Elsharkawy, A.M.
Petroleum Engineering Department, Kuwait University, P.O. Box 5969, Safat 13060,
Kuwait
Abstract
This paper presents the application of radial basis function neural network (RBFNM) to model the behavior of black oil systems. The RBFNM is trained using PVT analysis of numerous black-oil samples collected from various Kuwaiti oil fields. The model is tested using properties of other samples that have not been used during the training process. The accuracy of the model in predicting the PVT properties has been compared for training and testing samples to several PVT correlations. The comparison indicated that the RBFNM is much more accurate than published correlations in predicting the properties of the crude oils under study. The behavior of the model in capturing the physical trend of the PVT data has also been checked against experimentally measured PVT properties of the test samples.
References
1. Katz, D.L. (1942) Prediction of the Shrinkage of Crude Oils: API Drill. &
Prod. prac, 137.
2. Standing, M.B. Volumetric and phase behavior of oil field hydrocarbon systems (1977) SPE, 124. Richardson, Tex.
3. Ostermann, R.D., Ehlig-Economides, C.A., Owolabi, O.O. Correlations for the reservoir fluid properties of Alaskan crudes (1983) SPE 11703.
4. Abdul Majeed, G.H. Evaluation of PVT correlations (1985) Unsolicited SPE
Paper 14478
5. Abdul-Majeed, G.H., Salman, N.H. Statistical evaluation of PVT correlations - solution gas-oil ratio. (1988) J. CAN. PET. TECHNOL., 27 (4 ,
Jul.-Aug. 1988), pp. 95-100.
6. Saleh, A.M., Mahgoub, I.S., Assad, Y. Evaluation of empirically derived
PVT properties for Egyptian crudes (1987) SPE 15721.
7. Ahmed, T. (1989) Hydrocarbon Phase Behavior, pp. 163-164. Gulf
Publishing Co., Houston, Tex., USA
8. Sutton, Robert P., Farshad, F. Evaluation of empirically derived PVT properties for gulf of Mexico crude oils (1990) SPE Reservoir Engineering
(Society of . Petroleum Engineers), 5 (1), pp. 79-86 13172 .
9. Ghetto, G., Panone, F., Villa, M. Reliability analysis of PVT correlations
(1994) European Petroleum Conference. Paper SPE 28904, London, October
25-2

10. Elsharkawy, A.M., Elgibaly, A.A., Alikhan, A.A. Assessment of the PVT correlations for predicting the properties of Kuwaiti crude oils (1995) Journal of Petroleum Science and Engineering, 13 (3-4), pp. 219-232. Doi:
10.1016/0920-4105(95)00012-7
11. Mahmood, M.A., Al-Marhoun, M.A. Evaluation of empirically derived PVT properties for Pakistani crude oils (1996) Journal of Petroleum Science and
Engineering, 16 (4), pp. 275-290. Doi: 10.1016/S0920-4105(96)00042-3
12. Al-Kaabi, A.W., Lee, J.W. An artificial neural network approach to identify the well test interpretation model: Application (1990) 65th Ann. Tech. Meet.
Paper SPE 2055, New Orleans, LA, Sept. 23-26
13. Osborne, Debra A. Neural networks provide more accurate reservoir permeability (1992) Oil and Gas Journal, 90 (39), pp. 80-83. .
14. Accarain, P., Desbrandes, R. Nuro-computing help pore pressure determination (1993) Pet Eng. Int., pp. 39-42. Feb.
15. Juniardi, I.R., Ershaghi, Iraj Complexities of using neural network in well test analysis of faulted reservoirs (1993) 1993 Western Regional Meeting, pp.
711-721.
16. Zhou, I. Determining reservoir properties in reservoir studies using a fuzzy neural network (1993) 68th Ann. Tech. Meet. paper SPE 26430, Houston, TX,
Oct. 3-6
17. Ali, J.K. Neural networks: A new tool for the petroleum industry
(1994) European Per Conf. paper SPE 27561, Aberdeen, March 15-18
18. Kumoluyi. Identification of well test models using high order neural net
(1994) European Comp. Conf. Paper SPE 27558, Aberdeen (March 15-17)
19. Briones, I. Application of neural network in the prediction of reservoir hydrocarbon mixture composition from production data (1994) 69th Ann.
Tech. Meet. Paper SPE 28598, New Orleans, Sept. 25-28.