DOI : https://doi.org/10.18517/ijaseit.14.6.11982

Artificial Intelligence Models for Flowing Bottomhole Pressure Estimation: State-of-the-Art and Proposed Future Research Directions

Okorie Ekwe Agwu (1), Saad Alatefi (2), Ahmad Alkouh (3), Reda Azim (4)
(1) Universiti Teknologi PETRONAS, Petroleum Engineering Department, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
(2) Department of Petroleum Engineering Technology, College of Petroleum Studies, Paaet, Kuwait City, Kuwait
(3) Department of Petroleum Engineering Technology, College of Petroleum Studies, Paaet, Kuwait City, Kuwait
(4) Petroleum Engineering Department, American University of Kurdistan, Sumel, Iraq
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How to cite (IJASEIT) :
Agwu , Okorie Ekwe, et al. “Artificial Intelligence Models for Flowing Bottomhole Pressure Estimation: State-of-the-Art and Proposed Future Research Directions”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 1868-79, doi:10.18517/ijaseit.14.6.11982.
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Flowing bottom hole pressure (FBHP) is an important parameter during evaluation of conventional and unconventional oil and gas resources and is mainly used for production optimization, calculation of productivity index and assessment of well performance. FBHP in an oil well is a multifaceted parameter that has a number of factors affecting it. It is characterized by high stochasticity, non-linearity and non-stationarity. Traditionally, production engineers rely on physics-based models and empirical correlations calibrated with measurements from offset wells to calculate FBHP. However, in recent times, there has been a significant shift towards the use of cutting-edge artificial intelligence (AI) algorithms. The present study is designed to provide a historical account of past and present models developed with AI algorithms for estimating FBHP. To achieve this, a deep bibliographic survey was conducted using various peer reviewed journals and relevant oil and gas conference papers. The results of the review have been presented in tables to avoid ambiguity. To make the review novel, the merits and demerits of each of the AI models for FBHP prediction are highlighted and discussed in detail.  In this direction 54 models were isolated from the literature. The findings indicate that artificial neural network is the preferred algorithm by several researchers. However, the transparency and interpretability issues associated with the neural network algorithm has propelled researchers to explore the possibility of deploying physics informed machine learning techniques to model FBHP. This review would serve as a valuable reference for production engineers seeking information on AI models for FBHP estimation.

N. A. Sami and D. S. Ibrahim, “Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques,” Petroleum Research, vol. 6, no. 4, pp. 417–422, Dec. 2021, doi: 10.1016/j.ptlrs.2021.05.004.

I. N. Ponomareva, V. I. Galkin, and D. A. Martyushev, “Operational method for determining bottom hole pressure in mechanized oil producing wells, based on the application of multivariate regression analysis,” Petroleum Research, vol. 6, no. 4, pp. 351–360, Dec. 2021, doi: 10.1016/j.ptlrs.2021.05.010.

M. A. Ahmadi, M. Galedarzadeh, and S. R. Shadizadeh, “Low parameter model to monitor bottom hole pressure in vertical multiphase flow in oil production wells,” Petroleum, vol. 2, no. 3, pp. 258–266, Sep. 2016, doi: 10.1016/j.petlm.2015.08.001.

D.A.Martyushev, “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, vol. 332, no. 5, pp.110-119, 2021.

Z. Tariq, M. Mahmoud, and A. Abdulraheem, “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, vol. 10, no. 4, pp. 1411–1428, Jul. 2019, doi: 10.1007/s13202-019-0728-4.

T. Bikmukhametov and J. Jäschke, “Oil Production Monitoring using Gradient Boosting Machine Learning Algorithm,” IFAC-PapersOnLine, vol. 52, no. 1, pp. 514–519, 2019, doi:10.1016/j.ifacol.2019.06.114.

S. Baki and S. Dursun, “Flowing Bottomhole Pressure Prediction with Machine Learning Algorithms for Horizontal Wells,” SPE Annual Technical Conference and Exhibition, Houston, Texas, USA, Sep. 2022, doi:10.2118/210235-ms.

M. A. Ahmadi and Z. Chen, “Machine learning models to predict bottom hole pressure in multi‐phase flow in vertical oil production wells,” The Canadian Journal of Chemical Engineering, vol. 97, no. 11, pp. 2928–2940, Aug. 2019, doi: 10.1002/cjce.23526.

