Computational analysis for optimum multiphase flowing bottom-hole pressure prediction

Ugochukwu I. Duru; Dennis Delali Kwesi Wayo; Reginald Ogu; Chindera Cyril; Happiness Nnani

Ugochukwu I. Duru Department of Petroleum Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
Dennis Delali Kwesi Wayo Department of Petroleum Engineering, School of Mining and Geosciences, Nazarbayev University, Kazakhstan
Reginald Ogu Department of Software Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
Chindera Cyril Department of Petroleum Engineering, Federal University of Technology, Owerri, Imo State, Nigeria
Happiness Nnani Department of Petroleum Engineering, Federal University of Technology, Owerri, Imo State, Nigeria

Keywords: MATLAB, Artificial Neural Network, Multilinear Regression, Machine Learning, Multiphase Fluid Flow, Flowing Bottom-Hole Pressure

Abstract

Computer intelligent models are the order of the day for the manipulation of data to better understand the trend of complex situations under the questioned industry. The petroleum engineering is faced with multiple datasets from production logging tools and predictive analysis without these computer intelligent tools can be devastating. Errors of margins under these circumstances cannot be easily prevented, which may lead to some biases in the decision-making processes, thereby affecting the cost of operations and services in the industry. This study used an open-source dataset from a production well logging tool to evaluate and affirm the accuracy of a computer intelligent model, suitable for processing complex problems. However, an artificial neural network under the feedforward function and a model fitting-multilinear regression were used for this predictive analysis. Conclusively, the predictive analysis, whiles considering the coefficient of determination for these two models resulted that, the artificial neural network- feedforward function was better in predicting the flowing bottom-hole pressures than the multilinear regression. Multiphase flow under bottom-hole pressures can further be computed using CFD to determine variations of pressure drops, predicting flow patterns and geometry to enhance prudent decision-making analysis.

References

Abdul-Majeed, G. H., & Al-Mashat, A. M. (2000). A mechanistic model for vertical and inclined two-phase slug flow. Journal of Petroleum Science and Engineering, 27(1–2), 59–67. https://doi.org/10.1016/S0920-4105(00)00047-4

Abdul-Majeed, G. H., Kadhim, F. S., Almahdawi, F. H. M., Al-Dunainawi, Y., Arabi, A., & Al-Azzawi, W. K. (2022). Application of artificial neural network to predict slug liquid holdup. International Journal of Multiphase Flow, 150(February), 104004. https://doi.org/10.1016/j.ijmultiphaseflow.2022.104004

Abou-Sayed, A. (2012). Data Mining Applications in the Oil and Gas Industry. Journal of Petroleum Technology, 64(10), 88–95. https://doi.org/10.2118/1012-0088-jpt

Ahmadi, M. A., & Chen, Z. (2019). Machine learning models to predict bottom hole pressure in multi-phase flow in vertical oil production wells. Canadian Journal of Chemical Engineering, 97(11), 2928–2940. https://doi.org/10.1002/CJCE.23526

Ahmed, M. M., & Ayoub, M. A. (2014). A Comprehensive Study on the Current Pressure Drop Calculation in Multiphase Vertical Wells; Current Trends and Future Prospective. In Journal of Applied Sciences (Vol. 14, Issue 23, pp. 3162–3171). https://doi.org/10.3923/jas.2014.3162.3171

al Shehri, F. H., Gryzlov, A., al Tayyar, T., & Arsalan, M. (2020). Utilizing machine learning methods to estimate flowing bottom-holepressure in unconventional gas condensate tight sand fractured wells in Saudi Arabia. Society of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2020, RPTC 2020. https://doi.org/10.2118/201939-MS

Ali, J. K. (1994). Neural networks: A new tool for the petroleum industry? Society of Petroleum Engineers - European Petroleum Computer Conference 1994, EPCC 1994, 233–242. https://doi.org/10.2523/27561-ms

