Development and Application of a Wax Deposition Prediction Model for the BoZi Crude Oil Pipeline

Abstract The BoZi pipeline transports high-wax-content crude oil; however, the spatiotemporal evolution of wax deposition thickness along the pipeline remains poorly characterized. This study develops a modeling framework that integrates field operational data with theoretical formulations to simulate and predict pipeline production dynamics over a one-year period. The model, comprising 365 consecutive time points, accurately captures key operational phases including wax deposition growth, pigging operations, and pipeline shutdowns and restarts. Model predictions were validated against four key operational parameters: inlet pressure, outlet temperature, wax removal volume, and pigging pressure. The model exhibits high fidelity, with deviations below 1.01% for pressure, 1.9% for outlet temperature, 2% for pigging pressure, and 10.5% for wax removal volume. Based on the validated model, 11 pigging and shutdown–restart operations were implemented in the BoZi pipeline. The model further enables long-term projection of wax deposition evolution and provides a basis for optimizing pigging frequency over a 15-year horizon.