Zlotnik, Anatoly (Los Alamos National Laboratory) | Rudkevich, Aleksandr M. (Newton Energy Group) | Goldis, Evgeniy (Newton Energy Group) | Ruiz, Pablo A. (Boston University) | Caramanis, Michael (Boston University) | Carter, Richard (DNV-GL) | Backhaus, Scott (Los Alamos National Laboratory) | Tabors, Richard (Tabors Caramanis Rudkevich) | Hornby, Richard (Tabors Caramanis Rudkevich) | Baldwin, Daniel (Kinder Morgan)
As dependence of the bulk electric power system on gas-fired generation grows, more economically efficient coordination between the wholesale natural gas and electricity markets is increasingly important. New tools are needed to achieve more efficient and reliable operation of both markets by providing participants more accurate price signals on which to base their investment and operating decisions.
Today’s Electricity energy prices are consistent with the physical flow of electric energy in the power grid because of the economic optimization of power system operation in organized electricity markets administered by Regional Transmission Organizations (RTOs). A similar optimization approach that accounts for physical and engineering factors of pipeline hydraulics and compressor station operations would lead to location- and time-dependent intra-day prices of natural gas consistent with pipeline engineering factors, operations, and the physics of gas flow.
More economically efficient gas-electric coordination is envisioned as the timely exchange of both physical and pricing data between participants in each market, with price formation in both markets being fully consistent with the physics of energy flow. Physical data would be intra-day (e.g., hourly) gas schedules (burn and delivery) and pricing data would be bids and offers reflecting willingness to pay and to accept. Here, we describe the economic concepts related to this exchange, and discuss the regulatory and institutional issues that must be addressed. We then formulate an intra-day pipeline market clearing problem whose solution provides a flow schedule and hourly pricing, while ensuring that pipeline hydraulic limitations, compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. Furthermore, in order to support the practical application of these concepts, we provide a computational example of gas pipeline market clearing on a small interpretable model, and validate the results using a commercial pipeline simulator. Finally, we validate the modeling by cross-verifying simulations with SCADA data measured on a real pipeline system.
The paper concerns the problem of optimal control of a natural gas transmission system consisting of a compressor station and adjacent pipeline sections. Natural gas is supplied with two types of compressors, namely gas turbine driven centrifugal compressors and motor-compressors. For a given simulation scenario, the suction pressure, suction temperature, discharge pressure, and total compressor station mass flow are predicted from the non-isothermal transient gas flow model. Next the nonlinear programming problem with continuous and, in case of motor-compressors, discrete variables is solved to evaluate the type and the number of simultaneously operating compressors, while determining such a distribution of the capacity that the total unit fuel consumption in each time interval is minimized subject to the constraints imposed. The paper presents an algorithm of automatic search for the optimal values of the operating parameters of the compressor station. The method presented has been verified experimentally on the telemetry data.
INTRODUCTION AND BACKGROUND
Natural gas is usually transported by pipeline networks which serve as the most cost effective transportation means. Transmission systems usually have a linear topology corresponding to a linear arrangement of compressor stations. The fuel consumption of compressors is responsible for a large fraction of the costs of gas network operation. Luongo et al. (1989) reported that AGA estimates the operating cost of running the compressor stations to vary between 25% and 50% of the total company's operating budget, therefore minimizing fuel usage is a major objective in the control of gas transmission costs.
This work is concerned with the optimization of a single compressor station operated under transient conditions. More specifically, we consider variable boundary conditions, i.e. unsteady inlet and outlet pressures together with a variable flowrate through the compressor, and search for the optimal values of the operating parameters that minimize the running costs of the compressor station.
The objective of this paper is to describe a method that simulates an energy recovery system (ERS), which exploits water hydraulic power to boost inlet flow pressure. The impact of pipeline pressure surge (water hammer) on water treatment units was investigated. Surge pressure and pressure rise rate were calculated.
A novel methodology has been developed in this paper to simulate an energy recovery system and estimate pressure rise rate. This method integrated an energy recovery system into an existing pipeline simulation model. The energy recovery system model was developed using basic hydraulic pump equations. Actual system efficiency was used. Both maximum surge pressure and pressure rise rate are calculated each model time step. This same method can be used for other energy recovery systems hydraulic analysis.