Anderson, R. N. (2017). “Petroleum Analytics Learning Machine” for optimizing the Internet of Things of today’s digital oil field-to-refinery petroleum system. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018-Janua, 4542–4545. https://doi.org/10.1109/BigData.2017.8258496

Andrianova, A., Simonov, M., Perets, D., Margarit, A., Serebryakova, D., Bogdanov, Y., Budennyy, S., Volkov, N., Tsanda, A., & Bukharev, A. (2018). Application of machine learning for oilfield data quality improvement. Society of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2018, RPTC 2018. https://doi.org/10.2118/191601-18rptc-ms

Anifowose, F. A., Labadin, J., & Abdulraheem, A. (2017a). Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization. Journal of Petroleum Science and Engineering, 151, 480–487. https://doi.org/10.1016/J.PETROL.2017.01.024

Anifowose, F. A., Labadin, J., & Abdulraheem, A. (2017b). Hybrid intelligent systems in petroleum reservoir characterization and modeling: the journey so far and the challenges ahead. Journal of Petroleum Exploration and Production Technology, 7(1), 251–263. https://doi.org/10.1007/S13202-016-0257-3

Anifowose, F., Khoukhi, A., & Abdulraheem, A. (2017). Investigating the effect of training–testing data stratification on the performance of soft computing techniques: an experimental study. Journal of Experimental and Theoretical Artificial Intelligence, 29(3), 517–535. https://doi.org/10.1080/0952813X.2016.1198936

Ansari, A. M., Sylvester, N. D., Sarica, C., Shoham, O., & Brill, J. P. (1994). A Comprehensive Mechanistic Model for Upward Two-Phase Flow in Wellbores. SPE Production & Facilities, 9(02), 143–151. https://doi.org/10.2118/20630-PA

Asheim, H. (1986). MONA, An Accurate Two-Phase Well Flow Model Based on Phase Slippage. SPE Production Engineering, 1(03), 221–230. https://doi.org/10.2118/12989-PA

Awadalla, M., Yousef, H., Al-Hinai, A., & Al-Shidani, A. (2016). Prediction of Oil Well Flowing Bottom-hole Pressure in Petroleum Fields. Proceedings of the International Conference on Industrial Engineering and Operations Management, 8-10 March, 3007–3017.

Babanezhad, M., Taghvaie Nakhjiri, A., Rezakazemi, M., Marjani, A., & Shirazian, S. (2020). Functional input and membership characteristics in the accuracy of machine learning approach for estimation of multiphase flow. Scientific Reports, 10(1), 1–15. https://doi.org/10.1038/s41598-020-74858-4

Bahaa, M., Shokir, E., & Mahgoub, I. (2019). Soft computation application: Utilizing Artificial Neural Network to Predict the Fluid Rate and Bottom Hole Flowing Pressure for Gas-lifted Oil Wells. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2018, ADIPEC 2018. https://doi.org/10.2118/193052-MS

Barrufet, M. A., Rasool, A., & Aggour, M. (1995). Prediction of bottomhole flowing pressures in multiphase systems using a thermodynamic equation of state. Society of Petroleum Engineers - SPE Production Operations Symposium 1995, POS 1995, 1995-April, 369–380. https://doi.org/10.2523/29479-ms

Bhandari, J., Abbassi, R., Garaniya, V., & Khan, F. (2015). Risk analysis of deepwater drilling operations using Bayesian network. Journal of Loss Prevention in the Process Industries, 38, 11–23. https://doi.org/10.1016/J.JLP.2015.08.004

Cao, Q., Banerjee, R., Gupta, S., Li, J., Zhou, W., & Jeyachandra, B. (2016). Data driven production forecasting using machine learning. Society of Petroleum Engineers - SPE Argentina Exploration and Production of Unconventional Resources Symposium. https://doi.org/10.2118/180984-ms

