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Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared

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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).

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Abbreviations

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

P b :

Bubble point pressure

PSO:

Particle swarm optimization

R 2 :

Correlation coefficient

RMSE:

Root mean square error

R s :

Solution gas oil ratio

STD:

Standard deviation

T :

Temperature

γ g :

Gas specific gravity

γ o :

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

References

  • Adeleke, N., Ityokumbul, M. T., Adewumi, M. 2013. Blockage detection and characterization in natural gas pipelines by transient pressure-wave reflection analysis. SPE J, 18:–365.

    Google Scholar 

  • Ahmadi, M. A., Zendehboudi, S., Lohi, A., Elkamel, A., Chatzis., I. 2013. Application of hybrid genetic algorithm with particle swarm optimization and neural network for reservoir permeability prediction. J Geophys Prospect, 61:–598.

    Google Scholar 

  • AlAjmi, M. D., Alarifi, S. A., Mahsoon, A. H. 2015. Improving multiphase choke performance prediction and well production test validation using artificial intelligence: A new milestone. In: Proceedings of the SPE Digital Energy Conference and Exhibition: SPE-173394-MS.

    Google Scholar 

  • Al-Marhoun, M. A. 1988. PVT correlations for middle east crude oils. J Petrol Technol, 40:–666.

    Google Scholar 

  • Al-Marhoun, M. A., Osman, E. A. 2002. Using artificial neural networks to develop new PVT correlations for saudi crude oils. In: Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference: SPE-78592-MS.

    Google Scholar 

  • Almehaideb, R. A. 1997. Improved PVT correlations for UAE crude oils. In: Proceedings of the Middle East Oil Show and Conference: SPE-37691-MS.

    Google Scholar 

  • Al-Shammasi, A. A. 2001. A review of bubblepoint pressure and oil formation volume factor correlations. SPE Reserv Eval Eng, 4:–160.

    Google Scholar 

  • Arabloo, M., Amooie, M. A., Hemmati-Sarapardeh, A., Ghazanfari, M. H., Mohammadi, A. H. 2014. Application of constrained multivariable search methods for prediction of PVT properties of crude oil systems. Fluid Phase Equilibr, 363:–130.

    Google Scholar 

  • Asoodeh, M., Kazemi, K. 2013. Estimation of bubble point pressure: Using a genetic integration of empirical formulas. Energ Source Part A, 35:–1109.

    Google Scholar 

  • Atashnezhad, A., Wood, D. A., Fereidounpour, A., Khosravanian, R. 2014. Designing and optimizing deviated wellbore trajectories using novel particle swarm algorithms. J Nat Gas Sci Eng, 21:–1204.

    Google Scholar 

  • Bandyopadhyay, P., Sharma, A. 2011. Development of a new semi analytical model for prediction of bubble point pressure of crude oils. J Petrol Sci Eng, 78:–731.

    Google Scholar 

  • Basarir, H., Tutluoglu, L., Karpuz, C. 2014. Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Eng Geol, 173:–9.

    Google Scholar 

  • Bolondarzadeh, A., Hashemi, S., Solgani, B. 2006. The new PVT generated correlations of Iranian oil properties. In: Proceedings of the 4th Iranian Petroleum Engineering Student Conference.

    Google Scholar 

  • Boukadi, F. H., Bermani, A. S., Hashmi, A. 2002. PVT empirical models for saturated Omani crude oils. Petroleum Science and Technology, 20:–100.

    Google Scholar 

  • Broomhead, D. S., Lowe, D. 1988. Radial basis functions, multi-variable functional interpolation and adaptive networks (No. RSRE-MEMO-4148). Royal Signals and Radar Establishment Malvern (United Kingdom).

    Google Scholar 

  • Choubineh, A., Ghorbani, H., Wood, D. A., Robab Moosavi, S., Khalafi, E., Sadatshojaei, E. 2017. Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization. Fuel, 207:–560.

    Google Scholar 

  • Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function. Math Control Signals Syst, 2:–314.

    MathSciNet  MATH  Google Scholar 

  • Darwin, C. 1859. On the Origin of Species by Means of Natural Selection, or, the Preservation of Favoured Races in the Struggle for Life. London: John Murray.

    Google Scholar 

  • Deisman, N., Khajeh, M., Chalaturnyk, R. J. 2013. Using geological strength index (GSI) to model uncertainty in rock mass properties of coal for CBM/ECBM reservoir geomechanics. Int J Coal Geol, 112:–86.

    Google Scholar 

  • Dindoruk, B., Christman, P. G. 2004. PVT properties and viscosity correlations for gulf of Mexico oils. SPE Reserv Eval Eng, 7:–437.

