modelling lng train design

8/22/2019 Modelling LNG Train Design

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A PRACTICAL APPROACH TO MODELLING LNGTRAIN DESIGN FOR MINIMIZING MEASUREMENT

AND ALLOCATION UNCERTAINTY

Ronald Roberts

Instrumentation & Controls Engineer IIAtlantic LNG Company of Trinidad & Tobago

Point Fortin Trinidad & [email protected]

J ustin WalterSenior Measurement Consultant

Metco Services LimitedAberdeen, Scotland

J [email protected]

ABSTRACT

Our ability to measure accurately has formed the basis of trade in all industries. As a

consequence, the performance of measurement systems will affect the profitability of any

business. In the Oil and Gas Industry measurement systems are used for fiscal and

custody transfer purposes, for process plant operations and for plant efficiency

monitoring. It is of great importance therefore that these systems are engineered, operated

and maintained to industry standards and within equipment specifications.

This paper highlights the implications of measurement uncertainties on LNG product

allocations and how, through a study of these uncertainties and the implementation of a

Measurement Upgrade Enhancement Project, the final Allocation Uncertainty was more

than halved. The study addressed each of the metering elements associated with the

allocation process and identified which of these had the greatest impact (and/or required

attention). The measurement upgrade addressed issues affecting key meter performance

such as flow profile effects and valve noise with respect to ultrasonic measurement.

A major factor in the success of the project was the use of a Measurement Exposure

Model (MEM) which determined the measurement uncertainties associated with key

metering points, and the impact they had on the final LNG Product Allocation. By using

the MEM to focus on the effect of these measurement uncertainties, unnecessary

modifications/upgrades were avoided and savings made in the overall project.


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INTRODUCTION

1 MEASUREMENT AND ALLOCATION

The following are basic questions that should be asked of any LNG Production

facility:

What does X% uncertainty of LNG measurement mean and how can this beimproved?

To what extent do the plant measurement systems affect the LNG productallocation?

What is the minimum acceptable financial risk to J oint Venture (J V) partners onan LNG complex and how can this be improved?

These are some of the issues which have to be addressed among J V partners forequity on a multi-train LNG complex. This situation can arise if the ownership amongLNG trains and the commercial terms governing their operation are different. Thus a

single LNG facility using shared resources where applicable, may have multiple trains(plants) where there can be several owners and several feed gas streams, as is the caseat Atlantic LNG Company of Trinidad & Tobago.

The problem of accurately determining the LNG production of a train arises becauseof the following:

1. The absence of reliable LNG flow measurement at the outlet of the trains.2. Shared LNG storage at the facility.3. Recycling of LNG vapours from tank storage into the production streams.

The first constraint is dictated by the manufacturers products. Reliable dynamic

LNG measurement technology is actively being researched by a number of flowmeasurement vendors with the main challenges being the ability to accurately measureLNG leaving the trains and the ability to prove such a meter. In addition, there are nodynamic LNG flow measurement standards.

The second constraint is from a standpoint of practicality. It is good to have theoption to pump LNG production to several tanks. With the cost of an LNG storage tankbeing in the vicinity of 250-300 million USD it is prudent to share this facility since thiswould strengthen the project economics. However, this complicates ownershiptraceability where LNG production is commingled across several storage tanks.

The third constraint is the recycling of the LNG vapours from storage, loading andLNG cooldown to production facilities means that shared production from onetrain/owner/shipper can be allocated to another.

As a result of the above issues, the allocation process used to determine the LNGproduction of a train on a multi-train facility can be very complex. This product allocationwill be governed by the commercial terms and will be largely a function of mass, energyor standard volume balanced equations, for example,

Train Production = Inlet - Fuel Consumption-NGL Production - Train Losses

It is worth noting that static custody transfer measurement, as outlined by The

International Group of LNG Importers (GIIGNL), takes place on LNG tankers at anuncertainty of0.2-0.3%, however what about dynamic LNG train production?


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The key question then becomes, what are the uncertainties associated with these

measurement points and what impact does it have on the LNG product allocation?