Castiñeira, D., Toronyi, R., & Saleri, N. (2018). Machine learning and natural language processing for automated analysis of drilling and completion data. Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018. https://doi.org/10.2118/192280-MS

Chokshi, R. N., Schmidt, Z., & Doty, D. R. (1996). Experimental Study and the Development of a Mechanistic Model for Two-Phase Flow Through Vertical Tubing. All Days. https://doi.org/10.2118/35676-MS

D’Almeida, A. L., Bergiante, N. C. R., de Souza Ferreira, G., Leta, F. R., de Campos Lima, C. B., & Lima, G. B. A. (2022). Digital transformation: a review on artificial intelligence techniques in drilling and production applications. International Journal of Advanced Manufacturing Technology, 119(9–10), 5553–5582. https://doi.org/10.1007/s00170-021-08631-w

Dunlop, J., Isangulov, R., Aldred, W. D., Arismendi Sanchez, H., Sanchez Flores, J. L., Alarcon Herdoiza, J., Belaskie, J., & Luppens, J. C. (2011). Increased rate of penetration through automation. SPE/IADC Drilling Conference, Proceedings, 1, 442–452. https://doi.org/10.2118/139897-MS

Economi, M. J. (n.d.). Michael J Economi des. Measurement, 139–151.

Esmaili, S., & Mohaghegh, S. D. (2016). Full field reservoir modeling of shale assets using advanced data-driven analytics. Geoscience Frontiers, 7(1), 11–20. https://doi.org/10.1016/J.GSF.2014.12.006

Falcone, G., Hewitt, G. F., Alimonti, C., & Harrison, B. (2001). Multiphase Flow Metering: Current Trends and Future Developments. Proceedings - SPE Annual Technical Conference and Exhibition, 1291–1303. https://doi.org/10.2118/71474-MS

Feng, Q.-H., Gao, S.-C., Zhang, J.-C., & Ye, X.-C. (2016). Well bottom-hole flowing pressure evaluation method in 48 Block of S Gas field. Iceep, 186–189. https://doi.org/10.2991/iceep-16.2016.31

Firouzi, M., & Rathnayake, S. (2019). Prediction of the flowing bottom-hole pressure using advanced data analytics. SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference 2019, APUR 2019, November. https://doi.org/10.15530/ap-urtec-2019-198240

Gomez, L. E., Shoham, O., Schmidt, Z., Chokshi, R. N., Brown, A., & Northug, T. (1999). A Unified Mechanistic Model for Steady-State Two-Phase Flow in Wellbores and Pipelines. All Days. https://doi.org/10.2118/56520-MS

Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855–868. https://doi.org/10.1109/TPAMI.2008.137

Hajizadeh, Y. (2019). Machine learning in oil and gas; a SWOT analysis approach. Journal of Petroleum Science and Engineering, 176(December 2018), 661–663. https://doi.org/10.1016/j.petrol.2019.01.113

Hassanvand, M., Moradi, S., Fattahi, M., Zargar, G., & Kamari, M. (2018). Estimation of rock uniaxial compressive strength for an Iranian carbonate oil reservoir: Modeling vs. artificial neural network application. Petroleum Research, 3(4), 336–345. https://doi.org/10.1016/J.PTLRS.2018.08.004

Hørsholt, S., Nick, H., & Jørgensen, J. B. (2019). A hierarchical multigrid method for oil production optimization. IFAC-PapersOnLine, 52(1), 492–497. https://doi.org/10.1016/j.ifacol.2019.06.110

Kabir, C. S., & Hasan, A. R. (1986). A Study of Multiphase Flow Behavior in Vertical Oil Wells: Part II-Field Application. Society of Petroleum Engineers of AIME, (Paper) SPE, 2, 479–491. https://doi.org/10.2118/15139-MS

Karakurt, I., Aydin, G., & Aydiner, K. (2012). Sources and mitigation of methane emissions by sectors: A critical review. Renewable Energy, 39(1), 40–48. https://doi.org/10.1016/j.renene.2011.09.006