    Google Scholar 

  • Dixit, N., Zeng, D. L., Kalonia, D. S. 2012. Application of maximum bubble pressure surface tensiometer to study protein–surfactant interactions. Int J Pharmaceut, 439:–323.

    Google Scholar 

  • Dokla, M., Osman, M. 1992. Correlation of PVT properties for UAE crudes (includes associated papers 26135 and 26316). SPE Formation Eval, 7:–46.

    Google Scholar 

  • Dong, J., Feldmann, G., Huang, J., Wu, S., Zhang, N., Comerford, S. A., Gayyed, M. F., Anders, R. A., Maitra, A., Pan, D. 2007. Elucidation of a universal size-control mechanism in drosophila and mammals. Cell, 130:–1133.

    Google Scholar 

  • Dutta, S., Gupta, J. P. 2010. PVT correlations for Indian crude using artificial neural networks. J Petro Sci Eng, 72:–109.

    Google Scholar 

  • El-Sebakhy, E., Sheltami, T., Al-Bokhitan, S., Shaaban, Y., Raharja, P., Khaeruzzaman, Y. 2007. Support vector machines framework for predicting the PVT properties of crude-oil systems. In: Proceedings of the SPE Middle East Oil and Gas Show and Conference: SPE-105698-MS.

    Google Scholar 

  • Elsharkawy, A. M. 1998. Modeling the properties of crude oil and gas systems using RBF network. In: Proceedings of the SPE Asia Pacific Oil and Gas Conference and Exhibition: SPE-49961-MS.

    Google Scholar 

  • Elsharkawy, A. M., Elgibaly, A. A., Alikhan, A. A. 1995. Assessment of the PVT correlations for predicting the properties of Kuwaiti crude oils. J Petrol Sci Eng, 13:–232.

    Google Scholar 

  • Fainerman, V. B., Miller, R. 2004. Maximum bubble pressure tensiometry—An analysis of experimental constraints. Adv Colloid Interfac, 108–109:–301.

    Google Scholar 

  • Farasat, A., Shokrollahi, A., Arabloo, M., Gharagheizi, F., Mohammadi, A. H. 2013. Toward an intelligent approach for determination of saturation pressure of crude oil. Fuel Process Technol, 115:–214.

    Google Scholar 

  • Farshad, F., LeBlanc, J. L. Garber, J. D., Osorio, J. G. 1996. Empirical PVT correlations for colombian crude oils. In: Proceedings of the SPE Latin America/Caribbean Petroleum Engineering Conference: SPE-36105-MS.

    Google Scholar 

  • Gharbi, R. B., Elsharkawy, A. M. 1997. Neural network model for estimating the PVT properties of middle east crude oils. In: Proceedings of the Middle East Oil Show and Conference: SPE-37695-MS.

    Google Scholar 

  • Gharbi, R. B., Elsharkawy, A. M., Karkoub, M. 1999. Universal neural-network-based model for estimating the PVT properties of crude oil systems. Energ Fuel, 13:–458.

    Google Scholar 

  • Ghorbani, H., Moghadasi, J., Wood, D. A. 2017. Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm. J Nat Gas Sci Eng, 45:–271.

    Google Scholar 

  • Ghorbani, H., Wood, D. A., Choubineh, A., Tatar, A., Abarghoyi, P. G., Madani, M., Mohamadian, N. 2018. Prediction of oil flow rate through an orifice flow meter: Artificial intelligence alternatives compared. Petroleum, https://doi.org/10.1016/j.petlm.2018.09.003.

    Google Scholar 

  • Ghorbani, H., Wood, D. A., Moghadasi, J., Choubineh, A., Abdizadeh, P., Mohamadian, N. 2019. Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: An oil field case study. J Petrol Explor Prod Technol, 9:–1373.

    Google Scholar 

  • Glaso, O. 1980. Generalized pressure–volume–temperature correlations. J Petrol Technol, 32:–795.

    Google Scholar 

  • Goda, M. H., Shokir, E. M., Fattah, K. A., Sayyouh, M. H. 2003. Prediction of the PVT data using neural network computing theory. In: Proceedings of the Nigeria Annual International Conference and Exhibition: SPE-85650-MS.

    Google Scholar 

  • Gomaa, S. 2016. New bubble point pressure correlation for middle east crude oils. International Advanced Research Journal in Science, Engineering and Technology, 3:–9.

    Google Scholar 

  • Haykin, S. 1994. Neural Networks: a Comprehensive Foundation. Prentice Hall PTR Upper Saddle River.

