Abstract The integration of Renewable Energy Sources (RES) into the electric power system and the demand for efficient, clean and reliable production, transport and distribution of energy is connected with an increased coupling between the individual energy systems. The interdependency between natural gas and electric power systems, for instance, is increasing and expected to increase further in the near future, mainly due to the growing demand for flexible backup generation and the need for short- and long-term energy storage options to balance the fluctuations of variable and intermittent RES. The need for flexible backup power can be covered by Gas Fired Power Plants, while the demand for seasonal storage can be met by Power-To-Gas facilities. In this paper, we show how curtailment of RES can be reduced by operating Power-To-Gas facilities. Introduction To meet future decarbonization targets power system planners and operators have to integrate more variable renewable energy sources into their electric power systems. The stable and reliable operation of the power system, however, requires a balance between power generation and demand, since electric energy cannot be efficiently and economically stored in existing power system networks. Due to the uncertainty and variability in the generation from Renewable Energy Sources (RES) and at the same time the lack of energy storage, power system planners and operators must find new flexibility and storage options, which typically lie at the interface between different energy networks, such as natural gas, heat & power networks. Natural gas networks, for instance, have relatively large energy storage capacities (linepack, underground gas storage, LNG storage tanks), which can be leveraged by electricity networks to tackle the flexibility and energy storage challenges. The connection between the gas and electric power system can exist at Gas-To-Power plants (G2P) as Gas fired Power Plants (GPP), electric driven gas compressor stations (Power-To-Pressure - P2P) as well as Power-To-Gas facilities (P2G). G2P facilities have relatively short start-up and shut-down times, making them well suited as back-up generation sources in case of lack of wind and solar generation. P2G facilities, on the other hand, can be used to convert excess power generation from renewable sources into hydrogen (H2) and/or synthetic natural gas (SNG), which in turn can be injected into the natural gas pipeline network and/or underground gas storage reservoirs.

Abstract Leaks may occur at any time and location in a liquid transmission pipeline. One of the significant complexities in the leak detection process arises when the leak happens in a pipeline in the presence of phase change. A set of experiments are conducted on a 374-meter (1227-ft) pipe to examine the performance of CPM-based Leak-Detection algorithms. Different intensities of slack from the bubbly flow to the complete cavitating zone are managed to be present before performing various leak tests in terms of size and location. Flow and pressure values are measured at different locations along the length of the pipeline and used as the initial and boundary conditions or constraint points and fed to the algorithms. Two CPM-based leak detection algorithms are considered. The first algorithm performs the leak detection based on a CPM model, which does not model the hydraulics involving phase change. The second algorithm takes advantage of a CPM solver with a precise model to take phase change (cavitation) and the presence of vaporous phase (slack) into account. The ability and quality of these leak detection algorithms are examined against the experiments. The results show the importance of the phase change modelling ability of the CPM to avoid false positives and detect the leaks of different types, which would have otherwise been masked under operating conditions that involve phase change.

ABSTRACT Analysis of the operating risk due to hazardous liquid pipeline spills is a challenging task. One reason for this is the fact that the spill probability and the magnitude of the release are both difficult to quantify for any particular pipeline. One source of comprehensive information regarding spills in the United States is the Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA) hazardous incident database. This database is a mandatory compendium of spill incidents dating from 1968, and relies on self-reporting on the part of pipeline operators. We use this database to calculate historical average spill incident rates for various designated spill causes, and demonstrate a methodology using a tail risk analysis of the limited release orifice information supplied by the database to calculate a more complete aperture probability distribution for those causes. The incident rates developed by this analysis are then adapted to obtain more reliable incident rates for a particular pipeline by using the PHMSA results as a prior estimate and implementing a Bayesian posterior update based on historical sparse spill information for that system. We conclude by showing how the information obtained can be used in conjunction with pipeline hydraulic, leak detection performance, logistic response, and other models to perform a probabilistic cost-based risk analysis for the pipeline system.

