Abstract Crude oil pipelines can occasionally experience flow regimes in the critical section where the flow changes from a laminar streamlined flow to a transitional zone with the onset of turbulence where the Reynolds number is located in the range 2100 to 3000. In this range of operation the flow is considered unstable and currently there is no definitive method for evaluating the friction factor with certainty. The result for modeling purposes is that pressure predictions are rendered somewhat less reliable than if the pipe operated in either the laminar or transition zone or fully turbulent zones. In the critical zone, one approach for both providing a reasonable estimate and for maintaining numerical stability is to use a weighted average of the laminar and transitional friction factor equations. This paper intends to quantify the uncertainty in pressure prediction using this approach, by comparing with estimates for the friction factor based on actual instrumented data during both steady state and transient modes of operation in the critical zone. The first step in attempting to achieve this goal reliably would be to provide a verification base state where the uncertainties in all input parameters such as fluid properties, pipe roughness, temperature, elevation profile, pipe internal diameter, and distances have been reduced as feasibly as possible. The approach taken here involves application of the non-linear regression method, Levenberg-Marquardt. Historical data from an actual operating NPS 16 multi-station blended crude pipeline system was available to assist in this parametric study.

Abstract The paper demonstrates the construction, training, and uncertainty quantification analysis of an artificial neural network model for single phase liquid pipeline leak detection through pressure drop and flow rate monitoring. The demonstrated methods reached acceptable error levels efficiently using theoretical data produced by a theoretical physics model. The results demonstrate that the randomly simulated leakage can be efficiently detected using the trained ANN (Artificial Neural Network) model based on theoretical data derived from physical equations. However, complexity appears when simulated leakage with modeled uncertainty are used in the training of the AI models. The propagation and influence of the uncertainty in the input data on the ANN method are discussed. Randomness following certain probability distributions is introduced into the data to measure the influence on the efficiency and reliability of the training and results of the ANN model. The paper also discusses the influence of the range of input data on the predictability of the ANN model in leak detection. Introduction and Background Data driven Machine Learning technologies such as ANN (Artificial Neural Networks) provide great innovation opportunities towards the design and operation of oil and gas pipeline systems. ANN based models have proved to be efficient in predicting the pressure drop within a pipeline (Brkic, D., and Cojbasic, Z., 2016; Shayya, W.H., and Sablani, S.S, 1998; Salmasi, F., etc. 2012; Fadare, D.A., and Ofidhe, U.I., 2009) as well as solving the inverse problems such as detecting pipeline roughness progression, inner diameter change, and leakage (Cheng, D., Zeosky, D., 2019). The increasing interest towards machine learning models causes the need to highlight the concerns of uncertainty propagation into the engineering applications of the methods. Machine learning models are developed through analysis of data derived either from physics models or measurements.

Abstract This tutorial presentation will build on the introductory pulsation topics by expanding the basic theory into advanced topics in gas piping systems. Topics in this tutorial will address complex problems that many operators face today including: wet gas compression and pulsation bottle design, the impact of changing gas compositions for reverse direction flow on pulsations and horsepower requirements, adding pulsation control for integral (Enbloc) compressors, and mixed compression with different unit types sharing a common header. Each topic will be presented with an overview of the subject, case studies and mitigation techniques or analysis methodologies. In addition, an overview of the latest research and development for novel pulsation attenuation methods will be presented as well as the level of technology development for each concept. Introduction and Background Pulsations in piping systems can be generated by reciprocating compressors, centrifugal compressors, flow-induced vortex-shedding, pressure reducing valves, or turbulent flow. Of these sources, reciprocating compressors and flow-induced vortex-shedding produce the majority of pulsations that cause noise and vibration problems in pipelines. This paper will focus primarily on pulsations created in a gas piping system by positive displacement compressors. Pulsation is a periodic fluctuation in local pressure imposed on the fluid in a piping system by a machine or occurring locally as a result of a flow disturbance phenomenon. The Bernoulli principle describes that a variation in pressure will produce a corresponding variation in velocity (see Equation 1 for a simplified form). While the Bernoulli principle is rarely used in calculating pulsation amplitudes in piping systems, it is an important relation to understand when solving flow related problems. Figure 1 gives a visual example of the molecular redistribution of molecules imposed on a gas by a reciprocating compressor to create a pressure wave that travels through a piping system.

