Leveraging Data-driven Approach for Sand Production Prediction and Management in Oil and Gas Wells

Sand production poses a major challenge in wells completed within sandstone reservoirs, despite their inherent benefits. It could lead to erosion of surface and subsurface equipment, well plugging from sand grain deposition and accumulation, and potential collapse of sections in horizontal wells, resulting in non-productive time, costly cleanup, and remedial or work-over operations. Various researchers have proposed strategies and models to tackle sand production and its associated problems. While these models have successfully predicted the onset of sand production, cavity stability, and rock collapse, they often struggle to accurately forecast the sand production rate due to the complexity of influencing parameters and the difficulty in fully capturing all reservoir and wellbore processes. Early prediction of sand production before drilling and field development is critical for selecting appropriate equipment and mitigation strategies. A thorough evaluation of sanding potential based on key reservoir and wellbore properties is essential to inform field development plans. In this study, a ν-support vector classification (ν-SVC) model was used to classify sand-ing potential, achieving 100% accuracy on the training set and 87% on the test set. For wells identified as having sanding potential, the sand occurrence was predicted using critical drawdown pressure estimated by a Random Forest model, also with 100% training and 87% test accuracy. The expected volume of sand produced over time was then predicted using an AdaBoost regression model with equivalent performance metrics. Together, these models form a predictive pipeline for proactive sand control and management. Their effectiveness was validated using publicly available field data, demonstrating strong predictive capability for real-world applications. The results indicate that these machine learning models can support completion engineers in designing timely and effective sand management strategies, minimizing production losses and operational risks.