Time-Series Well Performance Prediction Based on Convolutional and Long Short-Term Memory Neural Network Model

Junqiang Wang, Xiaolong Qiang, Zhengcheng Ren, Hongbo Wang, Yongbo Wang and Shuoliang Wang
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Junqiang Wang: Jinan Bestune Times Power Technology Co., Ltd., Jinan 250000, China
Xiaolong Qiang: The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China
Zhengcheng Ren: The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China
Hongbo Wang: The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China
Yongbo Wang: The Second Gas Production Plant of PetroChina Changqing Oilfield Company, Yulin 719000, China
Shuoliang Wang: School of Energy, Faculty of Engineering, China University of Geosciences, Beijing 100083, China

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

Abstract: In the past, reservoir engineers used numerical simulation or reservoir engineering methods to predict oil production, and the accuracy of prediction depended more on the engineers’ own experience. With the development of data science, a new trend has arisen to use deep learning to predict oil production from the perspective of data. In this study, a hybrid forecasting model (CNN-LSTM) based on a convolutional neural network (CNN) and a Long Short-Term Memory (LSTM) neural network is proposed and used to predict the production of fractured horizontal wells in volcanic reservoirs. The model solves the limitation of traditional methods that rely on personal experience. First, the production constraints and production data are used to form a feature space, and the abstract semantics of the feature time series are extracted through convolutional neural network, then the LSTM neural network is used to predict the time series. The certain hyperparameters of the whole model are optimized by Particle Swarm Optimization algorithm (PSO). In order to estimate the model, some production dynamics from the Xinjiang oilfield of China are used for comparative analysis. The experimental results show that the CNN-LSTM model is superior to traditional neural networks and conventional decline curves.

Keywords: LSTM; CNN; PSO; time sequence data; production forecasting (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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