Abstract Air-water two-phase flow in circular pipes has been studied by many investigators. Because of the important effect of flow patterns on the flow and heat transfer, investigations on flow patterns are still very active. On the other hand, investigations of flow patterns in noncircular pipes are still very rare. Triangular tubes have found a great deal of applications in many fields of industry, especially in heat transfer equipment, such as compact heat exchanges, solar heat collectors, miniature heat pipes, nuclear reactor cores and electronic cooling systems. While a large number of studies have been made of turbulent single-phase flow in triangular geometries, very little has been done with two-phase flow. The present investigation was to study the effects of triangular pipe geometry and orientation on flow patterns, slug frequency, and liquid film dryout in gas-liquid flows. Experimental investigations on the flow patterns of air-water two-phase flow in horizontal circular and triangular pipes were carried out. The circular test section consists of a 2-inch (50.8 mm) ID, 28 ft (8.53 m) long clear PVC pipe followed by an equilateral triangular pipe 20 ft (6.1 m) in length having a side length of 2.7-inch (68.6 mm), corresponding to hydraulic diameter of 1.56-inch (39.6 mm). In addition, the effect of corners on the liquid distribution was investigated using two different orientations of triangular channel (apex upward and downward) with comparison to round tubes. Video cameras were positioned 60 and 120 diameters from the inlet of each test section to investigate the flow development. Superficial liquid and gas velocities that ranged from 0.3 to 6.6 ft/s and from 3.1 to 65.6 ft/s, respectively. Different understandings of two-phase flow regimes affected by the shapes of the cross-sections of the channels were obtained by flow visualization. The following flow patterns at different air and water superficial velocities were recognized for both pipe geometries: slug flow, pseudo-slug flow, stratified-wavy flow, and annular flow. Visual observations revealed that the slug frequency in the triangular channel is lower than the frequency obtained in the circular pipe.

Abstract There are occasions when one might want to use results from one simulator as input to another simulator, for example, when preparing data for a leak sensitivity study where data from an offline simulator is used to synthesize measurement data for a real time model. Alternatively, one might want to compare results between different simulators, as with the PSIG test cases. Clearly, ensuring the pipeline parameters and fluid parameters are the same in both simulators is an essential step in this process. However, nuances and differences in modelling philosophies can lead to differences in the results that can be difficult to explain. In this paper we present an incremental approach to ensuring that results between simulators match to within desired tolerances and, where they do not match, provide insight to where the source of the differences lie. The key parameters used to measure the agreement, or otherwise, between the simulators are explained and the effect of the different modeling parameters investigated. To demonstrate, the approach is applied to an offline single-phase simulator, an offline multiphase simulator, a spreadsheet model and a Real Time Transient Model. In the example presented the various steps in the process are described and differences in modelling approaches are highlighted. Finally, we demonstrate that, for the example used, excellent agreement between the different simulators can be achieved. Motivation and Overview The reasons for wanting to ensure that different simulators agree are many and varied. For example, we might want to generate data using one simulator for consumption by another simulator, such as when undertaking a leak sensitivity study. Another example would be independent verification of a flow assurance study.