ABSTRACT The Negative Pressure Wave (NPW) leak detection method has been increasing in popularity recently due to the promise of highly accurate leak location calculations compounded with response times on the order of seconds. Additionally, when compared to other leak detection methods such as Real Time Transient Models (RTTM), NPW can be implemented on many different pipelines in a relatively short period of time with only the need for pressure transmitters. However, as with all engineering applications, there are tradeoffs to consider. In this analysis, a NPW solution was evaluated on two different liquid pipelines running offline simulators: one pipeline transports crude oil and one pipeline transports diesel. It was observed that, under very specific operating and test conditions, NPW could perform adequately. The conditions which allow for accurate NPW performance are limited when considering real world operations. With this consideration, NPW can be a complimentary method to other leak detection methodologies. This analysis is intended to provide pipeline operating companies with a greater understanding of when and where an NPW solution may be appropriate, as well as the limitations and challenges associated with the NPW methodology.

ABSTRACT Refining NZ is New Zealand's only oil refinery and the leading supplier of refined petroleum products to the New Zealand market, including gasoline, diesel, jet fuel and other products. It supplies most of the fuel needs for Auckland through a 169 kilometre (105 mile) long, 273 millimetre (10.75 inch) diameter pipeline terminating at the Wiri Terminal in South Auckland. To meet the growing demand for jet fuel, Refining NZ started a pipeline capacity increase project in 2017. The first 2 of 3 phases of this project have been completed with the 3rd phase due to kick-off in 2019. Refining NZ rely on an Online Real-Time Transient Modelling (RTTM) system to provide decision support applications for the pipeline controllers such as batch tracking, scraper tracking and leak detection. The RTTM was updated in 2017 to include the changes made in the pipeline operation. On September 14th, 2017, Refining NZ experienced a significant incident on the pipeline which threatened their business. This paper details how Refining NZ use their RTTM as a decision support tool, some key learnings from the incident, including analysis of the incident using the RTTM, and what Refining NZ has done to improve its operations as a result of the incident and the subsequent analysis.

ABSTRACT The Negative Pressure Wave (NPW) methodology of leak detection has potential advantages over other pipeline leak detection technologies in terms of leak location accuracy and detection speed. This paper investigates the parameters affecting the uncertainty of the leak location inferred by the NPW methodology. Numerical tests based on data sets generated by an offline hydraulic simulation tool with different leak scenarios are applied to investigate the impact of various parameters on the prediction of the leak location, including noise level, data acquisition scanning rate, transient period of the leakage and wave speed. Based on the analysis of these parameters, this paper further studies how the leak location uncertainty is affected by two non-dimensional variables: Signal to Noise Ratio (SNR) and Transient-period to Scan-rate Ratio (TSR). Numerical tests containing random perturbations of these two variables are performed to evaluate their influence on the leak location uncertainty. This paper then discusses how leak characteristics and pipeline conditions are related to SNR and TSR, and how these affect the minimum detectable leak size. Through the investigation, this paper finds that the leak location uncertainty of the negative pressure wave system is affected significantly by the uncertainty of the wave speed estimation and the event time of detecting the pressure wave at the pressure sensor. The event time uncertainty is mainly determined by two factors: SNR and TSR. This investigation also suggests that the percentage of nominal flow rate is not sufficient for defining the minimum detectable leak size for a negative pressure wave system. It is more reasonable to define the minimum detectable leak size as the actual leak flow rate with respect to leak transient period, the pipe diameter, the type of fluid transferred by the pipeline and pressure noise level.

ABSTRACT We follow up on our previous paper describing an API RP 1130 compliant test method that imposes simulation-based leak perturbation signatures on archived SCADA data. The perturbation-based simulated leak testing (PSLT) approach combines a faithful representation of the leak with the real-world impacts of noise, calculation uncertainties, and measurement errors. The current paper expands on the previous work by providing an analysis and testing of hydraulic limitations as well as benchmarking against other commonly employed leak testing methods. The work continues to demonstrate that previous conclusions regarding its relatively low cost, high hydraulic fidelity, coverage, and flexibility are maintained, while producing extensive output metrics that include detailed leak detection sensitivity maps, false alarm rates, and supporting statistical analysis. INTRODUCTION AND BACKGROUND 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). Rapidly detecting the leak and determining its most probable location enables the pipeline operator to respond rapidly, effectively, and with precision to the spill, thus reducing its size and negative impacts. 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.

ABSTRACT 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.

