External corrosion in uninsulated pipelines is normally able to be prevented by cathodic protection (CP). Generally, external corrosion on buried pipelines cannot occur if CP current is getting onto the pipe. CP is an electrochemical means of corrosion control in which the oxidation reaction in a galvanic cell is concentrated at the anode and suppresses corrosion of the cathode (pipe) in the same cell. For instance, to make a pipeline a cathode, an anode is attached to it.
On an increasingly frequent basis, pipeline operators are using risk-based decision making to prioritize cross-company expenditures. Due to the long-term mitigation benefits of Cathodic Protection (CP), when planning external corrosion mitigation activities, pipeline operators typically prioritize mitigation of deeper anomalies for integrity expenditures due to their higher Probability of Failure (PoF). However, anomalies that are not receiving adequate CP or those experiencing electrical interference may remain unaddressed using this rationale. This paper presents both a qualitative and semi-quantitative approach to support the quantification of the risk reduction benefits gained from external corrosion prevention on pipelines. This can help in the efficient prioritization of both pro-active and re-active integrity repair activities. Supporting examples are also discussed to help explain the intended use of the methodology and the interpretation of the results.
Enbridge is proposing to develop a program that utilizes state-of-the-art technologies and proven inspection methods to prescribe interventions related to external corrosion mitigation using a predictive, integrated approach. This new program embraces complex problems by collecting, analyzing, and integrating environmental, pipeline integrity, and corrosion control data to predict external corrosion risk with sound engineering models (mechanistic, reliability and risk) to anticipate, prevent, and contain unexpected events.
CORRECTED VERSION AS OF 1/14/2022. Analysis of available and emerging technologies in the field of in-line inspection tools and review their status with respect to characteristics, performance, range of application, and limitations. This is a companion guide to SP0102.
The following corrections have been made in NACE Publication 35100-2017, “In-Line Inspection of Pipelines.”
Date: January 14, 2022
Reference 2 on p. 20 has been revised from “Specifications and Requirements for Intelligent Pig Inspection of Pipelines, Pipeline Operators Forum (POF), http://www.pipelineoperators.org/publicdocs/POF_specs_2009.pdf (Rijswijk, Netherlands: 2009).” to “Specifications and Requirements for Intelligent Pig Inspection of Pipelines, Pipeline Operators Forum (POF), 2016.”
Two citations of reference 2 in Appendix B (pp. 30 and 31) have been revised from “…from POF document. [ref.]” to “…from Table 2.1 of the POF document.2”
Metric-to-U.S. Customary unit conversions have been corrected throughout Appendix B.
Published as: NACE Publication 35100-2017 (Corrected Copy)
In-Line Inspection (ILI) technology is considered one of the safest and most efficient and reliable inspection method to inspect hydrocarbon pipelines. The retrieved data are usually validated and verified upon successful completion of the inspection. This paper is intended to introduce a new approach to validate the ILI run based on a statistical analysis comparing the new ILI run with a previous ILI run of the same pipeline by leveraging a root mean square (RMS) model to quantify the similarity between the datasets. API-1163 and Canadian Energy Pipeline Association (CEPA) offer consistent criteria as a validation methodology for a new ILI run. Also, this paper will demonstrate a new scoring criterion for accepting Magnetic Flux Leakage (MFL) runs with partial data loss as number of MFL runs experience unexpected data loss, which might affect the minimum reporting threshold of the tool. The approach will help pipeline operators to identify the criticality of the missed data via a detailed comparison with the previous MFL run for the same pipeline and detailed analysis of the behavior of the tool during the run. The scoring criteria is aligned with the Pipeline Operators Forum (POF) requirements for data loss. Multiple case studies extracted from actual data will be presented throughout the paper.