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Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 14–17, 2019

Paper Number: PSIG-1906

... = 2 2 If we use Blasius formula for the hydraulic resistance

**coefficient**then diameter can be expressed explicitly from this equation. It should be noted that the same law for the hydraulic resistance coeffiecent should be used both in the adaptation process and in the following modeling. Data...
Abstract

ABSTRACT Presently one of the methods of increasing pipeline capacity is using the drag reducing agents (DRA). DRA are typically high molecular mass polymers that are added at very low concentrations to reduce the pressure drop necessary to generate a given flow rate in a turbulent flow. They can be used in case if building of extra loops or pumping stations is impossible or the need to increase pipeline capacity is seasonal. Scheduling pumping regimes and calculating the amount of drag reducing additive necessary to achieve the specified pumping parameters, requires a mathematical model. This paper proposes a method that allows to integrate DRA into a mathematical model of viscous fluid motion in a pipeline. The model takes into account the degradation of DRA as the agents travel forward the pipeline. This article focuses on the question of theoretical model adjustment to the characteristics of a certain pipeline. Using the nominal information about DRA (provided by manufacturers) generally leads to a strong error and some information needed for the modeling might not be provided at all. Thus, the model needs to be tuned to the real DRA characteristics and the main source of data are real measurements of flow parameters (pressures, flow rates, etc.). Methods of using operational pipeline data for identifying DRA characteristics are considered. The issues of data collection and further data processing are discussed. The results of comparing modeling computations with real data from operating pipelines are presented. The characteristics of these pipelines are very diverse: internal diameters vary from 0.4 m to 1 m, different DRAs are used, and different types of liquid (oils, oil products and gas-condensates) are pumped.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 14–17, 2019

Paper Number: PSIG-1932

... procedures for computing the following: Isothermal bulk modulus as a function of base density, temperature and pressure. Isochoric specific heat as a function of base density, temperature and pressure.

**Coefficient**of thermal expansion as a function of base density, temperature and pressure Isobaric...
Abstract

ABSTRACT Ultrasonic flow meters, in addition to calculating flow rate, measure the speed of sound in the fluid at the local fluid pressure and temperature. It is desirable to be able to compute the crude oil specific gravity at base (i.e. standard temperature and pressure) conditions from this measurement. However, there is no straightforward way to do this, in large part because there is no accepted standard for computing crude oil sonic velocity as a function of pressure, temperature, and base density. The authors develop an approach for computing sonic velocity for crude oil. Using this as a foundation, they propose a calculation approach to compute crude oil specific gravity as a function of observed speed of sound, pressure, and temperature. OVERVIEW The motivation for this work was to be able to compute crude oil density from sonic velocity, pressure, and temperature. Pipeline companies often have ultrasonic flow meters at pump stations that, as part of measuring flow, compute sonic velocity. Because of cost, densitometers are usually much more sparsely spaced. Since elevation head depends directly on density, a measurement of fluid density can improve hydraulic modeling calculations.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 9–12, 2017

Paper Number: PSIG-1704

...-1 below. 9-1. y = -0.1793x + 0.9983 Repeating those steps to find the equations for the other 2 data sets give the following equations. 9-2. y = -0.3043x + 1.0807 9-3. y = -0.2754x + 1.0615 Defining the equation

**coefficients**in terms of the coordinates from the data sets gives an equation as shown...
Abstract

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.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 9–12, 2017

Paper Number: PSIG-1709

... resistance. It is well known that once installed in the piping system, the control valve characteristics (the relationship between valve flow

**coefficient**and valve opening will change) (Sines, 2009, Headley 2003). A so called installed valve**coefficient**is introduced to describe this behavior. The valve...
Abstract

ABSTRACT When designing a piping system, normally the inherent control valve characteristics, e.g. linear or equal percentage valve opening/closing curves, are considered. However, inherent valve curves only consider the control valve as a "bobble". The characteristics of the valve will change once it is installed with piping connections, meters, equipment, or other valves and fittings. The additional friction loss introduced by piping connections or valve combinations is normally a function of the flow rate instead of staying as constant. This will change the overall opening and closing characteristics of the control valve. It is well known that surge pressure is directly related to valve characteristics. The combination of control valve with other components may create undesirable surge scenarios in operation which is commonly neglected in the design. This paper examines how the connections of the control valve with other piping components can influence the installed valve characteristics and surge pressure level in valve closings. The focus is on two aspects: how other components such as an ESD valve immediately upstream or downstream can influence the surge behavior of the control valve closing; how the upstream or downstream control valve influences the surge behavior of the ESD or Mainline Block Valve closing. The paper will present how the installed valve characteristics are different from the inherent characteristics and how significant the increase in the pressure surge was developed. The results and conclusions provided in this paper will serve as a general guideline for valve arrangement and piping design for reducing potential surge pressure in liquid systems. INTRODUCTION AND BACKGROUND In piping design the control valves present unique influence to system hydraulics resistance. It is well known that once installed in the piping system, the control valve characteristics (the relationship between valve flow coefficient and valve opening will change) (Sines, 2009, Headley 2003). A so called installed valve coefficient is introduced to describe this behavior. The valve coefficient is normally tested in the shop as a "bobble".

