Study of Sand Transport in a Horizontal Pipeline Using Validated Computational Fluid Dynamics Simulations with Experimental Fiber-Optic Distributed Acoustic Sensing Data

Summary Sand management in wellbores is a significant challenge in the industry, notably impacting equipment integrity and operational safety—particularly in offshore oil and gas operations affected by the onset of sand production along with hydrocarbons. Recent advancements in fiber-optic sensing, especially through distributed acoustic sensing (DAS) experimental data, have enabled the continuous monitoring of sand ingress and migration. In this study, we use computational fluid dynamics (CFD) to accurately model sand transport by validating the simulations against the DAS data in a 40-ft-long, 2-in.-diameter experimental flow loop. The validation and verification (V&V) process demonstrates the CFD model’s accuracy in both steady-state and transient conditions, through predictions of key flow parameters such as sand slip velocity, sand concentration profiles, and sand arrival times against published experimental data, as well as verification of CFD methodology against similar simulation studies. Next, we used the CFD model to simulate the fiber-optic experimental DAS data for sand slurry transport in a pipe through an injection port with conditions of carrier fluid velocity = 0.93 m/s and dispersed phase (sand) particle diameter of 300 µm at a concentration of 0.001 v/v. To address uncertainties during sand production, a parametric study under transient conditions was conducted with varying boundary conditions in the CFD model. It examined fluid flow velocities at both 0.53 m/s and 0.93 m/s, below and above the critical settling velocity of the sand respectively, and the effects of varying sand particle diameters (125 µm and 600 µm). Our research represents a significant advancement in sand management strategies, offering a robust and cost-effective tool for simulating real-world scenarios to improve operational efficiency. By providing detailed insights into flow dynamics and enabling robust predictions across various conditions, this study contributes substantially to advancing sand management strategies that could effectively mitigate operational risks and optimize sand transport in real time.