Result: Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data.
Title:
Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data.
Authors:
Xun Z; China University of Geosciences (Beijing), Beijing, 100083, China. Zhiwei0914@outlook.com., Altalbawy FMA; Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia., Kanjariya P; Department of Physics, Marwadi University Research Center, Faculty of Science, Marwadi University, Rajkot, Gujarat, India., Manjunatha R; Department of Data Analytics and Mathematical Sciences, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India., Shit D; Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India., Nirmala M; Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India., Sharma A; Department of Applied Sciences-Mathematics, NIMS Institute of Engineering and Technology, NIMS University Rajasthan, Jaipur, India., Hota S; Department of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India., Shomurotova S; Department of Chemistry Teaching Methods, Tashkent State Pedagogical University Named After Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan., Sead FF; Department of Dentistry, College of Dentistry, The Islamic University, Najaf, Iraq.; Department of Medical Analysis, Medical Laboratory Technique College, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq.; Department of Medical Analysis, Medical Laboratory Technique College, The Islamic University of Babylon, Babylon, Iraq., Abbasi H; Chemistry Department, Herat University, Herat, Afghanistan. hojjatabbasimeybodi@gmail.com., Alam MM; Central Labs, King Khalid University, AlQura'a, P.O. Box 960, Abha, Saudi Arabia.; Department of Basic Medical Sciences, College of Applied Medical Science, King Khalid University, 61421, Abha, Saudi Arabia.
Source:
Scientific reports [Sci Rep] 2025 Jul 16; Vol. 15 (1), pp. 25825. Date of Electronic Publication: 2025 Jul 16.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Taha, A. A. & Malebary, S. J. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access 8, 25579–25587 (2020).
Cha, G.-W., Moon, H.-J. & Kim, Y.-C. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph18168530 (2021). (PMID: 348860508657383)
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Gouda, S. K. & Mehta, A. K. Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm. Int. J. Inf. Technol. 14(4), 2171–2182 (2022).
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Bangtang, Y. I. N. et al. Deformation and migration characteristics of bubbles moving in gas-liquid countercurrent flow in annulus. Pet. Explor. Dev. 52(2), 471–484 (2025).
Cao, D. et al. Correction of linear fracture density and error analysis using underground borehole data. J. Struct. Geol. 184, 105152 (2024).
Yin, B. et al. An experimental and numerical study of gas-liquid two-phase flow moving upward vertically in larger annulus. Eng. Appl. Comput. Fluid Mech. 19(1), 2476605 (2025).
Kim, S., Kim, T.-W. & Jo, S. Artificial intelligence in geoenergy: Bridging petroleum engineering and future-oriented applications. J. Petrol. Explor. Prod. Technol. 15(2), 35 (2025).
Alakbari, F. S. et al. Prediction of Poisson’s ratio for a petroleum engineering application: Machine learning methods. PLoS ONE 20(2), e0317754 (2025). (PMID: 3998295111844917)
Honarvar, B. et al. Smart water effects on a crude oil-brine-carbonate rock (CBR) system: Further suggestions on mechanisms and conditions. J. Mol. Liq. 299, 112173 (2020).
Gomez, S., Mansi, M. & Fahes, M. Quantifying the non-monotonic effect of salinity on water-in-oil emulsions towards a better understanding of low-salinity-water/oil/rock interactions. In Abu Dhabi International Petroleum Exhibition & Conference D031S088R002 (2018).
Nasr-El-Din, H. A. et al. Field treatment to stimulate an oil well in an offshore sandstone reservoir using a novel, low-corrosive, environmentally friendly fluid. J. Can. Pet. Technol. 54(05), 289–297 (2015).
Sualihu, M. A. et al. Financial planning and forecasting in the oil and gas industry. In The Economics of the Oil and Gas Industry 180–199 (Routledge, 2023).
Zhang, J. et al. Integrating petrophysical, hydrofracture, and historical production data with self-attention-based deep learning for shale oil production prediction. SPE J. 29(12), 6583–6604 (2024).
Hasankhani, G. M. et al. Experimental investigation of asphaltene-augmented gel polymer performance for water shut-off and enhancing oil recovery in fractured oil reservoirs. J. Mol. Liq. 275, 654–666 (2019).
Abbasi, P., Aghdam, S. K. Y. & Madani, M. Modeling subcritical multi-phase flow through surface chokes with new production parameters. Flow Meas. Instrum. 89, 102293 (2023).
Khezerlooe-ye Aghdam, S. et al. Mechanistic assessment of Seidlitzia Rosmarinus-derived surfactant for restraining shale hydration: A comprehensive experimental investigation. Chem. Eng. Res. Des. 147, 570–578 (2019).
Alkouh, A. et al. Explicit data-based model for predicting oil-based mud viscosity at downhole conditions. ACS Omega 9(6), 6684–6695 (2024). (PMID: 3837184210870379)
Chen, S.-S. & Chen, H.-C. Oil prices and real exchange rates. Energy Econ. 29(3), 390–404 (2007).
