This paper examines both the method and results of a leak detection sensitivity analysis for a liquid pipeline. A fractional factorial design is used to quantify both primary effects as well as confounding effects between parameters. The analysis examines the impact of uncertainty and bias in pressure and flow measurements, as well as spatial and temporal discretization on leak flow estimation. These are considered under conditions of transient pressures, the presence of a leak and with altered SCADA poll frequency. The results of the parametric study as well as the applicability of the general approach are discussed.
INTRODUCTION AND BACKGROUND
The ability of pipeline operators to swiftly detect pipeline leaks is critical to the safeguarding of public and environmental interests. One of the prevalent tools for achieving this ability within industry is the use of a real time transient model of the pipeline. A primary benefit of utilizing a real time transient model for pipeline leak detection is the ability to accurately represent the pressure profile of the pipeline under transient conditions (Learn, 2015). A more accurate representation of pipeline transients leads to a more accurate estimation of linepack and hence a lower error in the leak flow estimate. As a result, alarm threshold values can be lowered without increasing false alarm frequency, and a better leak detection sensitivity can be achieved.
One of the more challenging roles for a leak detection engineer is to assess and understand the multitude of parameters affecting the error in leak flow estimation. The most widely applied standard, API1149 (1993), provided an excellent theoretical framework for estimating leak flow uncertainty as a function of time averaging window and telemetry uncertainty. However, the most recent update to this standard recognizes that potentially many different parameters affect leak flow uncertainty and recommends a perturbation approach against a reference model. (Salmatanis, 2015)
Given the number of parameters which may affect leak detection sensitivity, a more efficient method is needed to assess the impact of such parameters. Assessing all the potential effects of all parameters within a large quantity of scenarios can be time consuming. It can be onerous to perform this analysis on pipelines in the early stages of project development, during which certain other design assumptions are yet to be confirmed. In addition, many projects may never progress beyond the prospecting stage despite significant design and analysis.
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.
The American Petroleum Institute (API) publication number 1149 (first published in 1993)  is perhaps the first accepted industry procedure for the numerical assessment of uncertainty in software-based Computational Pipeline Monitoring (CPM) leak detection systems (LDS). This publication remains valid and extremely valuable within its range of applicability. Generally speaking, it is designed for crude oil and refined products pipelines. It also focuses on (while also discussing other ancillary issues) single, straight pipelines and on the Material Balance method of CPM, particularly under steady state conditions.
A recent initiative sanctioned through American Petroleum Institute (API) and sponsored by the Pipeline Research Council International (PRCI) has developed a revision of procedure for the assessment of uncertainty in CPM techniques, in light of a number of recent technological developments and operational requirements. It is also directed at engineering uncertainty factors that prove, in practice, to have a significant effect on LDS uncertainty but that were not thoroughly addressed in the 1993 version.
The new procedure’s aim to follow the standard API and ASME measurement uncertainty practice of defining a Reference Value, a Bias and a Precision for LDS, just as with any other industrial measurement system. In particular, it is possible for the reference conditions of the pipeline to be estimated using a transient pipeline system simulation model – the Reference Model – that takes all the relevant engineering uncertainty factors into account. In this respect, the procedure is similar to a formalized, statistical Leak Sensitivity Study (LSS) as is often performed as part of the requirements analysis and design of a software-based LDS.This paper provides an overview of the procedure, with a focus on the utilization of transient pipeline simulation models as the Reference Model. The process of identifying and prioritizing the key areas of input uncertainty is highlighted. In particular, experiences in running the procedure for LVL liquids, HVL liquids and natural gas pipelines are discussed. Other areas of discussion and comment include how the new API 1149 update, technical report, can be used for a relative benefit analysis of different candidate CPM techniques for a specific pipeline; and how it might fit in with industry best practices as an API Technical Report (TR).
