A computational fluid dynamics based artificial neural network model to predict solid particle erosion

作者：Pandya D A; Dennis B H; Russell R D

来源：Wear, 2017, 378-379: 198-210.

DOI：10.1016/j.wear.2017.02.028

Solid particle erosion plays a critical role in the design and reliability of equipment employed in the oil and gas industry. Significant erosion occurs due to solid particle loading, especially in applications involving sand production. Low particle loading in drilling fluids (< 10%) is also a source of erosion inside downhole tools and in rig equipment at the surface. Accurate prediction of erosion rates can save money and lives by predicting failure accurately and helping to maintain the safety of the equipment. Empirical and mechanistic models to predict erosion were primarily developed based on observations of extensive experiments and field studies. Computational fluid dynamics (CFD) has emerged as an alternative tool to predict erosion in recent years. The ability to simulate multiphase flows in complex geometries using CFD makes it a valuable and less-expensive method to predict erosion flow loop experimentations and field trials. Various empirical relations have been established to predict erosion using CFD. These methods often predict erosion regions accurately, but typically are highly inaccurate in predicting an erosion rate. An order-of-magnitude error is observed in many cases.
This study employs machine learning approach along with CFD-based methodology to develop robust erosion models. A generalized model is developed based on experiments conducted on 90-degree elbows of 1-in. diameter and made from Inconel 718, Nickel Alloy 825, 25% Cr, Nickel Alloy 925, and 13% Cr L-80 materials. The Baker Hughes erosion model developed in 2008 is studied as a baseline. Statistical analysis was performed on CFD output parameters to identify those that most affect erosion rates. A correlation analysis and non-parametric statistical analysis is performed resulting in the development of two new regression models based on turbulent kinetic energy, and surface shear stress was developed. A 25-% improvement is observed in the predictions of cumulative erosion rate error compared to baseline. An artificial neural network with multilayer feed-forward model with the back-propagation algorithm and Levenberg-Marquardt training was developed. This model, along with Bayesian regularization, reduced cumulative error to less than 10%, compared to more than 40% in the baseline Baker Hughes model.

• 出版日期2017-5-15