Kern, C. P., & Nicholson, F. R. (1965). Practical Application of Calculated Multiphase Flowing Bottom-Hole Pressures. Journal of Petroleum Technology, 17(12), 1373–1378. https://doi.org/10.2118/1218-PA

Khudaier, Y. abbas, Kadhim, F. S., & Yousif, Y. K. (2020). Using Adaptive Neuro Fuzzy Inference System to Predict Rate of Penetration from Dynamic Elastic Properties. Journal of Engineering, 26(7), 45–61. https://doi.org/10.31026/j.eng.2020.07.04

Lin, Z., Liu, X., Lao, L., & Liu, H. (2020). Prediction of two-phase flow patterns in upward inclined pipes via deep learning. Energy, 210, 118541. https://doi.org/10.1016/j.energy.2020.118541

Liu, K., Xu, B., Kim, C., & Fu, J. (2021). Well Performance from Numerical Methods to Machine Learning Approach: Applications in Multiple Fractured Shale Reservoirs. Geofluids, 2021. https://doi.org/10.1155/2021/3169456

Liu, W., Liu, W. D., & Gu, J. (2019). Petroleum production forecasting based on machine learning. ACM International Conference Proceeding Series, 124–128. https://doi.org/10.1145/3373419.3373421

Liu, W., Liu, W. D., & Gu, J. (2020). A Machine Learning Method to Infer Inter-Well Connectivity Using Bottom-Hole Pressure Data. Journal of Energy Resources Technology, 142(10). https://doi.org/10.1115/1.4047304

Luo, G., Tian, Y., Bychina, M., & Ehlig-Economides, C. (2018). Production optimization using machine learning in bakken shale. SPE/AAPG/SEG Unconventional Resources Technology Conference 2018, URTC 2018, 2011. https://doi.org/10.15530/urtec-2018-2902505

Makhotin, I., Orlov, D., & Koroteev, D. (2022). Machine Learning to Rate and Predict the Efficiency of Waterflooding for Oil Production. Energies, 15(3). https://doi.org/10.3390/en15031199

Makinde, I., & Lee, W. J. (2019). Principal components methodology – A novel approach to forecasting production from liquid-rich shale (LRS) reservoirs. Petroleum, 5(3), 227–242. https://doi.org/10.1016/j.petlm.2018.08.002

Mamudu, A., Khan, F., Zendehboudi, S., & Adedigba, S. (2021). Dynamic risk modeling of complex hydrocarbon production systems. Process Safety and Environmental Protection, 151, 71–84. https://doi.org/10.1016/j.psep.2021.04.046

Marshall, C., & Thomas, A. (2015). Maximising economic recovery - A review of well test procedures in the North Sea. Society of Petroleum Engineers - SPE Offshore Europe Conference and Exhibition, OE 2015. https://doi.org/10.2118/175518-ms

Martyushev, D. A. (2021). Experimental study of the influence of bottomhole pressure of producing wells on reserve production from complicated carbonate reservoirs. Bulletin of the Tomsk Polytechnic University, Geo Assets Engineering, 332(5), 110–119. https://doi.org/10.18799/24131830/2021/05/3190

Memon, P. Q., Yong, S. P., Pao, W., & Pau, J. S. (2015). Dynamic well bottom-hole flowing pressure prediction based on radial basis neural network. Studies in Computational Intelligence, 591, 279–292. https://doi.org/10.1007/978-3-319-14654-6_17

Nait Amar, M., & Zeraibi, N. (2020). A combined support vector regression with firefly algorithm for prediction of bottom hole pressure. SN Applied Sciences, 2(1). https://doi.org/10.1007/S42452-019-1835-Z

