New Correlations of Dew-Point Pressure for Gas Condensate Reservoirs Using Hybrid Modelling

Salem O. Baarimah; Ghareb Hamada; Khaled Ba-Jaalah; Abdelrigeeb Al Gathe; Wahbi Al-Ameri

doi:10.52716/jprs.v16i2.1233

Authors

Salem O. Baarimah Department of Petroleum Engineering, Faculty of Engineering and Petroleum, University of Hadhramout, Yemen.
Ghareb Hamada Oil and Gas Engineering, Arab Academy for Science, Technology & Maritime Transport, Alexabdria, Egypt.
Khaled Ba-Jaalah Department of Petroleum Engineering, Faculty of Engineering and Petroleum, University of Hadhramout, Yemen
Abdelrigeeb Al Gathe Department of Petroleum Engineering, Faculty of Engineering and Petroleum, University of Hadhramout, Yemen.
Wahbi Al-Ameri Department of Petroleum Engineering, Faculty of Engineering and Petroleum, University of Hadhramout, Yemen.

DOI:

https://doi.org/10.52716/jprs.v16i2.1233

Keywords:

Artificial intelligences, Nonlinear multiple regression, Hybrid; PSONN, Dew-point Pressure.

Abstract

For the development of gas condensate reservoirs and the handling of gas condensate fluids, the dew-point pressure (DPP) is an essential characteristic. Many individual mathematical relationships and intelligent systems were also used to predict this property with a good accuracy, but applying the hybrid models is fewer. For these reasons, nonlinear multiple regression (NLMR) approach and hybrid intelligent models were proposed to predict dew-point pressure accurately. This hybrid technique is Particle Swarm Optimization with Neural Networks (PSONN). Around of 900 collected data points are utilized to develop these hybrid models. The temperature (T), the composition of hydrocarbon, specific gravity (SG) and molecular weight (Mw) of heptane plus were used as inputs to predict the dew-point pressure (DPP).

The performance of the both NLMR approach and PSONN model are compared with performance of the most published empirical correlations and artificial intelligences (AI) models in the literature. Based on the statistical error analysis results, the new hybrid PSONN models outperform the NLMR model and the most published empirical correlations and artificial intelligences (AI) in the literature. The result also confirmed the PSONN hybrid model achieved the best one with APRE (2.45%) and the highest CC (0.997).

References

L. K. Nemeth and H. T. Kennedy, “A Correlation of Dewpoint Pressure with Fluid Composition and Temperature”, Society of Petroleum Engineers Journal, vol. 7, no. 02, pp. 99–104, Jun. 1967. https://doi.org/10.2118/1477-PA.

A. A. Humoud and M. A. Al-Marhoun, “A New Correlation for Gas-Condensate Dewpoint Pressure Prediction”, in Proceedings of the SPE Middle East Oil Show, Manama, Bahrain, Mar. 2001, Paper SPE-68230-MS. https://doi.org/10.2118/68230-MS.

A. M. Elsharkawy, “Predicting the dew point pressure for gas condensate reservoirs: empirical models and equations of state”, Fluid Phase Equilibria, vol. 193, no. 1–2, pp. 147–165, Jan. 2002. https://doi.org/10.1016/S0378-3812(01)00724-5.

I. Marruffo, J. Maita, J. Him, and G. Rojas, “Correlations to Determine Retrograde Dew Pressure and C7+ Percentage of Gas Condensate Reservoirs on Basis of Production Test Data of Eastern Venezuelan Fields,” in Proceedings of the SPE Gas Technology Symposium, Calgary, Alberta, Canada, Apr. 2002, Paper SPE-75686-MS. https://doi.org/10.2118/75686-MS.

E. M. El- M. Shokir, “Dewpoint Pressure Model for Gas Condensate Reservoirs Based on Genetic Programming”, Energy and Fuels, vol. 22, no. 5, pp. 3194–3200, Sep. 2008. https://doi.org/10.1021/ef800225b.

