A hybrid machine learning approach based study of production forecasting and factors influencing the multiphase flow through surface chokes

Abstract

Surface chokes are widely utilized equipment installed on wellheads to control hydrocarbon flow rates. Several correlations have been suggested to model the multiphase flow of oil and gas via surface chokes. However, substantial errors have been reported in empirical fitting models and correlations to estimate hydrocarbon flow because of the reservoir's heterogeneity, anisotropism, variance in reservoir fluid characteristics at diverse subsurface depths, which introduces complexity in production data. Therefore, the estimation of daily oil and gas production rates is still challenging for the petroleum industry. Recently, hybrid data-driven techniques have been reported to be effective for estimation problems in various aspects of the petroleum domain. This paper investigates hybrid ensemble data-driven approaches to forecast multiphase flow rates through the surface choke (viz. stacked generalization and voting architectures), followed by an assessment of the impact of input production control variables. Otherwise, machine learning models are also trained and tested individually on the production data of hydrocarbon wells located in North Sea. Feature engineering has been properly applied to select the most suitable contributing control variables for daily production rate forecasting. This study provides a chronological explanation of the data analytics required for the interpretation of production data. The test results reveal the estimation performance of the stacked generalization architecture has outperformed other significant paradigms considered for production forecasting.

Keywords

Machine learning / Tree-based methods / Stacking ensemble / Multiphase flow / Data analytics / Wellhead choke variables

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] N. Chithra-Chakra, K.Y. Song, M.M. Gupta, D.N. Saraf, An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs), J. Pet. Sci. Eng. 106 (2013) 18-33, https://doi.org/10.1016/j.petrol.2013.03.004.

[2] S. Chaki, A. Routray, W.K. Mohanty, M. Jenamani,A novel multiclass SVMbased framework to classify lithology from well logs: a real-world application, in: In proceedings of the 2015 Annual IEEE India Conference (INDICON), 2015, https://doi.org/10.1109/INDICON.2015.7443653.

[3] S. Wang, Z. Chen, S. Chen, Applicability of deep neural networks on production forecasting in Bakken shale reservoirs, J of Petro Science and Eng 179 (2018) 112-125, https://doi.org/10.1016/j.petrol.2019.04.016.

[4] B. Guo, Petroleum Production Engineering, a Computer-Assisted Approach, Gulf Professional Publishing, 2011.

[5] B. Guo, A.S. Al-Bemani, A. Ghalambor, Improvement in Sachdeva's multiphase choke flow model using field data, J. Can. Petrol. Technol. 46 (5) (2007), https://doi.org/10.2118/07-05-01.

[6] Nasriani, Hamid Reza; Kalantari ASL, Azim. [Society of petroleum engineers SPE asia pacific oil and gas conference and exhibition -jakarta, Indonesia (2011-09-20)] SPE asia pacific oil and gas conference and exhibition -Two-Phase Flow Choke Performance in High Rate Gas Condensate Wells.

[7] A. Mirzaei-Paiaman, S. Salavati, The application of artificial neural networks to predict oil production flow rate, Energy Sources A 34 (2012) 1834-1843.

[8] A. Mirzaei-Paiaman, M. Nourani, Positive effect of earthquake waves on well productivity: case study: Iranian carbonate gas condensate reservoir, Sci. Iran. 19 (6) (2012) 1601-1607.

[9] A. Mirzaei-Paiaman, S. Salavati, A new empirical correlation for the sonic simultaneous flow of oil and gas through wellhead chokes for Persian oil fields, Energy Sources Part A Recov Util Environ Eff 35 (9) (2013) 817-825.

[10] J.D. Jansen, P.K. Currie, Modelling and optimization of oil and gas production systems, in:Lecture Notes for Course Ta4490, Production Optimization, Version 5c, March, Section Petroleum Engineering, Delft University of Technology, 2004. Assessed on 17-16-2022), https://documents.pub/document/production-optimisation-5659c95d63058.html?page=1.

[11] J.P. Brill, H.D. Beggs, Two-Phase Flow through Pipes, sixth ed., Tulsa University Press, Tulsa, OK, 1991.

