Pipelines in remote and ecologically sensitive regions pose special challenges for pipeline integrity monitoring. These challenges include difficulties of access, reliability issues of communication and instrumentation that may impact the leak detection technology applied in these regions. The selection, application and continuous testing of an appropriate technology to detect possible leaks are important to pipeline integrity monitoring. The paper reports theoretical assessment and extensive testing on an Enbridge subarctic liquid pipeline. It also reports the comprehensive cost-benefit analysis to guide the leak detection instrument design/configuration and the evaluation of the capabilities of alternative computational pipeline monitoring (CPM) technologies for leak detection. The selected test pipeline is an 869 kilometer (540 mile), NPS 12 inch pipeline that transports sweet crude through environmentally sensitive areas. This pipeline currently uses a real-time transient model (RTTM) style CPM as the leak detection system (LDS). The pipeline LDS is tested annually by a number of industry-recognized methodologies. These include fluid withdrawal tests, simulated leak tests and an API-1130 instrument adjustment approach. This pipeline is also assessed by API-1149 for its theoretical CPM leak detection sensitivity. A pilot project invited commercial CPM-style vendors to participate in an LDS test using data from fluid withdrawal and simulated leak tests. Five vendors responded and were included in the test suite. The paper describes the design and implementation of the test process. The results of the commercial systems are presented in aggregated form and the participating vendors remain anonymous. Performance assessment focuses on the LDS evaluation factors of sensitivity and accuracy. The paper concludes with a “lessons learned” review of issues associated with test design.

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The operation of Enbridge’s pipeline system is continuously monitored by a leak detection system termed the Material Balance System (MBS). The MBS is not only a regulatory requirement; it helps minimize the financial and environmental damages that could result from an oil leak. A team of MBS experts provides 24/7 support to Enbridge’s control centre, which manages pipeline operations. When MBS predicts or detects any hydraulic abnormality in the pipeline, it alerts the operations’ staff. Though the operators do some basic analyses, such abnormal situations are usually referred to the support team. The idea of the intelligent alarm analyzer is to provide a tool to analyze and present the most probable cause of the alarm. The human knowledge of MBS alarm analysis is transferred into a soft knowledge base, which is then used in combination with the real-time data to analyze the alarm causes. Logic was developed to identify a group of alarms caused by a single reason, which was important from the performance point of view as a set of alarms can result from one event. In parallel, some basic rules were designed to implement the procedure for analyzing these alarms. A detailed study of some typical alarm scenarios was done to build an example knowledge base. The “event” triggers the alarm analyzer, which uses the real-time data and the knowledge base to go through an extensive series of logical evaluations and mathematical manipulations (called “tests”) to decide on the probable cause of the alarms. The next task was to combine the power of computational techniques and human understanding to implement possible scenarios. At this stage more complex and realistic rules have been developed to facilitate real-time alarm analyses. The paper will include an example scenario of how the tool could be applied to a production system. The alarm analysis system described in this paper is currently being tested for production use later this year. Once in production, additional tuning and refinement of the tool will be a continuing process.

MBS, the software based leak detection system employed by Enbridge, is a real time transient model and as such requires fluid characteristics of the various batches that enter the pipeline. In the past, of the 25 plus pipelines modeled, only 4 received fluid identifiers from the field. These fluid identifiers are a sub-string of the batch identifiers stored in flow computers located at custody transfer locations. On the remaining pipelines, Enbridge used fluid density from the field to infer fluid type and therefore characteristics. In the past whenever a number of fluids had the same density, MBS assigned a best-guess of fluid type. The ‘MBS Real Time Injection Batch Data’ project was proposed to bring fluid identifiers to MBS on the remaining lines with the purpose of improving MBS’ selection of fluid properties. Since injection points on the remaining lines were not custody transfer there were no flow computers at these locations. An existing application called Commodity Movement Tracking, or CMT, was used to provide fluid names to the leak detection model. CMT holds past, present, and future injection batch information in an Oracle database. Batch identifiers are queried, placed into the SCADA system, and forwarded on to MBS. This paper explores the new approach, introduced by the ‘MBS Real Time Injection Batch Data’ project, of providing MBS with batch identifiers.

The subarctic location of Enbridge’s Norman Wells pipeline provides unique conditions affecting both construction and operations. These include the huge variations in annual air temperature, permanently frozen ground (permafrost), hundreds of river crossings and potential slope instability. The regulatory authorities recognized this environmental sensitivity and stringent conditions for construction and operation were applied. In this difficult environment, loss of integrity must be detected rapidly and at low thresholds. To ensure that integrity monitoring maintains or improves these thresholds, frequent testing is necessary. Testing of the integrity of this remote northern oil pipeline provides significant operational challenges. This remote 869km (540 mile) NPS12 crude oil pipeline has been operating in the Canadian subarctic since 1985. This paper will outline the implementation, assessment and future directions of the integrity monitoring testing of the pipeline’s leak detection capability. The history of this pipeline in the Canadian Northwest Territories will be outlined with emphasis on the special regulatory issues of this sensitive sub arctic environment. The development of a Computational Pipeline Modeling (CPM) leak detection system to meet these regulations will be summarized with reference to the guidelines of CSA Z662, Appendix E. A central component of meeting this regulatory requirement is an annual test program that uses controlled fluid withdrawal to test the CPM system and operational responses. The special methods and procedures used to meet the challenges of this program will be noted. The extent and frequency of testing make this probably one of the most tested liquid pipeline leak detection systems in the world. These controlled fluid withdrawal tests are used to enhance the Enbridge response to operational emergencies. Many factors must be considered when designing these tests. A detailed description of the preparation and field logistics required for the pipeline CPM test will be presented. The special needs of conducting tests in an environmentally sensitive region will also be outlined. A review of how these tests address the considerations of API 1149 and API 1155 are summarized. Since pipeline completion, over 70 test events have been conducted. A recent case study will detail some of the issues associated with testing. Future plans for enhancements using additional testing methodologies will be presented with particular mention of a simulation-based alternative.