Prediction of Sand Passage Rate Through Slotted Liner Using Machine Learning Algorithms

Document Type : Original Article

Author
Mohammadelyas Khodashenas
M.Sc. Student, Petroleum Engineering, School of Chemical Engineering, Oil and Gas, Shiraz University, Shiraz, Iran
20.1001.1/ijge.2026.2088830.1125
Abstract
Sand production from oil and gas wells reduces productivity and causes damage to production equipment. To control this phenomenon, mechanical sand screens, particularly slotted liners, are employed, and the performance of these screens is determined through the analysis of sand retention test results. In this study, machine learning algorithms were utilized to predict the amount of sand passing through the unit surface area of slotted liner coupons. To this end, the results of 519 sand retention test samples reported in previous studies were compiled as a dataset, encompassing various screen structures and designs, sand particle size distributions, and experimental conditions. The final dataset consisted of 499 preprocessed data samples. The models were constructed using the following algorithms: GB, XGBoost, RF, DT, KNN, SVM, MLP, and Polynomial Regression. The results demonstrated that the GB algorithm, with the highest accuracy (R² = 0.94) and the lowest error, alongside the XGBoost algorithm, delivered the best predictive performance. Furthermore, it was found that slot width, pressure differential, and flow rate are the most influential factors affecting the amount of produced sand. Additionally, the screen design type and slot width must be selected in accordance with the particle size distribution of the sand. The machine learning model developed in this research offers advantages such as enhanced prediction accuracy compared to previous methods, providing an interpretable model of screen performance, and increasing the speed of selection, thereby contributing to the control of sand production in wells.
Keywords
Sand retention test
Well completion
Sand production
Slotted liner
Machine learning
Subjects
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  • Receive Date 18 February 2027
  • Revise Date 07 June 2026
  • Accept Date 20 June 2026