Abstract Midstream gathering systems present several unique pipeline simulation modeling challenges. The first, and most significant challenge, is that systems are not static. In fact, systems are constantly changing as new wells are added and depleted. One method of handling this constant change is to use geographic information systems (GIS) to build models. This requires the simulation software to be capable of importing and exporting GIS data, fix topology errors on the fly, convert 2D GIS data to 3D using digital elevation models, and display the models over geo-referenced aerial backgrounds. The second challenge is that gathering systems are often multiphase, enabling liquids and solids to collect in low spots or low velocity areas. In addition, gathering systems are often looped and interconnected. The combination of multiphase and multipath networks makes predicting flows and pressure drops difficult. In order to accurately portray how a system would react in the real world requires the simulation to be tuned often. Unfortunately, manually tuning a complex gathering system is practically unfeasible. Automatic tuning algorithms utilizing SCADA data to tune the model is likely the only reliable method to accomplish this task. This makes it possible to determine the location and severity of liquid holdup, hydrates, or salt buildup. In addition, simulation software can be used to debug new models by identifying incorrect geometry, equipment, or operational parameter model inputs. The third challenge is analyzing and understanding the simulation results. This requires the application to have both the ability to create 2D or 3D color simulation maps and graphs for visual interpretation, as well as, export the results into spreadsheets for further analysis. Introduction One of the primary purposes of computerized pipeline simulation is to assist midstream companies to troubleshoot existing systems and design modifications or new systems. The tools available to the industry today enable companies to analyze and make decisions quicker, more reliably and efficiently.

ABSTRACT: Static models have been used in the last two decades to analyze the hydraulic behavior of gas gathering systems. The driving forces for building these models include the need to increase system awareness and enhance production. Building these systems in any production software and keeping them up to date are processes that require expertise and time; the latter depends on the system complexity. Manual data transfer can delay recognition of problems until changes have adversely impacted production. This paper describes the implementation of a systematic workflow to build the Raton basin gas gathering system model in PIPESIM* and to control and automate the model data transfer and optimization through Microsoft Visual Basic® macros. The results demonstrate how the modeling, combined with an automated interface, became an invaluable tool for updating, optimizing, and simulating scenarios for the gas gathering system. INTRODUCTION Pioneer Natural Resources USA Inc. owns and operates a low-pressure natural gas gathering system, in parallel with a produced-water gathering system, in Las Animas County, Colorado, west of the town of Trinidad in the Sangre de Cristo Range of the Rocky Mountains (Figure 1). The system services the Purgatoire coalbed methane field in the Raton basin and comprises about 1,000 miles of pipeline and 2,100 active wells. Field History Development of Raton basin field began in 1995. The most notable player was Evergreen Resources Inc., which drilled most of the wells and constructed and operated the bulk of the gathering system. Evergreen's growth was facilitated by several acquisitions of smaller well and gathering packages within the field. When Pioneer acquired Evergreen in 2003, there were 1,000 wells producing into the gathering system. Since the acquisition, the system was expanded further (Figure 2), continuing the philosophy of designing, constructing, and operating the gathering in-house. Gathering System Description The gathering system involves four discrete systems: Lorencito/Rita, Long's Canyon, Maxwell, and North of the River, or simply North; the latter is by far the most complicated of the four. Mainline (sales) compression consists of approximately 123,000 site-rated horsepower in nine stations. In addition, there are 10 (185-hp) screw units installed as lateral compressors, and about 300 wellhead boosters. The main line compressor stations follow two basic configurations: Conventional four-stage reciprocating units and stations with a screw or blower first-stage boosting into three-stage reciprocating units. The conventional reciprocating compressors boost from about 6 psig to sales pressures of about 1,100 psig. The "compound" stations hold a suction of 1 to 2 psig. All satellite compression, lateral, and wellhead units run at about 1-psig suction. Gathering System Evolution The evolution of the Raton basin system has presented many design challenges as a result of extreme mountain terrain and landowner, homeowner, and environmental issues. Responsible environmental stewardship, being a good neighbor to the ranches and subdivisions, and extremely difficult topography have frequently dictated pipeline routes. In fast-developing fields such as Raton basin, almost every step-out play requires a new trunkline sized to carry an unknown amount of production from an unknown number of wells.

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.