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 9–12, 2017

Paper Number: PSIG-1711

... magnitude

**coefficient**b Transient error decay**coefficient**Bias error**coefficient**The three**coefficients**determined in Equation 4 define all key performance characteristics of the leak detection system over all the averaging windows considered. To illustrate the fit, three cases are plotted in Figure...
Abstract

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.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 9–12, 2017

Paper Number: PSIG-1714

... Temperature gradient between the pipe wall and centerline flow is high High-yield flow stress occurring due to changes in flow behavior such as paraffin content, fluid viscosity, flow rates, gas/oil ratio, and heat transfer

**coefficient**Oil Assay Crude oil assays define physical and chemical characteristics...
Abstract

ABSTRACT In recent years, pipeline operators have faced reduced production environments caused by declining brownfield operations and capital constraints induced by oil prices, among other factors, which have led to pipelines operating well under their designed capacity and challenges such as congealing—the precipitation of wax solids in a crude oil pipeline. This paper discusses how models are built using scientific principles and how simulation may be used to predict where congealing is or may occur inside a pipeline. Finally, a case study from a major oil and gas company's site demonstrates how these modeling and simulation techniques may be effectively applied in the field. INTRODUCTION AND BACKGROUND Pipeline operators are currently challenged with operating pipelines safely in reduced production environments, which have been caused by declining brownfield operations, capital constraints brought on by oil prices, and the lack of drilling rigs to keep pipelines full. These present conditions result in pipelines operating well under their designed capacity and challenges such as congealing. Congealing refers to the precipitation and nucleation of wax solids in a crude oil pipeline. It is initiated by a temperature gradient between the pipe wall and the centerline flow, leading to high-yield flow stress and causing changes in flow behavior. This paper discusses the physical considerations that contribute and are necessary to detect congealing followed by a series of modeling steps to accurately simulate when and where congealing occurs in a pipeline while accounting for multiphase flow of differing compositions from multiple producers. In turn, this information can automatically be displayed as a visual pipeline profile, allowing operators to understand their entire pipeline operation from remote locations and view critical parameters and events, such as congealing, leak detection, and slugging. These modeling and congealing algorithms were implemented and validated at a major oil and gas company's site on a 150-km (~93.2 mi) commercial pipeline network used to transport roughly 50,000 BOPD (7,949 m 3 /day) from 11 gathering stations to a distribution tank farm. The main transportation pipeline was designed to transport 500,000 BOPD (79,490 m3/day). Congealing events were detected and verified by comparing the simulated and assayed pipeline data. Prediction time averaged between three and six hours in advance of the congealing event, allowing the pipeline operator take appropriate mitigation actions and reduce lost production opportunity (LPO).

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 10–13, 2016

Paper Number: PSIG-1606

... measured in centipoise (CP). Kinematic Viscosity (ν) – is a measure of the time required for a given volume of liquid to flow through an orifice or restriction. Often measure in Centistokes (CST). relief valve heavy crude closure software psig 1606 heat transfer

**coefficient**reservoir...
Abstract

Abstract As the world searches for more oil and energy, heavier oils are being found and moved through pipelines. Many of these are Non-Newtonian fluids which have very different modelling needs than Newtonian fluids. Others such as WCS (Western Canadian Select) or Alaskan crudes are truly Newtonian fluids when tested in the lab but can behave like non-Newtonian fluids in low flow and cold temperatures. Also, systems which are normally not modelled for refined products or light oils become issues for these heavy crudes especially in colder times of the year. There are other modeling traps which may result in poorly sized lines or unrealistic predictions of capacity of pipelines. This paper will address some of these issues and provide some back ground information on fluid properties. The modelling processes discussed are software package independent. Introduction and Background This section provides information on the physics and basic physical properties which combine to create issues when transporting heavy crudes and trying to effectively model them. Fluids A brief refresher on the fluid properties which have significant effects on the behavior of heavy crudes in pipeline systems is provided. Types of Fluids There are two basic types of fluids which can flow through pipelines Newtonian and Non-Newtonian. The basic definitions of the two are: Newtonian - Viscosity is independent of shearing force and rate and is a constant at a given temperature. The majority of crude oils are Newtonian. Non-Newtonian - Viscosity is dependent on shearing force and rate and, in some cases, can be time dependent. See Figure 1 - Fluid Viscosity of Newtonian and Non-Newtonian Fluids. Newtonian fluid viscosity goes through the origin of the curve. Bingham plastic fluids have an offset which is the gel strength of the fluid and then have a straight line relationship. Fluid Properties Viscosity Viscosity is the fluid property which enables it to resist a shearing force. It is a measure of the resistance to flow exerted by a fluid. In other words the more viscous a fluid the harder it is to get it flowing. See Figure 2 - Newtonian fluid Viscosity. There are two different measurements of viscosity: Dynamic Viscosity (μ) – Which is also called Absolute Viscosity, is the measure of the shearing stress necessary to induce a unit of velocity shear gradient in a fluid. Often measured in centipoise (CP). Kinematic Viscosity (ν) – is a measure of the time required for a given volume of liquid to flow through an orifice or restriction. Often measure in Centistokes (CST).