Abstract Gas pipelines want to provide transportation flexibility to meet demand, but are always focused on operational safety, reliability, system integrity, and environmental issues. Transportation networks need to ensure the components of gas are acceptable for delivery to end users. Local distribution companies want to meet customer demand and have no existing capacity to remove or extract hydrocarbons from their system. This paper introduces a method for calculating hydrate formation and the phase transfer between gas and liquid to allow the user to know how much liquid exists in a pipeline and the appropriate pigging scenario for the liquid fallout. The paper focuses on determining pigging and how the gas transfers to liquid and the amount of liquid hold up that occurs in the pipeline. Using these mathematical models and algorithms can allow an operator to know the correct timing for pipeline inspection and where in the pipeline system liquid is building up for potential safety monitoring. Introduction and Background Cleaning a pipeline during operation is a maintenance procedure that needs to be done on a regular predetermined frequency using pigs adequately designed for the proper cleaning application. The pigging operation will almost always increase the flow efficiency and reduce operating expenses. In pipelines that have low flow conditions it is more prevalent to see an increase in the collection of free water in the bottom of the pipeline, even in crude oil lines. In low flow conditions you need a mechanical means (pigs) to remove both the solids and liquids that collect in the bottom of the pipeline to help prevent the process of internal corrosion. When a pipeline goes online 100% efficiency cannot be expected, but a routine pigging can keep a pipeline operating at 90 – 95 percent capacity. A pigging requires manual labor and adds cost to the pipeline. An algorithm is needed to estimate the amount of liquid in a pipeline at any given moment and allow the user to know that it is time to pig the pipeline to help alleviate the issues of free water or condensate that is staying in the pipeline. Previous papers have already gone over how the simulation flow and gas condensation work in a gas pipeline. Below is the mathematical procedure to estimate the liquid hold up along with the amount of piggable liquid that is in the pipeline to give a better time to pig a pipeline.

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 Due to the constant need of improving flow, pressure and pipeline throughput for single phase flow in fluid pipelines and due to extensive injection of DRA into pipeline networks, whether to supply an increased demand of customers or uprate an overall throughput of a pipeline system, a need to find ways to optimize the effect of DRA injection on the flow rate and pressure has become essential in the pipeline industry. This study looked into the effect of drag reducing agents on the flow rate and pressure of single phase flow in fluid pipelines. Reviewing the literature helped a lot in planning a methodology that explains how the investigation is carried. The methodology started with selecting a pipeline network to be case studied and then utilizing an advance tool that can perform hydraulic pipeline calculations to simulate various cases. After that, results are evaluated, analyzed, and discussed to make sure that they match with the obtained current pipeline data. To optimize, a hydraulic simulation tool called SPS is used to simulate cases where effect of DRA on the flow rate and pressure is improved. Having gone through several investigations on how to optimize the effect of DRA on the flow rate and pressure of single phase fluid pipelines, a number of recommendations has been made to modify several factors that eventually lead to optimization of the effect of DRA. Initially, the definition and characteristics of DRAs has been made clear and understood to be able to know more about what is being researched and investigated. Additionally, the mechanics behind drag reduction have been understood and the effect of DRAs on the flow rate and pressure of pipelines once injected has been understood. Factors such as location of DRA injection, DRA type vs. %Dr, and DRA concentration have been proven to be effective in optimizing the effect of DRA based on the results of the simulated cases. Savings reaching up to $4 Million on spending for one type of polymeric DRA injection were the results of only one factor contributing to optimization which was the location of DRA injection.

ABSTRACT The Austin-Palfrey equation could be directly applied to a pipeline where both the Reynolds number and the pipe diameter are not varied along the entire length and where there is no initial mixing volume at the start point of the pipeline. If the Reynolds number, or the pipe diameter, or both are varied along a pipeline, the pipeline should be divided into segments based on the point along the pipeline where Reynolds number or the diameter change. The Austin-Palfrey equation should be revised to include effects of the initial mixing volumes in subsequent segments. In this study, the Austin-Palfrey equations were revised by introducing an initial pipe length or an initial length factor to incorporate the initial mixing volumes at the start point of each pipe segment. The revised equations could predict the mixing volume along the entire pipeline length. The initial length factor has an impact on the incremental mixing length in subsequent segments. When the initial length factor is larger than one, the mixing length in subsequent pipe segment tends to decrease. When the initial length factor is less than one, the mixing length in subsequent pipe segment tends to increase.

ABSTRACT Economically optimizing DRA within a crude pipeline is simple when using an optimization program, however it is challenging and important to understand how the optimization works. This paper will address a methodology of how DRA and power are optimized to minimize economic costs. Then an analysis will look into how velocity, fluid viscosity, DRA performance, pump spacing, pipe size, cost of electricity, and cost of DRA affect economic optimization results. Finally, this paper discusses several key factors that affect decisions such as building new stations or laying new pipelines versus increasing DRA injection rates into existing assets. APPROACH Energy is consumed in order to move a barrel of oil. DRA decreases the amount of energy that is required to move that barrel of oil. A price associated with both energy consumption to transport a barrel of oil and DRA utilized to reduce the energy consumption. The goal of optimizing DRA usage is to minimize the total cost to move each barrel of oil from origination to destination.

