Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs

Abstract

Bubble point pressure is of great significance in reservoir engineering calculations affecting the success of reservoir simulation. For determining this valuable parameter, experimental tests are the most reliable techniques; however, these measurements are costly and time-consuming. So, it is crucial to propose an empirical model for estimating bubble point pressure. The existing correlations mainly have large errors and develop based on restricted database from a specific geographical location. As a result, development of an all-inclusive correlation is essential. In current article, gene expression programming (GEP) was used to create a generalized model for bubble point pressure estimation. To do this, an all-inclusive source of data was utilized for training and testing the model from the petroleum industry. Several statistical approaches including both illustration tools and diverse error functions were utilized to show the supremacy of the developed GEP model. Consequently, the recommended model is the most accurate as compared to the similar correlations in literature with the average absolute relative error (AARE = 11.41%) and determination coefficient (R² = 0.96). Furthermore, the solution gas-oil ratio shows to be the most influencing variable on determining bubble point pressure according to sensitivity analysis. The results of contour map analysis demonstrate that most portions of the experimental region are predicted via the GEP equation with fewer errors as compared to two well-known literature correlations. Finally, the proposed GEP model can be of high prominence for accurate bubble point pressure estimation.

Cited as: Rostami, A., Daneshi, A., Miri, R. Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs. Advances in Geo-Energy Research, 2020, 4(2): 126-134, doi: 10.26804/ager.2020.02.02

References

Ahmadi, M.A., Pournik, M., Shadizadeh, S.R. Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence. Petroleum 2015, 1(4): 307-317.

Alakbari, F.S., Elkatatny, S., Baarimah, S.O. Prediction of bubble point pressure using artificial intelligence AI techniques. Paper SPE 184208 Presented at SPE Middle East Artificial Lift Conference and Exhibition, Manama, Kingdom of Bahrain, 30 November-1 December, 2016.

Al-Marhoun, M.A. PVT correlations for middle east crude oils. J. Pet. Technol. 1988, 40(5): 650-666.

Asoodeh, M., Bagheripour, P. Estimation of bubble point pressure from PVT data using a power-law committee with intelligent systems. J. Pet. Sci. Eng. 2012, 90-91: 1-11.

Bandyopadhyay, P., Sharma, A. Development of a new semi analytical model for prediction of bubble point pressure of crude oils. J. Pet. Sci. Eng. 2011, 78(3-4): 719-731.

Chok, N.S. Pearson’s versus spearman’s and kendall’s correlation coefficients for continuous data. Pittsburgh, University of Pittsburgh, 2010.

Elkatatny, S., Mahmoud, M. Development of a new correlation for bubble point pressure in oil reservoirs using artificial intelligent technique. Arab. J. Sci. Eng. 2018, 43(5): 2491-2500.

Ferreira, C. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. New York, USA, Springer, 2006.

Glaso, O. Generalized pressure-volume-temperature correlations. J. Pet. Technol. 1980, 32(5): 785-795.

Ikiensikimama, S.S., Ajienka, J.A. Impact of PVT correlations development on hydrocarbon accounting: The case of the niger delta. J. Pet. Sci. Eng. 2012, 81: 80-85.

Kamari, A., Pournik, M., Rostami, A., et al. Characterizing the CO2-brine interfacial tension (IFT) using robust modeling approaches: A comparative study. J. Mol. Liq. 2017, 246: 32-38.

Khoukhi, A., Albukhitan, S. PVT properties prediction using hybrid genetic-neuro-fuzzy systems. Int. J. Oil Gas Coal Technol. 2010, 4(1): 47-63.

Lasater, J.A. Bubble point pressure correlation. J. Pet. Technol. 1958, 10(5): 65-67.

McCain Jr., W.D., Soto, R.B., Valko, P.P., et al. Correlation of bubblepoint pressures for reservoir oils-A comparative study. Paper SPE 51086 Presented at SPE Eastern Regional Meeting, Pittsburgh, Pennsylvania, 9-11 November, 1998.

Moradi, B., Malekzadeh, E., Amani, M., et al. Bubble point pressure empirical correlation. Paper SPE 132756 Presented at Trinidad and Tobago Energy Resources Conference, Port of Spain, Trinidad, 27-30 June, 2010.

Ostermann, R.D., Owolabi, O.O. Correlations for the reservoir fluid properties of alaskan crudes. Paper SPE 11703 Presented at SPE California Regional Meeting, Ventura, California, 23-25 March, 1983.

Petrosky Jr, G.E., Farshad, F.F. Pressure-volume-temperature correlations for gulf of mexico crude oils. Paper SPE 26644 Presented at SPE Annual Technical Conference and Exhibition, Houston, Texas, 3-6 October, 1993.

Rafiee-Taghanaki, S., Arabloo, M., Chamkalani, A., et al. Implementation of SVM framework to estimate PVT properties of reservoir oil. Fluid Phase Equilib. 2013, 346: 25-32.

Rostami, A., Hemmati-Sarapardeh, A., Mohammadi, A.H. Estimating n-tetradecane/bitumen mixture viscosity in solvent-assisted oil recovery process using gep and gmdh modeling approaches. Pet. Sci. Technol. 2019, 37(14): 1640-1647.

Shokrollahi, A., Tatar, A., Safari, H. On accurate determination of PVT properties in crude oil systems: Committee machine intelligent system modeling approach. J. Taiwan Inst. Chem. Eng. 2015, 55: 17-26.

Standing, M.B. A pressure-volume-temperature correlation for mixtures of california oils and gases. Paper API-47-275 Presented at Drilling and Production Practice, New York, USA, 1 January, 1947.

Standing, M.B. Volumetric and Phase Behavior of Oil Field Hydrocarbon Systems. New York, USA, Society of petroleum engineers of AIME, 1977.

Talebi, R., Ghiasi, M.M., Talebi, H., et al. Application of soft computing approaches for modeling saturation pressure of reservoir oils. J. Nat. Gas Sci. Eng. 2014, 20: 8-15.

Vazquez, M., Beggs, H.D. Correlations for fluid physical property prediction. Paper SPE 6719 Presented at SPE Annual Fall Technical Conference and Exhibition, Denver, Colorado, 9-12 October, 1977.