Prediction of Sand Erosion in Elbows and Tees Using Direct Impingement Model

• Main Authors: Mohyaldinn, Mysara Eissa, Hamzah, Razali, Che Ismail, Mokhtar
• Format: Conference or Workshop Item
• Published: 2009
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utp.edu.my/1765/1/NACE_KL.pdf; http://eprints.utp.edu.my/1765/

When sand is produced from a bay zone in a petroleum field, sand control or sand management techniques are normally applied to avoid the subsequent problems of sand production. In the former, sand production is minimized to the least level, whereas in the later sand is allowed to be produced but the flow characteristics are managed to avoid the consequences. Erosion of subsurface and surface components is the most important consequence of sand production. When sand management technique is applied, sand erosion needs to be predicted at different conditions to set the limits of the process operation parameters. Many models were proposed for sand erosion prediction but the applicability of any model subjects to specific constraints. For example some models assume that the particle velocity is identical as fluid velocity. These models can only be applicable to gas flow or high gas-liquid ratio two phase flow but are not applicable for liquid flow. The direct impingement model is a mechanistic model developed by Erosion/Corrosion research center (E/CRC) in University of Tulsa to predict the penetration rate of direct impingement of elbows and tees after determining the direct impact velocity using a CFD-based equation of particles motion. The main attributions of the direct impingement model are its simplicity, its accounts to the sand trajectories along flow path, and its accounts to sand shapes and substrate materials. In this work, a friendly user-interface computational code has been developed using the direct impingement model and the results of the code have been validated using published measured data. It has been found that, the code results highly agree with the measured data.