In this study, a high-pressure feed pump with a discharge pressure of 630 psig was analyzed. The model was used to calculate the maximum surge pressure downstream of the ERS.
In this analysis, downstream of the ERS there is an RO (reverse osmosis) filtration system. The maximum pressure and rate of change of pressure must be controlled so as not to damage the filter membranes.
Different surge scenarios were investigated. For the cases analyzed it was possible to keep the maximum surge pressure to 1117 psig that is below the maximum membrane design pressure. It was also possible to keep the maximum pressure rise rate for all cases simulated to below 5.2 psi/second. The membrane warranty for the cases analyzed limited the pressure rise rate to 10 psi/second and stipulated a maximum pressure or 1200 psig. The simulation results also provide design parameters for sizing surge relief devices and designing the required control system.
Traditional surge analysis tools can properly estimate surge pressure within the pipeline system. However, energy recovery system behavior in a surge scenario was not simulated previously. The provided method can simulate energy recovery systems, calculate maximum surge pressure and pressure rise rate. The method sheds light on simulating energy recovery system and can be adopted for different simulation tools.
Barrera, Colleen (Pacific Gas & Electric Co.) | Richard, Molly (Pacific Gas & Electric Co.) | Bishop, Bill (Pacific Gas & Electric Co.) | Macias, Miguel (Pacific Gas & Electric Co.) | Lydon, Heidi (Pacific Gas & Electric Co.)
With the pipeline industry’s increased focus on pipeline safety and integrity, more frequent and complex pipeline outages are required to perform this safety work. How can dead-end, single feed natural gas systems be taken out of service to perform safety work while maintaining service to customers? It has become increasingly necessary to support customers using portable natural gas (PNG) in compressed (CNG) or liquefied (LNG) form. Pacific Gas & Electric Company (PG&E) has relied on PNG support during pipeline outages more frequently in recent years and recently undertook two separate projects that were unprecedented in scope and volume – in Santa Cruz and Redding, California.
PG&E’s Gas System Planning (GSP) department performed complex hydraulic analysis and operational support for the 2 major pipeline safety projects, each of which required more than 40,000+ customers to be supported solely with PNG for up to 4 weeks. In Redding, 130 tankers carried 110 mmscf of LNG to be injected into the pipeline, plus additional equipment provided 13 mmscf of CNG. Personnel traveled over 135,000 miles to support the project. In Santa Cruz, 119 loads of LNG injected a volume of 81 mmscf with personnel traveling 83,300 miles over the course of the outage.
This paper presents the analysis and tools used to perform extensive and complex gas system planning analysis. In the initial phases of the project, GSP provided input on the project schedule, how to phase the work to minimize customer impacts and maintain system reliability, and what additional PNG equipment would be needed to support the entire project. As we moved into the project design phase, GSP calculated total daily volumes and peak hourly flow rates, as well as found hydraulically feasible injection site locations and pressures for each tap off of the pipeline. Finally, during the outages, GSP provided real-time flow monitoring and operational support for field personnel.
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.
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”.
Potential routes, pipeline sizes, pump spacing, and numerous more details are scrutinized when designing Greenfield and expansion pipeline projects. These details are analyzed to create an accurate cost estimate to determine the economics of building a new pipe. Billions of dollars are spent constructing pipelines based on these estimates with detailed studies. However, one important pipeline parameter is generally kept at a default value when designing Greenfield and expansion pipeline projects – pipeline roughness.
Absolute pipeline roughness of 0.0018 inch (0.04572 mm) is often selected by default based on published industry information for hydrocarbon liquids. In practice, default pipeline roughness can be shown to vary based on product when pipeline roughness is used as a tuning factor. Various friction factor equations can be selected to reduce the variation in tuned pipeline roughness. However, light hydrocarbon liquids are not well suited to existing friction factor equations. Pipeline pressure losses will be overestimated if the roughness value of 0.0018 inch (0.04572 mm) is used. Overestimating pressure losses results in overdesigning pipe and pump requirements for new pipelines. Proposed projects may fail to start due to excessive material costs and projects that do get completed may have installed equipment that is not used after construction. More research is needed in order to determine exact correlations between product type and friction factor equation results.