El-Sebakhy, E. A. Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme. J. Petrol. Sci. Eng. 64(1), 25–34 (2009).
Agwu, O. E. et al. Carbon capture using ionic liquids: An explicit data driven model for carbon (IV) oxide solubility estimation. J. Clean. Prod. 472, 143508 (2024).
Agwu, O. E., et al. Applications of artificial intelligence algorithms in artificial lift systems: A critical review. In Flow Measurement and Instrumentation 102613 (2024).
Espinoza, R. Digital oil field powered with new empirical equations for oil rate prediction. In SPE Middle East Intelligent Oil and Gas Conference and Exhibition (2015).
Kargarpour, M. A. Oil and gas well rate estimation by choke formula: Semi-analytical approach. J. Petrol. Explor. Prod. Technol. 9(3), 2375–2386 (2019).
Farag, W. A. Virtual multiphase flow meter for high gas/oil ratios and water-cut reservoirs via ensemble machine learning. Exp. Comput. Multiphase Flow 8, 1–16 (2025).
Souza, B. G. Jr., da Fontoura, S. A. B. & Inoue, N. Adaptive criterion for iterative hydromechanical coupling in black-oil reservoir using pseudocompressibility. Int. J. Geomech. 25(5), 04025066 (2025).
Abugoffa, R. H., Almabruk, A. A. & Abozaid, H. H. Troubleshooting Techniques for Electric Submersible Pumps (ESPs).
Agwu, O. E. et al. Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps. J. Petrol. Explor. Prod. Technol. 14(5), 1205–1233 (2024).
Jiang, Y. et al. Predicting gas flow rates of wellhead chokes based on a cascade forwards neural network with a historically limited penetrable visibility graph. Appl. Intell. 55(6), 1–17 (2025).
Kurtz, P. W. et al. Low-energy electron beam modification of metallic biomaterial surfaces: Oxygen and silicon-rich amorphous carbon as a wear-resistant coating. J. Biomed. Mater. Res. Part A 113(2), e37849 (2025).
Sun, H. et al. Theoretical and numerical methods for predicting the structural stiffness of unbonded flexible riser for deep-sea mining under axial tension and internal pressure. Ocean Eng. 310, 118672 (2024).
Yanchun, L. I. et al. Surrogate model for reservoir performance prediction with time-varying well control based on depth generative network. Pet. Explor. Dev. 51(5), 1287–1300 (2024).
Yu, H. et al. Modeling thermal-induced wellhead growth through the lifecycle of a well. Geoenergy Sci. Eng. 241, 213098 (2024).
Yang, M. et al. Probing structural modification of milk proteins in the presence of pepsin and/or acid using small-and ultra-small-angle neutron scattering. Food Hydrocolloids 159, 110681 (2025).
Agwu, O. E. et al. Modelling the flowing bottom hole pressure of oil and gas wells using multivariate adaptive regression splines. J. Petrol. Explor. Prod. Technol. 15(2), 22 (2025).
Schlussel, E. J. et al. Flow characteristics in an optically accessible solid fuel scramjet. J. Propul. Power 8, 1–10 (2025).
Paxton, B. T., Sykes, J. & Rankin, B. A. Pattern Factor and Combustion Efficiency Measurements in a Full-Annular Partially Premixed Pre-Vaporized Small-Scale Combustor.
Hastie, T., et al., Ensemble learning. In The Elements of Statistical Learning: Data Mining, Inference, and Prediction 605–624 (2009).
Luna, J. M. et al. Building more accurate decision trees with the additive tree. Proc. Natl. Acad. Sci. 116(40), 19887–19893 (2019). (PMID: 315272806778203)
Zulfiqar, H. et al. Identification of cyclin protein using gradient boost decision tree algorithm. Comput. Struct. Biotechnol. J. 19, 4123–4131 (2021). (PMID: 345271868346528)
Ayyadevara, V. K. Gradient boosting machine. In Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R 117–134 (Apress, 2018).
Fan, J. et al. Light gradient boosting machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data. Agric. Water Manag. 225, 105758 (2019).
Taha, A. A. & Malebary, S. J. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine. IEEE Access 8, 25579–25587 (2020).
Cha, G.-W., Moon, H.-J. & Kim, Y.-C. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph18168530 (2021). (PMID: 348860508657383)
AlKhulaifi, D. et al. An overview of self-adaptive differential evolution algorithms with mutation strategy. Math. Modell. Eng. Problems 9(4), 84 (2022).
Srivastava, G. & Pradhan, N. Handling imbalanced class in melanoma: Kemeny-Young rule based optimal rank aggregation and self-adaptive differential evolution optimization. Eng. Appl. Artif. Intell. 125, 106738 (2023).
Brest, J., Maučec, M. S. & Bošković. B. Self-Adaptive Differential Evolution Algorithm with Population Size Reduction for Single Objective Bound-Constrained Optimization: Algorithm j21. IEEE.