ABSTRACT This tutorial session discusses a systematic approach to building a well balanced pipeline model. The presentation describes a step-by-step method for constructing a stable pipeline network simulation model. Explanation on local isolations, control conflicts, and pitfalls to avoid while building and setting up a good model, will also be explored. The audience is encouraged to participate in this dialogue as it pertains to gathering, distribution and transmission systems. BUILDING MODELS The purpose of this tutorial is to discuss some of the basic aspects of computer simulation of natural gas pipeline systems. It is written for the person who is familiar with the terminology and concepts of a pipeline, but who is new or in need of a refresher to modeling pipeline systems. First let's deline what pipeline simulation is. Pipeline simulation is the process whereby a computer model of a pipeline is used to simulate an actual pipeline system. The idea is to have the computer model duplicate or predict what is occurring in the actual pipeline system. The ability to predict what is going to happen with an actual pipeline system can be extremely useful in a number of ways. From an operational standpoint, pipeline simulation programs can be used to test operating changes on the model before implementing them on the actual pipeline system. Typical changes that can be tested with the model are variations in flow rates, pressures, temperature, gas quality, elevations, and compressor performance. You can also see what the effect of taking a line section out of service or turning off a compressor will be, by first trying it on the computer model. What it boils down to is that if you are unsure of something, you can experiment with the computer model of the pipeline system instead of the actual pipeline system itself. Another major use of pipeline simulation programs is in the desigu of pipeline systems. Pipeline simulation programs can be used to desigu a pipeline system from scratch or used to desigu an expansion to an existing system. While there are a few general rules you can follow when designing a pipeline system or expansion, there is still a lot of room for experimentation. Using a pipeline simulation program as an aid not only speeds up the desigu process, but also gives you more time to test different ideas and configurations, which in turn will usually result in a more efficient design. When you consider the high cost of buying and installing compressor units and pipe, even a small improvement in the design can yield considerable savings. STEADY STATE SIMULATION Steady state simulation is the process of simulating a pipeline system under steady state conditions, which simply means that the conditions are assumed to not change with time. Of course pipeline systems do not operate under perfectly steady conditions, but some pipeline systems do remain steady enough so that steady state simulation can be used to adequately model their behavior.

ABSTRACT Over the last ten years, energy companies, including gas gatherers, transmission and distribution companies have begun using pipeline simulation to design and operate pipeline systems. They have also begun using Geographic Information Systems ("GIS") to make maps, meet regulatory requirements, and as a business development and planning tool. Because GIS and pipeline simulation software have similar characteristics and use similar data these companies have begun integrating the two. This paper discusses the issues and experiences of creating pipeline simulation models from GIS. INTRODUCTION Who Is Targa Resources? Targa Resources, Inc. is one of the largest independent midstream natural gas and Natural Gas Liquids ("NGL") companies in the United States. Targa owns or operates over 11,300 miles of natural gas gathering and NGL pipelines and 22 natural gas processing plants with over 10 billion cubic feet of capacity in the United States. As a prudent and profitable operator, Targa has invested significant time and resources to develop and use information and engineering systems such as Geographic Information System ("GIS") and pipeline simulation to help operate its pipeline systems safely and efficiently. Who Is Gregg Engineering? Gregg Engineering, Inc. (GEI) is a developer and supplier of Pipeline Simulation Software for modeling all types of pipeline networks, including gathering, transmission, and distribution systems, both under Steady and Transient conditions, including Real Time. The Situation (1996) In 1996, we purchased GEI's Winflow pipeline simulation software to help design and operate our gas gathering pipeline systems. To get started we hired a consultant familiar with pipeline simulation software to build a model of a small part of one the gathering systems. The consultant obtained copies of the gathering system paper maps and began building the models by sketching the system free hand using the pipeline simulation software's graphical interface and entering information about the system such as pipe length and diameter by hand into the model. The consultant soon found that the maps were not up to date and in some cases were in error. After spending many months in the field updating and correcting the information in the model, the consultant developed a model that correctly represented the gathering system. When the consultant presented the information to the drafting department to update the maps the head of the drafting department said that the information was of no use because it had not been developed according to drafting standards. Unfortunately, the model was also not very useful as a map. The Plan (1997) As a result of our frustrations with building pipeline simulation models from our maps and keeping our maps up to date, we undertook a study of our business processes. At that time we used a Computer Aided Design (CAD) computer program to draw and maintain our maps. CAD represented a vast improvement over hand drawn maps and was supposed to

