Predictive modeling of oil rate for wells under gas lift using machine learning.

Journal: Scientific reports
Published Date: Jul 30, 2025

Abstract

Optimizing oil production in wells employing gas lift systems is a critical challenge due to the complex interplay of operational and reservoir parameters. This study aimed to develop robust predictive models for estimating oil production rates using a comprehensive dataset from oil fields in south-eastern Iraq, leveraging advanced machine learning techniques. The dataset, comprised of 169 rigorously validated samples, includes key features such as basic sediment and water content, choke size, pressures, gas injection characteristics, gas lift valve depth, oil density, and temperature. Input and output variables were normalized and split into training and test sets to ensure fairness and reliability. Multiple machine learning models (Decision Tree, AdaBoost, Random Forest, Ensemble Learning, CNN, SVR, MLP-ANN, and Lasso Regression) were trained and evaluated using 5-fold cross-validation and key statistical metrics (R², MSE, AARE%). The Random Forest model demonstrated superior performance, achieving a test R² of 0.867 and the lowest prediction errors (MSE: 18502 and AARE: 8.76%) for the testing phase, while other models were prone to overfitting or underfitting. Sensitivity analysis and SHAP interpretability methods revealed that basic sediment and water content, choke size, and upstream pressure had the greatest influence on oil output. These findings underscore the importance of both statistical rigor and model interpretability in oil production forecasting and provide actionable insights for optimizing gas lift operations in oil wells.

Authors

  • Famin Ma
    Shangluo University, Shangluo, 726000, Shannxi, China. mafamin_2007@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.
  • Pinank Patel
    Department of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi Universitly 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.
  • Rishiv Kalia
    Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
  • Shoira Formanova
    Department of Chemistry and Its Teaching Methods, Tashkent State Pedagogical University, Tashkent, Uzbekistan.
  • P Raja Naveen
    Department of Mechanical Engineering, Raghu Engineering College, Visakhapatnam, Andhra Pradesh, 531162, India.
  • Kamal Kant Joshi
    Department of Allied Science, Graphic Era Hill University, Dehradun, Uttarakhand, 248002, India.
  • Aashna Sinha
    School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal University, Dehradun, Uttarakhand, India.
  • Abdolali Yarahmadi Kandahari
    Faculty of Engineering, Kandahar University, Kandahar, Afghanistan. aliyarahmadiput21@gmail.com.
  • Taqi Mohammed Khattab Al-Rubaye
    Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq.
  • Mohammad Mahtab Alam
    Department of Basic Medical Science, College of Applied Medical Science, King Khalid University, Abha, Saudi Arabia.

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