Dann, Markus R.Birkland, Monica2019-09-062019-09-062019-08-30Birkland, M. (2019). A framework for pipeline corrosion growth modeling tailored to mass in-line inspection data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.http://hdl.handle.net/1880/110868The integrity management of existing oil and gas pipelines is vital to avoid failures with potentially severe consequences. As many existing pipelines are constructed of steel, corrosion poses a significant threat to the integrity of these pipelines, in-line inspections are periodically performed, and a corrosion growth analysis is used to predict the future state of the pipeline. Currently, there is no standard or guideline for a corrosion growth analysis; and since there are many uncertainties surrounding corrosion, it is difficult to navigate. Additionally, in-line inspections, particularly for upstream and subsea pipelines, often lead to large datasets and the reported data is subject to measurement error. This framework proposed in this thesis is a generalized, step-by-step approach for performing a corrosion growth analysis designed to accommodate for a mass dataset with measurement uncertainty while also allowing for other scenarios. The objectives of this research are primarily to provide consistency of the corrosion growth analysis process, while also improving the efficiency and to provide guidance and advice throughout. An example is carried out from start to finish and the results presented to demonstrate how the framework can be used in practice.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.CorrosionProbabilistic ModelingIntegrity ManagementPipelineEngineering--CivilA framework for pipeline corrosion growth modeling tailored to mass in-line inspection datamaster thesishttp://dx.doi.org/10.11575/PRISM/36946