ABSTRACT This paper identifies procedures which can be used to quantify reasonable and useful data arrays for head versus flow, efficiency versus flow, and BHP versus flow of a centrifugal pump for use in hydraulic simulation models. Often during the design phase of a liquid pipeline or distribution system, the designer is able to specify the steady state flowrate and head rise for the pumps in the system. However, for transient analysis the performance of the pump is needed throughout a wider range of flowrates from shutoff to runout conditions (or beyond), wherein both pump head and flow are positive. This paper does not discuss methods for quantifying pump performance in other quadrants of operation, wherein pump head and/or flow are negative. Data arrays for positive pump head, efficiency, and BHP versus flow can be fabricated reasonably well using statistical correlations based on a large collection of such data for various types of centrifugal pumps. This paper provides details of those performance correlations, with discussion of their applicability and limitations. Hydraulic head versus flowrate is characterized by two conjoined parabolic curves having functional continuity and which are based upon statistical data. Efficiency versus flow is characterized by two conjoined power curves having functional continuity and which are based upon statistical data. Brake Horse Power versus flow is determined mathematically from the defined equations which characterize head and efficiency versus flow. SUMMARY OF PREVIOUS METHODS In general, hydraulic simulation software assumes that modeler can characterize pump performance of head versus flow in one of the following ways: A horizontal line. This implies that pump head is constant regardless of flow. This method is limited only to some simple steady state distribution system hydraulics modeling software. An inverted parabola based on user-entered two data points of head versus flow, wherein the first datum occurs at shut-off head, the second datum occurs either at BEP or rated conditions. This method assumes that the slope of the curve is zero at shut-off head. An inverted parabola based on user-entered three data points of head versus flow, wherein the first datum occurs at shut-off head, the second datum usually occurs either at BEP or rated conditions, and the third datum usually occurs at runout. This method permits a non-zero slope at shut-off head. A user-entered array of many data points of head versus flow. This method assumes that the modeler already has a published pump manufacturer's curve in mind, or that the modeler can scale up or down a previously used pump curve so as to fit the intended application. Values of pump performance at positive flowrates other than the user-input data are determined either by linear interpolation or by polynomial spline fit of the input data. In general, hydraulic simulation software tends to make some overly simplifying assumptions about pump efficiency or horsepower, or requires the modeler to characterize efficiency or brake horsepower (BHP) versus flow either from a published pump manufacturer's curve, or assumes the modeler can scale such a curve so as to fit the modeled pump. In the absence of published pump efficiency or BHP data, the task of building data arrays to characterize efficiency or BHP versus flow can be an exercise in guesswork.

ABSTRACT During the last decades significant savings have been achieved for development of gas condensate fields by advances in the ability to transport untreated well fluid over long distances. Multiphase pipelines longer than 100 km are now not uncommon, and transient multiphase simulations have been an important factor in enabling these developments. This paper considers important elements in the modelling of gas condensate pipelines, both for steady state and for dynamic operations, such as rate changes and pigging. Among elements discussed is how to best create a pipeline profile based on the data available, the importance of the flow model, sensitivities to pipe inclination angle, flow rate and pipe diameter and the importance of boundary conditions for transient operations. The results of a set of steady state and transient are used to show the importance of proper modelling of the pipeline profile. In addition liquid handling during such transient operations as rate changes and pigging operations are discussed. The simulation results have been obtained using the transient multiphase simulator OLGA 2000 [1]. INTRODUCTION The traditional gas condensate development would have a process facility close to the field, and the fluids would be exported through a single phase gas and a single phase liquid line. This involved expensive process facilities at remote locations, often off-shore, and multiple pipelines. As the ability to design two phase (gas and hydrocarbon liquid) pipelines advanced it was sufficient to dehydrate the fluid close to the field, and the hydrocarbons were transported in a multiphase pipeline. This gave significant saving due to a simpler and smaller process facility and a single pipeline (MLNG in Malaysia, BLNG in Brunei, Sable Island in Canada and Nam Con Son in Vietnam). The last step in the development has been the ability to design three phase pipelines (gas, hydrocarbon liquid and water). This completely removes the need for the process facility, which are replaced by simple wellhead platforms (Huldra and Troll in Norway, South Pars in Iran, Ras Laffan and Qatar Gas in Qatar and Goldeneye in the UK) or complete sub-sea developments (Mensa and Canyon Express in the US, Scarab/Saffron in Egypt, TOGI, Snøhvit and Ormen Lange in Norway). The three main issues considered when designing gas condensate systems are liquid handling, pressure drop and hydrate prevention, and in this paper the main focus will be on liquid handling. Liquid handling is important because it determines the sizing of the slug catcher, the plant liquid processing capacity and the pipeline. In addition liquid handling is closely tied to how the pipeline can be operated, thereby strongly affecting the profitability of the development. Even with proper pipeline modelling there are still uncertainties in the simulations results. The paper summarizes some of the experiences from modelling of gas condensate pipelines, strengths and weaknesses in the models, and what uncertainties can typically be expected.