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 12–15, 2015

Paper Number: PSIG-1512

... input

**coefficient**, polytropic head**coefficient**and total flow**coefficient**as non-dimensional parameters that can not only model the compressor with all the variability mentioned above, but also indicate when there is a performance problem. This new approach proposes the use of real time input data and...
Abstract

Abstract Centrifugal compressors, especially variable speed machines, offer the user considerable advantages resulting from the availability of a wide range of operating flow and head. The performance map with multiple speed lines, for example, can increase from two (2) to three (3) dimensions by simply adding changes to suction conditions. The convenience afforded by the wide range of operating points, however, presents a challenge in modeling the performance. The traditional methods of modeling - table lookups and fan laws fail to sufficiently reduce and eliminate inaccuracies due to their tedious nature, the shape of the performance curve and interpolation method. They work, but not well enough. This paper proposes a new method of modeling compressor performance using non-dimensional performance parameters apart from lookup tables and fan laws. The case is made for utilizing work input coefficient, polytropic head coefficient and total flow coefficient as non-dimensional parameters that can not only model the compressor with all the variability mentioned above, but also indicate when there is a performance problem. This new approach proposes the use of real time input data and response capabilities. Through the methods put forward in this paper, a user will have a realistic performance model, and a performance monitoring and alert system that can be used as a real-time feedback mechanism into the gathering system model. Introduction In today's highly competitive low margin natural gas market gaining an edge by keeping production costs low is an imperative. Identifying and realizing any and all efficiencies in natural gas production and transmission could significantly improve the profit margin over long term operation. The centrifugal compressor, as a critical component in this process, provides the flexibility in capacity range and head as required by the process. Despite these benefits, it has been a challenge to monitor the aerodynamic health of the machine considering the number of variables affecting the operating condition of the compressor; especially the inlet conditions, compressor speed, head, and inlet mass flow. A common evaluation, for example, utilizes the compressor's OEM (Original Equipment Manufacturer) performance curves. While the OEM provides a variety of performance curves based on different sets of operating conditions, it leaves a lot of room for interpretation and, consequently, error. With all the variability mentioned above, finding the machine's current operating point on a predefined set of curves can be difficult. This generally leaves the engineer to make an educated guess regarding machine performance against the original design.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 12–15, 2015

Paper Number: PSIG-1516

... simulation interest group production control temperature profile pipeline psig 1516 upstream oil & gas fluid modeling deviation equation of state simulator reservoir surveillance simulation tool gas temperature clausen heat transfer

**coefficient**steady-state temperature prediction methane...
Abstract

Abstract Gassco supplies Norwegian natural gas to the European market through nearly 8,000 km (5000 miles) of large-diameter high-pressure subsea pipelines. The Norwegian export pipelines are between 300 and 900 km (200-560 miles) long, and have diameters up to 44 inches. Pressure transmitters, flow meters and temperature measurements are only located at the inlet and at the outlet. The state of the gas between those two points can only be predicted by computer models and simulators, which are very important in order to obtain optimal operation of the pipelines. One of the parameters calculated by these models is the temperature profile along the pipeline. This is a very important parameter when assessing the technical integrity of a pipeline by evaluating thermal expansion and buckling. The results from such evaluations are used to decide whether or not rock dumping is needed to stabilize certain parts of the pipeline. The cost associated with rock dumping is typically around 4MNOK (0.5 mill USD) per 100m. In this study we are comparing three different commercial pipeline simulation tools. The focus is on the ability of predicting gas temperature profiles during steady state operations. The study was initiated after observing quite large deviations between gas temperatures measured by a pipeline inspection tool (PIG) and simulated gas temperature profiles for two different pipelines in operation. In addition, noticeable deviations were observed among the different simulation tools. In order to resolve these issues a series of simulation cases were proposed, all running on a simplified pipeline model. The results obtained are surprising: after updating and aligning the input model parameters for the real pipeline, all three tools are predicting a much more rapid temperature drop than the measured temperature profiles from the PIG indicate. There are also deviations among the simulation tools, with the first tool (1) predicting a somewhat smaller temperature drop than tool no 2 and 3. On the other hand, the simulation results for the simplified pipeline model shows consistent behavior for simulation tool no 2 and 3. Both in the case of pure adiabatic cooling and high heat transfer from the ambient environment, the results comply with simple analytical models. For these cases, simulation tool no 1 is predicting a smaller temperature drop. The observed differences among the simulation tools might be related to the EoS and/or the chosen strategy for solving the energy equation. Based upon the results for the simplified pipeline model it is reasonable to question the ability of the PIG to measure a representative gas temperature profile. An initial investigation of the PIG itself and the mounted temperature sensor reveals several possible design flaws that might prevent the sensor from measuring a representative gas temperature. These possible design flaws will be checked in more detail in the future.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 6–9, 2014

Paper Number: PSIG-1403

... data. We discuss building daily demand models using monthly consumption data, including what inputs can be used so that model

