SPE Reservoir Evaluation & Engineering
Volume 14, Number 1, February 2011, pp. 129-137

SPE-141165-PA

Using a Committee Machine With Artificial Neural Networks To Predict PVT Properties of Iran Crude Oil

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DOI  More information 10.2118/141165-PA http://dx.doi.org/10.2118/141165-PA

Citation

  • Alimadadi, F., Fakhri, A., Farooghi, D., and Sadati, S.H. 2011. Using a Committee Machine With Artificial Neural Networks To Predict PVT Properties of Iran Crude Oil. SPE Res Eval & Eng  14 (1): 129-137. SPE-141165-PA. doi: 10.2118/141165-PA.

Discipline Categories

  • 6.2.2 Fluid Modeling, Equations of State

Keywords

  • Artificial neural network, committee machine , PVT properties , Density, Oil FVF (Bo)

Summary

Reservoir-fluid properties are very important in material-balance calculations, well testing, and reserves estimates. Ideally, those data should be obtained experimentally. Sometimes, the results obtained from experimental tests are not reliable or accessible.

In this study, we predict the pressure/volume/temperature (PVT) properties by a new artificial-neural-network (ANN) model using component mole percent, solution gas/oil ratio (GOR) (Rs), bubblepoint pressure (Pb), reservoir pressure, API oil gravity, and temperature as input data.

The employed ANN model is from the committee machine type. The designed model processes its inputs using two parallel multilayer perceptron (MLP) networks, and then recombines their results. The results obtained show that the committee-machine model is a dependable network for prediction of PVT properties in reservoirs among the other ANNs and empirical correlations.

© 2010. Society of Petroleum Engineers

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History

  • Original manuscript received: 22 July 2009
  • Revised manuscript received: 24 April 2010
  • Manuscript approved: 21 June 2010
  • Published online: 3 December 2010
  • Version of record: 21 February 2011