Top of the Line Corrosion (TLC) is a serious concern for the oil and gas industry and has been the cause of numerous pipeline failures. Many research projects have been developed in order to better understand the mechanisms and to develop accurate predictive tools for TLC. The corrosion mechanisms implemented in most of the available TLC prediction models are mostly based on laboratory based experimental data. Therefore, it is essential to validate the model’s capabilities by using field data. A new approach in comparing model predictions with field data is proposed in this work. Information collected from a sweet field having experienced TLC issues was analyzed processed and then used as an input for the TLC predictive model to simulate the evolution of temperature, pressure, water condensation rates (WCR) and TLC rate along the pipeline. The simulation results were then compared with in-line inspection (ILI) data. Challenges encountered in the analysis of the information about the field conditions (inaccuracy and variability of production data) as well as the ILI data are discussed and a coherent methodology for comparison with simulation results is proposed.
Top of the line corrosion (TLC) in dewing condition has been identified as the cause of numerous pipeline failures and, consequently, has become a growing concern for the oil and gas industry. TLC is a phenomenon encountered under condensation conditions in wet gas pipelines, operated in the stratified flow regime at low gas velocity. Corrosion prediction models were developed and used to provide an overall assessment of the severity of the corrosive conditions. However, the corrosion mechanisms implemented in the model are mostly based on laboratory experimental data. Consequently, it is necessary to evaluate model performance when applied to field experiences involving actual pipeline TLC failures.
