Influence of Sand Fines Transport Velocity on Erosion-Corrosion Phenomena of Carbon Steel 90-Degree Elbow

Erosion-corrosion is an ineluctable flow assurance problem confronted in hydrocarbon transportation and production systems. In this work, the effect of sand fines velocity on the erosion-corrosion behavior of AISI 1018 carbon steel long radius 90° elbows was experimentally and numerically investigated for liquid-solid flow conditions. Experiments were effectuated for sand fines of mean diameter 50 µm circulated in a flow loop with three different velocities (0.5, 1 and 2 m/s). To elucidate the erosion-corrosion mechanism and degradation rate, the material loss analysis, multilayer paint modeling (MPM) and microscopic imaging technique were employed, with computational fluid dynamics (CFD) and discrete phase modeling (DPM) also capacitating to evaluate the erosion distribution. It was perceived that increasing slurry velocity significantly changes the particle-wall impaction mechanism, leading to an increase in material degradation in the elbow bottom section up to 2 times in comparison to the low transport velocity. The erosion scars and pits development at the elbows internal surface was found to govern the wear mechanism in the carbon steel and made downstream section susceptible to erosion and corrosion. The material removal mechanisms were ascertained to change from cutting to pitting and plastic deformation with an increase of sand fines transportation velocity from 0.5 m/s to 2 m/s

A Feed-Forward Back Propagation Neural Network Approach to Predict the Life Condition of Crude Oil Pipeline

Pipelines are like a lifeline that is vital to a nation’s economic sustainability; as such, pipelines need to be monitored to optimize their performance as well as reduce the product losses incurred in the transportation of petroleum chemicals. A significant number of pipes would be underground; it is of immediate concern to identify and analyse the level of corrosion and assess the quality of a pipe. Therefore, this study intends to present the development of an intelligent model that predicts the condition of crude oil pipeline cantered on specific factors such as metal loss anomalies (over length, width and depth), wall thickness, weld anomalies and pressure flow. The model is developed using Feed-Forward Back Propagation Network (FFBPN) based on historical inspection data from oil and gas fields. The model was trained using the Levenberg-Marquardt algorithm by changing the number of hidden neurons to achieve promising results in terms of maximum Coefficient of determination (R2) value and minimum Mean Squared Error (MSE). It was identified that a strong R2 value depends on the number of hidden neurons. The model developed with 16 hidden neurons accurately predicted the Estimated Repair Factor (ERF) value with an R2 value of 0.9998. The remaining useful life (RUL) of a pipeline is estimated based on the metal loss growth rate calculations. The deterioration profiles of considered factors are generated to identify the individual impact on pipeline condition. The proposed FFBPN was validated with other published models for its robustness and it was found that FFBPN performed better than the previous approaches. The deterioration curves were generated and it was found that pressure has major negative affect on pipeline condition and weld girth has a minor negative affect on pipeline condition. This study can help petroleum and natural gas industrial operators assess the life condition of existing pipelines and thus enhances their inspection and rehabilitation forecasting