Introduction and background
Greenfield pipelines and expansions of existing pipelines are constantly analyzed. These projects are essential for providing pipeline access to newly developed oilfields and increasing transportation out of existing oilfields. Project budgets became much more restricted during the oil price collapse in 2015. As a result, projects to develop new Greenfield pipelines and to expand existing pipelines came under intensified scrutiny.
One way to improve the chance of a project’s success was to closely analyze the hydraulics of the projects. Close scrutiny of hydraulics from various projects revealed that pipeline roughness, which is used as a tuning factor, has consistently been kept at a default value of 0.0018 inches (0.04572 mm) and used the Colebrook-White friction factor equation for new liquid pipeline projects. Evaluation of existing pipelines showed that a default pipeline roughness value of 0.0018 inches (0.04572 mm) worked well for heavier hydrocarbon liquids. However, Natural Gas Liquids (NGLs) had tuned pipeline roughness values that were lower than the default value by as much as a magnitude of 10 with Colebrook-White. This observation showed that product type needed to be considered when deciding pipeline roughness for new pipeline design.
SGN is the second largest gas distribution company in the UK owning and operating two gas distribution networks made up of three Local Distribution Zones (LDZs) delivering natural and green gas through over 46,000 miles (74,000 km) of pipeline to 5.9 million homes and businesses across Scotland and the south of England. The distribution networks receive gas from the UK’s National Transmission System (NTS), multiple biomethane producers and an LNG terminal.
The SGN Gas Control Centre has a key role to ensure there is a safe and reliable network to enable them to meet the daily demand whilst operating the networks within designed limits. National Grid Gas UK Transmission (NGG UKT), which operate the National Transmission System (NTS), require the provision of an hourly schedule of gas volumes (hourly profiles) taken through each of the NTS Offtakes into the LDZ in the form of Offtake Profile Notifications (OPNs). Although these profiles can be altered throughout the day to reflect changes in the demand, there are strict rules as to how much the profiles may change. Each OPN is scrutinized against the Uniform Network Code (UNC)(2) rules on submission to UKT.
To gain further insight and enable more efficient operation of its networks, SGN has deployed a real-time Gas Network Modelling System (GNMS) on the high pressure (100-1000 psig (7-69 barg)) parts of its networks. As well as providing a valuable stock monitoring tool, the GNMS utilizes data from the OPNs to predict the performance of the networks over the period of the current and next gas days.
This paper will discuss the OPN generation tool developed by SGN and Emerson and the implementation of the GNMS. Despite limitations in instrumentation in certain parts of the networks early indications are the GNMS is providing accurate results: this too will be discussed.
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 m3/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).
Knowledge of natural gas quality in the short-term future (24 h) is expected by many of end users. Also, European Union Law requires to provide such information by Transmission System Operators. In case of multiloop network which is supplied with many sources and different gas compositions, the dynamic network simulation combined with forecasting of behavior all sources and offtakes, is necessary.
The article describes model of full chain of calculation. At entries, there are: productions, storages and interconnectors with more less stable gas compositions and LNG Terminals where gas composition smoothly change in function of time or significantly due to filing in from next vessel. At exits forecasting of demand relates to nomination processes (industrial end users) or forecasting systems (city gates to household areas). Between them the multiloop transmission network is dynamically simulated with full quality tracking model.
The paper contains also our practice experience based on Polish transmission system which has many entries from production, interconnectors, storages, more than 900 exits and new LNG Terminal. The multiloop network has also several compression stations and reduction points. There is a possibility of determining the degree of gas mixing i.e. providing clients with such information with simulation software. Such analyses are executed on a regular basis. Calculations are performed in three minute cycles (reconstruction network state) and future calculation are performed with the 15-minute step. For future calculations city gate exit points demand is obtained from the forecasting system (short-term forecasts - 10 days) or nominations used in supply points or industrial exits. Such values are compared up – to – date with the values obtained from chromatographs located in the transmission system network – reference chromatographs.
Finally, the article presents the case study of stream mixing degree depending on gas composition from different gas sources, exit point demand and settings of the non-linear network elements. The analyses were performed for both static and dynamic scenarios where one of the parameters is a dynamic change of the quantity and quality supply from LNG Terminal to the network.