent operational changes. This study proposed a Gated Recurrent Unit (GRU) neural network-based model to achieve batch production forecasting in M conglomerate reservoir of China, which tackles the limitations of traditional decline curve analysis and conventional time-series prediction methods. The model is trained by four features of production rate, tubing pressure (TP), choke size (CS), and shut-in period (SI) from 70 multistage hydraulic fractured horizontal wells. Firstly, a comprehensive data preprocessing is implemented, including excluding unfit wells, data screening, feature selection, partitioning data set, z-score normalization, and format conversion. Then, the four-feature model is compared with the model considering production only, and it is found that with frequent oilfield operations changes, the four-feature model could accurately capture the complex variance pattern of production rate. Further, Random Forest (RF) is employed to optimize the prediction results of GRU. For a fair evaluation, the performance of the proposed model is compared with that of simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural network. The results show that the proposed approach outperforms the others in prediction accuracy and generalization ability. It is worth mentioning that under the guidance of continuous learning, the GRU model can be updated as soon as more wells become available.

Keywords: GRU; time series; production forecasting; RF; deep learning (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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