    Google Scholar 

  • Hemmati, M. N., Kharrat, R. 2007. A correlation approach for prediction of crude oil PVT properties. In: Proceedings of the SPE Middle East Oil and Gas Show and Conference: SPE-104543-MS.

    Google Scholar 

  • Holcomb, C., Outcalt, S. 1999. Near-saturation (P, ρ, T) and vapor-pressure measurements of NH3, and liquid-phase isothermal (P, ρ, T) and bubble-point-pressure measurements of NH3+H2O mixtures. Fluid Phase Equilibr, 164:–106.

    Google Scholar 

  • Hush, D. R., Horne, B. G. 1993. Progress in supervised neural networks. IEEE Sig Proc Mag, 10:–39.

    Google Scholar 

  • Ikiensikimama, S. S., Ajienka, J. A. 2012. Impact of PVT correlations development on hydrocarbon accounting: The case of the Niger Delta. J Petrol Sci Eng, 81:–85.

    Google Scholar 

  • Ikiensikimama, S. S., Ogboja, O. 2009. New bubblepoint pressure empirical PVT correlation. In: Proceedings of the Nigeria Annual International Conference and Exhibition: SPE-128893-MS.

    Google Scholar 

  • Jang, J. S. R., Sun, C. T., Mizutani, E. 1997. Neuro-Fuzzy and Soft Computing. Prentice Hall:–368.

    Google Scholar 

  • Jang, J.-S. R. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE T Syst Man Cyb, 23:–685.

    Google Scholar 

  • Kartoatmodjo, T., Schmidt, Z. 1994. Large data bank improves crude physical property correlations. Oil and Gas Journal, 92(27).

  • Kennedy, J., Eberhart, R. 1995. Particle swarm optimization. In: Proceedings of ICNN’95 - International Conference on Neural Networks, 4:–1948.

    Google Scholar 

  • Kirkpatrick, S., Gelatt Jr., C. D., Vecchi, M. P. 1983. Optimization by simulated annealing. Science, 220:–680.

    MathSciNet  MATH  Google Scholar 

  • Kloubek, J. 1972. 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 Interf Sci, 41:–16.

    Google Scholar 

  • Lasater, J. A. 1958. Bubble point pressure correlation. J Petrol Technol, 10:–67.

    Google Scholar 

  • Li, H., Yang, D. T. 2012. Phase behaviour of C3H8-n-C4H10-heavy oil systems at high pressures and elevated temperatures. In: Proceedings of the SPE Heavy Oil Conference Canada: SPE-157744-MS.

    Google Scholar 

  • Liscic, B., Tensi, H. M., Canale, L. C. E., Totten, G. 2010. Quenching Theory and Technology, 2nd edn. CRC Press.

  • Malallah, A. M., Gharbi, R., Algharaib, M. 2006. Accurate estimation of the world crude oil PVT properties using graphical alternating conditional expectation. Energy Fuels, 20:–698.

    Google Scholar 

  • Mansouri, V., Khosravanian, R., Wood, D. A., Aadnoy, B. S. 2015. 3-D well path design using a multi objective genetic algorithm. J Nat Gas Sci Eng, 27:–235.

    Google Scholar 

  • McCain Jr., W. D. 1991. Reservoir-fluid property correlations - State of the art. SPE Reservoir Eng, 6: SPE-18571-PA.

  • Mehran, F., Movagharnejad, K., Didanloo, A. 2006. New correlation for estimation of formation vilume factor and bubblepoint pressure for Iranian oil fields. In: Proceedings of the 1st Iranian Pet. Eng. Conference.

    Google Scholar 

  • Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., Teller, E. 1953. Equation of state calculations by fast computing machines. J Chem Phys, 21:–1092.

    MATH  Google Scholar 

  • Mishchuk, N. A., Fainerman, V. B., Kovalchuk, V. I., Miller, R., Dukhin, S. S. 2000. Studies of concentrated surfactant solutions using the maximum bubble pressure method. Colloid Surface A, 175:–216.

    Google Scholar 

  • Moradi, B., Malekzadeh, E., Amani, M., Boukadi, F. H., Kharrat, R. 2010. Bubble point pressure empirical correlation. In: Proceedings of the Trinidad and Tobago Energy Resources Conference: SPE-132756-MS.

    Google Scholar 

  • Nnochiri, M. O., Lawal, K. A. 2010. How variable fluid PVT model affects the performance of an integrated production system. In: Proceedings of the SPE EUROPEC/EAGE Annual Conference and Exhibition: SPE-130881-MS.

    Google Scholar 

  • Onwunalu, J. E. O., Durlofsky, L. J. 2010. Application of a particle swarm optimization algorithm for determining optimum well location and type. Computat Geosci, 14:–198.

