Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Hamzeh Ghorbani; David A. Wood; Abouzar Choubineh; Nima Mohamadian; Afshin Tatar; Hamed Farhangian; Ali Nikooey

doi:10.1007/s42757-019-0047-5

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Research Article

Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

Hamzeh Ghorbani^¹, David A. Wood^²(

), Abouzar Choubineh^³, Nima Mohamadian^⁴, Afshin Tatar^⁵, Hamed Farhangian^⁶, Ali Nikooey^³

1Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

2DWA Energy Limited, Lincoln, United Kingdom

3Petroleum Department, Petroleum University of Technology, Ahwaz, Iran

4Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

5Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran

6Department of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran

Show Author Information

Abstract

Pressure-Volume-Temperature (PVT) characterization of a crude oil involves establishing its bubble point pressure, which is the pressure at which the first gas bubble forms on a fluid sample while reducing pressure at a stabilized temperature. Although accurate measurement can be made experimentally, such experiments are expensive and time-consuming. Consequently, applying reliable artificial intelligence (AI)/machine learning methods to provide an accurate mathematical prediction of an oil’s bubble point pressure from more easily measured characteristics can provide valuable cost and time savings.

This paper develops and compares four neurocomputing models applying algorithms consisting of a Multilayer Perceptron (MLP), a Radial Basis Function trained with a Genetic Algorithm (RBF-GA), a Combined Hybrid Particle Swarm Optimization-Adaptive Neuro-Fuzzy Inference System (CHPSO-ANFIS), and Least Squared Support Vector Machine (LSSVM) tuned with a coupled simulated annealing (CSA) optimizer. Based on a comprehensive analysis, although the four proposed models yield acceptable outputs, the CHPSO-ANFIS model has the best performance with the average absolute relative deviation of 0.846, the standard deviation of 0.0126, the root mean square error of 43.21, and the correlation coefficient of 0.9902. These algorithms are deployed for the accurate estimation of the bubble point pressure from the giant Ahvaz oil field (Iran).

Keywords

crude oil bubble point pressure (BPP)prediction of BPP neural network optimization LSSVM ANFIS MLP RBF learning networks neurocomputing/machine learning error analysis tuning network models

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Experimental and Computational Multiphase Flow

Volume 2 Issue 4,
December 2020

Pages 225-246

DOI: 10.1007/s42757-019-0047-5

Cite this article:

Ghorbani H, Wood DA, Choubineh A, et al. Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared. Experimental and Computational Multiphase Flow, 2020, 2(4): 225-246. https://doi.org/10.1007/s42757-019-0047-5

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Received: 13 July 2019

Revised: 01 September 2019

Accepted: 01 September 2019

Published: 18 December 2019