Nikitin, N. O., Revin, I., Hvatov, A., Vychuzhanin, P., & Kalyuzhnaya, A. v. (2022). Hybrid and automated machine learning approaches for oil fields development: The case study of Volve field, North Sea. Computers and Geosciences, 161. https://doi.org/10.1016/j.cageo.2022.105061

Noshi, C. I., & Schubert, J. J. (2018). The role of machine learning in drilling operations; a review. SPE Eastern Regional Meeting, 2018-October. https://doi.org/10.2118/191823-18ERM-MS

Nwachukwu, A., Jeong, H., Pyrcz, M., & Lake, L. W. (2018). Fast evaluation of well placements in heterogeneous reservoir models using machine learning. Journal of Petroleum Science and Engineering, 163(December 2017), 463–475. https://doi.org/10.1016/j.petrol.2018.01.019

Orkiszewski, J., & Houston, T. (1967). Predicting Two-Phase Pressure Drops in Vertical Pipe. Journal of Petroleum Technology, 19(06), 829–838. https://doi.org/10.2118/1546-PA

Osman, E. S. A., & Aggour, M. A. (2002). Artificial neural network model for accurate prediction of pressure drop in horizontal and near-horizontal-multiphase flow. Petroleum Science and Technology, 20(1–2), 1–15. https://doi.org/10.1081/LFT-120002082

Osman, E.-S. A. (2001). Artificial Neural Networks Models for Identifying Flow Regimes and Predicting Liquid Holdup in Horizontal Multiphase Flow. All Days. https://doi.org/10.2118/68219-MS

Ossai, C. I., & Duru, U. I. (2021). Applications and theoretical perspectives of artificial intelligence in the rate of penetration. Petroleum, 8(2), 237–251. https://doi.org/10.1016/j.petlm.2020.08.004

Patel, P., Odden, H., Djoric, B., Garner, R. D., & Vea, H. K. (2014). Model based multiphase metering and production allocation. Proceedings of the Annual Offshore Technology Conference, 4, 3336–3344. https://doi.org/10.4043/25457-ms

Ponomareva, I. N., Galkin, V. I., & Martyushev, D. A. (2021). Operational method for determining bottom hole pressure in mechanized oil producing wells, based on the application of multivariate regression analysis. Petroleum Research, 6(4), 351–360. https://doi.org/10.1016/j.ptlrs.2021.05.010

Putcha, V. B., & Ertekin, T. (2018). A hybrid integrated compositional reservoir simulator coupling machine learning and hard computing protocols. Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018. https://doi.org/10.2118/192368-ms

Rathnayake, S., Rajora, A., & Firouzi, M. (2022a). A machine learning-based predictive model for real-time monitoring of flowing bottom-hole pressure of gas wells. Fuel, 317(February), 123524. https://doi.org/10.1016/j.fuel.2022.123524

Rathnayake, S., Rajora, A., & Firouzi, M. (2022b). A machine learning-based predictive model for real-time monitoring of flowing bottom-hole pressure of gas wells. Fuel, 317(February), 123524. https://doi.org/10.1016/j.fuel.2022.123524

Ristanto, T. (2018). Machine Learning Applied to Multiphase Production Problems. June, 1–71.

Salem, K. G. S. K. G., Abdulaziz, A. A. M. A. M., & Dahab, A. S. D. A. S. A. (2019). Prediction of hydraulic properties in carbonate reservoirs using artificial neural network. Society of Petroleum Engineers - Abu Dhabi International Petroleum Exhibition and Conference 2018, ADIPEC 2018. https://doi.org/10.2118/193007-MS

Sami, N. A., & Ibrahim, D. S. (2021). Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques. Petroleum Research, 6(4), 417–422. https://doi.org/10.1016/j.ptlrs.2021.05.004

Sanusi, S., Omisore, A., Blankson, E., Anyanwu, C., & Eremiokhale, O. (2021). Estimation of Bottom Hole Pressure in Electrical Submersible Pump Wells using Machine Learning Technique. Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition 2021, NAIC 2021. https://doi.org/10.2118/207122-MS

Seong, Y., Park, C., Choi, J., & Jang, I. (2020). Surrogate model with a deep neural network to evaluate gas-liquid flow in a horizontal pipe. Energies, 13(4), 1–12. https://doi.org/10.3390/en13040968

Shirangi, M. G. (2012). Applying Machine Learning Algorithms to Oil Reservoir Production Optimization. Stanford University, 1–5.