ABSTRACT It is inevitable that pipeline companies will be relying increasingly upon simulation models in the near future to provide timely information for business decisions. Before models can provide this information, they will need to be implemented within the gas control environment, providing operational information. For real-time models to be effective, they must be maintained. This can only be practically accomplished by the gas controllers. Gas controllers have traditionally been resistant to the integration of simulation models in the gas control environment. Their reasons are: Models can't tell an operator with 20 years experience anything about the pipeline. Controllers already have enough to do without having to baby-sit models. Models require too much expertise to keep them running. Recognizing that the participation of gas control is essential to the successful integration of simulation models in the operational and business processes within a company, it is crucial to address these barriers to acceptance of the technology. In this workshop, methods will be explored through which each barrier is either mitigated or removed completely. Participants will be encouraged to share experiences in which models have been successfully introduced in and used by gas control. Outline I. Involvement A. Get Gas Control Involved in the Specifications. The success or failure of the simulation implementation will depend on its acceptance in gas control. Getting the controllers involved during the "shopping" phase of the project will insure buying a relevant product. B. Involvement in the implementation. The difference between what is delivered and what was originally envisioned can be significant (the devil's in the details). Having gas control involvement throughout the implementation phase can avoid a lot of problems. C. Involvement in the testing and integration. The ability to spend significant time with the developers of the system can be an important opportunity to really learn how and where the system performs best. It is also the best possible training for senior gas control personnel that will greatly exceed anything they might experience through after the fact training. II. Ease of Use A. Control screens look like SCADA. Making the control and viewing of system results look as much like the gas controllers' SCADA displays is critical to success. It will reduce the learning curve and maximize user acceptance of the system. It will also increase the overall level of integration of the system into the gas control environment. B. Buttons and short menus. Gas controllers have a lot to do. Making the control and viewing of results easy to accomplish will enhance acceptance. Cryptic computer like commands are strictly verboten. C. Intuitive Operation - pipeline terms rather than modeling terms. Too often, modeling systems rely on mathematical rather than pipeline terminology (boundary or external rather than supply or delivery). The simulation system is supposed to be reflecting and projecting what is actually going on in the pipeline. Make the operator's interaction with the system as close as possible to the way he/she interacts with the pipeline. III. Reduced Responsibility - Useful Results

ABSTRACT A simulation model is being developed to track the operation of a two-phase wet gas pipeline in Indonesia. The on-line model continually receives SCADA updates assuring accurate conformance to the actual pipeline. The information generated by the model supports the Pipeline Management System (PMS) applications, which include Inventory Management, Line Pack, Over/Under Pressure Analysis, Instrument Analysis, and Look-A-Head Analysis. The simulation model is capable of simulating gas pipelines, liquid pipelines, as well as transient operation in either single or two-phase. The simulation model is run in conjunction with the SCADA system. All alarms are presented to the operator through the SCADA alarm management system. All data, trends, and profiles from the simulation model are also presented to the pipeline operators through the SCADA graphics management system. The paper concludes with a discussion of the dynamic testing of transients in the two-phase model when multiple components are present. West Java Gas Transmission Pipeline Overview The West Java gas transmission pipeline was put into operation in 1977. The 280 mile (740 km) pipeline network, transports natural gas through cities and suburbs while serving heavy industrial customers; fertilizer plants, cement plants, steel plants, power plants and other industries in West Java. The onshore pipeline network is composed of 9 sources/inputs, 5 compressor stations and 18 consumers/outputs receiving up to 400 MMSCFD of natural gas. The pipeline, delivering gas in an east to west direction, having a maximum design pressure of 720 psi (50 kg/cm2) is composed of X52 and X42 grade pipe segments. The diameter of these segments varies from 8 to 32 inches, with most of the mainline piping at 24 inches. The pipeline is externally coated, cathodically protected and buried 3 to 5 ft (1 to 1.5 meters) underground. 2 Compressor stations are composed of centrifugal units driven by gas turbine engines. High pressure and low-pressure sources supply gas from both onshore and offshore gas fields. Wet gas is mixed with dry gas after the scrubbing and dehydrating processes. Gas component information is available at source and consumer locations via chromatography readings. System Architecture Data from the individual RTU's will be polled and gathered by the communications server of the SCADA system. This data is presented to the simulation model at the same time it is presented to the Graphics and Alarm systems for the operator to see. The simulation model processes the data and performs its application calculations every quarter of a second. Upon completion of the application calculations, the model submits the results to the Graphics and Alarm systems allowing the operator to interact with the simulation model and respond to alarms exclusively through the regular operator console and environment. The operators will never interact physically with the Pipeline Management System (PMS) computer that houses both the model and the PMS applications. Data from the master or host SCADA communications server will be submitted to the PMS computer. Output from the PMS computer will be sent back to the host computer.