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 Sand management technology has become more prevalent in the petroleum industry to increase well production. Sand may deposit on pipeline walls, causing problems such as pipe blockage, reduced flow area, structural pipe issues, abrasion, corrosion, and low production in wells. This paper explores the multiphase model and computation algorithms used to simulate multiphase flow with sand particles through a subsea pipeline to predict the pressure gradient, transport velocity, and sand deposition. Being able to predict sand accumulation and understanding real-time sand velocity in a pipeline can help define design criteria for subsea pipelines and help pipeline operators maximize production while minimizing well and pipeline downtime. INTRODUCTION Sandstone reservoirs are the source for most of the world's hydrocarbons; however, sand is a common by-product of oil and gas production, especially when producing from reservoirs with low formation strength. When rock around the well perforations is weak, the reservoir fluids can push sand or solid material into the wellbore. Sand in the produced oil and gas enters the pipeline network, which can increase frictional pressure loss, pipeline damage due to erosion, potential equipment failure, and sand deposition (causing flow assurance problems and pressure changes). The traditional sand management method involves preliminary sand extraction from the produced fluid before it is released into the main pipeline. Unfortunately, this method is expensive and not applicable in all cases, such as deep, subsea pipelines. As a result, newer sand management methods focus on tracking the amount of sand in the pipeline and operating the pipeline at conditions that minimize the risks. With these new sand management methods, pipeline simulations become an even more powerful tool for pipeline operators to identify the influence sand has on pressure and temperature changes in the pipeline, detecting erosion and sand deposition, and understanding the impact different operating conditions have on the pipeline performance and safety. This paper discusses a set of sand management modeling and simulation algorithms and approach to detect sand deposition and erosional velocity. The approach was applied to a cluster of offshore platforms and wells connected by a subsea pipeline; in the case described, these simulated sand deposition and erosional velocity events were compared to real-time pipeline data—including theoretical and actual pipeline pressure loss—and verified, allowing the pipeline operator to take appropriate mitigation actions and reduce lost production opportunity.

ABSTRACT This paper presents specialized simulation techniques addressing some of the unique challenges related to modeling of heavy crude (dilbit) pipeline systems. These techniques were developed to improve the accuracy and effectiveness of modeling efforts associated with simulation of seasonal operation and product blending, and include methods to modify fluid properties and other characteristics with simulation coding. INTRODUCTION AND BACKGROUND Heavy crude generally refers to oil of density higher than 900 kg/m 3 . Pipelines shipping products from oil sands production sites typically transport fluids heavier than those servicing conventional oil fields. The bitumen produced either by open mining or SAGD method is often mixed with light condensate ("diluent") to meet pipeline specifications. The mixture of bitumen and diluent is called "dilbit". The dilbit system discussed in this paper can be viewed in Figure 1 . This system originates at the SAGD facilities in northern Alberta and terminates in Edmonton. It includes: LACT (Lease-Automatic Custody Transfer) facilities at each of the SAGD sites. 300 km (186 mile) of NPS42 pipeline ("Upstream Pipeline") 50 km (31 mile) of NPS30 pipeline ("Delivery Pipeline") Trim blending (diluent injection) and product quality monitoring facilities located between the Upstream Pipeline and the Delivery Pipeline. Mainline block valve stations Pig launchers and receivers for each pipeline This paper will discuss the major considerations and simulation techniques in connection with the unique challenges associated with modelling dilbit systems.

