Total deliverability gas storage analysis methodology and case study

Transcription

1 Risk, Reliability and Societal Safety Aven & Vinnem (eds) 2007 Taylor & Francis Group, London, ISBN Total deliverability gas storage analysis methodology and case study B. Haukelidsæter & S. Gaard Statoil ASA, Kårstø, Norway T. Aven University of Stavanger, Norway ABSTRACT: A total gas chain deliverability model has been developed in order to predict deliverability in each exit point of the subsea dry gas transmission network from the Norwegian Continental Shelf (NCS) to the European Continent. The complexities in the operation of the gas value chain experienced by the shippers and operators have increased over time. Increased exploitation ratio of the network imply reduced survival time, system capacity becomes more sensitive as the necessary conditions are harder to fulfil. Furthermore, gas is collected at fewer nodes reducing the redundancy in the gas supply. In this regime the deliverability of the NCS machine is challenged and additional compensating elements like gas storages is needed by shippers of gas to maintain the system s performance. This paper presents and discusses a deliverability analysis methodology for identifying the optimal locations and capacities for new gas storages. Determining the location and capacity of gas storage facilities is challenging as several factors affect the performance of the system, including: Equipment downtimes Routing options and constraints Compensating elements; amount, distribution and operation Distribution and operation of the gas supply Delivery nodes redundancy in the gas supply Gas quality Short term gas market deviations A case study utilizing the methodology is also presented. 1 INTRODUCTION The capacity utilization of the gas transportation system at the Norwegian Continental Shelf (NCS) system is increasing. This is leading to reduced system survival time, more strained operation of the fields and thereby increased regularity challenges. By regularity we mean the capability of the system of meeting the demand for deliveries or performance. On regularity consult Hjorteland et al (2006). Production availability, deliverability or other appropriate measures can be used to express regularity. At the same time the operational flexibility at the fields is decreasing, new fields are tied-in to existing installations creating large nodes that increase system vulnerability. Some of these new collective nodes are old installations and are in a deterioration process (experiencing ageing). In such a system it can be difficult to always deliver as planned. The shippers have few compensating elements at hand. Commercial compensating means, such as paper storages, buying gas in the spot market and standby capacity agreements, are widely used, but could be expensive. Physical compensating means include excess pipeline inventory, spare capacity at fields and gas storages. Additional gas regularity storages may be profitable in the new regime where regularity and the ability to deliver are challenged. Other uses for gas storages include market optimization and use during planned operations like maintenance, but these are not discussed in this paper. In case of unsatisfactory regularity, the option is either to buy expensive gas on the spot markets or to give large refunds to the customers. However, other compensating means are needed. Gas storages are one such compensating element that may increase the value of a shipper s gas portfolio. The main issues that 535

