Toward connectionist model for predicting bubble point pressure of crude oils: Application of artificial intelligence

Abstract

Knowledge about reservoir fluid properties such as bubble point pressure (P_b) plays a vital role in improving reliability of oil reservoir simulation. In this work, hybrid of swarm intelligence and artificial neural network (ANN) as a robust and effective method was executed to determine the P_b of crude oil samples. In addition, the exactly precise P_b data samples reported in the literatures were employed to create and validate the PSO-ANN model. To prove and depict the reliability of the smart model developed in this study for estimating P_b of crude oils, the conventional approaches were applied on the same data set. Based on the results generated by PSO-ANN model and other conventional methods and equation of states (EOS), the PSO-ANN model is a reliable and accurate approach for estimating P_b of crude oils. This is certified by high value of correlation coefficient (R²) and insignificant value of average absolute relative deviation (AARD%) which are obtained from PSO-ANN outputs. Outcomes of this study could help reservoir engineers to have better understanding of reservoir fluid behavior in absence of reliable and experimental data samples.

Keywords

Bubble point pressure / Swarm intelligence / Crude oil / Artificial intelligence / Reservoir fluid

References

[1] N. Adeleke, M.T. Ityokumbul, M. Adewumi, Blockage detection and characterization in natural gas pipelines by transient pressure-wave reflection analysis, SPE J. 18 (2) (2013) 355-365.

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

[3] N. Dixit, D.L. Zeng, D.S. Kalonia, Application of maximum bubble pressure surface tensiometer to study proteinesurfactant interactions, Int. J. Pharm. 439 (1-2) (2012) 317-323.

[4] V.B. Fainerman, et al., Application of themaximumbubble pressure technique for dynamic surface tension studies of surfactant solutions using the Sugden two-capillary method, J. Colloid Interface Sci. 304 (1) (2006) 222-225.

[5] V.B. Fainerman, et al., Dynamic surface tension measurements of surfactant solutions using the maximum bubble pressure method e limits of applicability, Colloids Surfaces A Physicochem. Eng. Aspects 250 (1-3) (2004) 97-102.

[6] C.D. Holcomb, S.L. Outcalt,Near-saturation (P,r,T) and vapor-pressure measurements of NH3, and liquid-phase isothermal (P,r,T) and bubblepoint-pressure measurements of NH3+H₂O mixtures, Fluid Phase Equilibria 164 (1) (1999) 97-106.

[7] J. Kloubek, Measurement of the dynamic surface tension by the maximum bubble pressure method. III. Factors influencing the measurements at high frequency of the bubble formation and an extension of the evaluation to zero age of the surface, J. Colloid Interface Sci. 41 (1) (1972) 7-16.

[8] H. Li, D. Yang, Phase behaviour of C3H8/n-C4H10/Heavy-oil systems at high pressures and elevated temperatures, J. Can. Petroleum Technol. 52 (1) (2013) 30-40.

[9] N.A. Mishchuk, et al., Studies of concentrated surfactant solutions using the maximum bubble pressure method, Colloids Surfaces A Physicochem. Eng. Aspects 175 (1-2) (2000) 207-216.

[10] M. Simjoo, et al., Novel insight into foam mobility control, SPE J. 18 (3) (2013) 416-427.

[11] H. Sun, et al., Investigation of bubble-point vapor pressures for mixtures of an endothermic hydrocarbon fuel with ethanol, Fuel 84 (7-8) (2005) 825-831.

[12] A.Ö. Yazaydın, M.G. Martin, Bubble point pressure estimates from Gibbs ensemble simulations, Fluid Phase Equilibria 260 (2) (2007) 195-198.

[13] A. Farasat, et al., Toward an intelligent approach for determination of saturation pressure of crude oil, Fuel Process. Technol. 115 (0) (2013) 201-214.

[14] R.O. Baker, C. Regier, R. Sinclair,PVT error analysis for material balance calculations, in:Canadian International Petroleum Conference, 2003. Calgary, Alberta.

[15] N. Deisman, et al., Cased wellbore tools for sampling and in situ testing of cement/formation flow properties, Int. J. Greenh. Gas Control 16 (Suppl. 1(0)) (2013) S62-S69.

