Experimental study on erosion behavior of fracturing pipeline involving tensile stress and erosion prediction using random forest regression

Abstract

Damage induced by erosion wear is an inevitable problem in the oil and gas industry. During the hydraulic fracturing operation, the fracturing pipeline is not only subjected to tensile stress caused by the high internal pressure, but it also inevitably suffers from erosion damage from solid particles carried by the fracturing fluid. Due to the lack of accurate erosion prediction methods for fracturing pipelines in operation conditions, it is difficult to prevent the failure of pipe fittings caused by erosion wear. Therefore, in this paper, erosion wear experiments of fracturing pipelines under varying conditions (including impact angle, tensile stress, target material, flow velocity and particle concentration) were carried out. Results indicate that the tensile stress plays a crucial role in affecting the erosion wear rate. Furthermore, erosion wear prediction models were proposed on basis of the sufficient experimental data by using different machine learning algorithms. The prediction results were validated in comparison with experiments via error analysis. A good performance in prediction accuracy and generalization ability was observed in the random forest regression (RFR) approach, making it a potential solution in predicting the slurry erosion wear of fracturing pipelines and may be developed to all the high-pressure pipelines.