Erosion Behavior and Prediction Model of Intelligent Filling Tools Under Flow-Path Switching Conditions

Flow-path switching is a key operating condition that enables flow regulation, zonal conversion, and filling-path redirection in sand-control completions. The associated flow characteristics directly govern the operational stability and service reliability of intelligent filling tools. Most existing studies have addressed erosion only under simple geometries such as pipe contractions and expansions, leaving the dominant erosion-controlling factors and rapid erosion-rate prediction methods for sand-control filling tools under flow-path switching conditions insufficiently understood. This study developed a Fluent-based numerical model of solid–liquid two-phase erosion for intelligent filling tools and characterizes the wall-erosion distribution pattern during flow-path switching. Guided by field practice, multi-factor simulations were performed over the reduction angle, cutting particle size, inlet flow capacity, flow-switching direction, opening area, and structural form. On this basis, a maximum-erosion-rate prediction model was constructed using a logarithmic transformation combined with a stepwise quadratic response-surface method. This regression-based approach was deliberately chosen over machine-learning black-box models, whose limited interpretability and small-sample reliability make it difficult to reveal the underlying physical mechanisms; in contrast, the proposed model yields an explicit algebraic expression whose significant interaction and quadratic terms directly reflect the coupling between structural and operating parameters, while the logarithmic transformation accommodates erosion-rate fluctuations spanning several orders of magnitude. The results show that the factors rank in influence as follows: flow-switching direction > opening area > reduction angle > inlet flow capacity > cutting particle size > structural form. The established prediction model attained a coefficient of determination of about 0.951 and an adjusted coefficient of determination of about 0.901; combined with the significance test and a residual analysis, the model can effectively characterize the coupling influence of structural parameters and operating parameters on the erosion rate. These findings provide quantitative guidance for the erosion-resistant structural design, field operating-parameter selection, and preliminary service-life assessment of intelligent filling tools in offshore sand-control well-completion operations. The prediction model is further validated through jetting-erosion bench tests; the measured erosion rates agree closely with the model-predicted values, confirming the practical reliability of the model and its capability to serve as an engineering reference for the erosion-resistant design and service-life assessment of intelligent filling tools in sand-control well-completion operations.