Abstract This work focus on the simulation of gas pipeline dynamic models in view to develop a leakage detection tool. The gas dynamics in the pipes is represented by a system of nonlinear partial differential equations. The linear partial differential equations is reduced to a transfer function model. Taking advantage of an electrical analogy, a pipeline can be represented by a two port network where gas mass flows behave like electrical currents and pressures like voltages. Thence, four transfer functions quadripole models are found to describe the gas pipeline dynamics, depending on the variable of interest at the boundaries. These models are simple enough to be used in the control and management of the network. These models have been validated using operational data and used to simulate a leakage. Introduction Natural gas sustains one quarter of the global energy necessities [8] with the world gas networks representing many kilometers of pipelines, manifold branches, pumps, compressors, valves, among others; being therefore a large scale and complex system to control and maintain [1]. The control and the safe operation of these systems are crucial due to grave consequences that may result from default operation [5], mainly leaks. Model-based (MB) software methods continually measure pressure and/or mass flow signals frequently at the intake and outtake of the pipeline, so instrumentation is usually limited to the extremes of the pipelines and consequently economical to implement. The most reliable among these methods are based on nonlinear partial differential equations (PDE) to describe the gas dynamics [3]. These PDE are not modular and its resolution can lead to a non-efficient and high complexity models; nevertheless PDE can be linearized and leading to simple and accurate models [6,3]. In this paper, we propose a model focus on the modelling and simulation of the pipeline dynamics, and able to improve leakage detection/location techniques. Therefore, we describe four quadripole models which are derived using an electrical analogy. These models bestow boundary values of pressure and mass flow, as well as intermediate values, allowing for obtain mass flow and pressure calculated profiles. The analysis of the discrepancies between calculated and measured values allows investigating the effect of a leakage. The article is organized as follows: In Section 2, transfer function (TF) models are derived from a nonlinear first order hyperbolic PDE. Four different TF quadripole models are described in Section 3. In Section 4 the described models are validated using real data. An approach to determine intermediate pressure and mass flow values is presented in Section 5 and adapted to the leakage situation in Section 6. The last section provides some brief conclusions and directions for future work.

Abstract Operator Training Systems (OTS) are traditionally used for training pipeline controllers in how to deal with normal and abnormal operating conditions, however OTS can serve to provide multiple uses in an Oil & Gas pipeline software environment. One of these is as a test platform for a Liquid Computational Pipeline Model Leak Detection System (CPM LDS). This paper demonstrates such a use and outlines the benefits of providing a testing environment that takes into consideration normal operating conditions (NOC) as well as abnormal operating conditions (AOC) and their impact on LDS performance. LDS performance maps can then be generated for most anticipated pipeline conditions. Details of hydraulic modeling, control system emulation, and SCADA telemetry simulation will be discussed as well as their impact on the fidelity of the OTS response and subsequent accuracy of LDS predicted performance. Review of the level of abnormal operating conditions and their implementation on the OTS will also be outlined and the resulting OTS behavior. Lastly, the determination of expected LDS performance for a range of normal and abnormal operating conditions will be summarized. Introduction and Background The use of offline methods to establish performance metrics for leak detection systems has been common in the pipeline simulation industry. Regardless of the leak detection method, the necessity to define the anticipated performance and validation of the design of the system is important. Several papers have addressed this topic. A previous study has used historical SCADA data provided to the LDS and analyse the response of the LDS with the use of statistical methods to superimpose leak events (1). In the absence of real pipeline data, as in the case of greenfield projects, the LDS vendor is left to wait until such data is available to be able to create an initial performance map for the LDS. Conditions of the pipeline that are not normally encountered are also problematic as the historical data would not exist. This pertains to failure modes not normally encountered or combined effects of transients and failure modes. To address this, we propose to use the OTS as a leak detection test bed. Approach The use of the operator training environment as a test bed for defining LDS performance is based on the assumption that the training environment provides a reliable platform that will allow for realistic recreation of pipeline operating conditions. Based on Schneider Electric's SimSuite Pipeline™ modeling tool the test bed used to illustrate this has several components of the real pipeline operating environment. These are the duplication of the SCADA system and its functionality, the duplication of the field response in terms of PLC/RTU algorithm and device switch gear response, and lastly the modeling of the pipeline hydraulics. All these components integrated together serve as the test bed that will provide a high level of fidelity to the real pipeline response as seen by a LDS in the production environment. The Figure 1 shows the functional architecture of these components.

