Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations.

Journal: Scientific reports
Published Date: Jul 28, 2025

Abstract

Mud loss during drilling operations poses a significant problem in the oil and gas industry due to its contributions to increased costs and operational risks. This study aims to develop a reliable predictive model for mud loss volume using machine learning techniques to improve drilling efficiency and reduce non-productive time. The dataset consists of 949 field records from Middle Eastern drilling sites, incorporating variables such as borehole diameter, drilling fluid viscosity, mud weight, solid content, and pressure differential. Initial data analysis included statistical evaluation, outlier detection using leverage diagnostics, and data normalization to ensure validity and consistency. A Gradient Boosting Machine (GBM) served as the core predictor, with its hyperparameters fine-tuned using four optimization strategies: Evolution Strategies (ES), Batch Bayesian Optimization (BBO), Bayesian Probability Improvement (BBI), and Gaussian Process Optimization (GPO). Model performance was evaluated using k-fold cross-validation, with metrics including R², mean squared error and average absolute relative error percentage. Results demonstrated that the GBM-BPI achieved the strongest test performance (R² = 0.926, MSE = 1208.77, AARE% = 26.73), outperforming other approaches in accuracy and stability. Feature importance assessed through SHAP analysis revealed that hole size, formation type, and pressure differential were the most influential variables, while solid content had minimal effect.

Authors

  • Xiaozhi Lu
    School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200000, China. Lxzh192126@163.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.
  • Tarak Vora
    Department of Civil Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, 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.
  • Shirin Shomurotova
    Department of Chemistry Teaching Methods, Tashkent State Pedagogical University Named After Nizami, Bunyodkor Street 27, Tashkent, Uzbekistan.
  • Akshay Kumar
  • Atreyi Pramanik
    School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, Uttarakhand, India.
  • Ajay Sharma
    Department of Veterinary Medicine and Surgery, College of Veterinary Medicine, University of Missouri, Columbia, MO, 65211, USA.
  • Raed H C Alfilh
    Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq.
  • Samim Sherzod
    Faculty of Engineering, Nangarhar University, Nangarhar, Afghanistan. samimsherzod@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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