[12] R. Tangren, C. Dodge, H. Seifert, Compressibility effects in two-phase flow, J. Appl. Phys. 20 (1949) 637-645, https://doi.org/10.1063/1.1698449.

[13] W.E. Gilbert, Flowing and gas-lift well performance. API Drill, Produc. Pract. 20 (1954) 126-157.

[14] N.C.J. Ros, An analysis of critical simultaneous gas/liquid flow through a restriction and its application to flow metering, Appl. Sci. Res. 9 (1960) 374, https://doi.org/10.1007/BF00382215.

[15] L.B. Achong, Revised bean and performance formula for lake maracaibo wells. Shell internal report, 1961.

[16] P.B. Baxendell,Bean performancedlake wells. Shell Internal Report, 1957.

[17] F. Fortunati, Two-phase flow through wellhead chokes, in: In proceedings of SPE European Spring Meeting, Amsterdam, Netherlands, 1972, https://doi.org/10.2118/3742-MS.

[18] A.B. Al-Towailib, A new correlation for two-phase flow through chokes, in: Master of Science Thesis, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia, 1992.

[19] A.A. Pilehvari, Experimental study of critical two-phase flow through wellhead chokes. Fluid Flow Projects Report, University of Tulsa, Tulsa, OK, 1981.

[20] H.H. Al-Attar, G.H. Abdul-Majeed, Revised bean performance equation for East Baghdad oil wells, SPE Prod. Eng. 3 (1) (1988) 127-131, https://doi.org/10.2118/13742-PA.

[21] D.A. Martyushev, I.N. Ponomareva, L.A. Zakharov, T.A. Shadrov, Application of machine learning for forecasting formation pressure in oil field development, Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering 332 (10) (2021) 140-149, https://doi.org/10.18799/24131830/2021/10/3401.

[22] I.N. Ponomareva, D.A. Martyushev, S.K. Govindarajan, A New Approach to Predict the Formation Pressure Using Multiple Regression Analysis: Case Study from Sukharev Oil Field Reservoir e Russia, J. Eng. Sci. King Saud Univ (2022), https://doi.org/10.1016/j.jksues.2022.03.005.

[23] L.А. Zakharov, D.А. Martyushev, I.N. Ponomareva, Predicting dynamic formation pressure using artificial intelligence methods, Journal of Mining Institute 253 (2022) 23-32, https://doi.org/10.31897/PMI.2022.11.

[24] Cao Q.; Banerjee R.; Gupta S.; Li J.; Zhou W.; Jeyachandra B.[Society of petroleum engineers SPE Argentina exploration and production of unconventional resources symposium -buenos aires, Argentina (2016-06-01)] SPE Argentina Exploration and Production of Unconventional Resources Symposium -Data-Driven Production Forecasting Using Machine Learning.

[25] A. Choubineh, H. Ghorbani, D.A. Wood, M.S. Robab, E. Khalafi, E. Sadatshojaei, Improved predictions of wellhead choke liquid critical-flow rates: modelling based on hybrid neural network training learning-based optimization, Fuel 207 (2017) 547-560, https://doi.org/10.1016/j.fuel.2017.06.131.

[26] R.G. Gorjaei, R. Songolzadeh, M. Torkaman, M. Safari, G. Zargar, A novel PSOLSSVM model for predicting the liquid rate of two-phase flow through wellhead chokes, J. Nat. Gas Sci. Eng. 24 (2015) 228-237, https://doi.org/10.1016/j.jngse.2015.03.013.

[27] M.A. Elhaj, F. Anifowose, A. Abdulraheem, Single gas flow prediction through chokes using artificial intelligence techniques, SPE Saudi Arab. Sect. Annu. Tech. Symp. Exhib (2015). Society of Petroleum Engineers.

[28] H. Ghorbani, J. Moghadasi, D.A. Wood, Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm, J. Nat. Gas Sci. Eng. 45 (2017) 256-271, https://doi.org/10.1016/j.jngse.2017.04.034.

[29] Saeed Rashid, Ali Ghamartale, Jassem Abbasi, Hoda Darvish, Afshin Tatar, Prediction of multiphase critical choke flow behavior by using a rigorous artificial neural network method, Flow Meas. Instrum. (2019), 101579, https://doi.org/10.1016/j.flowmeasinst.2019.101579.