**coefficients**are identifiable. Significantly more accurate models are built using two temperature inputs instead of a single temperature input. This approach fits the data much...
Abstract

ABSTRACT In this paper we discuss methods and limitations of building daily natural gas demand models from historical monthly consumption data for the purpose of predicting demand on design day conditions. (Here we define the design day as the bitter cold day that produces the highest demand the system is designed to handle.) Effective gas system planning for design day operations and design day design requires accurate design day demand forecasts. For regional segments of the gas system, often only cumulative demands over billing cycles is available and may not include periods with extreme cold weather events. This paper provides an overview of new methods for building design day forecasting models based on this coarse data. We discuss building daily demand models using monthly consumption data, including what inputs can be used so that model coefficients are identifiable. Significantly more accurate models are built using two temperature inputs instead of a single temperature input. This approach fits the data much better on extreme cold days showing a higher design day demand. We also discuss observations on recent multiday near design day events showing higher than modeled demand on the peak day, known as the heck-with-it-hook, which impacts confidence levels. Results are presented comparing model forecasts made with models parameterized on monthly data and on daily data. These test results include models built with no near design day conditions in the training data set but evaluated on testing set data with such data included. Results of forecasts from various gas systems are discussed and the use of proxy data from these gas systems for use on other gas systems.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, April 16–19, 2013

Paper Number: PSIG-1315

... liquid motion containing DRA is proposed. The model takes into account the gradual breakup of DRA in a pipeline flow. The influence of DRA is considered as a dependence of hydraulic resistance

**coefficient**on the DRA concentration; the DRA concentration in turn depends on a travel distance in a pipeline...
Abstract

ABSTRACT One of the methods currently used to increase pipeline transfer capacity, when the option of extra loops or pumping stations is unavailable, is the use of the drag-reducing agents (DRA). DRA are long-chain polymeric compounds that reduce turbulent frictional losses. Nowadays there is no complete theory that explains this phenomenon, known as the Toms effect. In the course of the DRA flow in a pipeline the efficiency of the turbulence suppression decreases, most likely due to breakup of the long molecular chains into shorter ones. In this paper a mathematical model of viscous liquid motion containing DRA is proposed. The model takes into account the gradual breakup of DRA in a pipeline flow. The influence of DRA is considered as a dependence of hydraulic resistance coefficient on the DRA concentration; the DRA concentration in turn depends on a travel distance in a pipeline.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 15–18, 2012

Paper Number: PSIG-1210

... the United States ethane futures are sold on the NYMEX electronic futures exchange. downstream oil & gas pipeline ethane phase envelope psia ethylene plant pump station shale gas complex reservoir gas monetization thermal expansion

**coefficient**lng hydrocarbon propane psig...
Abstract

ABSTRACT: The authors will discuss the challenges involved in the design and predictive modeling of a proposed 1,200 mile (1,900 kilometer) liquid ethane pipeline. Comparisons between ethane and other common liquid product streams will be presented. The implications of these differences will be shown on how they relate to design consideration. Commercial considerations as well as project timing will also be discussed. WHAT IS ETHANE At the standard conditions of 60°F and 14.7 psia, ethane is an odorless colorless hydrocarbon gas. Ethane is an alkane that is a common component of natural gas. Ethane has a relatively high heating value of 1630 BTU. During the processing of natural gas, ethane and heavier hydrocarbons are removed from the rich natural gas stream in order to meet the pipeline quality specifications of a net heating value of 1100 BTU. Ethane is one of the lighter hydrocarbon components of natural gas as depicted in Table 2. One can see that the progression of heavier hydrocarbons from methane adds one carbon atom and 2 hydrogen atoms and a corresponding ~14.03 g/mol molecular weight increase. The removal of heavier hydrocarbons from the rich natural gas stream creates a lean natural gas stream and mixture of hydrocarbons liquids (natural gas liquids). Often this mixture of ethane, propane, butane and heavier hydrocarbons is referred to as a demethanized natural gas liquids (NGL) mixture. A majority of the ethane is transported in this NGL mixture to centralized locations. A small remainder of the ethane stays in the leaner natural gas stream. At these centralized locations, component product streams are created through fractionation of the demethanized NGL mixture, and sold as a commodity. The commoditized components are then delivered to the end user for use. In the United States ethane futures are sold on the NYMEX electronic futures exchange.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 15–18, 2012

Paper Number: PSIG-1213

... number of these papers[3],[4],[8] also present investigations into various simplifications that can be made to the physics of the thermal model and to what extent these simplifications affect the accuracy. average absolute relative difference simulation relative difference

