Nicholas, Ed (Nicholas Simulation Services) | Carpenter, Philip (Great Sky River Enterprises LLC) | Henrie , Morgan (MH Consulting, Inc.) | Hung, Daniel (Enbridge Pipelines, Inc.) | Kundert, Kris (Enbridge Pipelines, Inc.)
Testing of pipeline leak detection systems can be challenging. It is also a critical activity which provides key information on the systems capability for communications to regulators and key stakeholders. The authors describe an API RP 1130 compliant test method that relies on the development of a limited number of realistic "leak signatures" that are superimposed on archived SCADA data in a way that preserves not only a faithful representation of the leak, but the real-world impacts of noise, calculation uncertainties, and measurement errors as well. In addition to maintaining high hydraulic fidelity, coverage and flexibility, this procedure is performed at low cost while potentially providing a greater degree of insight into the detailed performance of the leak detection system than can be achieved with other methods.
INTRODUCTION AND BACKGROUND
The Need for Testing of Pipeline Leak Detection Systems
A leak detection system (LDS) is a safety and integrity-critical component of an operating pipeline that is designed to help mitigate negative consequences following an unplanned commodity release. Its intended purpose is to reduce the potential negative impacts from a breach in pipeline hydraulic integrity (e.g., a leak with its resulting spill). Reducing these potential negative impacts is achieved by rapidly detecting the leak and determining its most probable location. Determination of these factors in as short as time frame as possible provides key information that is critical in terms of enabling the pipeline operator to respond faster, more effectively, and with greater precision. Note that the most commonly applied method for leak detection is via Computational Pipeline Monitoring (CPM) systems, which are the explicit focus of this document.
As part of the operator’s overall spill response plan the organization should be able to quantify the leak detection system’s predicted performance. This allows the operator to identify areas where further leak detection improvements are desirable and refine location specific response plans. It also provides a mechanism by which the LDS performance can be monitored and tracked over time.
Quantifying the leak detection performance requires testing. As stated in the American Petroleum Institute recommended practice 1130 (API 1130), “[t]he primary purpose of testing [quantifying] is to assure that the CPM system will alarm if a commodity release occurs.” Note that while API 1130 is specific to Computational Pipeline Monitoring leak detection systems, the quantification of system testing is applicable to all leak detection systems.
We describe a prototype application that uses Bayesian statistics to improve Trans Alaska Pipeline System (TAPS) controller response time and reduce control room workloads by automating the process of evaluating and attributing the causes of Pipeline Leak Detection (PLD) system alarms. Pipeline Leak Detection System for Alarm Validation and Evaluation (PLDSAVE) was developed with the support of Alyeska Pipeline Service Company (APSC), operator of TAPS, and is designed to work in conjunction with the existing TAPS leak detection system. The basis of the TAPS PLDSAVE application is an underlying probabilistic description of TAPS. This probabilistic description encompasses instrument and PLD system modeling errors, a leak condition statistical model and a description of the physical constraint conditions applicable to TAPS configuration. The stochastic model raw data is TAPS' instrument measurements, PLD system state information, plus modeling data and leak alarms provided by the PLD system. A Bayesian inference process is regularly applied to this stochastic model in such a way that the system determines a leak probability or validity value, and the most likely current TAPS leak and measurement/modeling error state. From the user perspective, any PLD system alarm is accompanied by the calculated leak probability value combined with an estimate of the current measurement and modeling error magnitudes that apply to the PLD system if the system is assumed not to be in a leak condition. The goal of PLDSAVE is that TAPS safety and integrity are therefore enhanced as minimizing cost and operational distraction by (1) allowing the controller to respond in shorter time frames and assertively to high probability leak conditions, and (2) by assisting the user in the PLD system alarm attribution process by pointing to the most likely instrumentation or modeling alarm causes and conditions when a leak is of low assessed probability.