[30] M. Rajabi, S. Beheshtian, S. Davoodi, et al., Novel hybrid machine learning optimizer algorithms to prediction of fracture density by petrophysical data, J. Pet. Explor. Prod. Technol. 11 (2021) 4375-4397, https://doi.org/10.1007/s13202-021-01321-z.

[31] A.A. Alakeely, R.N. Horne, Application of deep learning methods to estimate multiphase flow rate in producing wells using surface measurements, J. Petrol. Sci. Eng. 205 (2021), 108936, https://doi.org/10.1016/j.petrol.2021.108936.

[32] T.G. Dietterich, An Experimental Comparison of three methods for constructing ensembles of decision trees: bagging, Boosting, and randomization,Mach. Learn. 40 (2) (2000) 139-157, https://doi.org/10.1023/A:1007607513941.

[33] M. Skurichina, R.P.W. Duin, Bagging, boosting and the random subspace method for linear classifiers, Pattern Anal. Appl. 5 (2001) 121-135, https://doi.org/10.1007/s100440200011.

[34] M.P. Sesmero, A.I. Ledezma, A. Sanchis, Generating ensembles of heterogeneous classifiers using a stacked generalization, Wiley Interdiscip Rev Data Min Knowl Discov 5 (1) (2015) 21-34, https://doi.org/10.1016/j.flowmeasinst.2019.101579.

[35] Y. Xie, C. Zhu, W. Zhou, Z. Li, X. Liu, M. Tu, Evaluation of machine learning methods for formation lithology identification: a comparison of tuning processes and model performances, J. Petrol. Sci. Eng. 160 (2018) 182-193, https://doi.org/10.1016/j.petrol.2017.10.028.

[36] S. Tewari, U.D. Dwivedi, Ensemble-based big data analytics of lithofacies for automatic development of petroleum reservoirs, Comput. Ind. Eng. 128 (2018) 937-947, https://doi.org/10.1016/j.cie.2018.08.018.

[37] S. Tewari, U.D. Dwivedi, A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies, J. Pet. Explor. Prod. Technol. 10 (2020) 1849-1868, https://doi.org/10.1007/s13202-020-00839-y.

[38] C. Hedge, H. Daigle, H. Millwater, H. Gray, Analysis of the rate of penetration in drilling using physics-based and data-driven models, J. Petrol. Sci. Eng. 59 (2017) 295-306.

[39] F.H. Poettmann, R.L. Beck, New charts developed to predict gas-liquid flow through chokes, World Oil 184 (1963) 95-100.

[40] R. Omana, C. Housesiere Jr. K. E. Brown, J.P. Brill, R.E. Thompson,Multiphase Flow through chokes, SPE Paper No. 2682, in: SPE 44th Annual Meeting, Denver, Colorado, September 28eOctober 1, 1969.

[41] F.E. Ashford, An evaluation of critical multiphase flow performance through wellhead chokes, J. Petrol. Technol. 26 (8) (1974) 843-850, https://doi.org/10.2118/4541-PA.

[42] H. Al-Attar, Performance of wellhead chokes during the sub-critical flow of gas condensates, J. Pet. Sci. Eng. 60 (3-4) (2008) 205-212, https://doi.org/10.1016/j.petrol.2007.08.001Get.

[43] D.W. Surbey, B.G. Kelkar, J.P. Brill, Brill, Study of subcritical flow through multiple-orifice valves, SPE Prod. Eng. 3 (1988) 103-108, https://doi.org/10.2118/14285-PA.

[44] M.E. Osman, M.E. Dokla, Gas condensate flow through chokes, in: Eur. Pet. Conf., Society of Petroleum Engineers, 1990.

[45] T.K. Perkins, Critical and subcritical flow of multiphase mixtures through chokes, SPE Drill. Complet. 8 (1993) 271-276, https://doi.org/10.2118/20633-PA.

[46] A.I. Al-Towailib, M.A. Al-Marhoun, A new correlation for two-phase flow through chokes, J. Can. Pet. Technol. 33 (5) (1994), https://doi.org/10.2118/94-05-03.