ABSTRACT Liquid pipelines through mountainous terrain often experience slack flow: the pressure at the high point drops to the fluid's vapor pressure and a bubble of vapor separates out of the liquid. The bubble grows and shrinks as conditions in the line change and as hydraulic transients move through. Accurately simulating this situation is necessary for leak detection or any other transient model application on such a system. Physically, the start of the vapor bubble is usually "pinned" to the top of a hill with the bubble extending downhill and downstream. As hydraulic transients move through, the bubble grows longer and shorter at the downstream end. The movement of the bubble end over a model time step is typically much smaller than a model's distance step, so it is a challenge to model it accurately. Traditionally, slack models have approximated the movement of the bubble end by fudging the amount of liquid in the mesh intervals around the end so that mass was conserved. This work describes a new model that ties a moving mesh point to the downstream end of the vapor bubble, so as to exactly capture the transient variations of bubble size. The paper will discuss the challenges in simulating this moving knot in a semi-explicit model; extension of this model to the complete draining and filling of a pipe; dealing with bad or limited knowledge of the pipe elevation profile, which is critical to the behavior of the two-phase region; and will also show comparisons between this simulation and slack-region draining and filling on a real pipeline. INTRODUCTION AND BACKGROUND Many liquid pipelines experience slack. Slack is combined vapor and liquid flow that occurs when the pressure in the pipeline drops to the vapor pressure of the liquid. It is also commonly called "slack-line flow", "column separation", and "vapor pockets". Slack can be caused in several ways. It can happen in a flowing pipeline, even in steady conditions, when the pipe goes over a large mountain; it can happen in a shut-in pipeline as it cools (even in the absence of any large elevation changes); and it can happen downstream of a valve that is suddenly closed, due to pressure drop from the surge. All of these types of slack will be discussed in detail below.

ABSTRACT A common concept for oil production and export comprises the use of Floating Production Storage and Offloading (FPSO) vessels. These vessels, whether purpose built or, alternatively, converted tankers receive live produced oil which is then processed on board in preparation for export. On the FPSO, gas is separated from the oil and is typically used for fuel gas, lift gas, or injection gas. There may be times when fuel gas production is forecast to reach a deficit. To address this, additional produced gas may be spiked into the oil export pipeline to the FPSO and used as a source of fuel gas. During the front-end engineering and design (FEED) phase of a recent project, dynamic multiphase pipeline simulation showed that the oil pipeline would experience slugging across a wide range of operating conditions when fuel gas is spiked into the oil export pipeline. To mitigate slugging, a slug control valve was introduced at the outlet of the oil export pipeline. Further simulation demonstrated that using the slug control valve, slugging can be effectively mitigated. The primary objective for the study was to recommend the simplest possible operating procedure to manage slugging in the oil export pipeline after the introduction of fuel gas spiking. In addition to avoiding slugging, the recommended slug control valve positions also needed to ensure that the pressure at the inlet of the oil export pipeline would not exceed a given threshold. Further simulation was also necessary to ensure that the slug control valve operating procedure would work irrespective of which gas is being used to spike the oil export line given that two different sources of gas with differing compositions are available. The results for only one composition will be presented here.

ABSTRACT Assessing the discharged volume during third party digging damages with efficiency and acceptable accuracy is a challenge for multiple reasons. It involves complex physics phenomena, requires taking the right assumptions and has to be perform relatively quickly as the number of cases to compute each year is important. This paper summarizes the application of a comprehensive methodology used to perform this task. The first part deals with the required field measurements needed to be obtained, the selection of an adequate physics equation as a function of the flow regime and the linkage between an analytical equation and a commercial CFD software to obtain a valid network pressure at the damage point. In the second section, a validation attempt between computed results and field measurements is made using two different sets of data. First, for some very specific incidents where the pipeline damage is close to a gate station with SCADA recording, it is possible to obtain an hourly flow profile at the break. Second, simple configurations of pipe rupture have been replicated in laboratory and tested with air. For most of the cases, the described methodology shows a good match with experimental data with typical discharge coefficient values in the range of 0.61 to 0.92. INTRODUCTION AND BACKGROUND Gaz Metro « GM » is the main natural gas distributor in the province of Quebec on the east part of Canada. The territory is connected to the TCPL Mainline (figure 1) at the very end of the transmission system. GM distributes 97% of all natural gas in Quebec (figure 2) to over 200 000 customers located in more than 300 municipalities [1]. This can be achieved by an asset of 10 000 km of underground network pipeline with more than 90% being distribution mains and services, mostly small diameter plastic pipes. Along this distribution network, digging damages by third party represent by far the primary reason for unplanned emergency response and an important part of the total non-fugitive annual gas loss. Each year, 300 to 400 rupture cases are reported and analyzed.