Evaluating the effectiveness of a CPM implementation via leak testing is paramount to confirm that the performance of the CPM system is acceptable based on a pipeline company's risk profile for detecting leaks. However, leak testing of a CPM system is challenging due to the complexity of the CPM design, as well as the need to stress test the CPM over the breadth of operational scenarios to assess the robustness of the CPM, where test coverage includes steady state threshold sensitivity, transient threshold sensitivity and the threshold switching action. This paper reviews the leak testing challenges encountered during CPM implementation and evaluation, outlines its limitations, and proposes a novel approach to an API RP 1130 recommended test method that can be applied to stress test CPM sensitivity, providing an evaluation of CPM robustness over a range of varying operating scenarios. The concept of the new testing methodology, along with a feasibility study on the automation of the test process, is discussed. Extensive tests are carried out to evaluate and assess the new testing methodology, and a comparison is made with other API RP 1130 recommended leak test methodologies such as parameter manipulation tests, simulated leak tests, and fluid withdrawal tests. The results indicate that the proposed technique has far wider testing coverage compared to existing approaches to leak testing while providing similar sensitivity measurement results and appears promising for use in stress testing sensitivity of CPM systems to gain an understanding of CPM robustness, which in turn has improved the sensitivity and robustness of Enbridge's current leak detection systems.
This paper examines the feasibility of Real Time Transient Model (RTTM) based methods for gas pipeline leak detection, elucidates the factors that must be managed for effective gas pipeline leak detection, and examines factors that impact leak detection and location sensitivity.
A growing regulatory focus on minimizing the impacts of ruptures in natural gas commodity pipelines is increasing the pressure on the operators of such systems to provide means of rapidly detecting and locating such leaks. Leak detection systems have become standardized components of liquid commodity pipelines over the last few decades, but have not been emphasized for natural gas systems.
Although many methods have been used to detect leaks in liquid systems, the most commonly used approach uses real time transient models and a mass-balance approach to detect commodity losses. The approach is extensible to gas systems in a fairly straightforward manner, and this paper will discuss such implementation. However, it is worth nothing that gas systems have certain differences that make them distinct from liquid pipeline systems. One difference is that gas pipelines, especially if they are part of or support gas distribution, have the potential to be far more highly networked, branched and looped than liquid transportation systems. Gas is a far more highly compressible commodity than most liquids are, and this has ramification for desired levels of instrumentation and speed of response. Finally, gas pipelines are more highly typified by maintenance requirements that can interfere with or degrade the performance of RTTM systems.
Another significant different between liquid and gas pipeline leak detection requirements is that generally, in a liquid line, a very large leak may be quickly identified by rate of change alarms. In contrast, a large leak in a gas pipeline, because of the compressibility of the gas, will cause much slower changes in the pipeline pressure. A gas pipeline therefore, may need to rely on an RTTM based leak detection system even for the reliable and timely detection of very large leaks.This paper attempts to illuminate these issues and equip the reader to understand and deal with them.
Significant financial and environmental consequences often result from line leakage of oil product pipelines. Product can escape into the surrounding soil as even the smallest leak can lead to rupture of the pipeline. From a health perspective, water supplies may be tainted by oil migrating into aquifers. A joint academic-industry research initiative funded by (Pipeline and Hazardous Materials Safety Association) PHMSA(1) has led to the development and refinement of a free-swimming tool which is capable of detecting leaks as small as 0.01 L/min (0.03 gallons) in oil product pipelines. The tool swims through the pipeline being assessed and produces results to the end user at a significantly reduced cost compared to current leak detection methods. Above Ground Markers (AGM’s) capture low frequency acoustic signatures and digitally log the passage of the tool through a pipeline. A tri-axial accelerometer system gives the odometric position of the ball, and has the accuracy of standard instrumented pigs. Several other types of sensors like temperature, and pressure, are also present in the ball and collect useful data.