    MATH  Google Scholar 

  • Petrosky Jr., G. E., Farshad, F. F. 1993. Pressure–volume–temperature correlations for gulf of Mexico crude oils. In: Proceedings of the SPE Annual Technical Conference and Exhibition: SPE-26644-MS.

    Google Scholar 

  • Proett, M. A., Chin, W. C., Mandal, B. 2000. Advanced dual probe formation tester with transient, harmonic, and pulsed time-delay testing methods determines permeability, skin, and anisotropy. In: Proceedings of the International Oil and Gas Conference and Exhibition in China: SPE-64650-MS.

    Google Scholar 

  • Sayahi, T., Tatar, A., Bahrami, M. 2016. A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater. Int J Therm Sci, 99:–194.

    Google Scholar 

  • Simjoo, M., Dong, Y., Andrianov, A., Talanana, M., Zitha, P. L. J. 2013. Novel insight into foam mobility control. SPE J, 18:–427.

    Google Scholar 

  • Standing, M. B. 1947. A pressure–volume–temperature correlation for mixtures of California oils and gases. Drilling and Production Practice, API-47–275:–287.

    Google Scholar 

  • Sun, H., Fang, W., Guo, Y., Lin, R. 2005. Investigation of bubble-point vapor pressures for mixtures of an endothermic hydrocarbon fuel with ethanol. Fuel, 84:–831.

    Google Scholar 

  • Tadeusiewicz, R. 1995. Neural networks: A comprehensive foundation. Control Eng Pract, 3:–747.

    Google Scholar 

  • Valkó, P. P., McCain Jr., W. D. 2003. Reservoir oil bubblepoint pressures revisited; solution gas–oil ratios and surface gas specific gravities. J Petrol Sci Eng, 37:–169.

    Google Scholar 

  • Vapnik, V. 2013. The Nature of Statistical Learning Theory. Springer Science & Business Media.

    MATH  Google Scholar 

  • Vasquez, M., Beggs, H. D. 1980. Correlations for fluid physical property prediction. J Petrol Technol, 32:–970.

    Google Scholar 

  • Velarde, J., Blosingame, T. A., McCain Jr., W. D. 1997. Correlation of black oil properties at pressures below bubble point pressure - A new approach. In: Proceedings of the Annual Technical Meeting: PETSOC-97–93.

    Google Scholar 

  • Xavier-de-Souza, S., Suykens, J. A. K., Vandewalle, J., Bolle, D. 2010. Coupled simulated annealing. IEEE T Syst Man Cy B, 40:–335.

    Google Scholar 

  • Yasari, E., Pishvaie, M. R., Khorasheh, F., Salahshoor, K., Kharrat, R. 2013. Application of multi-criterion robust optimization in water-flooding of oil reservoir. J Petrol Sci Eng, 109:–11.

    Google Scholar 

  • Yavari, H., Sabah, M., Khosravanian, R. Wood, D. A. 2018. Application of adaptive neuro-fuzzy inference system and mathematical ROP models for prediction of drilling rate. Iranian Journal of Oil & Gas Science and Technology, 7:–100.

    Google Scholar 

  • Yazaydin, A. Ö., Martin, M. G. 2007. Bubble point pressure estimates from Gibbs ensemble simulations. Fluid Phase Equilibr, 260:–198.

    Google Scholar 

  • Zoveidavianpoor, M., Samsuri, A., Shadizadeh, S. R. 2013. Adaptive neuro fuzzy inference system for compressional wave velocity prediction in a carbonate reservoir. J Appl Geophys, 89:–107.

    Google Scholar 

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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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Authors and Affiliations

  1. Young Researchers and Elite Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran

    Hamzeh Ghorbani

  2. DWA Energy Limited, Lincoln, UK

    David A. Wood

  3. Petroleum Department, Petroleum University of Technology, Ahwaz, Iran

    Abouzar Choubineh & Ali Nikooey

  4. Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran

    Nima Mohamadian

  5. Young Researchers and Elite Club, North Tehran Branch, Islamic Azad University, Tehran, Iran

    Afshin Tatar

  6. Department of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran

    Hamed Farhangian

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  1. Hamzeh Ghorbani
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  2. David A. Wood
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  3. Abouzar Choubineh
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  4. Nima Mohamadian
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Ghorbani, H., Wood, D.A., Choubineh, A. et al. Performance comparison of bubble point pressure from oil PVT data: Several neurocomputing techniques compared. Exp. Comput. Multiph. Flow 2, 225–246 (2020). https://doi.org/10.1007/s42757-019-0047-5

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