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

• Published in: Energies (Basel) Vol. 16; no. 1; p. 499
• Main Authors: Wang, Junqiang, Qiang, Xiaolong, Ren, Zhengcheng, Wang, Hongbo, Wang, Yongbo, Wang, Shuoliang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Algorithms; Boundary conditions; CNN; Comparative analysis; Data science; Deep learning; Engineering; Engineers; Horizontal wells; Long short-term memory; LSTM; Mathematical models; Mathematical optimization; Methods; Neural networks; Oil and gas fields; Oil fields; Oil recovery; Oil wells; Optimization; Optimization algorithms; Petroleum production; production forecasting; Production methods; Productivity; PSO; Reservoir engineering; Reservoirs; Semantics; Simulation; Support vector machines; time sequence data; Time series; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16010499

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.