Fister, I. et al. Design and implementation of parallel self-adaptive differential evolution for global optimization. Logic J. IGPL 31(4), 701–721 (2023).
Gouda, S. K. & Mehta, A. K. Software cost estimation model based on fuzzy C-means and improved self adaptive differential evolution algorithm. Int. J. Inf. Technol. 14(4), 2171–2182 (2022).
Mohaideen Abdul Kadhar, K. et al. Parameter evaluation of a nonlinear Muskingum model using a constrained self-adaptive differential evolution algorithm. Water Pract. Technol. 17(11), 2396–2407 (2022).
Yang, Z., Tang, K. & Yao, X. Self-Adaptive Differential Evolution with Neighborhood Search. IEEE.
Deng, W. et al. An improved self-adaptive differential evolution algorithm and its application. Chemom. Intell. Lab. Syst. 128, 66–76 (2013).
Fan, Q. & Yan, X. Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Trans. Cybern. 46(1), 219–232 (2015). (PMID: 25775502)
Hansen, N., Arnold, D. V. & Auger, A. Evolution Strategies. Springer Handbook of Computational Intelligence 871–898 (2015).
Beyer, H.-G. & Schwefel, H.-P. Evolution strategies–a comprehensive introduction. Nat. Comput. 1, 3–52 (2002).
Sui, X., Chen, Q. & Gu, G. Adaptive bias voltage driving technique of uncooled infrared focal plane array. Optik 124(20), 4274–4277 (2013).
Zhao, L.-C. et al. Fast and sensitive LC-DAD-ESI/MS method for analysis of Saikosaponins c, a, and d from the roots of Bupleurum falcatum (Sandaochaihu). Molecules 16(2), 1533–1543 (2011). (PMID: 213178436259614)
Zhu, B., et al. KNN-Based Single Crystal High Frequency Transducer for Intravascular Photoacoustic Imaging. IEEE.
Fang, T. et al. Multi-scale mechanics of submerged particle impact drilling. Int. J. Mech. Sci. 285, 109838 (2025).
Zhang, L. et al. Seepage characteristics of broken carbonaceous shale under cyclic loading and unloading conditions. Energy Fuels 38(2), 1192–1203 (2023).
Mezura-Montes, E. & Coello, C. A. C. An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int. J. Gen. Syst. 37(4), 443–473 (2008).
Jiang, L. et al. Improving tree augmented Naive Bayes for class probability estimation. Knowl. Based Syst. 26, 239–245 (2012).
Ament, S. et al. Unexpected improvements to expected improvement for bayesian optimization. Adv. Neural. Inf. Process. Syst. 36, 20577–20612 (2023).
Laitila, P. & Virtanen, K. Improving construction of conditional probability tables for ranked nodes in Bayesian networks. IEEE Trans. Knowl. Data Eng. 28(7), 1691–1705 (2016).
Alatefi, S., Agwu, O. E. & Alkouh, A. Explicit and explainable artificial intelligent model for prediction of CO
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Contributed Indexing:
Keywords: Choke flow modeling; Crude oil production; Machine learning; Optimization; SHAP analysis
Entry Date(s):
Date Created: 20250716 Latest Revision: 20250716
Update Code:
20250717
DOI:
10.1038/s41598-025-10060-8
PMID:
40670502
Database:
MEDLINE
Further information
Accurate prediction of oil production rates through wellhead chokes is critical for optimizing crude oil production and operational efficiency in the petroleum industry. The central thrust of this investigation involves the systematic creation of machine learning (ML) paradigms for the robust prediction of choke flow performance. This endeavor is rigorously informed by comprehensive data acquired from an operational petroleum production facility in the Middle East. Within the dataset, produced gas-oil ratio (GOR), choke size, basic sediment and water (BS&W), wellhead pressure (THP), and crude oil API stand out as key parameters. Each plays a vital role in forecasting the oil production rate. To ensure reliability, robust data preprocessing was conducted using the Monte Carlo outlier detection (MCOD) method to recognize and manage data outliers. The models were trained using 198 data points, employing K-fold cross-validation (five folds) to ensure generalization. Gradient boosting machine (GBM) models were optimized using advanced algorithms like self-adaptive differential evolution (SADE), evolution strategy (ES), Bayesian probability improvement (BPI), and Batch Bayesian optimization (BBO). Among these, SADE demonstrated superior performance based on metrics such as average absolute relative error (AARE%), R <sup>2</sup> , and mean squared error (MSE). Furthermore, SHAP (SHapley Additive exPlanations) analysis was used to interpret the models and highlight the dominant influence of choke size and THP on the predictions. Overall, this research work presents a data-driven framework for highly accurate and interpretable predictions, significantly contributing to production optimization initiatives in the oil and gas sector.
(© 2025. The Author(s).)
Declarations. Competing interests: The authors declare no competing interests.