Experimental and Computational Multiphase Flow

, Volume 2, Issue 4, pp 225–246 | Cite as

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

  • Hamzeh Ghorbani
  • David A. WoodEmail author
  • Abouzar Choubineh
  • Nima Mohamadian
  • Afshin Tatar
  • Hamed Farhangian
  • Ali Nikooey
Research Article


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).


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 



API gravity


Bubble point pressure


Oil formation volume factor


Average absolute relative deviation


Average absolute error


Mean square error


Bubble point pressure


Particle swarm optimization


Correlation coefficient


Root mean square error


Solution gas oil ratio


Standard deviation




Gas specific gravity


Oil specific gravity


Fuzzy inference system


Least squared support vector machine


Artificial neural network


Support vector machine


Adaptive neuro-fuzzy inference system


Fuzzy C-means


Radial basis function networks


Multilayer perceptron networks


Predictive networks


Genetic algorithm


Coupled simulated annealing


Coupled local minimizer


Simulated annealing


Forward back-propagation neural network



The authors wish to express special thanks to the National Iranian Oil Company (NIOC) and to Dr. Jamshid Moghadasi, Mr. Saeed Kooti, Mr. Pejman Ghazaeipour Abaghoei from NIOC for their advice.


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Copyright information

© Tsinghua University Press 2019

Authors and Affiliations

  • Hamzeh Ghorbani
    • 1
  • David A. Wood
    • 2
    Email author
  • Abouzar Choubineh
    • 3
  • Nima Mohamadian
    • 4
  • Afshin Tatar
    • 5
  • Hamed Farhangian
    • 6
  • Ali Nikooey
    • 3
  1. 1.Young Researchers and Elite Club, Ahvaz BranchIslamic Azad UniversityAhvazIran
  2. 2.DWA Energy LimitedLincolnUK
  3. 3.Petroleum DepartmentPetroleum University of TechnologyAhwazIran
  4. 4.Young Researchers and Elite Club, Omidiyeh BranchIslamic Azad UniversityOmidiyehIran
  5. 5.Young Researchers and Elite Club, North Tehran BranchIslamic Azad UniversityTehranIran
  6. 6.Department of Chemical Engineering, Oil and GasShiraz UniversityShirazIran

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