Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach

Sunday Agbons Igbinere; İkponmwosa Ohenhen; Edobor Frankie Christopher

doi:10.54287/gujsa.1669814

Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach

Abstract

This study investigates the relationship between operational parameters like choke size etc. and sand production in some oilfields, aiming to optimize efficiency while minimizing sand-related challenges. Data visualization revealed trends in sand cut behaviour under varying gross rate, net rate, and BS&W conditions. Three machine learning models artificial neural network (ANN), Random Forest (RF) and extreme gradient boosting (XGBoost) were developed, with XGBoost achieving the highest accuracy. Extreme gradient boosting (XGBoost) outperformed the others by achieving the highest R-squared value of 0.952 and the lowest mean absolute error (MAE) and mean squared error (MSE), demonstrating its superior accuracy in predicting sand cut values. Shapley additive exPlanations (SHAP) analysis highlighted key parameters like manifold pressure, gas rate, and BS&W in predicting sand cut. Optimization using the Mealpy Genetic Algorithm yielded an optimal configuration gross rate of 750blpd, sand cut of 0.25pptb, and BS&W of 5.5%. Sensitivity analysis emphasized monitoring separator pressure and gas rate. The findings demonstrate the potential of integrating machine learning and optimization to enhance decision-making, reduce risks, and improve production efficiency. Recommendations for implementation and future research are provided to ensure sustainable operations.

Keywords

Supporting Institution

University of Benin

Ethical Statement

Academic Standards and Excellence

Thanks

A special appreciation to Dergipark and Gazi University

References

Abdelghany, W. K., Hammed, M. S., Radwan, A. E., & Nassar, T. (2022). Implications of machine learning on geomechanical characterization and sand management: A case study from Hilal Field, Gulf of Suez, Egypt. Journal of Petroleum Exploration and Production Technology. https://doi.org/10.1007/s13202-022-01551-9
Ali, M., Prasad, R., Xiang, Y. & Yaseen, Z. M. (2020). Complete Ensemble Empirical Mode Decomposition Hybridized with Random Forest and Kernel Ridge Regression Model for Monthly Rainfall Forecasts, J. Hydrol. 584(2020) 124647, https://doi.org/10.1016/J.JHYDROL.2020.124647
Al-Shaaibi, S. K., Al-Ajmi, A. M., & Al-Wahaibi, Y. (2013). Three dimensional modeling for predicting sand production. Journal of Petroleum Science and Engineering, 109, 348–363.
Alshboul, O., Shehadeh, A., Almasabha, G., & Almuflih, A. S. (2022). Extreme Gradient Boosting-Based Machine Learning Approach for Green Building Cost Prediction. Sustainability, 14(11), 6651. https://doi.org/10.3390/su14116651
Ani, M., Oluyemi, G., Petrovski, A., & Rezaei-Gomari, S. (2016). Reservoir Uncertainty Analysis: the Trends from Probability to Algorithms and Machine Learning. SPE Intelligent Energy International Conference and Exhibition, 6-8 September 2016, Aberdeen, UK.
Anifowose, F. A., Labadin, J. & Abdulraheem, A. (2017a). 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) (2017), 251-263.
Anifowose, F. A., Labadin, J. & Abdulraheem, A. (2017b). Ensemble Machine Learning: An Untapped Modeling Paradigm for Petroleum Reservoir Characterization. J. Petrol. Sci. Eng., 151(2017), 480-487.
Anirbid, S., Kriti, Y., Kamakshi, R., Namrata, B. & Hemangi, O. (2021). 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