**coefficient**...
Abstract

ABSTRACT: In recent years there have been a number of papers that have addressed various topics within the general subject area of thermal modeling in a pipeline. These have all been worthy papers and certainly present the PSIG membership with a reasonably comprehensive view of the subject. However, the approach to solving the temperature equation together with the hydraulic equations has only been briefly discussed and relatively few comparisons made between the alternative strategies. This paper investigates the differences between a fully coupled system, in which all three equations used to describe the flow of fluid in a pipe are solved simultaneously, and a decoupled system, in which the thermal equation is solved separately from the hydraulic equations. The advantages of such a decoupling are reduced complexity and improved computational speed. But what is the cost? Is the accuracy of a decoupled system compromised? However, can a properly constructed decoupled system produce solutions that are indistinguishable from those produced by a fully coupled system? To compare the different approaches a comprehensive set of test cases has been developed. As well as highlighting specific thermal modeling phenomena, the results of these tests demonstrate where differences in the solutions lie and the magnitude of such differences: ultimately the tests are used to determine the credence of decoupling the thermal solution from the hydraulic solution. INTRODUCTION Over the past 30 years there have been many papers presented at PSIG on the subject of thermal modeling ranging from tutorials on the physics and thermodynamics[1],[2], to comparison of different solution methods[3],[4], verification[5] and accuracy[6] and why thermal modeling is important in the real world[7],[8]. A number of these papers[3],[4],[8] also present investigations into various simplifications that can be made to the physics of the thermal model and to what extent these simplifications affect the accuracy.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 24–27, 2011

Paper Number: PSIG-1107

... turbine is not mechanically coupled with the part load turbine, which permits the turbine operates under the characteristic of a unique running line. The correlation is modeled by mean of linear functions with the

**coefficients**adjusted using an ordinary least square for each turbine unit. The operational...
Abstract

ABSTRACT: The intent of this work is to propose a simple alternative to estimate the fuel consumption in the compressions stations making use of the available data at pipeline control room. The basic idea is to take advantage of the correlation that exists for fuel flow rate to air inlet temperature and axial compressor speed of the turbine. This approach is applied in two-shaft gas turbines where the gas producer turbine is not mechanically coupled with the part load turbine, which permits the turbine operates under the characteristic of a unique running line. The correlation is modeled by mean of linear functions with the coefficients adjusted using an ordinary least square for each turbine unit. The operational data is acquired, processed to remove noise and low quality values and regressed to obtain the linear coefficients. To verify the proposed model, the fuel flow is calculated and compared with data provided by flow meters. This comprises an operational window of seven consecutive days where is shown the total fuel consumed per day of all unit in operation and the total consumed per unit during the seven days. Additionally, to analyze the behavior of the model in transient conditions is presented a comparison and the difference between measured and estimated is plotted for the time window. The first comparison reveals an error lower than 5% per day, while the error accumulated per unit is lower than 8%. However in the transient analysis larger differences are noted mainly during abrupt changes in the turbine speed. As a general remark the model could also be used in the identification of fuel metering failure and prevent the spurious measurement data being considered. INTRODUCTION The Bolivia-Brazil pipeline (Gasbol) was designed to transport up to 1.1 billion cubic feet (31 million cubic meters) per day of natural gas. Part of the gas flowing through the pipeline is necessary to conduct its operation. A small part is used as source of energy to feed the city gates‘ heaters while a significant amount of gas is necessary to operate the compression stations by mean of heaters, generators and engines to drive the compressors. When the pipeline is operating at maximum transport capacity the pressure loss is increased due to friction and consequentially the fuel consumption. In this scenario, the volume of gas necessary to operate the compression stations may reach up to 5% of the total volume transported. Figure 1 gives an overview of the fuel usage grouped per equipment consumption representing a typical operational profile. The compressor engines are responsible for 93% of the total energy consumed that is significantly higher than the other equipment. This volume of gas extracted from the system should be correctly computed due to a lot of motives. Given that TBG is not the owner of the gas transported it is extremely necessary to reduce the uncertainties regarding the measurement chain. Moreover, at the end of the operative day, it is highly desirable to close the mass balance equation on the Gasbol.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 24–27, 2011

Paper Number: PSIG-1119

... thermal model comparison

**coefficient**piping simulation pipeline upstream oil & gas numerical diffusion temperature front propagation production monitoring distance step thickness gas pipeline pipeline temperature piping design cylindrical shell ground model time constant...
Abstract