Abstract In the recent decades, new leak detection technologies for hydrocarbon pipelines are emerging constantly, and conventional technologies are evolving to achieve better performance. It gives pipeline operators a good chance to detect leaks in the early phase and protect the environment from contamination by leaked hydrocarbons. However, this also makes the selection of leak detection methods more complicated. Two major tasks are required during the selection of leak detection methods: appropriately defining the selection criteria, and evaluating the leak detection capability of candidate technologies. For internal leak detection technologies, API 1130 (API, 2012) provides guidance to industry on the selection criteria and API 1149 (API, 1993) attempts to define the evaluation technique. Applying an evaluation technique that considers all likely operating conditions in its risk assessment is advisable. The process of combining of all the potential performance scores of a leak detection technology for various operating conditions into a single fitness factor can be complicated. This paper recommends the use of a decision tree to calculate the leak detection methods' fitness factor. In this analysis, many facets of leak detection are considered. They include the physical principles of the technologies as well as their performance under different operation conditions. The paper describes a systematic approach of evaluating the various leak detection methodologies against specific design criteria. The appendix includes descriptions of the strengths and limitations that the various leak detection techniques may have given a particular set of leak characteristics, operating conditions, physical design considerations, commercial implications, construction costs and operational costs. The paper proposes that a qualitative or semi-quantitative analysis on individual pipelines over all expected operating conditions could determine the best technology(s) to apply in mitigating the risk of undetected leaks in pipelines. Introduction There are multiple real time leak detection technologies, and they all have strengths and weaknesses. Leaks can present themselves in different ways, and every pipeline has different physical design and operational parameters. Determining the best leak detection technology(s) for a specific application can be complex. Every leak detection technology can cover different portions of the risk spectrum but no single technology will mitigate all of the risk as illustrated in Figure 1.

Abstract 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. Introduction In the operation of hydrocarbon liquid pipelines, Computational Pipeline Monitoring (CPM) systems are either mandated by legislation (e.g. in Alberta) or must meet regulatory requirements when they are installed (e.g. the current situation in United States). Software-based algorithmic monitoring tools can be used to enhance the abilities of a Pipeline Controller to recognize hydraulic anomalies in the operations of a pipeline, such as a leak or commodity release.

Abstract The American Petroleum Institute (API) publication number 1149 (first published in 1993) [1] 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).

ABSTRACT Different leak detection technologies offer different benefits and limitations. Popular options include real-time transient models, statistical analysis, and negative pressure wave systems. This paper outlines the successful integration of a statistical volume balance system and a negative pressure wave system on a crude oil pipeline. The results of the Site Acceptance Test validate the combined system's increased reliability, detection speed, location accuracy and sensitivity. This paper will also examine the benefits and technical challenges of combining these two technologies. The field application of the two systems on a 170 km (105.63 mi) crude oil pipeline will be explained in detail, along with the results of controlled product withdrawal tests on the pipeline. INTRODUCTION The US Department of Transportation Pipeline and Hazardous Materials Safety Administration Final Report of Leak Detection Study [1] correctly suggests that the recurring theme of false leak alarms in pipeline leak detection can be solved by the combination of technologies - utilizing multiple redundant and independent leak detection systems. Different pipeline types require different leak detection methods to assure detection. Key factors that influence the performance of different leak detection technologies include: Number of sensors on the pipeline Topology of the pipeline Accuracy, repeatability and response time of sensors on the pipeline Sensor digitization Availability and quality of a telecommunication system End conditions such as tanks at the outlet or positive displacement pumps at the inlet The impact of the above factors will vary depending on the location of the leak and the operating condition of the pipeline at the time of the leak. For this reason, the leak detection technologies that Atmos offers can be combined to make a specific multi-method leak detection system that is optimized to detect all types of leaks on a pipeline.