O. E. Agwu, E. E. Okoro, and S. E. Sanni, “Modelling oil and gas flow rate through chokes: A critical review of extant models,” Journal of Petroleum Science and Engineering, vol. 208, p. 109775, Jan. 2022, doi: 10.1016/j.petrol.2021.109775.

C. P. Vieira and L. A. Digiampietri, “Machine Learning post-hoc interpretability: a systematic mapping study,” Proceedings of the XVIII Brazilian Symposium on Information Systems, pp. 1–8, May 2022, doi:10.1145/3535511.3535512.

W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proceedings of the National Academy of Sciences, vol. 116, no. 44, pp. 22071–22080, Oct. 2019, doi: 10.1073/pnas.1900654116.

A. Vellido, J. D. Martín-Guerrero and P. J. G. Lisboa, "Making machine learning models interpretable", European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges (Belgium), 25-27 April 2012. pp. 163-172, Apr. 2012.

C. Molnar, Interpretable Machine Learning: A Guide for Making Black Box Models Explainable, Jun. 2018, [online] Available: https://christophm.github.io/interpretable-ml-book/.

M.A. Ayoub, “Development and testing of an artificial neural network model for predicting bottomhole pressure in vertical multiphase flow”. MSc Thesis, Department of Petroleum Engineering, King Fahd University of Petroleum and Minerals, Saudi Arabia, 2004. [Online]. Available: https://eprints.kfupm.edu.sa/id/eprint/9768/. [Accessed: Apr. 9, 2024].

F. H. Al Shehri, A. Gryzlov, T. Al Tayyar, and M. Arsalan, “Utilizing Machine Learning Methods to Estimate Flowing Bottom-Hole Pressure in Unconventional Gas Condensate Tight Sand Fractured Wells in Saudi Arabia,” Paper presented at the SPE Russian Petroleum Technology Conference, Virtual, October 29, 2020, Oct. 2020, doi:10.2118/201939-ms.

M. Zolfagharroshan and E. Khamehchi, “Accurate artificial intelligence-based methods in predicting bottom-hole pressure in multiphase flow wells, a comparison approach,” Arabian Journal of Geosciences, vol. 14, no. 4, Feb. 2021, doi: 10.1007/s12517-021-06661-y.

E. E. Okoro, S. E. Sanni, T. Obomanu, and P. Igbinedion, “Predicting the effects of selected reservoir petrophysical properties on bottomhole pressure via three computational intelligence techniques,” Petroleum Research, vol. 8, no. 1, pp. 118–129, Mar. 2023, doi:10.1016/j.ptlrs.2022.07.001.

C. C. Nwanwe, U. I. Duru, C. Anyadiegwu, and A. I. B. Ekejuba, “An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores,” Petroleum Research, vol. 8, no. 3, pp. 370–385, Sep. 2023, doi:10.1016/j.ptlrs.2022.10.004.

C. C. Nwanwe and U. I. Duru, “An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores,” Petroleum, vol. 9, no. 4, pp. 629–646, Dec. 2023, doi: 10.1016/j.petlm.2023.03.003.

T. Zhao, Y. Zheng, and Z. Wu, “Improving computational efficiency of machine learning modeling of nonlinear processes using sensitivity analysis and active learning,” Digital Chemical Engineering, vol. 3, p. 100027, Jun. 2022, doi: 10.1016/j.dche.2022.100027.

E. A. Osman, M. A. Ayoub, and M. A. Aggour, “Artificial Neural Network Model for Predicting Bottomhole Flowing Pressure in Vertical Multiphase Flow,” SPE Middle East Oil and Gas Show and Conference, Kingdom of Bahrain, Mar. 2005, doi: 10.2118/93632-ms.

A. Althnian et al., “Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain,” Applied Sciences, vol. 11, no. 2, p. 796, Jan. 2021, doi:10.3390/app11020796.

S. Alatefi, O. E. Agwu, R. A. Azim, A. Alkouh, and I. Dzulkarnain, “Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure,” Chemical Engineering Research and Design, vol. 205, pp. 672–694, May 2024, doi: 10.1016/j.cherd.2024.04.033.

M. Firouzi and S. Rathnayake, “Prediction of the Flowing Bottom-Hole Pressure Using Advanced Data Analytics,” Proceedings of the SPE/AAPG/SEG Asia Pacific Unconventional Resources Technology Conference, Brisbane, Australia, 2019, doi: 10.15530/ap-urtec-2019-198240.

