A Comprehensive Approach to 3D Sand Production Prediction: Integrating Petrophysical Data for Depth-Based Modelling

Abstract The reliable prediction of sand production in wellbores remains a critical challenge in the oil and gas industry, as sand production can impact on productivity, equipment integrity, and increase operating costs. Traditional prediction models often rely on single-point depth calculations and simplified failure criteria, which fail to capture the complex and heterogeneous conditions of real reservoirs. This study presents a three-dimensional (3D), depth-based sand prediction model that integrates petrophysical and geomechanical data to improve the accuracy of sanding onset prediction and guide targeted sand management strategies. In contrast to conventional tools, the model utilizes pressure depletion trends and calculates depth-specific rock properties such as porosity, friction angle, unconfined compressive strength (UCS), and critical drawdown pressure (CDP) from petrophysical logs. It employs 3D analytical approaches based on Mogi–Coulomb and Mohr–Coulomb failure criteria to evaluate sand production potential for both open-hole and cased-hole conditions. Implemented in Python, the model automates 3D CDP profile calculation and incorporates variables like inclination, azimuth, friction angle, and dipping angle to account for directional stresses critical to wellbore stability. Results include visualizations such as CDP envelopes and depth-based CDP profiles, which provide precise predictions of high-risk sand production zones. The CDP envelope distinctly categorizes the wellbore environment into two zones: a safe zone where the wellbore remains stable under the current stress conditions, and a sand-prone zone where the stresses exceed the rock strength, indicating potential sand production. These visualizations provide a quantitative evaluation of the critical thresholds for wellbore stability. The classification of these zones offers engineers a practical tool to optimize drilling trajectories, and wellbore geometry to avoid entering the sand-prone zone. It also informs the determination of suitable well completion designs during early-stage field development planning. Furthermore, the depth-based CDP profiles enhance the analysis by identifying weak intervals along the wellbore, enabling targeted interventions. These insights support optimization of the drilling trajectories and targeted sand management strategies, supporting early-stage field development planning. By integrating measured well and field data with empirical correlations for unavailable parameters, this model enables reliable predictions of sanding onset pressure. It offers a reliable and practical tool for engineers for effective field development planning and well completion design, enhancing reservoir performance and operational efficiency.