ABSTRACT Model calibration is the act (some might say "art") of adjusting model parameters in such a way that the model's behavior matches as closely as possible the behavior of the real-world system that it represents. In order to successfully calibrate a hydraulic model, certain hydraulic conditions must be known in order to have a defined calibration solution. Pipes that run parallel to each other (i.e. from the same upstream location to the same downstream location in roughly the same right-of-way) can pose serious difficulties to this requirement, especially when no inline flow measurement on any of the parallel lines exist, as the lack of knowing the exact flow distribution between the parallel lines means that the calibration problem either has no finite solution, or the finite solution is exceedingly difficult to determine. A potential solution to this problem involves utilizing multiple data sets. Each data set will have a particular range of possible solutions, and by comparing the solution ranges of multiple data sets, a single solution can easily be found. This paper will describe this method and provide examples with the intent of enabling the reader to apply the methodology to his or her own hydraulic calibration challenges. INTRODUCTION AND BACKGROUND Most engineers involved with hydraulic simulation are probably quite familiar (too familiar?) with the Darcy-Weisbach flow equation that describes head loss in terms of flow, pipe length, and pipe diameter. A form of the equation is shown below, as understanding the equation will be crucial to understanding the fundamental difficulty of calibrating parallel pipes.

ABSTRACT New designs or modifications of existing pump or gathering facilities are required frequently in many industries due to new operating conditions, increase in product demand, pump-pipeline interaction issues, pulsations problems, Net Positive Suction Head (NPSH) requirements, and so forth. In many cases, harsh transient events are experienced due to the lack of proper design, change in operating conditions, or unexpected process conditions. Piping systems are frequently subject to transient events such as slugs, water hammer, and cavitation that can create high amplitude forces and pressure spikes. Liquid systems are more susceptible to damaging forces during transients than gas systems due to the high density and incompressibility of the operating fluid. These events often result in high impact forces and vibrations that sometimes cause failures. This paper presents a general approach for modeling critical transient events in liquid and multiphase piping systems. In addition, case studies will be presented to identify the critical areas of the modeling, show model-field data comparison, results interpretation, and present some possible mitigation actions. INTRODUCTION AND BACKGROUND Transient events can introduce large pressure forces and rapid fluid accelerations into large pipeline systems, small manifolds, or distribution systems. These disturbances may result in different types of failures in pump, pipes, and devices such as pipe rupture or components reduced life due to cycle fatigue. Many transient events can lead to column separation or water hammer, which can result in catastrophic pipeline failures. Thus, transient flow simulation has become an essential requirement for increasing reliability and ensuring the safe operation of different pipeline systems and processes. Liquid systems are sometimes difficult to analyze for transient events as these can be dependent on factors that are not always known or finalized in the design stage or change during the lifetime of the system. Dependent variables include the characteristics of the fluid, the piping geometry, pressure reducing valves, operating conditions and nature of the events, scenarios and flow rates. Various modeling tools exist to provide reliable predictions and cost-effective solutions for hydraulic transient events, vibration or pulsation problems, but often multiple types of analyses must be used in combination to evaluate the system as a whole or solve complex problems. The traditional method of analyzing transient events in liquid systems is to only use commercially available software to predict the extreme pressure conditions; however, often these transient fluid models need to be combined with mechanical or thermal stress analyses; dynamic pressure versus time inputs, and sensitivity studies or field data evaluations to be effective in resolving problems and ensuring pipeline integrity.

