استفاده از تحقیق در عملیات به منظور بهینه سازی بهره برداری از سیستم گاز طبیعی نروژ
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|6939||2010||10 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 6330 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Natural Gas Science and Engineering, Volume 2, Issue 4, September 2010, Pages 153–162
Decisions regarding natural gas production, processing and transportation depend on each other, and knowledge about how partial changes in a gas transmission network influence the network capacity and flexibility is crucial in ensuring efficient system operation. SINTEF has developed a decision support tool, GassOpt, which is based on mixed-integer optimisation. The model objective is to maximise the flow throughput or profit for a given technical state of a natural gas network. The objective of this work has been to develop extensions to the GassOpt model mainly related to modelling of gas processing and energy consumption related to compression, and to analyse their impact on network operation. The extended GassOpt model represents and analyses a gas transport network in more detail, in particular in discovering bottlenecks, related to gas quality, contaminants and energy efficiency, which have obtained increased focus in recent time. GassOpt is a general tool for gas network optimisation, but applied on the Norwegian gas transport network specifically. The GassOpt tool is used to evaluate the current network as well as possible network extensions. Our approach ensures optimal operation of the network by considering the complete system and provides valuable insights in the dependencies between the different parts of the system. Tests show that the model represents actual network operation in a very good way.
1.1. Problem statement Decisions regarding natural gas production, processing and transportation depend on each other, and knowledge about how partial changes in a gas transmission network influence the network capacity and flexibility is of major importance for system operation. A decision support tool, GassOpt, is previously developed by SINTEF (Tomasgard et al., 2007 and Rømo et al., 2009) to optimise the network configuration and routing of gas for the main Norwegian shipper of natural gas, Statoil, and the independent network operator, Gassco. The basic theory and principles are outlined in Tomasgard et al. (2007). The objective of this optimisation tool is to maximise the flow throughput or profit for a given technical state of the Norwegian natural gas system. Recently, there has been increased focus on the importance of gas quality impacts on network capacity. Restrictions regarding gas quality are acknowledged as a major challenge when introducing new fields with larger variations in quality and higher CO2 content into the existing network. Also, greater attention is being paid to energy efficiency and environmental emissions in gas export. Operating efficiency is the major way to reduce emissions and energy use, in addition to its impact on system operating costs. All these aspects require refinement of the gas quality and energy consumption modelling in GassOpt. In this way, the analyst will get a significantly better tool for evaluating throughput potential by adding more complex bottlenecks in the network. In general there is also achieved an increased value by representing a more precise physical representation of the system. The model results then communicate better with a wide range of engineering specialists working with these systems. The objective of the work presented here has been to develop extensions to the GassOpt model, mainly related to modelling of gas processing and compression, and analyse their impacts on system optimisation and operation. Our approach ensures technically and economically optimal operation of the network by considering the complete system and provides valuable insights in the dependencies between the different parts of the system. The recent extensions in GassOpt will make solutions on system operation richer. This paper will describe these model extensions and their impacts on system optimisation. 1.2. Background The gas transport system on the Norwegian continental shelf (NCS) consists of 7800 km of subsea pipelines and is the largest offshore network of its kind in the world (see Fig. 1). In addition to pipelines, the gas transport system includes offshore platforms and land-based processing terminals, which process and compress natural gas. Dry gas is exported from these terminals through pipelines to exit terminals in the UK and continental Europe, where the gas is delivered for sale. Meeting these sales gas commitments is important. Failure to do so would result in gas sale losses, as well as reducing deliverability1 and hurting the reputation of gas shippers.2 Gas flowing through the Norwegian network represents approximately 16% of European gas consumption, and the system has a capacity of 120 billion scm3 a year. Dry gas exports from the NCS totalled 96.1 billion scm in 2008 (Nordvik et al., 2009). That makes Norway the second largest net gas exporter on world basis (IEA, 2009). The Norwegian gas transport system with its interconnected pipelines and processing terminals is large and complex. Variation in gas quality adds complexity to the problem. This makes it challenging to operate the system at maximum capacity. System effects are prevalent, and the network must be analysed as a whole to achieve optimal operation (Midthun et al., 2007). That requires detailed knowledge of network integration, operational flexibility, the relationship between customer nominations, pipeline flow and pressure, gas processing, compressor station operation, and the effects on optimal operation and energy efficiency. Therefore, it is important and necessary with clear procedures and models showing how to operate the system to optimality, securing flexibility, capability and availability of the gas export system. The GassOpt tool allows the users to graphically model their network with nodes and arcs in a graphical modelling environment, and to easily run optimisations for finding the best solutions quickly. Graphical network presentation is also used to give the users easier access and understanding of the results of the network optimisation. Fig. 2 shows the graphical modelling of the Norwegian natural gas network. Squared nodes in the illustration contain subsystem with further nodes and pipelines. The figure also illustrates the network complexity. Statoil and Gassco use the model to analyse the gas network, and to identify and reduce capacity and quality bottlenecks. It also has a major role in assessment of possible network extensions. Gassco uses the model to estimate future capacity of the network as input to the capacity booking process for the gas shippers. Statoil estimates the accumulated savings related to their use of GassOpt to be in the order of NOK 10 billion (USD 1800 billion) (Rømo et al., 2009).
نتیجه گیری انگلیسی
Decisions regarding natural gas production, processing and transportation depend on each other, and few publications exists on addressing how partial changes in the gas transmission network effect the network capacity and flexibility. Our approach ensures optimal operation of the network by considering the complete system and provides valuable insights in the dependencies between the different parts of the system. The extended GassOpt optimisation model can model and analyse a gas transport network or part of it more detailed than the former model, in particular in discovering bottlenecks related to gas quality, contaminants and energy efficiency. Refinement of processing plant modelling has become increasingly important due to increased focus in recent time on carbon extraction, and energy consumption in operating these energy demanding large scale systems. Tests show that the model represents actual system operation in a good way. In general there is achieved an increased value by representing a more precise physical representation of the system. The results then communicate better with a wide range of engineering specialists working with these systems