2 a shipper has to decide upon when he is to build a gas storage are: What is the optimal gas storage location given the shipper s gas portfolio, gas blending issues, bookings, customers, etc.? What is the optimal gas storage parameters that negate the effect of shortfalls related to unplanned events? What kind of gas storage technology is possible in the different locations and how does that affect the possible gas storage dimensions and the required investment? These issues are discussed in this paper. The last mentioned issue regarding technology choice is exemplified but not studied in detail in the paper. However, the issue regarding technology choice motivates the methodology used in the paper.the type of gas storage technology realizable varies from location to location. Salt caverns can only be used if salt caverns are present, depleted fields can only be used if there are a depleted field nearby, etc. The aim of the paper is to present and discuss a Monte-Carlo, Vose (1996), based methodology for identifying the optimal locations and capacities for new gas storages, from a regularity point of view. There are few relevant papers written in the field of gas value chain regularity analysis known to the authors. Some papers like Pedersen et al (2000) mention the method as a potential promising one in the architectural development of a gas value chain, but the authors has not found any papers presenting any concrete methodology regarding gas value chains and regularity other than Haukelidsæter et al (2006). The reliability modelling- and simulation tool used in the case study is Miriam Regina, reference to CognIT (2006) is made. 2 FACTORS TO BE REFLECTED IN THE METHODOLOGY (MODEL) The methodology should take into account: The entire gas value supply chain from field to customer The complete gas portfolio of the shipper All alternatives (to the new gas storage) compensating means The shipper s booking in transport capacity The shipper s delivery obligations Gas network physics and routing options Gas quality specifications Short-term gas markets The entire gas value supply chain need to be included in order to identify all regularity contributors and regularity bottle-necks.the sum of all bottle-necks will affect the delivery points differently depending on factors like delivery pressure and shipper booking. Additionally, different gas quality specification at exit points may affect deliveries differently even if the exit points are near each other and are prone to many of the same regularity bottle-necks. Also, a shipper s position in the gas supply must relate to his obligations in the delivery points. It plays an important role in determining his ability to deliver. All alternative compensating means should be included to evaluate what part of the curtailment volume is left for the gas storage to handle. Of course, some compensating means may be more expensive than gas storages and may therefore be left out of the analysis to cater for the curtailment volumes that the gas storage is determined not to handle. The capacity booking, the short-term market and the shipper s obligations all put a restriction on the usefulness of gas storage at a particular location and the possible gas storage size. If the booked capacity or obligations are low, the shippers ability fill up the storage or to move curtailments to the location are limited. If the short-term market is calm and the need for gas is low then the basis for a storage is limited because the amount of gas that the storage can compensate for there is low. However, if the gas storage is built at a location where a shipper has a high capacity booking, many obligations and the gas market craves gas, then the storage built there may be very large and the amount of curtailments that may be moved to the point are large. If the supply of gas is not challenged in either regularity directly or indirectly by gas quality specification, the increased regularity potential with a new gas storage is still limited. Gas network physics and gas routing options determine the gas value chain s flexibility, e.g. the ability to move gas where it is needed. During an average winter season the gas sales customer nominations, i.e. how much gas a customer orders from the shipper, vary from day to day within and between the exit points. One day, the deliveries towards exit point A may be strained, while the next day the deliveries towards exit point D are strained while deliveries to exit point A are relatively low. A single unplanned event may therefore result in different curtailment situations. Since all exit point capacities and thereby transport system capacities are not fully utilized every day, there are flexibility in the system. In a regularity perspective this flexibility may be utilized to negate the consequences of unplanned events. Pipeline inventory will be used by the operator of the transportation network, the system may change state giving increased capacities some places and placing restrictions in other. In this way deliveries towards the given customer nominations could be optimized and thereby minimizing curtailments. 536

