Field-Informed Prediction of Sand Erosion and Dynamic Risk Assessment for Tight-Gas Pipelines in a Gathering Station

Sand production and proppant flowback in tight-gas reservoirs introduce gas–liquid–sand three-phase flow into wellbore and surface gathering systems, where particle-laden streams simultaneously cause mechanical erosion, electrochemical corrosion, and their synergistic wall-thickness loss at bends, tees, chokes, and separators. This study develops a novel field-informed prediction framework that, for the first time, couples gas–liquid–sand three-phase flow, orientation-dependent sand transport probability (STP), erosion–corrosion models with multi-size particle superposition, and Bow-Tie/Bayesian-network-based dynamic risk assessment into a single operating-envelope calculation. The key innovations include: (i) a unified erosion–corrosion coupling model that integrates mechanical erosion, electrochemical CO2 corrosion, and their synergistic interaction through particle-size-distribution-weighted superposition; (ii) a four-pipeline-type differentiation (inlet, gas outlet, sand outlet, water outlet) revealing that erosion–corrosion severity varies by more than a factor of three among pipeline types; and (iii) a Poisson–Gamma dynamic risk updating model that tracks failure probability evolution under time-varying operating conditions. The framework was applied to three tight-gas wells and their gathering pipeline network. Field sieve analysis constrained particle-size distributions (70–160 mesh), and the sand samples were confirmed as quartz-rich proppant (Mohs hardness 7) based on sieve analysis and visual inspection. Horizontal-section STP ranged from 76.25% to 91.43%, and vertical-section STP reached 100% in all wells. The predicted erosion–corrosion rate for the KQ51H pipeline was 1.866 mm/a, exceeding the critical threshold and classifying it as High risk. Model predictions of wall-thickness loss at 11 monitoring locations agreed with ultrasonic thickness measurements within 10% error. The Bow-Tie model identified sand mass rate, particle-size distribution, and corrosion-product film failure as critical risk drivers. Dynamic Bayesian updating tracked failure probability evolution over 12 months, with the highest-risk segment showing a probability increase from 0.0149 to 0.0325. The framework supports differentiated inspection scheduling and mitigation prioritization across pipeline segments in a gathering station.