Machine Learning Applications in the Prediction and Management of Sand Production in Oil and Gas Wells-a Review Study

Document Type : Review Article

Authors
Mohammadelyas Khodashenas 1
Meysam Mohammadzadeh Shirazi 2
Behnam Shahsavani 2
1 M.Sc. student, Department of Petroleum Engineering, School of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran
2 Assistant Professor, Department of Petroleum Engineering, School of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran
20.1001.1/ijge.2025.725750
Abstract
Sand production in oil and gas wells leads to numerous issues, including reduced well productivity and equipment damage. To mitigate the challenges and consequences of sand production, implementing sand prediction and control processes in wells is recommended. These processes involve evaluating multiple factors, such as well depth, formation failure gradient, and other geological parameters. Due to the vast volume of data and the complexity of analysis, conventional traditional methods are time-consuming and prone to significant uncertainties and errors. Consequently, the adoption of more efficient methods has gained considerable importance. In recent years, the application of machine learning techniques for analyzing large and complex datasets has emerged as an effective and accurate approach to improving prediction accuracy. As a result, production engineers have utilized machine learning algorithms to analyze data related to sand production. This study provides a review of previous research on the application of machine learning in managing sand production. To facilitate better understanding, the referenced studies have been categorized and organized based on their focus. Furthermore, the algorithms and validation methods used in each study are specified to serve as a guide. Therefore, this study can serve as a reference for future research in this field.
Keywords
Sand Production Prediction
Production and Productivity
Oil Production
Artificial Intelligence
Machine Learning
Subjects
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  • Receive Date 18 February 2025
  • Revise Date 20 May 2025
  • Accept Date 07 June 2025