ABSTRACT A typical approach taken to modeling gas field production gathering networks is to input "fixed" flow rates at a particular point in time to size pipelines and/or identify restrictions to flow. A numerical reservoir simulation model is often used to forecast the production rates for each well in the field. The flowing conditions in the surface gathering network can vary significantly over the life of reservoir due to the field development strategy being pursued, pressure depletion of the reservoir, and the volumes of the gas and liquid that are produced. Until recently, the "best" means available to account for changes in flow rate potential or pressure losses in the reservoir and surface gathering network has either been to use a "simplistic" description of the reservoir inflow performance coupled to a "simplistic" surface network or, to run the reservoir model and pipeline model separately, manually exchanging pressures and flow rates data in a "trial-and-error" fashion until the models converge. This paper summarizes the recent "movement" by reservoir simulation software vendors to allow their models to couple to 3rd party pipeline and reservoir simulation models. The Sexsmith Gas Condensate Field located in Alberta, Canada is used to illustrate how these "integrated" models can more closely approximate the production characteristics of the reservoir and act as a focal point for planning and management of the surface gathering network and the transportation pipelines located downstream. The paper also offers an opinion of where further effort should be focused to make these modeling tools more useful to the end user. INTRODUCTION The design of a multiphase flow gathering network requires an estimate of flow potential from each well. Rarely is the aerial extent of the reservoir so well defined at the outset that all potential well locations can be identified. The flow potential of the fluid phases produced up the wellbore into the gathering network vary between wells depending upon the characteristics of the reservoir, and pressure depletion or "drive" mechanism of the reservoir. Accordingly, the efficient transport of reservoir fluids through the gathering network is usually difficult to predict over the producing life of the wells. The forecast of production rates from wells using a numerical reservoir simulation model is likewise dependent on backpressures caused by pressure losses in the wellbore and surface gathering network. The historical approach taken by the reservoir engineer has been to prepare flowing bottomhole pressure versus flow rate (VFP) "look-up" tables to approximate the backpressure caused by the wellbore and surface network. Each table is unique with respect to tubular dimensions, lengths, etc. When dealing with multiphase flow many permutations (e.g. different water-gas ratios, condensate-gas ratios) are often required to construct a VFP table over potential range of flowing conditions. Such an approach is suitable for single well, single flow line gathering networks however, such configurations are not typical of most pipeline networks.

ABSTRACT Centra Gas, a Local Distribution Company in Winnipeg, Canada, has installed an integrated transient simulation model and SCADA system. This is a completely Windows NT@ pipeline operating system that allows the simulation model to recall historical transient pipeline conditions and replay historically saved SCADA data from a transient. This system does not require or assume steady state conditions. The system is designed to allow operators to review the transient as it occurred, or to intervene and train on their response to the transient condition Centra Gas Manitoba Inc. Centra Gas Manitoba Inc. is the sole natural gas distribution company for the entire province of Manitoba, Canada. The company provides naturai gas service to approximately 230,000 customers in more than 90 towns and locations throughout the province. Centra Gas Manitoba Inc. is a wholly-owned subsidiary of Westcoast Energy Inc. The company's headquarters are located at 444 St. Mary Avenue in Winnipeg, with regional offices in the cities of Brandon, Dauphin, Portage la Prairie, Selkirk, Steinbach, and Winkler. It's operations are regulated by the Public Utility Board of Manitoba. There are more than 600 employees working for Centra Gas Manitoba. These employees are responsible for the supervision of the safe operation, maintenance, and gas distribution of the pipeline system, and providing the very best in customer service. Distribution Network The Centra Gas pipeline system is a combination of both transmission and local distribution networks. The natural gas is received from the Tram+Canada Pipeline system and is the sole natural gas delivery system for the population of Manitoba. Its operations include the management of more than 5,300 kilometers (3,180 miles) of transmission mains and distribution pipelines. The customer base for Centra Gas is made up of approximately 70% industrial and 30% residential consumption. The pipeline network delivers natural gas with significant seasonal swings in demand, the capacity ranges from 5.38 Million Cubic Meters per day with 1.0 Million being delivered to customers and the remaining 4.38 Million being placed in storage during the summer to approximately 16.0 Million Cubic Meters being delivered to customers during the winter months. SCADA Replacement Centra Gas has begun a process of modernizing and upgrading its system to provide additional capacity and monitoring of existing stations. The legacy SCADA system is approximately 11 years old and was designed and installed by Fisher. The recent announcement by Fisher that they would no longer support their SCADA systems installed base and that they would no longer implement SCADA systems proved to be an impediment to the planned upgrade program. The present pipeline system uses two different types of monitoring devices, ROMET EVC and HP 48000 RTU's. Centra Gas is replacing the ROMET RTU's with newer Mercury EC-AT RTU's and will be replacing the HP 48000's in the near future. The Fisher SCADA system is a closed system and does not support the communications protocols for the new RTU's.