O. N. Godwin, “A New Analytical Method for Predicting Dew Point Pressures for Gas Condensate Reservoirs,” in Proceedings of the Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, Aug. 2012, Paper SPE-162985-MS. https://doi.org/10.2118/162985-MS.

A. Kamari, M. Sattari, A. H. Mohammadi, and D. Ramjugernath, “Rapid method for the estimation of dew point pressures in gas condensate reservoirs”, Journal of the Taiwan Institute of Chemical Engineers, vol. 60, pp. 258–266, Mar. 2016. https://doi.org/10.1016/j.jtice.2015.10.011.

M. A. Ahmadi and M. Ebadi, “Evolving smart approach for determination dew point pressure through condensate gas reservoirs”, Fuel, vol. 117, Part B, pp. 1074–1084, Jan. 2014. https://doi.org/10.1016/j.fuel.2013.10.010.

M. A. Ahmadi and A. Elsharkawy, “Robust correlation to predict dew point pressure of gas condensate reservoirs,” Petroleum, vol. 3, no. 3, pp. 340–347, Sep. 2017. https://doi.org/10.1016/J.PETLM.2016.05.001.

M. Alarouj, O. Alomair, and A. Elsharkawy, “Gas condensate reservoirs: Characterization and calculation of dew-point pressure”, Petroleum Exploration and Development, vol. 47, no. 5, pp. 1091–1102, Oct. 2020. https://doi.org/10.1016/S1876-3804(20)60120-3.

A. González, M. A. Barrufet, and R. Startzman, “Improved neural-network model predicts dewpoint pressure of retrograde gases”, Journal of Petroleum Science and Engineering, vol. 37, no. 3–4, pp. 183–194, Mar. 2003. https://doi.org/10.1016/S0920-4105(02)00352-2.

M. K. Akbari, F. J. Farahani, and Y. Abdy, “Dewpoint Pressure Estimation of Gas Condensate Reservoirs, Using Artificial Neural Network (ANN)”, in 69th European Association of Geoscientists and Engineers Conference and Exhibition 2007: Securing the Future, Incorporating SPE EUROPEC 2007, vol. 6, pp. 3422–3432, Jun. 2007. https://doi.org/10.2118/107032-MS.

M. Al-Dhamen and M. Al-Marhoun, “New Correlations for Dew-Point Pressure for Gas Condensate”, in Proceedings of the SPE Saudi Arabia Section Young Professionals Technical Symposium, Dhahran, Saudi Arabia, Mar. 2011, Paper SPE-155410-MS. https://doi.org/10.2118/155410-MS.

H. Kaydani, A. Hagizadeh, and A. Mohebbi, “A Dew Point Pressure Model for Gas Condensate Reservoirs Based on an Artificial Neural Network”, Petroleum science and technology, vol. 31, no. 12, pp. 1228–1237, Jun. 2013. https://doi.org/10.1080/10916466.2010.540616.

A. Rabiei, H. Sayyad, M. Riazi, and A. Hashemi, “Determination of dew point pressure in gas condensate reservoirs based on a hybrid neural genetic algorithm”, Fluid Phase Equilibria, vol. 387, pp. 38–49, Feb. 2015. https://doi.org/10.1016/J.FLUID.2014.11.027.

S. Nowroozi, M. Ranjbar, H. Hashemipour, and M. Schaffie, “Development of a neural fuzzy system for advanced prediction of dew point pressure in gas condensate reservoirs”, Fuel Processing Technology, vol. 90, no. 3, pp. 452–457, Mar. 2009. https://doi.org/10.1016/J.FUPROC.2008.11.009.

S. Ghassemzadeh, M. Shafflie, A. Sarrafi, and M. Ranjbar, “The Importance of Normalization in Predicting Dew Point Pressure by ANFIS”, Petroleum science and technology, vol. 31, no. 10, pp. 1040–1047, May 2013. https://doi.org/10.1080/10916466.2011.598895.