[16] C. Dong, et al., New downhole fluid analyzer tool for improved reservoir characterization, in: Offshore Europe, Society of Petroleum Engineers, Aberdeen, Scotland, U.K., 2007.

[17] M.O. Nnochiri, K.A. Lawal, How variable fluid PVT model affects the performance of an integrated production system, in: SPE EUROPEC/EAGE Annual Conference and Exhibition, Society of Petroleum Engineers, Barcelona, Spain, 2010.

[18] M.A. Proett, et al., New dual-probe wireline formation testing and sampling tool enables real-time permeability and anisotropy measurements,in:SPE Permian Basin Oil and Gas Recovery Conference, Society of Petroleum Engineers Inc, Midland, Texas, 2000. Copyright 2000.

[19] X.Q. Guo, et al., Equation of state analog correlations for the viscosity and thermal conductivity of hydrocarbons and reservoir fluids, J. Petroleum Sci. Eng. 30 (1) (2001) 15-27.

[20] M. Nikookar, G.R. Pazuki, L. Sahranavard,Prediction of Gas condensate properties by a new equation of state, in:Canadian International Petroleum Conference, 2008. Calgary, Alberta.

[21] A.P. Pires, R.S. Mohamed, G. Ali Mansoori, An equation of state for property prediction of alcoholehydrocarbon and waterehydrocarbon systems, J. Petroleum Sci. Eng. 32 (2-4) (2001) 103-114.

[22] R. Sarkar, A.S. Danesh, A.C. Todd,Phase behavior modeling of Gas-Condensate fluids using an equation of state, in:SPE Annual Technical Conference and Exhibition, 1991. Dallas, Texas.

[23] L.-S. Wang, J. Gmehling, Improvement of the SRK equation of state for representing volumetric properties of petroleum fluids using Dortmund Data Bank, Chem. Eng. Sci. 54 (17) (1999) 3885-3892.

[24] P. Wang, G.A. Pope,A modified equation of state for gas-condensate systems, in:SPE Eastern Regional Meeting, 2000 (Morgantown, West Virginia).

[25] P. Wang, G.A. Pope, Proper use of equations of state for compositional reservoir simulation, J. Petroleum Technol. 53 (7) (2001) 74-81.

[26] M.B. Standing, A pressure-volume-temperature correlation for mixtures of California oils and gases, Drill. Prod. Pract. (1947) 275-287.

[27] J.A. Lasater, Bubble Point pressure correlation, J. Petroleum Technol. 10 (5) (1958) 65-67.

[28] O. Glaso, Generalized pressure-volume-temperature correlations, J. Petroleum Technol. 32 (5) (1980) 785-795.

[29] J. Velarde, T.A. Blasingame, J.W.D. McCain,Correlation of black oil properties at pressures below bubble point pressure e a new approach, in:Annual Technical Meeting, 1997. Calgary, Alberta.

[30] R.B.C. Gharbi, A.M. Elsharkawy, Neural network model for estimating the PVT properties of middle east crude oils, SPE Reserv. Eval. Eng. 2 (3) (1999) 255-265.

[31] R.B. Gharbi, A.M. Elsharkawy, M. Karkoub, Universal neural-network-based model for estimating the PVT properties of crude oil systems, Energy Fuels 13 (2) (1999) 454-458.

[32] E.A. El-Sebakhy, et al., Support vector machines framework for predicting the PVT properties of crude-oil systems,in:SPE Middle East Oil and Gas Show and Conference, Kingdom of Bahrain, 2007.

[33] P. Coulibaly, F. Anctil, B. Bobée,Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol. 230 (3-4) (2000) 244-257.

[34] I.N. Daliakopoulos, P. Coulibaly, I.K. Tsanis, Groundwater level forecasting using artificial neural networks, J. Hydrol. 309 (1-4) (2005) 229-240.

[35] M. Zoveidavianpoor, A. Samsuri, S.R. Shadizadeh, Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir, J. Appl. Geophys. 89 (0) (2013) 96-107.