3 For each possible gas storage location the following quantities should be assessed: The total curtailment volume (the sum of all undelivered volumes) The required working volume The required injection capacity The required withdrawal capacity The potential increase in deliverability for the entire gas portfolio Available gas for filling the gas storage The total curtailment volume represents the amount of undelivered volumes due to unplanned events. Injection capacity is the rate at which the injection compressors may put gas into the gas storage. The withdrawal capacity is the rate at which gas may be withdrawn from the gas storage. The working volume is the part of the volume that can be withdrawn from the storage. It is not the entire volume in the gas storage. The rest of the volume in the gas storage is used to pressurize the storage. The potential increase in deliverability for the entire gas portfolio of a shipper represents the storage location s potential for increasing the shippers deliverability, and thereby the usefulness of a gas storage location. The range of potential ways of getting gas into the storage to keep it full must be assessed. For a given location this includes booking extra capacity for storage injection purposes, night and daytime variations in the capacity utilization, periodic and short term variations in customer off take (including winter and summer variations and weekday weekend variations throughout the year), volatility of the gas market, potential swapping- or bilateral agreements, etc. A shipper may build a gas storage that takes care of the bulk of the curtailment volume, rather than the entire curtailment volume and thereby end up with a cheaper storage (with less withdrawal and/or injection and/or working volume) with a higher circulation of gas and thereby better economics. There are however many other factors than economics that also need to be taken into account when determining the best location for a gas storage. Some of the most important are: Ability to route gas away from the point given capacity restrictions How the exit point is related to the parts of the system with poor reliability Some gas quality curtailment aspects like CO 2 limitations The parts of the system that experience frequent events and the parts that experience more severe but less frequent events The way the physics of the system increase an exit point s ability to receive curtailments The way the gas market in the different exit points play a role in the regularity picture In what way a company s position in the fields affect the regularity in the different exit points where the company has contracts The booking in an exit point determines how much gas that may be taken out there and thereby also how much gas the gas storage can work with 3 METHODOLOGY A methodology is developed reflecting the above factors. The methodology is Monte-Carlo based. Complete gas infrastructure scenarios (volumes, booking, capacities, availability numbers, physics, etc.) are developed describing the performance of the system, e.g. its ability to deliver at demand. The gas storage in itself is not included in the scenarios. For each given location (or set of locations) a simulation is performed. In each simulation the given gas storage location is prioritized the lowest for deliveries. Hence, during curtailment situations the curtailment volume is moved to this location if possible. Note that the main objective is still to optimize total deliveries. Also, capacity restrictions upstream or downstream may prevent some curtailments from being moved to the location or the curtailment is so large that only a proportion of the loss may be realized in the location. Thereby the highest possible curtailment volume and the potential increase in deliverability at each location is obtained. In addition a downtime profile for each such location (or set of locations) is obtained. This downtime profile describes: The extent to which each event affects the deliveries at the exit point The distribution of durations for each (unplanned) event The frequency for each set of event duration and event size The deliverability of the exit point The relative contribution to downtime for each event size category The optimal location for a new gas storage is found in several steps. The first step involves identifying the locations that has the highest potential for increased gas portfolio deliverability, i.e. the location that given a gas storage that handles all curtailments increases the shipper s deliverability the most. Hence, the optimal storage location is understood as the location that has the potential of increasing a shipper s deliverability the most. The second step involves identifying a set of gas storage parameters related to handling all the curtailments described in the downtime profile of each gas 537

4 storage, e.g. the injection rate, withdrawal rate and working volume in each given exit point. Identifying these parameters require several steps. First the downtime profile is consulted and a methodology for dimensioning the parameters is used. This methodology is presented later in the paper. There are several possible gas storage dimensions. Identifying the optimal set of gas storage parameters is understood as identifying the parameters that lead to the greatest cycling of the storage, where cycling is defined as the number of times the storage is emptied and filled up again per unit of time, for example per winter season. The third step involves identifying the possible gas storage technologies (salt caverns, depleted fields, etc) that may realize the gas storage with the given storage parameters. Then the economics is done for each type of gas storage and the most cost effective solution is found. This step is, however, not treated in the paper. Technology choice is an important part of the design process. It may not be easy to realize the gas storage that has been identified in step one and two. Most exit point has for example different delivery pressure. Some exit points require the gas to be delivered at up to 90 barg, while other exit points only require about 50 barg. This affects the possible gas storages at the exit point, because the different technologies may have limitations due to pressures. Salt caverns, for instance, yield the highest withdrawal rates when they deliver towards low pressure points and at the same time are deep underground such that the pressure in the cavern is high. In addition operational costs come into consideration. For example injecting gas into very deep, high pressure caverns requires much energy and hence has a high operational cost. Due that the possible type of technology varies from location to location and the locations set the premises for the type of storage the technology choice is defined as the last step in this methodology. The next parameter to dimension is the working volume. To decide this amount each event size category is considered separately. In the downtime profile the simultaneousness is already accounted for. That means that each event size category already consists of many simultaneous events added together. Hence, each event size category may be considered by itself and a virtual storage may be defined for each event size category. By defining a virtual storage for each size category the problem is simplified. For each event size category the remaining capacity and mean downtime (MDT) is found. From these two and the nominal maximum delivery in the given location the expected shortfall per event can be derived for one virtual storage: where E(V loss ) is the expected shortfall for the mean event in each event size category, C max is the maximum capacity of the pipeline that is utilizable by the shipper due to booking and obligations restrictions, C remaining is the remaining capacity utilizable by the shipper for each particular event size category, MDT is the mean downtime of the events in each particular event size category. Hence, a virtual storage with a given working volume for each event size category is obtained. The third and last parameter is the injection capacity. When identifying the injection capacity some conservatism is incorporated. It is postulated that the injection capacity should be large enough for each virtual storage to reach full working volume capacity when the probability for the time to the occurrence for the next unplanned event in the particular size category (or virtual storage) is 0.5, i.e. at the median of the distribution. Of course, more or less conservatism may be incorporated by lowering or raising this value. Then the injection rate for one virtual storage can be written 3.1 Dimensioning gas storages The main tool used when identifying the gas storage parameters is the downtime profile for each identified gas storage location. The key gas storage parameters are: Injection capacity Withdrawal capacity Working volume Dimensioning the withdrawal capacity is straightforward. Consider the downtime profile for the storage location in question. Pick the largest event size category that the gas storage is supposed to negate. The gas storage withdrawal capacity must be equal to this amount in order to be able to negate the volumetric loss for such events. where t 0.5 is the median of the probability distribution function of the next unplanned event for each virtual storage, and I correction is a correction parameter that represents the additional injection capacity that must be added to account for the time when other virtual storages withdraws gas and the injection capacity may not be used. Two approaches have been applied when identifying the injection capacity for each virtual storage. In the first approach it is assumed that it is possible to inject gas into the gas storage the entire time, even during unplanned events. When making this simplification the independency of each virtual storage can be assumed and the I correction parameter is defined 538