H. Rostami-Hosseinkhani, F. Esmaeilzadeh, and D. Mowla, “Application of expert systems for accurate determination of dew-point pressure of gas condensate reservoirs”, Journal of Natural Gas Science and Engineering, vol. 18, pp. 296–303, May 2014. https://doi.org/10.1016/J.JNGSE.2014.02.009.

A. Najafi-Marghmaleki, A. Tatar, A. Barati-Harooni, M. J. Choobineh, and A. H. Mohammadi, “GA-RBF model for prediction of dew point pressure in gas condensate reservoirs”, Journal of Molecular Liquids, vol. 223, pp. 979–986, Nov. 2016. https://doi.org/10.1016/j.molliq.2016.08.087.

A. K. Manshad, H. Rostami, S. M. Hosseini, and H. Rezaei, “Application of artificial neural network particle swarm optimization algorithm for prediction of gas condensate dew point pressure and comparison with Gaussian processes regression particle swarm optimization algorithm”, Journal of Energy Resources Technology, vol. 138, no. 3, Art. no. 032903, May 2016. https://doi.org/10.1115/1.4032226.

S. Aghamiri, M. Tamtaji, and M. J. Ghafoori, “Developing a K-value equation for predict dew point pressure of gas condensate reservoirs at high pressure”, Petroleum, vol. 4, no. 4, pp. 437–443, Dec. 2018. https://doi.org/10.1016/J.PETLM.2017.08.002.

A. N. El-hoshoudy, S. Gomaa, and S. M. Desouky, “Prediction of Dew Point Pressure in Gas Condensate Reservoirs Based on a Combination of Gene Expression Programming (GEP) and Multiple Regression Analysis”, Petroleum & Petrochemical Engineering Journal, vol. 2, no. 3, 2018. https://doi.org/10.23880/ppej-16000163.

M. R. Khan, S. Kalam, Z. Tariq, and A. Abdulraheem, “A Novel Empirical Correlation to Predict the Dew Point Pressure Using Intelligent Algorithms”, in Proceedings of the Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC), Abu Dhabi, UAE, Nov. 2019, Paper SPE-197951-MS. https://doi.org/10.2118/197951-MS.

A. Ali and L. Guo, “Adaptive neuro-fuzzy approach for prediction of dewpoint pressure for gas condensate reservoirs”, Petroleum Science and Technology, vol. 38, no. 9, pp. 673–681, May 2020. https://doi.org/10.1080/10916466.2020.1769655.

M. Haji-Savameri, N. A. Menad, S. Norouzi-Apourvari, and A. Hemmati-Sarapardeh, “Modeling dew point pressure of gas condensate reservoirs: Comparison of hybrid soft computing approaches, correlations, and thermodynamic models”, Journal of Petroleum Science and Engineering, vol. 184, p. 106558, Jan. 2020, https://doi.org/10.1016/J.PETROL.2019.106558.

H. Han, L. Luo, S. Sarvazizi, and S. Saeed, “Applying Optimized ANN Models to Estimate Dew Point Pressure of Gas Condensates,” International Journal of Chemical Engineering, vol. 2022, Art. no. 1929350, pp. 1–8, 2022. https://doi.org/10.1155/2022/1929350.

M. Mirzaie, H. Esfandyari, and A. Tatar, “Dew point pressure of gas condensates, modeling and a comprehensive review on literature data”, Journal of Petroleum Science and Engineering, vol. 211, p. 110072, Apr. 2022. https://doi.org/10.1016/J.PETROL.2021.110072.

Z. Esmaeili-Jaghdan, A. Tatar, A. Shokrollahi, J. Bon, and A. Zeinijahromi, “Machine learning modelling of dew point pressure in gas condensate reservoirs: application of decision tree-based models”, Neural Computing and Application, vol. 36, no. 4, pp. 1973–1995, Feb. 2023. https://doi.org/10.1007/S00521-023-09201-9.