ABSTRACT: This paper investigates the impact of modeling assumptions on the fidelity of thermal transient simulations. Numerical diffusion and the impact of ignoring the heat capacity of the pipe and ground are examined. An RC time constant formulation for the ground around the pipe is presented. Simulations examine temperature front propagation and pressure induced thermal transients. Introduction Thermal effects can significantly influence both steady state conditions and the propagation of transients through the pipeline. Often neglected is the impact of the transient response of the pipeline surroundings on the propagation of transients through the pipeline. It is tempting to focus is on the transient modeling of the pipeline fluid without considering the transient modeling of the pipeline surroundings. Using buried natural gas pipelines as an example, this paper will demonstrate how the pipeline surroundings influence the in-pipe transients. Because of ground effects, the time required for a buried pipeline to move from one steady temperature profile to another is often measured in weeks or even months. The most common approximation to representing the ground around a buried pipeline is as a resistive heat loss to an ambient "ground" temperature. In this approach, the heat flux to the ground is represented by a heat transfer coefficient (HTC). We will examine the validity of this approximation both through analysis and simulation. We will demonstrate that the time response of the pipe wall and the ground can be represented by a characteristic time constant. We will show how this time constant can be computed from the density, specific heat, and thermal conductivity of the pipe and ground. This paper also examines the numerical diffusion that results from modeling the pipeline using an Eulerian formulation. The Eulerian formulation represents the pipeline state at fixed points along the pipeline. The alternative, a Lagrangian formulation, represents the pipeline state using points that move at the velocity of the fluid. We examine numerical diffusion by simulating the pipeline adiabatically (i.e. no heat flow from the pipeline fluid to the pipe wall or the pipe surroundings). Using this approach, we can observe how numerical diffusion affects the modeling of a temperature front moving through the pipeline independently from other real smoothing affects such as the absorption of the fluid heat by the pipe wall and ground. After demonstrating the very real effects of numerical diffusion, we move on to demonstrate how the pipe wall and pipe surroundings modify the transient thermal effects. We demonstrate that, at least for buried pipelines, the thermal mass of the pipe and ground have such great affect that numerical diffusion can in fact be dealt with by appropriate selection of the modeling distance step. Three pipeline conditions are examined: An 8 inch buried gas pipeline with 8 feet of soil between the pipeline and "ambient" temperature. A 24 inch buried gas pipeline with 8 feet of soil between the pipeline and "ambient" temperature. A 24 inch buried gas pipeline with 4 feet of soil between the pipeline and "ambient" temperature.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 24–27, 2011

Paper Number: PSIG-1125

... effective reservoir pressure psig 1125 well interference field pressure reservoir flow rate inventory reservoir performance

**coefficient**machine learning reservoir pressure pinnacle reef reservoir observation well pressure interference artificial intelligence modeling...
Abstract

ABSTRACT: The Underground Storage Department at Union Gas Limited, a Spectra Energy Company, has long recognized that well interference effects during extended flow periods are difficult to simulate using simplified tank models. Although more complex and rigourous reservoir models can be used to predict well interference, these models are cumbersome when simulating multiple reservoirs flowing to common delivery points at varying operating pressures. Using seasonal field data from SCADA including flow rates and station pressures for each reservoir, partial regression coefficients were developed to predict well interference using simplified tank type model methodology. The results of this study show that the simplified methodology incorporated into a hydraulic modeling software program using linear regression can provide reasonable well interference results. The results lead to a direct improvement in forecasting reservoir performance which will better predict: Storage withdrawal capability Storage pool inventory depletion Compressor horsepower requirements BACKGROUND Pinnacle reef natural gas storage reservoirs contain gas in porous, heterogeneous rock. The ability of a pinnacle reef reservoir to cycle gas is a function of pressure and flow rate governed by reservoir properties such as porosity, permeability, water saturation, etc. A reservoir's cycling capability is also a function of the number of injection/withdrawal wells, well diameters and well spacing. The cycling capability of a pinnacle reef reservoir is variable ranging from high to low performance depending on the reservoir properties and facilities. The size and pressure operating range of a pinnacle reef storage reservoir is easily defined on a pressure/z vs. inventory graph where z represents the compressibility of gas. Using a tank model as an ideal example of a storage reservoir, the pressure/z vs. inventory curve follows a straight line defined by the reservoir index "K" as the reservoir is filled and emptied. A pinnacle reef's reservoir pressure falls along the reservoir index after a period of stabilization as measured at a ‘key’ or observation well ("observation well"). Shut-in stabilized reservoir pressures for a typical pinnacle reef reservoir are plotted in Figure 1. Under normal operating conditions (unstablized conditions), measured field pressures at static observation wells do not follow along the path of the reservoir index. The deviation of the measured static field pressures from the reservoir index during injections and withdrawals is referred to as the hysteresis effect as shown in Figure 2. Tutt and Dereniewski (1978) proposed a practical regression model of hysteresis to forecast deliverability for Michigan Stray sand reservoirs. This study evaluates the accuracy of using observation well pressures (i.e. hysteresis) as a predictive tool in estimating deliverability in pinnacle reef reservoirs. DISCUSSION Stabilized steady-state deliverability of individual wells in a reservoir can be determined using a back-pressure equation as defined by Equation 2 (Tek, 1987). The performance coefficient ( c ) can be calculated using wellhead pressures when pipe friction is negligible (Katz et al., 1959). For shallow pinnacle reef reservoirs (less than 2,500 ft) Equation 2 can be modified to reasonably calculate the flow performance based on wellhead pressures as defined in Equation 3 (Katz & Coats, 1968).

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 11–14, 2010

Paper Number: PSIG-1010

...

