Sand production is a critical concern in the petroleum industry, often leading to costly equipment damage, production downtime, and safety risks. Accurate prediction of sand-prone zones is essential for proactive sand management and optimized well design. Although several empirical and machine learning-based models exist to estimate static Young’s modulus (Es) and static Poisson’s ratio (νs)—key inputs for sand production prediction methods such as the Sand Production Index (B), shear modulus to bulk compressibility ratio (G/Cb), and Schlumberger Sand Index (S/I)—their performance varies widely, and their reliability has not been studied. This study addresses that gap by conducting a comparative evaluation of multiple estimation models for Es and ν s from the literature, using a dataset of 100 samples with measured Es and ν s values from existing models. The study investigates how different input models affect the accuracy of B, G/Cb, and S/I sand production predictions and rock type identification. Results demonstrate that while many models yield inconsistent outputs and often misclassify sanding zones, one of the evaluated models achieves near-perfect agreement with measured data (coefficient of determination = 0.9998, minimal root mean square error = 2.78E-17), leading to significantly more reliable sand production forecasts across all methods evaluated. By quantifying prediction inconsistencies and strengths among widely used models, this work provides critical insights into model selection for well-log interpretation. It highlights the risks of relying on poorly calibrated methods. The findings offer practical guidance for improving sand risk evaluation in the field.
Alakbari et al. (Sat,) studied this question.