[47] M. Al-Khalifa, Application of neural network for two-phase flow through chokes, in: Paper Presented at the SPE Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, 2009, https://doi.org/10.2118/169597-MS.

[48] I. Nejatian, M. Kanani, M. Arabloo, A. Bahadori, S. Zendehboudi, Prediction of natural gas flow through chokes using support vector machine algorithm, J. Nat. Gas Sci. Eng. 18 (2014) 155-163, https://doi.org/10.1016/j.jngse.2014.02.008.

[49] H. Ghorbani, D.A. Wood, N. Mohamadian, S. Rashidi, S. Davoodi, A. Soleimanian, A.K. Shahvand, M. Mehrad, Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes, Flow Meas. Instrum. 76 (2020), 101849, https://doi.org/10.1016/j.flowmeasinst.2020.101849.

[50] B. Bhattacharya, R.C. Timothy, M. Pal, Comparison of Supervised and unsupervised approaches for mudstone lithofacies classification: case studies from the Bakken and Mahantango-Marcellus Shale, USA, J. Nat. Gas Sci. Eng. 33 (2016) 1119-1133, https://doi.org/10.1016/j.jngse.2016.04.055.

[51] Fatai Anifowose, Jane Labadin, Abdulazeez Abdulraheem, Improving the prediction of petroleum reservoir characterization with a stacked generalization ensemble model of support vector machines, Applied Soft Computing 26 (2015) 483-496.

[52] J. Brown,Ensemble Learning Algorithms with Python: Make Better Predictions with Bagging, Boosting, and Stacking, 2021. Accessed online [08-06- 2022], https://books.google.co.in/books?id=IUkrEAAAQBAJ&printsec=copy right&redir_esc=y#v=onepage&q&f=false.

[53] D.H. Wolpert, Stacked generalization, Neural Network. 5 (2) (1992) 241-259.

[54] J. Kittler, M. Hatef, R.P.W. Duin, J. Matas, On combining classifiers, Proceedings in IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3) (1998) 226-239, https://doi.org/10.1109/34.667881.

[55] E. Bauer, R. Kohavi, An empirical comparison of voting classification algorithms: bagging, boosting, and variants, Mach. Learn. 36 (1999) 105-139, https://doi.org/10.1023/A:1007515423169.

[56] T. Sun, Z.H. Zhou, Structural diversity for decision tree ensemble learning, Front. Comput. Sci. China 12 (3) (2018) 560-570, https://doi.org/10.1007/s11704-018-7151-8.

[57] L.Y. Yang, Classifiers selection for ensemble learning based on accuracy and diversity, Procedia 955 Eng 15 (2011) 4266-4270, https://doi.org/10.1016/j.proeng.2011.08.800.

[58] M. Ravasi, I. Vasconcelos, A. Curtis, A. Kritski, Vector-acoustic reverse time migration of Volve ocean-bottom cable data set without up/down decomposed wavefields, Geophysics 80 (4) (2015) S137-S150, https://doi.org/10.1190/geo2014-0554.1.

[59] M.B. Diaz, K.Y. Kim, T.H. Kang, H.S. Shin, Drilling data from an enhanced geothermal project and its pre-processing ROP forecasting improvement, Geothermics 72 (2018) 348-357, https://doi.org/10.1016/j.geothermics.2017.12.007.

[60] R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Proc Int Joint Conf Artif Intell 2 (1995) 1137-1143.

[61] J.R. Quinlan, Induction of decision trees, Mach. Learn. 1 (1986) 81-106, https://doi.org/10.1007/BF00116251.

[62] T.K. Ho,Random decision forests, in:Proceedings of 3rd International Conference on Document Analysis and Recognition 1, 1995, pp. 278-282.

[63] J.H. Friedman, Greedy function approximation: a gradient boosting machine, Ann. Stat. 29 (5) (2001) 1189-1232, https://doi.org/10.1214/aos/1013203451.

[64] P. Geurts, D. Ernst, L. Wehenkel, Extremely randomized trees, Mach. Learn. 63 (2006) 3-42, https://doi.org/10.1007/s10994-006-6226-1.