This presentation was made at the 2019 NAFEMS World Congress in Quebec Canada
Sand entrainment in extracted oil and gas from the oil well is inevitable. The presence of sand or any other solid particles is a source of various problems including damage to pipelines, fittings (elbows, chock valves etc.) and other control equipment due to erosion. Monitoring, predicting, and controlling erosion is important to avoid hampering the production process or even shutdown for extended periods of time. Sand erosion is a complicated phenomenon, which depends on many factors such as flow and turbulence field, particle properties, and surface condition. Computational Fluid Dynamics (CFD) is routinely used to predict the erosion rate at various locations within the system. In this paper, CFD fundamentals were applied to model sand erosion on elbow geometries and results were validated against the experimental data from University of Tulsa, to establish the confidence in CFD simulations.
CFD analysis generally tends to take relatively long time and requires significant computational resources, which makes it difficult for real-time monitoring or predictive maintenance. In addition, prior knowledge/experience is required to perform CFD simulations and extract meaningful results. Reduced Order Models (ROMs) can significantly reduce these requirements. ROM is a simplification of a high fidelity computational model that preserves essential behaviour and dominant effects, for the purpose of reducing solution time or storage capacity required for the more complex model.
In this paper a series of simulations were performed for varying fluid flow rates, fluid properties, and particle properties to train the solver. Once the solver is trained, a reduced order model for erosion rate on the pipe surface of elbow was extracted. This three dimensional non-linear ROM can be used for real-time monitoring and to perform ‘what-if’ analysis within seconds without compromising on accuracy. This multiscale modelling of erosion, from 3D high fidelity models to ROMs, paved the way to digitize the asset and perform real-time monitoring as well as predictive maintenance.
|Date||18th June 2019|