ABSTRACT In the post 636 environment, many pipeline companies have elected to install real-time and predictive simulation models in the gas control room. The technology is needed to properly control pipelines in the face of operations being dictated to a large extent by customer scheduled volumes. Pipeline companies have largely concluded that the execution of these simulation tools requires engineering expertise and an amount of effort which is beyond what most pipeline controllers are willing to undertake given their pipeline responsibilities. This has led to the widespread belief that an engineer in the control room is necessary to get useful results from the models. This paper details the efforts of Texas Eastern Transmission Corporation to develop applications which simplify the execution of commercial on-line simulation models. A user interface is developed which limits the flexibility of the control of the simulation to a similar level of control the operator is able to maintain over the pipeline itself. Operators are not only capable of maintaining the data flow to the real-time model, but are also able to perform predictive model runs, normally without the assistance of simulation engineers, obtaining useful information needed to efficiently run the pipeline. INTRODUCTION The Project At the end of 1995, Texas Eastern Transmission Corporation (TETCO) undertook a project to modernize many of their business process to better adapt to the new post Order-636 environment. This was done in recognition that the response cycle for running a natural gas pipeline in the U.S. was quickly becoming "within day". It was further recognized that simulation of the pipeline would be an integral part of the operation since this technology provided an enhanced level of visibility of the pipeline which was required. Pipeline Simulation It was guncrally accuptcd that unless the gas controllers embraced the simulation technology and hocame comIbrtahlc with its use and its demands. the project would be jeopardized. It was not feasible at TETCO for engineering support to be supplied to gas control on an around the clock basis. Therefore, the models had to be usable by gas control day-to-day operation of the pipeline without continuous engmeermg support. The Texas Eastern System The TETCO pipeline system consists of approximately 9,700 miles of pipeline, including 74 compressor stations. The system extends from the Mexican border near McAllen, Texas to Staten Island, NY. TETCO's pipeline takes two separate routes. The 30" system parallels the Texas and Louisiana Gulf Coast before turning northeast across Mississippi, Tennessee, and Kentucky into southeastern Ohio at the Berne Station. The 24" system runs from Texas through Arkansas, Missouri, Illinois, Indiana, and into southeastern Ohio, joining the 30" system at the Berne station. The Beme compressor station is the start of the Penn-Jersey system which represents the market end of the TETCO system. Penn-Jersey is designed to be primarily a peaking system. In the Penn-Jersey system, compressor stations are very close together, spaced only by about 30 miles. TETCO's as well as third party market area storage fields also reside in this area.