5 as zero. The validity of this simplification depends on the type of gas storage involved. If the gas storage is supposed to negate a few very large events, or many very short events, then the assumption may be a good approximation. To determine the injection rate the following procedure is used. Each event size category is considered separately as before and the total frequency of events per year in each category is found. From the total frequency of events per year it is easy to derive the Mean Time To Failure (MTTF). To find t 0.5 an exponential distribution is used. From the equation 1 exp{ t 0.5 /MTTF}=0.5, we obtain t 0.5. Having obtained t 0.5 the required injection capacity to realize the postulate is readily obtained from formula (2). The second approach involves adjusting the injection capacity of each virtual storage so that the injection capacity incorporates the time between two events when other events occur (and it is not possible to inject gas into the storage), e.g. identifying the parameter I correction. This approach is however not considered any further in this paper. Lastly, the different event size categories that the storage is supposed to negate have their virtual working volumes and injection rates added together to obtain the total working volume and injection rate required. The three gas storage parameters are not linearly dependant. If the working volume is doubled the injection rate may not be halved. The injection rate is strongly connected to the working volume, however not linearly. To have a large volume capacity requires a large injection rate to fill up the storage to be able to utilize the large working volume. This means that with a given storage volume and varying injection rate and vice versa the varying parameter may be increased arbitrarily above the optimal level without further improving deliverability. 4 CASE STUDY Consider a gas transportation system with exit points A, B, C, D, E, F, G, H and I. The shipper in question has not any booking or obligations in exit point H and I. Therefore he considers only building gas storage in the other exit points. A deliverability model describing the gas value chain and his complete gas supply portfolio is developed. The scenario is a prognosis of the winter season five years ahead in time. Monte-Carlo simulation is performed for each exit point and the results obtained for exit point C are shown in Table 1. From the table it is seen that 1281 MSm 3 of curtailment volumes were possible to move to exit point C, the largest amount in all simulations. It is seen that the total deliverability for the entire shipper s portfolio is almost 97%. Table 1. Monte-Carlo simulation. Planned Lost/delayed Exit Deliverability delivery production Point [%] [MSm 3 /d] [MSm 3 /year] A B C D E F G Total shipper Table 2. Exit point C with optimal gas storage. Planned Lost/delayed Exit Deliverability delivery production Point [%] [MSm 3 /d] [MSm 3 /year] A B C D E F G Total shipper It is then assumed that an optimal gas storage is designed that removes all curtailment volumes from exit point C. FromTable 2 it is seen that if such gas storage is built, then the shipper may raise his deliverability by 0.85 percent points, e.g. notice that the deliverability of exit point C has been set to 100% in Table 2. The next step is to identify the type of gas storage needed and its cost. Table 3 shows the downtime profile of exit point C. The downtime profile describes all unplanned events in exit point C. There are 10 remaining capacity levels. For each such level there is a frequency and a mean downtime (MDT). The frequency expresses the amount of expected unplanned events each winter season and the MDT describes the mean duration of each unplanned event. The unplanned events in the table represent all the curtailment volumes that the gas storage should negate. It is seen that the majority of the curtailment volumes occur at 15.48% and up. For the event size categories 0% and 5.3% the relative loss in only 0.3% and 0.2%. The bulk of curtailment volumes may be negated by building a storage that handles the unplanned events sized 15.5% of full capacity and up. The rest of the events may be handled by other compensating means. Note that the gas storage may handle a part of the events of 0.0% and 5.3% limited by the withdrawal capacity when designing gas storage at 15.5%. 539