Q.-F. Di, W. Chen, J.-N. Zhang, W.-C. Wang, and H.-J. Chen, “An Artificial Intelligence Prediction Method of Bottomhole Flowing Pressure for Gas Wells Based on Support Vector Machine,” Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016), 2017, doi: 10.2991/mme-16.2017.28.

O. Gómez-Carmona, D. Casado-Mansilla, F. A. Kraemer, D. López-de-Ipiña, and J. García-Zubia, “Exploring the computational cost of machine learning at the edge for human-centric Internet of Things,” Future Generation Computer Systems, vol. 112, pp. 670–683, Nov. 2020, doi: 10.1016/j.future.2020.06.013.

P. Spesivtsev et al., “Predictive model for bottomhole pressure based on machine learning,” Journal of Petroleum Science and Engineering, vol. 166, pp. 825–841, Jul. 2018, doi: 10.1016/j.petrol.2018.03.046.

D. I. Ignatov, K. Sinkov, P. Spesivtsev, I. Vrabie, and V. Zyuzin, “Tree-Based Ensembles for Predicting the Bottomhole Pressure of Oil and Gas Well Flows,” Analysis of Images, Social Networks and Texts, pp. 221–233, 2018, doi: 10.1007/978-3-030-11027-7_22.

X. Li, J. L. Miskimins, and B. T. Hoffman, “A Combined Bottom-hole Pressure Calculation Procedure Using Multiphase Correlations and Artificial Neural Network Models,” SPE Annual Technical Conference and Exhibition, Amsterdam, The Netherlands, Oct. 2014, doi:10.2118/170683-ms.

C. Zhang et al., “Bottom hole pressure prediction based on hybrid neural networks and Bayesian optimization,” Petroleum Science, vol. 20, no. 6, pp. 3712–3722, Dec. 2023, doi:10.1016/j.petsci.2023.07.009.

L. Goliatt, R. S. Mohammad, S. I. Abba, and Z. M. Yaseen, “Development of hybrid computational data-intelligence model for flowing bottom-hole pressure of oil wells: New strategy for oil reservoir management and monitoring,” Fuel, vol. 350, p. 128623, Oct. 2023, doi: 10.1016/j.fuel.2023.128623.

P. Q. Memon, S.-P. Yong, W. Pao, and J. S. Pau, “Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network,” Intelligent Systems in Science and Information 2014, pp. 279–292, 2015, doi: 10.1007/978-3-319-14654-6_17.

R. Yin and S. Zhang, “A novel method for prediction of flowing pressure of multi-stage fracturing horizontal well,” AIP Advances, vol. 11, no. 7, Jul. 2021, doi: 10.1063/5.0057337.

S. A. Marfo, S. Asante-Okyere, and Y. Y. Ziggah, “A new flowing bottom hole pressure prediction model using M5 prime decision tree approach,” Modeling Earth Systems and Environment, vol. 8, no. 2, pp. 2065–2073, Jun. 2021, doi: 10.1007/s40808-021-01211-7.

O. E. Agwu, K. A. Elraies, A. Alkouh, and S. Alatefi, “Mathematical modelling of drilling mud plastic viscosity at downhole conditions using multivariate adaptive regression splines,” Geoenergy Science and Engineering, vol. 233, p. 212584, Feb. 2024, doi:10.1016/j.geoen.2023.212584.

M. Mohammadpoor, Kh. Shahbazi, F. Torabi, and A. Qazvini, “A New Methodology for Prediction of Bottomhole Flowing Pressure in Vertical Multiphase Flow in Iranian Oil Fields Using Artificial Neural Networks (ANNs),” SPE Latin American and Caribbean Petroleum Engineering Conference, Lima, Peru, Dec. 2010, doi:10.2118/139147-ms.

R. Ashena, J. Moghadasi, A. Ghalambor, M. Bataee, R. Ashena, and A. Feghhi, “Neural Networks in BHCP Prediction Performed Much Better Than Mechanistic Models,” International Oil and Gas Conference and Exhibition in China, Jun. 2010, doi: 10.2118/130095-ms.

M. Romero-Salcedo, J. Ramirez-Sabag, H. Lopez, D. A. Hernandez, and R. Ramirez, “Analysis of Prediction of Pressure Data in Oil Wells Using Artificial Neural Networks,” 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference, pp. 51–55, Sep. 2010, doi: 10.1109/cerma.2010.17.