Kulkarni, Mohan G. (ExxonMobil Upstream Research Company) | Buitrago, Jaime (ExxonMobil Upstream Research Company) | Arslan, Haydar (ExxonMobil Upstream Research Company) | Bardi, Francois C. (ExxonMobil Production Company)
Prompt leak detection is an essential element of the integrity management process for any hazardous materials pipeline. If leaks are not detected at an early stage, they will result in progressively worse damage to the environment and can endanger the local population. Ultimately they can grow into a far more serious failure of the pipeline if not repaired in a timely manner. Therefore a variety of leak detection and/or leak prevention systems are used, including monitoring flow and pressure levels, surface monitoring
and networks of sensors located along the line. However, no single system is able to detect and accurately locate all small leaks when they first occur, and many require installation and maintenance of substantial levels of costly equipment on the pipeline. For this reason, the industry is interested the in advancement of alternative approaches in leak detection.
SmartBall is a radical new technology that has been successfully deployed in water transmission pipelines since 2004. It is now offered as a solution to the problem of leak detection in oil and gas lines too. It is a spherical acoustic device that travels through the pipeline propelled by the product flow and will detect the acoustic signature of any release of pressurized product to the environment (i.e. a leak). Its low cost, ease of deployment and the ability to immediately locate pinhole leaks to within a meter offer major benefits to pipeline integrity managers worldwide.
The paper discusses a number of case histories from the early deployment of SmartBall. The complexities of transferring the technology from a water environment to the hydrocarbons industry are addressed.
The leakage of hydrocarbon products from a pipeline not only represents the loss of natural resources, but it also is a serious and dangerous environmental pollution and potential fire disaster. Quick awareness and accurate location of the leak event are important to reduce losses and avoid disaster.
A leak-detection method using transient modeling is introduced in this paper. This method is suitable for both gas and liquid pipelines, with comprehensive consideration of the transient-flow features of compressible flows and stochastic processing and noise filtering of the meter readings. The correlations for diagnosing the leak location and amount are derived on the basis of the online real-time observation and the readings of pressure, temperature, and flow rate at both ends of the pipeline. As an online real-time system, great attention has been paid to the stochastic processing and noise filtering of the meter readings and the models to reduce the impact of signal noise. It is also essential for the robust real-time pipeline observer to have the self-study and adjustment abilities needed to respond to the large varieties of pipeline configuration, pipeline operation conditions, and fluid properties.
Real application cases are presented here to demonstrate this leak-detection method. For example, in the leak detection of a crude-oil pipeline of 34.5 km long and 219mm in diameter, this method located the leak at 16.6 km from the pipeline upstream end, which is only 0.6 km away from the actual leak location.
When there is a leak in the pipeline, the event will transfer to both upstream and downstream along the pipeline at the acoustic velocities. As a result, the measurements at the pipeline ends will change. The different location and rate of the leak will result in different meter readings at the pipeline ends. This is why the pipeline internal thermodynamic flowing features can be used to identify the appearance of a leak and determine its location.
It is essential for a leak-detection method and system to be sensitive to a small leak and insensitive to the system and measurement noise. To issue reliable and accurate alarms, great efforts have been paid to the stochastic processing, filtering the noise of the meter readings and the models and reducing the impact of signal noise.
Fig. 1 shows how this method works on the system control and data acquisition system (SCADA). An online real-time pipeline observer, which will always be leakage-free, is running and putting out the expected readings for the pipeline without leakage (such as flow rates at the pipeline ends) according to the measured inputs (such as pressures and temperatures measured at the upstream and downstream ends). When there is a leakage, the observer outputs are different from the meter readings, and the discrepancies between the observer outputs and the meter measurements can be used to identify the appearance, rate, and location of the leak (Wang and Wang 1996,Wang 1998).
Because the leak-detection of this method is based on the comprehensive internal flow features of the pipeline, it can be applied to the pipeline without concern for the upstream and downstream connections. The advantage of this method over the pressure-point-analysis method is that it continues detecting the leak during the entire time it exists. Therefore, this method has more opportunity to locate the leak accurately and issue the alarm reliably.