ABSTRACT Following the history of steady state optimization applied to OMV's distribution and transmission systems, the development of an optimization method, based on Sequential Linear Programming will be described. An iterative cooperation between a detailed simulation model and a simplified optimization model enables the application to fulfill any desired degree of accuracy and complexity. The optimization goal is the minimization of operating costs. Practical experiences made during development and application as well as general problems of steady state optimization will be pointed out. Finally conclusions and suggestions on how to overcome some of these problems are given. Biography Erwin Sekirnjak holds a Ph.D. in Physics from the University of Vienna. After one year of experience in designing optical systems he joined the Austrian Oil and Gas Company, OMV. For 27 years he has been working in various areas of computer applications with an emphasis on operations research methods. His responsibility covered applications like reservoir simulation, refinery optimization, optimization for long-term company investments, gas control systems, planning and optimization models for gas transportation systems. 1. Introduction OMV's aas transnortation system: OMV operates a national distribution system as well as two international transmission systems (see Fig. 7). Gas is moved from external sources, few domestic fields, 5 storage fields, through 800 miles of pipelines, 10 compressor stations and various gas treatment plants to local distribution companies and international delivery stations. The transmission systems represent a small, but important part of the European network as shown in Fig. 8. They connect several entry / exit locations to our neighboring countries: Slovakia (the major entrypoint of Russian gas supply), Italy (TAG), Germany (WAG), Slovenia (SOL), and Hungary (HAG). The Austrian portion of the transmission systems currently consist of 600 miles of high-pressure pipelines, and 4 compressor stations, delivering 630 MMMCF/year, whereas the national delivery amounts to 220 MMMCF/year. How does Optimization support decision making? Searching for good decisions dispatchers and planners are dealing with various entities: the real world: pipelines, compressors, plants, companies, people…. current requirements: demands, technical and economic constraints (contracts, schedules, forecasts) etc. models: data from the scads system, graphic images, mental images (experience), physical laws, mathematical and computer based images etc. Decision processes typically consist of the following phases: knowing and understanding the current situation finding alternative strategies which fulfill the current requirements evaluating and comparing the alternatives (testing) selecting the most appropriate solution implementing the selected strategy in the real world, feedback on performance Phases 1 and 2 are the traditional areas supported by simulation. A well tuned online model permanently promotes good knowledge and understanding of the current situation and predictive models are especially useful for planning of transient operations. But even if the decisions concern steady state conditions it is often wise to apply transient simulation in phases 3 or 4 to ensure technically feasible transition from the current to the new state.

Abstract In 1985, Ruhrgas AG launched the DYNOS (mamic @r-Line Simulation) project to develop computer systems to support its dispatching activities. By the end of 1989 a major step was completed and a transient on-line simulation model was installed. This step was the subject of a paper presented at the 1990 PSIG annual meeting. Several enhancements have been implemented or are under development based on more than 6 years of operational experience. Simulation models have proven to be excellent tools for anticipating possible grid reactions to planned control actions. Due to the extent of the Ruhrgas grid, the complexity of technical equipment and contractual obligations, a dispatcher is faced with problems similar to controlling the grid itself when using simulation. The problems may accumulate as simulation confronts the dispatcher with several questions at the same time. The challenges to overcome are: To design a reliable interface with the SCADA system for continuous cyclic online operations which avoids or minimizes user interaction. To provide a user interface to quickly set up simulation scenarios, easing the interpretation of results and decision making. To provide easy to use, highly accurate models of technical equipment. This paper highlights these problems and their solutions by describing some of the developments already carried out and planned projects at Ruhrgas. It presents the operational experience and the conclusions that resulted in the above mentioned developments and reports the results and benefits gained or expected from these developments. Ruhrgas Activities With annual gas sales of over 580 billion kWh (1,979 TBTU), Ruhrgas AG is one of the major energy companies in Germany. Its activities focus on the supraregional supply of gas and it has been instrumental in developing the market for natural gas in Germany. Ruhrgas is a pioneer of international natural gas trading in Europe and is, today, one of the largest gas importers in the world. Ruhrgas purchases its natural gas from indigenous fields as well as from various production areas in western Europe - the Netherlands, Norway, Denmark - and Russia. It therefore has a highly diversified supply structure ensuring security of supplies. Ruhrgas contracts the natural gas from these sources alone or together with other German or west European gas companies. The agreements run for terms of 20 years and more; some of the present contracts even continue beyond the year 2020. Ruhrgas constantly explores the feasibility and viability of buying additional natural gas from existing and new sources. Some Operating highlights of Ruhrgas are contained in figures 2 and 3. Improved On-Line Capabilities by Implementing a Windows Interface Simulation models have proven to be excellent tools for anticipating possible grid reactions to planned control actions. Due to the extent of the Ruhrgas grid, the complexity of technical equipment and contractual obligations, a dispatcher is faced with problems similar to controlling the grid itself when using simulation. The problems may accumulate as simulation confronts the dispatcher with several questions at the same time.