Simcenter STAR-CCM+. (2021). Segregated Flow Guidelines. STAR CCM+ Help Guide, 2.

Sina, R. G., Ani, M., Oluyemi, G., & Petrovski, A. (2016). Reservoir uncertainty analysis: The trends from probability to algorithms and machine learning. Society of Petroleum Engineers - SPE Intelligent Energy International Conference and Exhibition. https://doi.org/10.2118/181049-MS

Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021a). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379–391. https://doi.org/10.1016/j.ptlrs.2021.05.009

Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021b). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379–391. https://doi.org/10.1016/j.ptlrs.2021.05.009

Syed, F. I., Alnaqbi, S., Muther, T., Dahaghi, A. K., & Negahban, S. (2021). Smart shale gas production performance analysis using machine learning applications. Petroleum Research, xxxx. https://doi.org/10.1016/j.ptlrs.2021.06.003

Syed, F. I., AlShamsi, A., Dahaghi, A. K., & Neghabhan, S. (2021). Machine Learning techniques to Model Geomechanics and Petrophysical Properties of Shale Reservoirs – A Systematic Literature Review. Petroleum, 8. https://doi.org/10.1016/j.petlm.2020.12.001

Tadjer, A., Bratvold, R. B., Hong, A., & Hanea, R. (2021). Application of machine learning to assess the value of information in polymer flooding. Petroleum Research, 6(4), 309–320. https://doi.org/10.1016/j.ptlrs.2021.05.006

Tariq, Z. (2018). An automated flowing bottom-hole pressure prediction for a vertical well having multiphase flow using computational intelligence techniques. Society of Petroleum Engineers - SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition 2018, SATS 2018. https://doi.org/10.2118/192184-MS

Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2020a). Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques. Journal of Petroleum Exploration and Production Technology, 10(4), 1411–1428. https://doi.org/10.1007/s13202-019-0728-4

Tariq, Z., Mahmoud, M., & Abdulraheem, A. (2020b). Real-time prognosis of flowing bottom-hole pressure in a vertical well for a multiphase flow using computational intelligence techniques. Journal of Petroleum Exploration and Production Technology, 10(4), 1411–1428. https://doi.org/10.1007/s13202-019-0728-4

Tariq, Z., Mahmoud, M., Abdulraheem, A., Al-Shehri, D., Khan, M. R., & Janjua, A. N. (2018). An intelligent solution to forecast pressure drop in a vertical well having multiphase flow using functional network technique. Society of Petroleum Engineers - PAPG/SPE Pakistan Section Annual Technical Conference and Exhibition 2018, PATC 2018. https://doi.org/10.2118/195656-ms

Teixeira, A. F., & Secchi, A. R. (2019). Machine learning models to support reservoir production optimization. IFAC-PapersOnLine, 52(1), 498–501. https://doi.org/10.1016/J.IFACOL.2019.06.111

Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/J.2517-6161.1996.TB02080.X

Wang, H., Zhang, M., & Yang, Y. (2020). Machine learning for multiphase flowrate estimation with time series sensing data. Measurement: Sensors, 10–12(July), 100025. https://doi.org/10.1016/j.measen.2020.100025

Wu, B., Firouzi, M., Rufford, T. E., & Towler, B. (2019). Characteristics of counter-current gas-liquid two-phase flow and its limitations in vertical annuli. Experimental Thermal and Fluid Science, 109, 109899. https://doi.org/10.1016/J.EXPTHERMFLUSCI.2019.109899