A. Al-Shammari, “Accurate Prediction of Pressure Drop in Two-Phase Vertical Flow Systems using Artificial Intelligence,” SPE/DGS Saudi Arabia Section Technical Symposium and Exhibition, Al-Khobar, Saudi Arabia, May 2011, doi: 10.2118/149035-ms.

R. Ashena and J. Moghadasi, “Bottom hole pressure estimation using evolved neural networks by real coded ant colony optimization and genetic algorithm,” Journal of Petroleum Science and Engineering, vol. 77, no. 3–4, pp. 375–385, Jun. 2011, doi:10.1016/j.petrol.2011.04.015.

I. Jahanandish, B. Salimifard, and H. Jalalifar, “Predicting bottomhole pressure in vertical multiphase flowing wells using artificial neural networks,” Journal of Petroleum Science and Engineering, vol. 75, no. 3–4, pp. 336–342, Jan. 2011, doi: 10.1016/j.petrol.2010.11.019.

R. Nasimi, R. Irani, and B. Moradi, “An Improved Ant Colony Algorithm–Based ANN for Bottom Hole Pressure Prediction in Underbalanced Drilling,” Petroleum Science and Technology, vol. 30, no. 13, pp. 1307–1316, Apr. 2012, doi:10.1080/10916466.2010.497790.

X. Li, “A combined bottom-hole pressure calculation procedure using multiphase correlations and artificial neural network models”. MSc Thesis, Dept. of Petroleum Eng., Colorado school of Mines, USA, 2013. [Online]. Available: https://hdl.handle.net/11124/79529 [Accessed: Apr. 9, 2024].

A. R. Adebayo, A. Abdulraheem, and A. T. Al-Shammari, “Promises of Artificial Intelligence Techniques in Reducing Errors in Complex Flow and Pressure Losses Calculations in Multiphase Fluid Flow in Oil Wells,” SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, Aug. 2013, doi: 10.2118/167505-ms.

O. Bello and T. Asafa, “A Functional Networks Softsensor for Flowing Bottomhole Pressures and Temperatures in Multiphase Flow Production Wells,” SPE Intelligent Energy Conference & Exhibition, Utrecht, The Netherlands, Apr. 2014, doi: 10.2118/167881-ms.

M. A. Ayoub, B. M. Negash, and I. M. Saaid, “Modeling Pressure Drop in Vertical Wells Using Group Method of Data Handling (GMDH) Approach,” ICIPEG 2014, pp. 69–78, 2015, doi: 10.1007/978-981-287-368-2_6.

M.A.M. Awad, “Pressure Drop in Vertical Multiphase Flow using Neuro Fuzzy Technique; A Comparative Approach”. Bachelor of Engineering project, Dept of Pet. Eng., Universiti Teknologi PETRONAS, Malaysia 2014. [Online]. Available: https://utpedia.utp.edu.my/id/eprint/14569/, [Accessed: Apr. 9, 2024].

M. Awadalla and H. Yousef, “Neural Networks for Flow Bottom Hole Pressure Prediction,” International Journal of Electrical and Computer Engineering (IJECE), vol. 6, no. 4, p. 1839, Aug. 2016, doi: 10.11591/ijece.v6i4.pp1839-1856.

E. A. Antonelo, E. Camponogara, and B. Foss, “Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells,” Neural Networks, vol. 85, pp. 106–117, Jan. 2017, doi:10.1016/j.neunet.2016.09.009.

W. Chen, Q. Di, F. Ye, J. Zhang, and W. Wang, “Flowing bottomhole pressure prediction for gas wells based on support vector machine and random samples selection,” International Journal of Hydrogen Energy, vol. 42, no. 29, pp. 18333–18342, Jul. 2017, doi:10.1016/j.ijhydene.2017.04.134.

M. Bahaa, E. Shokir, and I. Mahgoub, “Soft Computation Application: Utilizing Artificial Neural Network to Predict the Fluid Rate and Bottom Hole Flowing Pressure for Gas-lifted Oil Wells,” Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, November 13, 2018, Nov. 2018, doi: 10.2118/193052-ms.

Z. Tariq, “An Automated Flowing Bottom-Hole Pressure Prediction for a Vertical Well Having Multiphase Flow Using Computational Intelligence Techniques,” SPE Kingdom of Saudi Arabia Annual Technical Symposium and Exhibition, Dammam, Saudi Arabia, Apr. 2018, doi: 10.2118/192184-ms.