6 Table 3. Downtime profile exit point C. Remaining capacity [%] SUM Remaining capacity NA [MSm 3 /d] Frequency [per winter season] MDT [hours] NA Total downtime [hours] Unavailability [%] Total mean volume loss Table 4. Dimensioning the gas storage. Remaining P 50 Time to Expected P 50 Injection Export Remaining capacity Failure MDT loss one capacity capacity capacity [%] [MSm 3 /d] [hours] [hours] event [MSm 3 ] [MSm 3 /d] [MSm 3 /d] SUM NA NA It is seen that the two top event size categories, 87.5% and 96.0% have a very high frequency and this could make it hard to inject enough gas into the storage. For these two event categories it was decided that they should not be handled by the gas storage, but by other compensating means. From Table 4 it is seen that to negate all events from category 15.5% and up the withdrawal capacity should be 59.2 MSm 3 /d. It was decided that the working volume of the storage should include all curtailment volumes that the gas storage is to handle including a part of the volume of category 0% (25.6 MSm 3 ) and category 5.3% (20.7 MSm 3 )upto the withdrawal capacity limitation of the gas storage, but not the two event categories at the bottom of the table, 87.5% and 96.0%. That means 25.6 MSm 3 per event for the 0% category and 20.7 MSm 3 per event for the 5.3% category is included, but the 3.7 MSm 3 and 1.4 MSm 3 from the two categories at the bottom of the table is not. The total working volume required is then MSm 3. The injection capacity of the first two events is also modified with the withdrawal capacity limitation and the last two categories are not included. The total injection capacity required is then 8.78 MSm 3 /d. Note that the simplification assuming that it may be injected into the gas storage at all times is made. Having removed the two event size categories at the bottom of the table the frequency of events is about 60 per winter season or about every three days. This means that between each event the gas storage is filled up with about 27 MSm 3. In the table it is seen that it is only the 15.5% event that requires a volume larger than this volume to negate an average event. This should indicate that the gas storage is suitably designed to handle the curtailment volumes presented by the downtime profile. In Table 4 the notation P 50 is used. This refers to the conservatism that was incorporated into the estimate of the injection rate. It was postulated that the injection rate should be large enough to fill the storage before it was 50% probable that the next unplanned event occurred. Hence, MeanTimeTo Failure is not used, but 540

7 the lower P 50 Time To Failure is used. The P 50 injection capacity represents the injection capacity that satisfies the demand for conservatism. REFERENCES Haukelidsæter B. and Gaard S. 2006, Methodology in Total Deliverability of the NCS Gas Value Chain, ESREL 2006, Volume 3, p Hjorteland, A., Aven, T. and Østebø, R. 2006, On how to treat uncertainty in regularity analyses, in different project phases. Reliability Engineering and System Safety, to appear. Pedersen, B., Sjøen, K. & Lokna, T. 2000, Reliability guided design and operation of large natural gas production and supply networks. International Gas Union World Gas Conference Papers 21/15p. Vose, David 1996, QUANTITATIVE RISKANALYSIS : a guide to Monte Carlo simulation modelling. Chichester: John Wiley. CognIT AS 2006, Miram Regina website, Products Miriam Regina. 541

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