Optimization of inflow performance relationship curves for an oil reservoir by genetic algorithm coupled with artificial neural-intelligence networks. (November 2021)

• Record Type: Journal Article
• Authors: Chen, Huiwei; Liu, Shumei; Magomedov, Ramazan Magomedovich; Davidyants, Alla Andronikovna
• Is Part Of: Energy reports. Volume 7(2021)
• Page Start: 3116
• Page End: 3124
• Publication Date: 2021-11
• Subjects: Inflow performance relationship curves -- Training data -- Artificial neural network -- Absolute open flow; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2021.05.028
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: In this paper, the effect of parameters on the IPR (Inflow Performance Relationship) curves of a well is analyzed using a multilayer perceptron artificial neural network. Genetic algorithms were used to train the network to adjust the weights and bias of the network so that the RMSE (Root Mean Squared Error) of the data assigned to the network test was minimized. The algorithm also optimized the network structure (including the number of hidden layers and the neurons of each layer). Based on Eclipse software, one of the oil reservoirs was first analyzed and then by changing the average reservoir pressures (assuming the oil well is predicted to reach such pressures above the average reservoir) and pressures. For this purpose, 48 experiments were conducted concerning the research variables implemented by each of them, providing inputs and outputs for network training and testing. Finally, 85% of the data were allocated to the training section and 15% to the software-generated software using the artificial neural network test section. The basis for deciding the error rate of the test data was considered. About 100% accuracy in the regression indicates that the neural network is reliable in other performances. The defined error is less than 2%, which indicated the efficiency of the genetic algorithm to predict IPR curves. This is rooted in applying genetic algorithms as a training function because it has increased the accuracy of data training. The magnitude of ANN (Artificial Neural Network) error for each scenario is also shown in the following curve. For example, the error of the sixth experiment is reported to be around 8%.