[36] R. Agarwal, Y.K. Li, L. Nghiem, A regression technique with dynamic parameter selection for phase-behavior matching, SPE Reserv. Eng. 5 (1990) 115-120.

[37] T. Ahmed, Hydrocarbon Phase Behavior, Gulf Publishing, Houston, 1989.

[38] K.H. Coats, G.T. Smart, Application of a regression-based EOS PVT program to laboratory data, SPE Reserv. Eng. 1 (1986) 277-299.

[39] A. Danesh, D.H. Xu, A.C. Todd, A grouping method to optimize oil description for compositional simulation of gas-injection processes, SPE Reserv. Eng. 7 (1992) 343-348.

[40] A. Danesh, D.H. Xu, A.C. Todd, Comparative study of cubic equations of state for predicting phase behaviour and volumetric properties of injection gas-reservoir oil systems, Fluid Phase Equilibria 63 (1991) 259-278.

[41] J. Drohm, W. Goldthorpe, R. Trengove, Enhancing the Evaluation of PVT Data, Offshore South East Asia Show, Singapore, 1988.

[42] A.M. Elsharkawy, An empirical model for estimating the saturation pressures of crude oils, J. Petroleum Sci. Eng. 38 (2003) 55-77.

[43] A. Hoffman, J. Crump, C. Hocott, Equilibrium constants for a gascondensate system, J. Petroleum Technol. 5 (1953) 1-10.

[44] K.C. Hong, Lumped-component characterization of crude oils for compositional simulation, in: Symp. On Enhanced Oil Recovery, Tulsa, OK, 1982.

[45] R. Jacoby, V. Berry Jr., A method for predicting pressure maintenance performance for reservoirs producing volatile crude oil, Trans. AIME 213 (1958) 59.

[46] B. Jhaveri, G. Youngren, Three-parameter modification of the PengeRobinson equation of state to improve volumetric predictions, SPE Reserv. Eng. 3 (1988) 1033-1040.

[47] Y.K. Li, L. Nghiem, A. Siu, Phase behaviour computations for reservoir fluids: effect of pseudo-components on phase diagrams and simulation results, J. Can. Petroleum Technol. 24 (1985).

[48] H.M. Moharam, M.A. Fahim, Prediction of viscosity of heavy petroleum fractions and crude oils using a corresponding states method, Ind. Eng. Chem. Res. 34 (1995) 4140-4144.

[49] K.S. Pedersen, A.L. Blilie, K.K. Meisingset, PVT calculations on petroleum reservoir fluids using measured and estimated compositional data for the plus fraction, Ind. Eng. Chem. Res. 31 (1992) 1378-1384.

[50] K.S. Pedersen, A. Fredenslund, P. Thomassen, Properties of Oil and Natural Gases, Gulf Publishing, Houston, 1989.

[51] W.G. Riemens, A.M. Schulte, L.N.J. Jong, Birba field PVT variations along the hydrocarbon column and confirmatory field tests, J. Petroleum Technol. 40 (1988) 83-88.

[52] J.L. Vogel, L. Yarborough, The effect of nitrogen on the phase behavior and physical properties of reservoir fluids, in: SPE/DOE Enhanced Oil Recovery Symposium, Tulsa, Oklahoma, 1980.

[53] C. Williams, E. Zana, G. Humphrys, Use of the PengeRobinson equation of state to predict hydrocarbon phase behavior and miscibility for fluid displacement, in: SPE/DOE on Enhanced Oil Recovery, Tulsa, OK, 1980.

[54] R. Wu, L. Rosenegger, Integrated oil PVT characterization d lessons from four case histories, J. Can. Petroleum Technol. 38 (1999).

[55] R. Wu, L. Rosenegger, Comparison of PVT properties from equation of state analysis and PVT correlations for reservoir studies, J. Can. Petroleum Technol. 39 (2000).

[56] M.A. Ahmadi, S. Zendehboudi, M. Dusseault, I. Chatzis, Evolving simple-to-use method to determine water-oil relative permeability in petroleum reservoirs, Petroleum (2015a), http://dx.doi.org/10.1016/j.petlm.2015.07.008.