Azad M., Zargar G. & Arabjamaloei, R. (2011). A New Approach to Sand Production Onset Prediction Using Artificial Neural Networks. Petroleum Science and Technology, 29:19, 1975-1983. https://doi.org/10.1080/10916460903551081
Carlson, J., Gurley, D., King, G., Price-Smith, C., & Waters, F. (1992). Sand Control: Why and How. Oilfield Review; Netherlands, 4(4).
Completion Technology for Unconsolidated Formations, Revision 2, (1995)
Completion Technology for Unconsolidated Formations, Revision 3, (1997). https://fr.scribed.com
Dickson, R. L., Raymond, C. A., Joe, W., & Wilkinson, C. A. (2003). Segregation of Eucalyptus Dunnii Logs using Acoustics. Forest Ecology and Management, 179(1-3), 243-251.
Fattahpour, V., Moosavi, M., & Mehranpour, M. (2012). An experimental investigation on the effect of rock strength and perforation size on sand production. Journal of Petroleum Science and Engineering, 86–87, 172–189.
Fuh, G., & Morita, N. (2013). Sand production prediction analysis of heterogeneous reservoirs for sand control and optimal well completion design. International Petroleum Technology Conference. https://doi.org/10.2523/16940-MS
Gharagheizi, F., Mohammadi, A., Arabloo, M., & Shokrollahi, A. (2017). Prediction of sand production onset in petroleum reservoirs using a reliable classification approach. Petroleum, 3(2), 280–285. https://doi.org/10.1016/j.petlm.2016.02.001
Han, G., Shepstone, K., Harmawan, I., Er, U., Jusoh, H., Lin, L. S., Pringle, D., et al. (2011). A comprehensive study of sanding rate from a gas field, from reservoir to completion, production, and surface facilities. SPE Journal, 16(2), 463–481.
van den Hoek, P. J., Hertogh, G. M. M., Kooijman, A. P., de Bree, Ph., Kenter, C. J., & Papamichos, E. (2007). A new concept of sand production prediction, theory and laboratory experiments. SPE Drilling & Completion, 15(4), 261–273.
Kanj, M. Y. & Abousleiman, Y. (1999). Realistic Sanding Predictions: A Neural Approach. Paper Presented at the SPE Annual Technical Conference and Exhibition, October 3–6, 1999. SPE-56631-MS. https://doi.org/10.2118/56631-MS
Kessler, N., Wang, Y., & Santarelli, F. J. (1993). A simplified pseudo 3D model to evaluate sand production risk in deviated cased holes. SPE Annual Technical Conference and Exhibition.
Ketmalee, T., & Bandyopadhyay, P. (2018). Application of neural network in formation failure model to predict sand production. In Offshore Technology Conference Asia 2018 (pp. 1–10). https://doi.org/10.4043/28506-ms
Khamehchi, E. Kivi, I. R. & Akbari, M. (2014). A Novel Approach to Sand Production Prediction using Artificial Intelligence. Journal of Petroleum Science and Engineering. 123(2014), 147-154. https://doi.org/10.1016/j.petrol.2014.07.033
Kozhagulova, A., Shabdirova, A., Minh, N. H., & Zhao, Y. (2021). An integrated laboratory experiment of realistic diagenesis, perforation and sand production using a large artificial sandstone specimen. Journal of Rock Mechanics and Geotechnical Engineering.
Morita, N., Whitfill, D. L., Fedde, O. P., & Lovik, T. H. (1989). Parametric study of sand-production prediction: Analytical approach. SPE Production Engineering, 4(1), 25–33. https://doi.org/10.2118/16990-PA
Ngwashi, A. R., Ogbe, D. O., & Udebhulu, D. O. (2021). Evaluation of machine-learning tools for predicting sand production. In SPE Nigeria Annual International Conference and Exhibition 2021, NAIC 2021, 1–16. https://doi.org/10.2118/207193-MS
Nosakhare, A., Igemhokhai, S., Aimhanesi, S., Ugbodu, F. & Iyore, N. (2024). Heliyon Data-Driven Intelligent Modeling, Optimization, and global sensitivity analysis of a xanthan gum biosynthesis process. Heliyon, 10(3), e25432. https://doi.org/10.1016/j.heliyon.2024.e25432
Nouri, A., Vaziri, H., Belhaj, H., & Islam, R. (2006). Sand-production prediction, a new set of criteria for modeling based on large-scale transient experiments and numerical investigation. SPE Journal, 11(2), 26–29.
Papamichos, E., & Furui, K. (2019). Analytical models for sand onset under field conditions. Journal of Petroleum Science and Engineering, 172, 171–189.
Shabdirova, A., Minh, N. H., & Zhao, Y. (2022). Role of plastic zone porosity and permeability in sand production in weak sandstone reservoirs. Underground Space (China). https://doi.org/10.1016/j.undsp.2021.10.005
Shabdirova, A., Kozhagulova, A., Samenov, Y., & others. (2024). Sand production prediction with machine learning using input variables from geological and operational conditions in the Karazhanbas oilfield, Kazakhstan. Natural Resources Research, 33, 2789–2805. https://doi.org/10.1007/s11053-024-10389-3
Sheridan, R. P., Wang, W. M., Liaw, A., Mu, J. & Gifford, E. M. (2016). Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationship. Journal of Chemical Information and Modeling, 56(12), 2353-2360. https://doi.org/10.1021/acs.jcim.6b00591
Skjaerstein, A., Tronvoll, J., Santarelli, F. J., & Joranson, H. (1997). Effect of water breakthrough on sand production, experimental and field evidence. In Proceedings - SPE Annual Technical Conference and Exhibition Pi, 565–575.
Song, J., Li, Y., Liu, S., Xiong, Y., Pang, W., He, Y. & Mu, Y. (2022). Comparison of Machine Learning Algorithms for Sand Production Prediction: An Example for a Gas-Hydrate-Bearing Sand Case. Energies 2022, 15(18), 6509. https://doi.org/10.3390/en15186509
Speiser, J. L., Miller, M. E., Tooze, J. & Ip, E. (2019). A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling. Expert Systems with Applications, 134, 93–101. https://doi.org/10.1016/j.eswa.2019.05.028
Tiab, D. & Donaldson, E. C. (2004). Petrophysics: Theory and Practice of Measuring Reservoir Rock and Fluid Transport Properties, Gulf Professional Publishing, Elsevier (2nd. Ed., pp. 554-670)
Tixier, M.P. (1949) Evaluation of Permeability from Log Resistivity Gradients. Oil and Gas Journal, 48, 113-122.
Wang, Y., & Dusseault, M. B. (2010). Sand production potential near inclined perforated wellbores. In 47th Annual Technical Meeting of the Petroleum Society. https://doi.org/10.2118/9670
Weingarten, J. S., & Perkins, T. K. (2007). Prediction of sand production in gas wells, methods and Gulf of Mexico case studies. Journal of Petroleum Technology, 47(7), 596–600.
Wu, B., Choi, S. K., Denke, R., Barton, T., Viswanathan, C., Lim, S., Zamberi, M., & Shaffee, S. (2016). A new and practical model for amount and rate of sand production. Offshore Technology Conference.

