Prediction of Sand Production in Vertical Oil Well Using Supervised Machine Learning Models

Abstract

Sand production has become a significant concern in the hydrocarbon recovery process from unconsolidated reservoirs which may result in equipment damage, flow restrictions, and costly operational downtime in vertical oil wells. Accurate prediction of sand production is vital to optimize well integrity. Conventional geomechanical and empirical models frequently fail to capture the highly non-linear interactions among reservoir pressure, multiphase flow rates, rock mechanical properties, and dynamic operating conditions. This study addresses the identified research gap by developing and systematically comparing four supervised machine learning classifiers for binary prediction of sand production occurrence using routine well-test data from a single vertical oil well in the Niger Delta basin. A total of 235 validated well test observations consisting of 19 recorded variables which include date and operational parameters such as production rates, pressure conditions, choke size, and fluid properties were pre-processed and analyzed. The target variable was formulated as a binary classification problem with the operational threshold sand rate > 0 lb/1000 bbl to enable early detection of any sanding event. Four machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine were developed and evaluated following feature optimization and model tuning. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that ensemble and kernel-based methods significantly outperform linear and single-tree models, with the Random Forest classifier achieving the best model prediction accuracy of 93.62%. This strong performance demonstrates the model's robustness in capturing the complex, nonlinear interactions governing sand production behavior. This study demonstrates that machine learning classifiers can be effectively utilized in a manner that enables proactive sand management strategies, including choke adjustment, artificial lift optimization and selective sand control deployment, ensuring a minimal risk of equipment failure and increasing overall well productivity and hydrocarbon production.