ABSTRACT Effective risk and cost management are paramount concerns for pipeline owners and operators. Leak detection systems (LDS), while having no effect on the probability of a leak occurring, limit the scope of potential damage and reduce risk. The authors propose using a stochastic simulation to quantify the economic effectiveness of these systems in mitigating these low-probability, high impact events for liquid pipelines. When considering any capital investment, it is useful to estimate its expected value; the authors propose using a stochastic simulation to perform an economic analysis on these systems - analyzing the ability of these systems to mitigate the impact of oil spills. For this methodology, each leak detection system is measured in terms of the reduction to total oil spill impact. This is accomplished by using leak probabilities to generate theoretical leaks that mimic the expected leak behavior of a pipeline. Next, probability maps are used to generate theoretical onset-to-response times, where the generated times mimic the expected behavior of the LDS. These generated values are used as input to a cost model to estimate the total spill costs for each leak, and repeated application results in an expected value for each subsystem. The costs are converted to a present value using the internal rate of return specified by the operating company. Risk is estimated with and without a proposed change to the current leak detection system; the difference between the two total spill risks is the value or impact of the proposed change. Comparing risk reduction values provides a meaningful way of evaluating LDSs and potential changes to LDSs. Additionally, it can justify the removal of unnecessary LDS components and allow comparison of proposed leak detection projects with other investment options.

ABSTRACT Noise is prevalent in all pipeline instrumentation and measurement system data and has been shown to affect Computational Pipeline Modeling (CPM) based leak detection (LD) systems in practice. Noise present in data can be random or repetitive, occurring continuous or in isolated bursts and can have a multitude of sources. At Enbridge, the leak detectability of CPM LD system is notably affected by the presence of noise in the pipeline data. Data smoothing of noisy pipeline data is a non-trivial process; these techniques are used not only to eliminate "noise" from the operating data, but also to extract real trends and patterns within the data, and to maintain important characteristics of the signal itself, particularly during transient time periods. The data smoothing initiative began within the Leak Detection group around August 2011 and the wavelet transformation technique was adopted as the data smoothing methodology to remove or attenuate the noise present in the pipeline data to improve the leak detectability of current LD system (MBS or Mass Balance System). Extensive offline studies and pilot tests including simulated leak tests, fluid withdrawal tests, and API 1149/API 1130 calculations have been carried out to prove this concept [1]. The offline studies have shown promising results and Enbridge has decided to pilot this technology with real time SCADA data in 2013. If successful this pilot will be the catalyst for the full implementation of this technology on all Pipelines. Online implementation is a non-trivial process as it includes not only modifications to the real time communication architecture between the SCADA system and the leak detection CPM, but also includes code integration, windows service development, and dealing with performance issues arising from the online assessment and evaluation. To properly assess performance it has also been commited to performing a full fluid withdrawal test during this Pilot to assess real time performance of Data Smoothing. This paper reviews the data smoothing methodology employed, and discusses the issues faced during the online implementation as well as the online test results. Extensive pre-implementation and online tests have been carried out to evaluate and assess this methodology. These test results indicate that a data smoothing technique has a substantial effect on the data quality which in turn has improved the detectability of current leak detection system.

ABSTRACT LDS, detecting pressure drop surge seem to be the most effective for the leak detection in pipelines. In fact, such systems have very high sensitivity. Despite that there are false leak detections, when disturbances in the pipeline are not related to leakage (such disturbances might be caused by changes in the frequency of pumps, pressure regulating systems work, etc.) Present article addresses the problem of minimizing false leak detections, as well as increasing sensitivity of algorithms, based on the pressure drop surge.

ABSTRACT: Leak detection systems come in many "flavors", each with distinct advantages, disadvantages and sometimes unique features and capabilities. Needless to say, performance and cost vary greatly as well. It can be a daunting task to decide which technology to invest in for a specific pipeline and a few wrong turns in the decision making process can end up being very costly. It is easy to say " I want a leak detection system capable of detecting a ten gallon spill in less than a minute, locating it within an accuracy of 10 feet ". It might even be possible to install a system that can achieve this, but what about the cost? If this system costs ten times as much as another system with slightly lower performance, is it really worth spending the extra money? What is the difference in the total cost of a specific incident when you factor in the various cost components? This paper puts some of the leak detection metrics and selection criteria into perspective and illustrates the importance of various factors under different conditions. This is accomplished by using some hypothetical, yet realistic, examples of various leak incidents. BACKGROUND American Petroleum Institute's publication "API RP 1130" provides an excellent overview of most of the leak detection methods available for liquid pipelines - and many of these apply to gas pipelines as well. API RP 1130 also provides a definition of the performance metrics and other selection criteria that should be considered during the selection phase. Sometimes the selection criteria are not carefully considered and prioritized, which can lead to a poorly written Request for Quotation (RFQ) issued to various system vendors. To make matters worse, this is often done using a format that originally was part of API 1155 – now an appendix to API RP 1130.