**coefficient**. There exists an analytical steady-state solution for k for a cylinder near a half-plane, which corresponds to the geometry of a buried pipeline [8]. Nevertheless, it is a common practice to calculate k as for a set of concentric cylindrical layers with the distance between the boundary of an...
Abstract

ABSTRACT Predictions of the gas flow rate, temperature and pressure profiles along the pipeline under transient conditions are vital to the operation of gas transmission pipelines. Available simplified models for the calculation of these profiles are evaluated. Numerical solution with the method of lines is adopted to allow for estimation of the magnitude of the model terms. Sensitivity of pipeline flow model to the choice of heat transfer model, and to the accuracy of compressibility factor, heat capacity and friction factor calculations is investigated. The influence of the selection of different equations of state on pipeline line-pack results is also demonstrated. Equations of state commonly used in the gas industry were investigated, i.e.: Soave-Redlich-Kwong, Benedict-Webb-Rubin, AGA-8 and SGERG-88. The predictions from the numerical solution are compared to the field data from the Yamal-Europe pipeline. INTRODUCTION Predicton of the gas flow-rate, temperature and pressure profiles along the pipelines under transient conditions requires adequate mathematical models from the class of systems with distributed parameters. Numerical methods rather than analytical ones are used for their solution. Discretization of the models is usually carried out through the finite difference methods, leading to the systems of "stiff equations", which need specific numerical methods of solution. In this article, we focus on the accuracy of a non-isothermal transient gas flow model. The impact of heat transfer model on the accuaracy flow parameters is demonstrated. The effect of the selection of different equations of state is also discussed. The results of the model solution are compared to the field data from the Yamal-Europe pipeline. Heat-transfer model In the energy equation, the heat transfer term q represents the amount of heat exchanged between unit mass of gas and the surroundings per unit time. Application of Fourier's law to calculate the overall heat-transfer between the gas and the ground for a discretization section of a pipeline yields where U is an overall heat transfer coefficient. There exists an analytical steady-state solution for k for a cylinder near a half-plane, which corresponds to the geometry of a buried pipeline [8]. Nevertheless, it is a common practice to calculate k as for a set of concentric cylindrical layers with the distance between the boundary of an outer layer and the pipe equal to the burial depth of the pipe. The ambient temperature is fixed and equal to the ground temperature at the same horizontal level as the pipe axis, and at a sufficient lateral distance from the pipe. This technique for simplified heat transfer modelling and its applicability to calculate accurate temperature profiles in gas work. The process of heat transfer from the gas to the surrounding environment is described using unsteady heat transfer model so that the description of heat flux could take into consideration the effect of heat capacity of the surroundings of a pipeline.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 11–14, 2010

Paper Number: PSIG-1013

... same way the pump characteristic

**coefficients**, diameter of the tank and capacity of the valve are considered to be known. Model equations We will consider the current in the pipeline as an isothermal flow of the poorly compressible fluid. Under these conditions the flow process can be described using...
Abstract

ABSTRACT The pipeline state estimation problem is posed to considered by the example of the simplest pipeline is formulated. It contains the simplified Navie-Stokes equations for the interior points of the pipe, and two boundary conditions for the fluid flow through the pump and valve. The main principles of the difference scheme for solving these model equations are described. The iterative quasi-linearization is proposed. The state estimation procedure is build on the basis of quasi-linearized model. Some results of numerical experiments are given. INTRODUCTION In the past few years the state estimation problem for pipeline systems has become one of current importance. These are different approaches to this problem, using the online models. Most of them are built on the correct statement of boundary conditions [6]. This paper represents a new technique for online state estimation based on the pipeline model and a latesh history of date, obtained from pressure sensors and flowmeters installed on the pipeline. The point is that in spite of the fact the modern oil pipelines are equipped with high tech equipments the accidents and oil spills keep happen, damaging the environment. To prevent these accidents one should have clear understanding about what is going on inside the pipeline. That is why mathematical models are attracted. They are generally accepted as the cheapest way of the investigation using mathematical modeling once can model almost all dangerous situation that take place in the real pipeline. But this is not enough. The pipeline when is use is the complex dynamical system, with many input and output signals. The input signals are the operator's commands, such as valve shutting, pump start, tank changeover, etc. output signals are the measurements of pressure and flow in situ, using special pressure sensors and flowmeters. As the case may be the pipeline can reach different states, depending on what command the operator has input. The wrong command can entail serious consequences. For example, the wrong stop of the pump can entail pipeline breakdown and therefore oil spill. But if it was possible for operator to control the flow process in the pipeline using its' online model one would avoid the accident. Mathematical model of a pipeline as a system with distributed parameters is represented with the hyperbolic system of partial equations and boundary conditions. To solve this system means to obtain the system state in the future time moments. But solving the hyperbolic system of partial equations is impossible without known initial state and boundary conditions at the next time moments. To know the state of the pipeline means to know the flow velocity and pressure distributions along the pipe. Despite the boundary conditions the initial state cannot be measured directly. Hence, one has to use identification methods to obtain the current state and to make a prediction on the basis of one. Identification of the current state can be carried out on the basis of some measurements from the past, i.e. using the latest history of date, obtained from pressure sensors.