ABSTRACT Performance test provides important information about whether a newly constructed pipeline can achieve the design capacity and transportation efficiency. The field performance test also provides mechanical performance correction factors for compressors as part of the commissioning procedure of natural gas transmission pipelines. The EPC contractors are usually asked to ensure that the pipeline can achieve the delivery pressures and flow rates under the protocols agreed upon. Pipelines are normally designed to achieve certain flow rates in different phases. However, it is not uncommon to see pipelines falling short on gas supply or user demand during the time period that the performance test is scheduled for. The available pipeline inlet pressure and flow rate are lower than the guarantee conditions described in the performance test protocols. The quality of the gas supply may also create unexpected problems on the interpretation and evaluation of the performance test results. Therefore, it is necessary to conduct the performance test at lower pressures and lower flow rate conditions. This paper presents the methodology, strategy, procedure as well as a real life example of a simulation based performance test of a gas pipeline. The method presented offers a practical tool to validate pipeline performance by evaluating the fundamental physical parameters. This method can also be used in assessing the pipeline hydraulics conditions for potential corrosion issues (ID change), roughness changes, BSW deposition or other issues influencing pressure drops in the pipeline. INTRODUCTION AND BACKGROUND Performance test is an important part of the commissioning procedure of natural gas transmission pipelines after their construction. The EPC contractors are responsible of ensuring the pipeline can achieve the delivery pressures and flow rates under the protocols agreed upon. Pipelines are normally designed to achieve certain flow rates in different phases. However, it is not uncommon to see pipelines falling short on gas supply during the time in which the performance test is scheduled. During the economic down turns, the pipeline users may also have difficulty accepting the gas at the flow rate required to achieve the pipeline guarantees. In these circumstances, the available pipeline inlet pressure and pipeline flow rate are lower than the guarantee conditions described in the performance test protocols. The quality of the gas supply may also create unexpected problems in the interpretation and evaluation of the performance test such as the presence of black powder. Therefore, it is necessary to conduct the performance test at lower than anticipated pressures and flow rate conditions. The question is how to assess whether the pipeline performance will achieve the guarantees with the data collected at a lower pressure and lower flow conditions? The answer lays in the assessment of the pipeline parameters using measurement data at lower pressures and flow rates, along with gas pipeline hydraulic simulations at guarantee conditions. This paper presents the methodology, strategy, procedure as well as a real life example of a simulation based performance test of a gas pipeline.

Abstract For many years, the pipeline industry has worked on leak detection technologies to accurately predict and mitigate the occurrence of leaks in pipelines. However, this is not an easy task since many systems have different characteristics and the existing technologies have specific ranges of applications. Nevertheless, the technological progress has reduced the gap between accurate leak predictions and false alarms in single-phase systems. A new challenge has been growing leak detection technologies with multiphase production systems since many of the current leak detection techniques limit their applications to single-phase systems. Although, there are some technologies that claim multiphase capabilities; it is not clear yet how they overcome the challenges imposed by the hydrodynamics of a multiphase system. Therefore, the main objective of this paper is to assess a multiphase system and simulate leak events to gain a better understating of its hydrodynamic behavior during the transient leak event. The studied multiphase system operates with three phases: oil, water, and gas; thus, flow patterns vary from stratified to severe slugging for some operating conditions and locations. The 30-in. pipeline system starts from an offshore production-separation platform which is located approximately 55 miles offshore and delivers its production to a separation processing plant on-shore. A leak condition was simulated in the middle of the trunkline to determine its effect on the hydrodynamics of the system and both ends facilities. Different gas oil ratios (GOR) and pipeline pressures were evaluated to determine the effect of those conditions on the leak detectability. One key parameter to accurate determination of a leak is to precisely differentiate or filter a leak signal (pressure, flow rate, pressure wave, etc.) from normal pipeline data noise. In this case that was even more challenging since a slugging condition was observed in the normal operation of the multiphase system which generates flow and pressure fluctuations that could overlap the leak data. This paper presents the results obtained for the steady-state prior leakage and transient leakage cases. In addition, a sensitivity analysis of different gas oil ratios is presented as well to map out its effect on the leak detectability. Introduction and Background A concern for operating a multiphase production system under higher pressures and gas-oil-ratios was expressed during a capacity increase study since that would reduce its reliability and integrity while increasing the operational risk. The offshore field demonstrated a potential for producing 5-7% more crude oil and gas than that of its original design capacity and the capacity study indicated a good feasibility of operating at those conditions. Therefore, a risk assessment of the system was required to ensure it function within safety compliance and quantify the risk associated with these new operating conditions. In addition, a part of the IMP possible mechanical failures (leaks) and their effect has to be evaluated to determine the consequence of a leak and how to better control it. Consequently, a leak simulation analysis was conducted to understand how the system will behave under leak conditions, to estimate the time to possibly detect a leak with the current technology, and to get an approximation of the amount of product spill for mitigation actions.