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Naveshki, Mohammad, Naghiei, Ali, Soltani Tehrani, Pezhman, Ahmadi Alvar, Mehdi, Ghorbani, Hamzeh, Mohamadian, Nima, Moghadasi, Jamshid. (1400). Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models. , 55(2), 203-222. doi: 10.22059/jchpe.2021.314719.1341
Mohammad Naveshki; Ali Naghiei; Pezhman Soltani Tehrani; Mehdi Ahmadi Alvar; Hamzeh Ghorbani; Nima Mohamadian; Jamshid Moghadasi. "Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models". , 55, 2, 1400, 203-222. doi: 10.22059/jchpe.2021.314719.1341
Naveshki, Mohammad, Naghiei, Ali, Soltani Tehrani, Pezhman, Ahmadi Alvar, Mehdi, Ghorbani, Hamzeh, Mohamadian, Nima, Moghadasi, Jamshid. (1400). 'Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models', , 55(2), pp. 203-222. doi: 10.22059/jchpe.2021.314719.1341
Naveshki, Mohammad, Naghiei, Ali, Soltani Tehrani, Pezhman, Ahmadi Alvar, Mehdi, Ghorbani, Hamzeh, Mohamadian, Nima, Moghadasi, Jamshid. Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models. , 1400; 55(2): 203-222. doi: 10.22059/jchpe.2021.314719.1341

Prediction of Bubble Point Pressure Using New Hybrid Computational Intelligence Models

Journal of Chemical and Petroleum Engineering
مقاله 2، دوره 55، شماره 2، اسفند 2021، صفحه 203-222    اصل مقاله (1.22 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jchpe.2021.314719.1341
نویسندگان
Mohammad Naveshki1؛ Ali Naghiei2؛ Pezhman Soltani Tehrani3؛ Mehdi Ahmadi Alvar4؛ Hamzeh Ghorbani* 5؛ Nima Mohamadian6؛ Jamshid Moghadasi7
1Department of Petroleum Engineering, Sahand University of Technology, Tabriz, Iran
2Faculty of Engineering, University of Garmsar, Garmsar, Iran
3Department of Petroleum Engineering, Kish International Campus, University of Tehran, Kish, Iran
4Department of computer Engineering, Faculty of Engineering, , Shahid Chamran University, Ahwaz, Iran
5Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran
6Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran
7Department of Petroleum Engineering Petroleum Industry University, Ahvaz, Iran
چکیده
Determining BPP is one of the critical parameters for the development of oil and gas reservoirs and have this parameter requires a lot of time and money. As a result, this study aims to develop a new predictive model for BPP that uses some available input variables such as solution oil ratio (Rs), gas specific gravity (γg), API Gravity (API). In this study, two innovatively combined hybrid algorithms, DWKNN-GSA and DWKNN-ICA, are developed to predict BPP. The outcomes of the study show the models developed are capable of predicting BPP with promising performance, where the best result was achieved for DWKNN-ICA (RMSE = 0.90276 psi and R2 = 1.000 for the test dataset). Moreover, the performance comparison of the developed hybrid models with some previously developed models revealed that the DWKNN-ICA outperforms the former empirical models with respect to perdition accuracy. In addition to presenting new techniques in the present study, the effect of each of the input parameters on BPP was evaluated using Spearman's correlation coefficient, where the API and Rs have the lowest and the highest impact on the BPP.
کلیدواژه‌ها
Bubble Point Pressure Prediction؛ DWKNN-ICA؛ Hybrid Computational Intelligence؛ DWKNN-GSA؛ Machine Learning
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