[57] M.A. Ahmadi, A. Bahadori, Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool, Petroleum (2015b), http://dx.doi.org/10.1016/j.petlm.2015.06.004.

[58] M.A. Ahmadi, M.R. Ahmadi, S.M. Hoseini, M. Ebadi, Connectionist model predicts porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence, J. Petroleum Sci. Eng. 123C (2014a) 181-198.

[59] M.A. Ahmadi, M. Ebadi, A. Yazdanpanah, Robust intelligent tool for estimation dew point pressure in retrograded condensate gas reservoirs: application of particle swarm optimization, J. Petroleum Sci. Eng. 123C (2014b) 5-17.

[60] M.A. Ahmadi, M. Masoumi, R. Kharrat, A.H. Mohammadi, Gas analysis by in situ combustion in heavy oil recovery process: experimental and modeling studies, J. Chem. Eng. Technol. 37 (3) (2014c) 1-11.

[61] M.A. Ahmadi, M. Ebadi, A. Shokrollahi, S.M.J. Majidi, Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir, Appl. Soft Comput. 13 (2) (2013b) 1085-1098.

[62] M.A. Ahmadi, S.R. Shadizadeh, New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept, Fuel 102 (0) (2012) 716-723.

[63] M.A. Ahmadi, Neural network based unified particle swarm optimization for prediction of asphaltene precipitation, Fluid Phase Equilibria 314 (2012) 46-51.

[64] M.A. Ahmadi, Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm, J. Petroleum Explor. Prod. Technol. 1 (2011) 99-106.

[65] M.A. Ahmadi, M. Golshadi, Neural network based swarm concept for prediction asphaltene precipitation due natural depletion, J. Petroleum Sci. Eng. 98-99 (2012) 40-49.

[66] M.A. Ahmadi, S. Zendehboudi, A. Lohi, A. Elkamel, I. Chatzis, Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization, Geophys. Prospect. 61 (2013a) 582-598.

[67] A. Bain, Mind and Body: the Theories of Their Relation, D. Appleton and Company, New York, 1873.

[68] W. James, The Principles of Psychology, H. Holt and Company, New York, 1890.

[69] S. Zendehboudi, M.A. Ahmadi, O. Mohammadzadeh, A. Bahadori, I. Chatzis, Thermodynamic investigation of asphaltene precipitation during primary oil production: laboratory and smart technique, Ind. Eng. Chem. Res. 52 (2013b) 6009-6031.

[70] S. Zendehboudi, M.A. Ahmadi, A. Bahadori, A. Shafiei, T. Babadagli, A developed smart technique to predict minimum miscible pressuredEOR implication, Can. J. Chem. Eng. 91 (7) (2013a) 1325-1337.

[71] S. Zendehboudi, M.A. Ahmadi, L. James, I. Chatzis, Prediction of condensateto-gas ratio for retrograde gas condensate reservoirs using artificial neural network with particle swarm optimization, Energy Fuels 26 (6) (2012) 3432-3447.

[72] N. Garcia-Pedrajas, C. Hervas-Martinez, J. Munoz-Perez, COVNET: a cooperative co evolutionary model for evolving artificial neural networks, IEEE Trans. Neural Netw. 14 (2003) 575-596.

[73] M.T. Hagan, H.B. Demuth, M. Beal, Neural Network Design, PWS Publishing Company, Boston, 1966.

[74] K. Hornick, M. Stinchcombe, H. White, Multilayer feed forward networks are universal approximators, Neural Netw. 2 (1989) 359-366.

[75] K. Hornik, M. Stinchcombe, H. White, Universal approximation of an unknown mapping and its derivatives using multilayer feed forward networks, Neural Netw. 3 (5) (1990) 551-600.

[76] H.R. Vallés, A Neural Networks Method to Predict Activity Coefficients for Binary Systems Based on Molecular Functional Group Contribution,Master thesis, University of Puerto Rico, 2006.

[77] M.A. Ahmadi, S. Zendehboudi, L. James, A. Elkamel, M. Dusseault, I. Chatsiz, A. Lohi, New tools to determine bubble Point pressure of crude oils: experimental and modeling study, J. Petroleum Sci. Eng. 123C (2014d) 205-214.