ABSTRACT Colorado Interstate Gas Company has developed a fully integrated Gas Control Planning System. It incorporates an automatically dialed weather forecast service, a gas load forecaster, a gas supply manager with storage scheduler, and an extensive computerized SCADA system. The output of these programs plus real-time data make up the input to steady-state and transient flow pipeline models that provide the Gas Controllers with a 48- hour forecast of pipeline operations. This paper describes the development and implementation of the project, the development of the load forecaster and supply manager programs, and use of the system by Gas Controllers for daily operations planning. It includes descriptions of the menu system, reports, and graphical output. INTRODUCTION Colorado Interstate Gas Company (CIG) was formed in 1927 and began operations in 1928. Its original function was moving natural gas from the Texas Panhandle to Colorado Fuel and Iron Company in Pueblo, Colorado, and to the city of Denver, Colorado. The company is headquartered in Colorado Springs, Colorado. In 1973, CIG became a subsidiary of Coastal States Gas Corporation, now The Coastal Corporation, with headquarters in Houston, Texas. CIG operates as a federally regulated interstate transmission company with major gas supplies in Wyoming, Utah, Colorado, Kansas, Oklahoma, and Texas. The principal markets are along the Front Range of the Rocky Mountains from Cheyenne, Wyoming, to Raton, New Mexico. Figure 1 illustrates CIG's pipeline in its supply and market areas. CIG operates over 6,000 miles of pipeline and over 324,000 horsepower for gas compression. Approximately one-half of the pipeline mileage is for transmission and the remainder is for gathering and storage. Similarly, about one-half of the compression horsepower is for transmission and the remainder is for gathering and storage. CIG has developed and operates four underground storage facilities: three in eastern Colorado and one in western Kansas. These storage fields can supply a peak-day volume of 550 MMcf per day and an annual volume of 30 Bcf. The locations of the storage fields are indicated by triangles on Figure 1. In addition to its role as principal supplier of natural gas to the Rocky Mountain Front Range, CIG has become a major transporter of gas for other gas producers and pipeline companies. In 1987, 60 percent of the gas transported by CIG was Transportation & Exchange (T&E) volumes. OPERATIONS CONTROL BEFORE CURRENT PROJECT In the late 1970s and early 1980s, CIG adopted and implemented a distributed data processing and control philosophy. Eight DEC PDP 11/23+s and 11/24+s were established at widespread district field locations. We call these computers Remote Masters (RMs). The RMs gather data from Remote Terminal Units (RTUs) installed at key locations along the pipeline. The RTUs are incorporated in CIG's in-house-developed Supervisory Control and Data Acquisition (SCADA) system.

ABSTRACT The Statpipe gas transmission system comprises totally 880 km offshore pipelines and an onshore NGL fractionation-plant. Gas from several fields located in the North Sea are transported in the system to buyers on the European Continent. Part of the gas is transported in dense phase to the NGL-plant for processing to sales gas and NGL products specifications. The gas transport through the system is monitored and controlled from a Pipeline Control Centre (PCC) located in Norway. There is implemented a real time simulator in the PCC SCADA-computer. SCADA data are used as boundary conditions for solving the transient flow equations of the real time simulator. In addition, several application programs are available for offline simulations. The present paper focus on operational use and experience with the real time simulator and applications programs. The Statpipe System The Statpipe system is a gas gathering and transmission system for transportation and treatment of gas from the Statfjord, Gullfaks and Heimdal fields in the North Sea. It includes 880 kilometers of pipeline, two offshore riser platforms and an onshore terminal for gas treatment and NGL extraction. The system was put into operation in 1985. Figure 1 shows the main dimensions and lengths. Associated rich gas is transported from the Gullfaks and Statfjord oil fields to the Kårstø NGL plant. The rich gas is transported in dense phase, i.e. at pressures higher than the cricondenbar (see figure 2). Typical operating pressures are in the range 120 to 167 barg. Design pressure of the pipeline is 172 barg. At Kårstø the heavier components of the rich gas is separated and fractionated into propane, normal-butane, iso-butane and natural gasoline. These products are stored in tanks and shipped from the gas terminal. The dry gas, mainly a mixture of methane and ethane, is sent through a pipeline to the riser platform 16/11S. At this platform the dry gas pipeline from the Heimdal field ties in, and the mixed gas is moving further to the Ekofisk Centre via riser platform 2/4S. From there on, the gas is transported in the Norpipe system to Emden in Germany, and distributed to consumers in Germany, The Netherlands, Belgium, France and Austria. The operational pressure range for the dry gas system is 60 to 116 barg. By installing additional compression facilities, the transport capacities can be increased. The design pressure for the Kårstø to 16/11S leg is 172 barg, and the Heimdal to 16/11S leg and 16/11S to 2/4S leg have design pressures of 156.8 barg. Statpipe Coordination The day to day operation of the Statpipe system is monitored and coordinated from a Pipeline Control Centre (PCC) at Karmøy, close to the Kårstø terminal, see figure 3. Information to/from the productions platforms, the gas terminal, the riser platforms and the Norpipe system operators are transferred via a telecommunication system to the control centre. This includes data, telex and voice traffic. The main goal of the operation is to meet the daily nominations of gas given by the buyers on the European Continent.