Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm
Hazbeh, O., Ahmadi Alvar, M., Khezerloo-ye Aghdam, S., Ghorbani, H., Mohamadian, N., Moghadasi, J. (2021). Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm. EKB Journal Management System, 23(1), 17-30. doi: 10.21608/jpme.2021.52149.1062
Omid Hazbeh; Mehdi Ahmadi Alvar; Saeed Khezerloo-ye Aghdam; Hamzeh Ghorbani; Nima Mohamadian; Jamshid Moghadasi. "Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm". EKB Journal Management System, 23, 1, 2021, 17-30. doi: 10.21608/jpme.2021.52149.1062
Hazbeh, O., Ahmadi Alvar, M., Khezerloo-ye Aghdam, S., Ghorbani, H., Mohamadian, N., Moghadasi, J. (2021). 'Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm', EKB Journal Management System, 23(1), pp. 17-30. doi: 10.21608/jpme.2021.52149.1062
Hazbeh, O., Ahmadi Alvar, M., Khezerloo-ye Aghdam, S., Ghorbani, H., Mohamadian, N., Moghadasi, J. Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm. EKB Journal Management System, 2021; 23(1): 17-30. doi: 10.21608/jpme.2021.52149.1062

Hybrid computing models to predict oil formation volume factor using multilayer perceptron algorithm

Journal of Petroleum and Mining Engineering
Article 4, Volume 23, Issue 1, June 2021, Page 17-30  XML PDF (2.07 MB)
Document Type: Full-length article
DOI: 10.21608/jpme.2021.52149.1062
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Authors
Omid Hazbeh1; Mehdi Ahmadi Alvar2; Saeed Khezerloo-ye Aghdam3; Hamzeh Ghorbani email orcid 4; Nima Mohamadian5; Jamshid Moghadasi6
1Faculty of earth sciences, Shahid Chamran University, Ahwaz, Iran
2Faculty of Engineering, Department of computer Engineering, Shahid Chamran University, Ahwaz, Iran
3Department of petroleum engineering, Amirkabir University of Technology, Tehran, Iran
4Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
5Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
6Petroleum Engineering Department Petroleum Industry University, Ahvaz, Iran
Abstract
Achieving important and effective reservoir parameters requires a lot of time and cost, and also achieving these devices is sometimes not possible. In this research, a dataset including 565 datapoints collected from published articles have been used. The input data for forecasting oil formation volume factor (OFVF) were solution gas oil ratio (Rs), gas specific gravity (γg), API gravity (API0) (or oil density γo), and temperature (T). We have tried to introduce two hybrid methods multilayer perceptron (MLP) with artificial bee colony (ABC) and firefly (FF) algorithms to predict this parameter and compare their results after extraction. After essential investigations in this study, the results show that MLP-ABC gives the best accuracy for predicting OFVF. For MLP-ABC model OFVF prediction accuracy in terms of RMSE < 0.002573 bbl/STB and R2 = 0.998 for this test dataset. After comparing the results of the experimental equations, it was concluded that the Dokla and Osman model gives the best results and Based on Spearman’s correlation coefficient relationships all input parameters have a positive effect on OFVF prediction, which are as follows: Rs> T> API> γg and these results show that the effect of Rs is more than other input variables and the effect of γg is the lowest.
Keywords
Oil formation volume factor; artificial intelligence; hybrid model; MLP
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