Details

Primary Language

English

Subjects

Reservoir Engineering

Journal Section

Research Article

Authors

Sunday Agbons Igbinere ^*
0000-0003-1908-389X
Nigeria

İkponmwosa Ohenhen
0000-0002-5938-8973
Nigeria

Edobor Frankie Christopher
0009-0000-5319-891X
Nigeria

Early Pub Date

June 15, 2025

Publication Date

June 30, 2025

Submission Date

April 3, 2025

Acceptance Date

May 2, 2025

Published in Issue

Year 2025 Volume: 12 Number: 2

DOI

https://doi.org/10.54287/gujsa.1669814

IZ

https://izlik.org/JA35GP54KJ

Cite

RIS / Bibtex

APA

Igbinere, S. A., Ohenhen, İ., & Christopher, E. F. (2025). Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach. Gazi University Journal of Science Part A: Engineering and Innovation, 12(2), 541-561. https://doi.org/10.54287/gujsa.1669814

AMA

1.Igbinere SA, Ohenhen İ, Christopher EF. Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach. GU J Sci, Part A. 2025;12(2):541-561. doi:10.54287/gujsa.1669814

Chicago

Igbinere, Sunday Agbons, İkponmwosa Ohenhen, and Edobor Frankie Christopher. 2025. “Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (2): 541-61. https://doi.org/10.54287/gujsa.1669814.

EndNote

Igbinere SA, Ohenhen İ, Christopher EF (June 1, 2025) Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach. Gazi University Journal of Science Part A: Engineering and Innovation 12 2 541–561.

IEEE

[1]S. A. Igbinere, İ. Ohenhen, and E. F. Christopher, “Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach”, GU J Sci, Part A, vol. 12, no. 2, pp. 541–561, June 2025, doi: 10.54287/gujsa.1669814.

ISNAD

Igbinere, Sunday Agbons - Ohenhen, İkponmwosa - Christopher, Edobor Frankie. “Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach”. Gazi University Journal of Science Part A: Engineering and Innovation 12/2 (June 1, 2025): 541-561. https://doi.org/10.54287/gujsa.1669814.

JAMA

1.Igbinere SA, Ohenhen İ, Christopher EF. Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach. GU J Sci, Part A. 2025;12:541–561.

MLA

Igbinere, Sunday Agbons, et al. “Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 2, June 2025, pp. 541-6, doi:10.54287/gujsa.1669814.

Vancouver

1.Sunday Agbons Igbinere, İkponmwosa Ohenhen, Edobor Frankie Christopher. Optimizing Choke Size to Minimize Sand Production in Oil Wells: A Machine Learning Approach. GU J Sci, Part A. 2025 Jun. 1;12(2):541-6. doi:10.54287/gujsa.1669814