ABSTRACT: This paper explores the ability of detecting a leak during shut-in conditions for both long and short sections of liquid phase pipelines, using the API 1149 report as a basis. The presentation will begin by briefly introducing the API 1149 report, describing a simple mathematical model for predicting temperature and pressure changes with instrument uncertainties during the shut-in process, followed by application case studies with leak and no leak conditions. The results will be examined with regard to providing recommendations for leak sensitivity. INTRODUCTION The API 1149 Report [1] provides guidance for predicting leak detection thresholds for a flowing pipeline. In reality, steady state conditions are not typically the case with shut-in pipelines due to the pressure change over time being a direct function of continually changing temperature. Additionally, uninsulated above ground pipe station yard piping can result in fluid pressure being a direct function of the ambient diurnal temperature variation. The API 1149 Report threshold determination is fundamentally based on the propagation of uncertainty principle; in contrast this paper offers an alternative solution to precision or random uncertainty estimation by exploring the more significant effect where the measured pressure change does not keep balance with the modeled temperature change. This paper begins by describing API 1149 report briefly, along with some limitations and then a formal foundation is made for shut-in pipeline simulation. Comparisons are made with the simulation results and an example API 1149 calculation. Overall, focus is placed on theoretical aspects which are typically performed on pipelines not-yet-in service in an attempt to provide some initial expectation for leak detectability. API 1149 Overview The API 1149 Report provides a very methodical approach for determining theoretical mass-balance leak detection sensitivity for any liquid phase refined products or crude oil pipeline.

Abstract: This paper describes a computational toolset designed to quantify and simulate potential spill volumes along a pipeline under a set of plausible operational and design strategies aimed at minimizing spill volume and impact time. The user specifies critical leak detection and spill response factors such as the leak orifice size, pipeline hydraulic design and operating envelope, leak detection system description, and critical logistical/design issues involving the time for initial response/call out, system isolation, and pipeline draindown and repair. The calculator determines the various impact times and spill volumes applicable to the system for a range of user specified imposed leak locations. This allows the operator to perform what-if scenarios as required to determine optimum set of design and operational parameters to minimize the spill impacts. A secondary tool that allows users to simulate and review the impact of a single leak via animated pipeline charts is also described. INTEGRATED SPILL MANAGEMENT Hazardous liquid spills can represent one of the most disruptive and costly events that many pipeline operators are ever likely to experience. Consequences to other parties both public and private can be significant, ranging from damage to property, watersheds, and environment to injury and death. Consequences of oil spills to the pipeline operator are typically financial in nature. These can extend from the simple costs of cleanup, which, for large spills, can be consequential, through the unpredictable results of court litigation, loss of community support and reduction in the value of the operator brand, to very significant revenue losses associated with the operator's "license to operate" the pipeline on a temporary or even permanent basis. In the limit where criminal liability is found, corporate officers can find themselves facing fines and jail time. Because of these significant impacts, it is in the interest of the operator to minimize the consequences of a spill. One part of this equation, of course, involves implementation of proper pipeline design, operating procedures and inspection measures oriented around minimizing the probability of having a leak in the first place. The other side involves being able to deal with the consequences of the spill once it has started. This paper addresses the latter part of the spill management process. Once a leak has started, it triggers a complex series of events or processes that, depending on the efficiency with which they occur, will define the total impact of the spill from both a time and spilled volume perspective. These include: Detection of the leak Pipeline shutdown Isolation of the affected segment Draindown of the segment Repair of the pipe Each of these critical activities in turn is dependent on potentially complex design and logistic constraints that differ for each portion of the spill response process. The activities may involve interplay between various subsystems involving, for example, pipeline hydraulics, signal detection, and human factors analysis. Thus, it is important to understand how design, operational and logistic constraints impact the spill quantifiers on both a global and local basis.