Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data.

Journal: Scientific reports
Published Date: Jul 16, 2025

Abstract

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, 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.

Authors

  • Zhiwei Xun
    China University of Geosciences (Beijing), Beijing, 100083, China. Zhiwei0914@outlook.com.
  • Farag M A Altalbawy
    National Institute of Laser Enhanced Sciences (NILES), University of Cairo, Giza 12613, Egypt; Department of Chemistry, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia.
  • Prakash Kanjariya
    Department of Physics, Marwadi University Research Center, Faculty of Science, Marwadi University, Rajkot, Gujarat, India.
  • R Manjunatha
    Department of Data Analytics and Mathematical Sciences, School of Sciences, JAIN (Deemed to be University), Bangalore, Karnataka, India.
  • Debasish Shit
    Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
  • M Nirmala
    Department of Biomedical Engineering, Dr.N.G.P Institute of Technology, Coimbatore, 641048, India.
  • Ajay Sharma
    Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.
  • Sarbeswara Hota
    Department of Computer Application, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
  • Shirin Shomurotova
    Department of Chemistry Teaching Methods, Tashkent State Pedagogical University Named After Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan.
  • Fadhil Faez Sead
    Department of Dentistry, College of Dentistry, The Islamic University, Najaf, Iraq.
  • Hojjat Abbasi
    Chemistry Department, Herat University, Herat, Afghanistan. hojjatabbasimeybodi@gmail.com.
  • Mohammad Mahtab Alam
    Department of Basic Medical Science, College of Applied Medical Science, King Khalid University, Abha, Saudi Arabia.

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