O. Akinsete and B. A. Adesiji, “Bottom-Hole Pressure Estimation from Wellhead Data Using Artificial Neural Network,” Nigeria Annual International Conference and Exhibition held in Lagos, Nigeria, August 07, 2019, Aug. 2019, doi: 10.2118/198762-ms.

M. Nait Amar and N. Zeraibi, “A combined support vector regression with firefly algorithm for prediction of bottom hole pressure,” SN Applied Sciences, vol. 2, no. 1, Dec. 2019, doi: 10.1007/s42452-019-1835-z.

E. S. Baryshnikov et al., “Adaptation of Steady-State Well Flow Model on Field Data for Calculating the Flowing Bottomhole Pressure,” SPE Russian Petroleum Technology Conference, Virtual, October 27, 2020, Oct. 2020, doi: 10.2118/201942-ms.

B. Zhang, J. Wang, and N. Zhang, “New Method for Flow Rate and Bottom-Hole Pressure Prediction Based on Support Vector Regression,” Proceedings of the International Field Exploration and Development Conference 2019, pp. 3812–3829, 2020, doi:10.1007/978-981-15-2485-1_345.

S. Krishna, S. Ridha, and P. Vasant, “Prediction of Bottom-Hole Pressure Differential During Tripping Operations Using Artificial Neural Networks (ANN),” Intelligent Computing and Innovation on Data Science, pp. 379–388, 2020, doi: 10.1007/978-981-15-3284-9_41.

E. Khamehchi and A. Bemani, “Prediction of pressure in different two-phase flow conditions: Machine learning applications,” Measurement, vol. 173, p. 108665, Mar. 2021, doi:10.1016/j.measurement.2020.108665.

H. Liang, G. Liu, J. Zou, J. Bai, and Y. Jiang, “Research on calculation model of bottom of the well pressure based on machine learning,” Future Generation Computer Systems, vol. 124, pp. 80–90, Nov. 2021, doi: 10.1016/j.future.2021.05.011.

E. Eltahan, R. Ganjdanesh, W. Yu, K. Sepehrnoori, R. Williams, and J. Nohavitsa, “Machine Learning Approach to Improve Calculated Bottom-hole Pressure,” SPE/AAPG/SEG Unconventional Resources Technology Conference, Houston, Texas, USA, 2021, doi:10.15530/urtec-2021-5645.

D. Molinari and S. Sankaran, “Merging Physics and Data-Driven Methods for Field-Wide Bottomhole Pressure Estimation in Unconventional Wells,” Proceedings of the 9th Unconventional Resources Technology Conference, 2021, doi: 10.15530/urtec-2021-5261.

R. Gorbachev, A. Gubaev, A. Lubnin, A. Chorny, and V. Kurbanov, “Selecting and Modifying Multiphase Correlations for Gas-Lift Wells Using Machine Learning Algorithms,” SPE Russian Petroleum Technology Conference, Virtual, October 14, 2021, Oct. 2021, doi: 10.2118/206531-ms.

S. Rathnayake, A. Rajora, and M. Firouzi, “A machine learning-based predictive model for real-time monitoring of flowing bottom-hole pressure of gas wells,” Fuel, vol. 317, p. 123524, Jun. 2022, doi: 10.1016/j.fuel.2022.123524.

O. Olamigoke and D. C. Onyeali, “Machine learning prediction of bottomhole flowing pressure as a time series in the volve field,” International Journal of Frontiers in Engineering and Technology Research, vol. 2, no. 2, pp. 020–039, Jun. 2022, doi: 10.53294/ijfetr.2022.2.2.0039.

Z. Zhu et al., “A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling,” Applied Sciences, vol. 12, no. 13, p. 6728, Jul. 2022, doi: 10.3390/app12136728.

M. Jin, H. Emami-Meybodi, and M. Ahmadi, “Flowing Bottomhole Pressure During Gas Lift in Unconventional Oil Wells,” SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, Oct. 2023, doi: 10.2118/214832-ms.

H. Sun, Q. Luo, Z. Xia, Y. Li, and Y. Yu, “Bottomhole Pressure Prediction of Carbonate Reservoirs Using XGBoost,” Processes, vol. 12, no. 1, p. 125, Jan. 2024, doi: 10.3390/pr12010125.

M. Jin, H. Emami-Meybodi, and M. Ahmadi, “Flowing Bottomhole Pressure during Gas Lift in Unconventional Oil Wells,” SPE Journal, pp. 1–13, Feb. 2024, doi: 10.2118/214832-pa.

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