Pipeline integrity assessments in upstream pipelines is a challenging activity that requires considerable amount of data and its proper interpretation. An important factor, usually ignored during the data collection process, is a data integration and data correlation analysis. This process becomes of high importance when trying to collect meaningful and representative pipeline information. The Success criteria of the integrity assessment highly depend on the quality of the data gathered, and the appropriate selection of significant variables in the corrosion mechanism. This approach aims to improve the decision-making framework on the internal corrosion of pipelines. This report encompasses a statistical analysis of oil pipelines metal loss due to localized corrosion. Different Machine Learning (ML) methods and statistical approaches, like Decision Tree, Logistic Regression and Random Forests were compared to identify the statistical significance of different predictor variables, such as, geochemical parameters, operation parameters, and mechanistic simulation outputs. The results showed that meaningful optimization of significant predictor variables enhance the ML model prediction accuracy.
Energy producing companies use pipelines to transport energy from point A to point B. When the pipeline thickness at a location falls below a certain threshold, there is risk of leakage that could result in serious economic losses, personal injury, or damage to the environment. Pipeline integrity management is a performance-based process that handles pipeline serviceability and failure prevention1. Pipeline integrity is a method of assessing and defining the likelihood of a pipeline incident and the potential consequences on safety, health, environmental and financial impacts of a specific incident. An adequate pipeline integrity management program will improve the pipeline serviceability over the time as it can advertise and prevent future failures. A pipeline integrity assessment can improve the effectiveness and efficiency of maintenance resource utilization to maintain the pipeline reliability. Pipeline operators, and oil producers around the globe try different approaches to develop their own integrity program. One way the pipeline operators have is the detailed analysis of the inline inspection to be converted into the maintenance programs1-3. For oil producers, however, the story change as they operate more segments of short distances and sizes that, sometimes, are not design to accommodate an ILI tool. The pipeline design becomes one big reason of the need of integrity models to assess the condition of the operating pipelines. An indirect method to determine the integrity of the pipeline is then required to assess the condition and serviceability of the pipeline.