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 12–15, 2009

Paper Number: PSIG-0901

... heat transfer modeling, and how this affects the estimated gas temperature. The importance of a correct total heat transfer

**coefficient**for different conditions has been studied, and the most important parameters associated with this**coefficient**have been identified. A recommendation regarding which...
Abstract

ABSTRACT Gassco supplies Norwegian natural gas to the European market through nearly 5,000 miles of large-diameter high-pressure subsea pipelines. In 2007 3·106 MMSCF of gas were exported from the Norwegian Continental Shelf (NCS). During the winter, demand for gas usually exceeds the estimated transport capacity of the pipelines. More accurate modeling of the flow can lead to improved use of available network capacity. The pipelines in Gassco's network are typically 200 - 560 miles long, and the gas temperature is only measured at the inlet and outlet. Consequently, the calculated gas temperature along the pipeline depends on the accuracy of the assumed ambient temperature and the estimated heat transfer. This paper mainly focuses on heat transfer modeling, and how this affects the estimated gas temperature. The importance of a correct total heat transfer coefficient for different conditions has been studied, and the most important parameters associated with this coefficient have been identified. A recommendation regarding which parameters to focus on under different conditions such as different burial depths and flow rates is given. INTRODUCTION The Norwegian gas is transported in seven large diameter sub-sea pipelines to United Kingdom and continental Europe, covering around 15 % of the European natural gas consumption. The transportation network is operated by the state-owned company Gassco. The Norwegian export pipelines are between 200 and 560 miles long, and have diameters up to 44 inches. Pressure transmitters, flow meters and temperature measurements are only located at the inlet and at the outlet. To know the state of the gas between those two points one has to rely solely on computer models and simulators, which are very important in order to obtain optimal operation of the pipelines. The computer models are used for general monitoring of the gas transport, providing estimated arrival times for possibly unwanted quality disturbances and ‘pigs’, predictive simulations when the operational conditions changes and for transport capacity calculations. The transport capacity is usually made available to the shippers of the gas many years in advance, and accurate calculations early in a pipeline's lifetime are appreciated and valuable. High accuracy in the transport capacity calculations is important to ensure optimal utilization of invested capital in the pipeline infrastructure. The calculations need to be as close to, but not higher than, the true capacity as possible. This will ensure optimal utilization of invested capital. As soon as a pipeline is built, the true capacity is determined by the diameter, length, available inlet compression, gas temperature and other physical parameters. A lot of effort is put in to estimate this capacity figure exactly. In 2004 a research program was launched to optimize the gas transport modeling involving high flow rates, high pressures, large diameters and very low roughnesses. The R&D Foundation Polytec and Gassco have worked together for several years to optimize the gas transport modeling. In the project several subtasks have been conducted or are ongoing; to improve the friction factor correlation, viscosity measurements and implement new viscosity correlation, use of more accurate ambient temperatures and more accurate modeling of heat transfer. This paper will focus on work performed related to heat transfer. After realising that the simulation tool used in the project did not model the heat transfer for partially buried pipelines, a literature survey was conducted to identify an appropriate model. It seems that very few published articles discuss this topic [1, 2]. An analytical model [1] was identified, able to

Proceedings Papers

Publisher: Pipeline Simulation Interest Group

Paper presented at the PSIG Annual Meeting, May 12–15, 2009

Paper Number: PSIG-09A1

... properties of the pipeline and its environment, such as thermal

**coefficients**and yield stresses. The data required for each of these types may be further differentiated. In some cases small data variation leads to large discrepancies, whilst in other large differences yield smaller variations. During this...
Abstract

ABSTRACT Data Gathering Exercises Data used in simulation is researched, derived, calculated, estimated, measured, or simply guessed at for the majority of pipeline physical parameters. In pipeline simulation, first-time users often find themselves in a quagmire of data gathering exercises. They spend many valuable hours researching •as built• documentation and performing laborious calculations with the expectation of constructing a highly accurate pipeline model. This paper is intended to help the new, and perhaps, the experienced modeler cut through what is important and what is superfluous. Data Requirements What is frequently not clear is how much of the collected and collated data is actually necessary and what level of accuracy. Additionally, how data quality will affect the simulation is often not recognized early in the process. At the initial stage of data gathering, other important decisions must also be made; these decisions can affect, or be affected by, the practice of simulation within an organization. Examples are the selection of the equation of state, the friction factor equation, or the reference conditions. Data Sensitivity This paper discusses these issues by describing an approach that allows model sensitivity to be performed at an early enough stage in the modeling process to ensure that the key parameters are well understood. This process is demonstrated through examples of both gas and liquid pipeline simulations. Various "universal" parameters are presented. INTRODUCTION Data required for pipeline simulation can be categorized in general types. The physical geometry of the pipeline, such as leg lengths, diameters and elevation profile. Fluids properties, for example gas compositions or liquid physical properties such as density and viscosity Material properties of the pipeline and its environment, such as thermal coefficients and yield stresses. The data required for each of these types may be further differentiated. In some cases small data variation leads to large discrepancies, whilst in other large differences yield smaller variations. During this tutorial generic forms of pipeline simulation equations will be presented, Data variations are examined and discussed. In some cases filtering data with the aim of simplifying the output may be appropriate. During this tutorial we will primarily be looking at data requirements for offline simulation. For real time simulation where detailed model hydraulics may be necessary, particularly for leak location applications, a different and more detailed set of data is required. PIPELINE CONFIGURATION Pipeline simulation software normally uses a node/equipment description to configure the model of the pipeline. A node is simply the connection point between pieces of equipment. Nodes have no physical dimension, although set-points or constraints may be configured at a node.