Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model

Coiled tubing has been widely used in oilfield development because it can significantly improve oil well productivity and recovery efficiency. However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. The results show that the main influencing factors for coiled tubing erosion rate are impact velocity, impact angle, and sand concentration. In addition, the SSA-BPNN model shows a high goodness of fit (R) and a good fit with the experimental data. The SSA-BPNN model underwent standard statistical validation tests, effectively predicting the erosion rate of coiled tubing with a high coefficient of determination and low errors, demonstrating a robust consistency between predicted and actual values. This study is of great significance to oilfield engineers, pipeline designers, and oilfield developers, and provides effective support for optimizing oilfield development and pipeline maintenance. The main users include oil companies, engineering consulting institutions and related industry personnel, and may also attract the interest of scientific research institutions and academia, providing a useful reference for the technological progress of the oil industry.