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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
  • 126 Downloads

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 

Nomenclature

API0

API gravity

BPP

Bubble point pressure

Bo

Oil formation volume factor

AARD%

Average absolute relative deviation

err%

Average absolute error

MSE

Mean square error

Pb

Bubble point pressure

PSO

Particle swarm optimization

R2

Correlation coefficient

RMSE

Root mean square error

Rs

Solution gas oil ratio

STD

Standard deviation

T

Temperature

γg

Gas specific gravity

γo

Oil specific gravity

FIS

Fuzzy inference system

LSSVM

Least squared support vector machine

ANN

Artificial neural network

SVM

Support vector machine

ANFIS

Adaptive neuro-fuzzy inference system

FCM

Fuzzy C-means

RBF

Radial basis function networks

MLP

Multilayer perceptron networks

PN

Predictive networks

GA

Genetic algorithm

CSA

Coupled simulated annealing

CLM

Coupled local minimizer

SA

Simulated annealing

FBPNN

Forward back-propagation neural network

Notes

Acknowledgements

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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