A. Embaireeg, “A Reliable Multilayer Perceptron Neural Network Estimator of Gas Condensate Reservoir Initial Composition and Dew Point Pressure”, in Proceedings of the Abu Dhabi International Petroleum Exhibition and Conference (ADIPEC), Abu Dhabi, UAE, Oct. 2023, Paper SPE-216467-MS. https://doi.org/10.2118/216467-MS.

S. O. Baarimah and A. O. Baarimah, “PVT Properties for Yemeni Reservoirs Using an Intelligent Approach”, in 2021 3rd International Sustainability and Resilience Conference: Climate Change, pp. 368–372, 2021. https://doi.org/10.1109/IEEECONF53624.2021.9668185.

S. O. Baarimah, A. A. Al-Gathe, and A. O. Baarimah, “Modeling Yemeni Crude Oil Reservoir Fluid Properties Using Different Fuzzy Methods”, 2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022, pp. 761–765, 2022. https://doi.org/10.1109/ICDABI56818.2022.10041519.

S. O. Baarimah, A. G. Abdelrigeeb, and A. B. Binmerdhah, “Predict Reservoir Fluid Properties of Yemeni Crude Oils Using Fuzzy Logic Technique”, 2019 1st International Conference of Intelligent Computing and Engineering: Toward Intelligent Solutions for Developing and Empowering our Societies, ICOICE 2019, pp. 1-5, Dec. 2019. https://doi.org/10.1109/ICOICE48418.2019.9035131.

S. O. Baarimah, N. M. Al-Aidroos, and K. S. Ba-Jaalah, “Using Chemical Composition of Crude Oil and Artificial Intelligence Techniques to Predict the Reservoir Fluid Properties”, in First International Conference of Intelligent Computing and Engineering (ICOICE), pp. 1–5, 2019. https://doi.org/10.1109/ICOICE48418.2019.9035135.

S. O. Baarimah, A. O. Baarimah, W. S. Alaloul, M. A. Bazel, F. Mohammed and M. Bawahab, "A Bibliometric Analysis on the Applications of Artificial Intelligence in Petroleum Engineering", 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI), Bahrain, pp. 152-159, 2023. https://doi.org/10.1109/ICDABI60145.2023.10629472.

G. M. Hamada, A. A. Al-Gathe, and A. M. Al-Khudafi, “Hybrid Artificial Intelligent Approach for Determination of Water Saturation using Archies Formula in Carbonate Reservoirs”, Journal of Petroleum & Environmental Biotechnology, vol. 6, no. 6, pp. 1–7, 2015. http://dx.doi.org/10.4172/2157-7463.1000250.

A. A. Al-Gathe, S. O. Baarimah, and A. M. Al-Khudafi, “Modelling gas compressibility factor using different fuzzy methods”, in AIP Conference Proceedings, AIP Publishing LLC, vol. 2443, no. 1, p. 030031, 2022. https://doi.org/10.1063/5.0092029.

A. A. Al-Gathe, S. O. Baarimah, A. M. Al-Khudafi, M. Bawahab, and H. Dmour, “Hybrid approach for gas viscosity in Yemeni oil fields”, Earth Science Informatics, vol. 17, no. 1, pp. 475–482, 2024. https://doi.org/10.1007/s12145-023-01121-5.

A. A. Al-Gathe, K. A. Abd-El Fattah, A. H. El-Banbi, and K. A. El-Metwally, “A Hybrid Neuro-Fuzzy Approach for Black Oil Viscosity Prediction”, International Journal of Innovation and Applied Studies, vol. 13, no. 4, pp. 946-957, Dec. 2015.

A. Al-Gathe Abedelrigeeb, A. El-Banbi, K. A. Abdel Fattah, and K. A. El-Metwally, “An Artificial Intelligent Approach for Black Oil PVT Properties”, Journal of Physics: Conference Series, vol. 1962, no. 1, Art. no. 012026, 2021. https://doi.org/10.1088/1742-6596/1962/1/012026.