This paper highlights the implications of measurement uncertainties on LNG productallocations and how the use of a Measurement Exposure Model (MEM) assisted in theimplementation of a Measurement Upgrade Enhancement Project thereby reducing theLNG and NGL product allocation uncertainty by an average of 2 %. In executing thismeasurement upgrade, the issue of valve noise and ultrasonic measurement (inlet feedgas meters) was also addressed since the correct installation and operation of inlet feedgas meters had the greatest impact on the LNG Allocation.

2 BUILDING A MEASUREMENT EXPOSURE MODEL (MEM)

A Measurement Exposure Model (MEM) is a mathematical tool that monitors thefacilitys measurement points and product allocation equations to determine theindividual and/or collective impact of each measurement point on the allocation of

products.

In order to build the MEM, an accurate assessment had to be made of themeasurement systems associated with the Allocation System. This was accomplishedby completing a comprehensive Audit of the metering elements feeding into theAllocation System for the LNG Pant and assessing their respective measurementuncertainties. The current operating Measurement Uncertainties of meters used forCustody and Allocation purposes were then used as an input by the Model (the MEM) toestablish the final Allocation Uncertainties in order to quantify the financial risk topartners.

Figure 1 - Typical MEM Mimic Screen

3 MEASUREMENT UNCERTAINTY ASSESSMENT

It should be appreciated that no measurement can be absolutely precise, since there

are inevitable biases/inaccuracies introduced as a result of the measurementinstruments chosen, their calibration and installation. Uncertainty then is an estimate of


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the limits to which one can expect an error to go, under a given set of conditions as partof the measurement process. Whilst the determination of measurement uncertainty isindependent of the MEM, it forms an essential input into the Model, if an accurateassessment of an existing Allocation Systems Performance is required. All relevantMeasurement systems are independently assessed and their Measurement Uncertaintyestablished using proven Calculation Techniques (API and ISO) complying with therelevant Measurement System Guidelines for Design, Operation and Maintenance.

The calculation process requires such information as:

Calibration Procedures, frequency and tolerance Primary Measurement Element Uncertainties (Orifice, Turbine Meter, Coriolis,

USM, Etc.) Secondary Instrument Uncertainties (DP, Pressure, Temperature, Densitometer,

Flow Computer, etc.) Correction Algorithms, Process Simulation Assumptions, etc. Laboratory Analysis Uncertainties

This information is used to calculate the individual Measurement SystemUncertainties and their consequent contribution to the overall LNG Train MeteringAllocation Uncertainty (calculated separately by the MEM).

Typical Custody & Allocation Measurement Systems include:

Gas Pipeline Meters (Custody) Train Inlet Meters (TIM) (Allocation) Train Liquid Output Meters (Allocation) Train Output Meters (Allocation) Storage Tank Measurements (Stock/Allocation) Fuel Gas Meters (Allocation Flare Gas Meters (Allocation) Gas Recycle Meters - where fitted (Allocation) Tanker Loading Measurements (Custody)

Figure 2 - Typical Input Screens for Uncertainty Assessment


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Figure 3 - Typical Output Screens of the Uncertainty Assessment

Using the above Output Data, the MEM user can enter the estimated measurementuncertainty relating to the metering element Operating Condition, thus enabling theModel to simulate most working conditions and reflect the additional uncertainty inAllocation for those conditions. This can be particularly helpful where the operator needsto be cognisant of the effect that instrument malfunctions or other failures may have onthe final Allocation uncertainty for the system.

4 MEASUREMENT EXPOSURE MODEL (MEM)

The Measurement Uncertainty Assessment forms an essential input to the MEM. Incases where the LNG Plant is not yet designed, design estimates of measurementuncertainty can be entered into the MEM such that the Design Engineers can accuratelyassess their impact on the Plant Allocation. This aspect of the MEM is not to beunderestimated, as it could save considerable Capital Expenditure on unnecessarydesign for uncritical elements of the Plant Measurement System. In the case of anexisting LNG Plant, weaknesses in the measurement systems provided can beidentified, thus providing substantial justification for change, in terms of misplacedAllocation Revenue.

Typically the MEM attributes measurement uncertainties to each of the

measurement points, then calculates the flow weighted impact these uncertainties willhave on Plant Balance and Allocation. These calculations can be based on estimated (ormeasured) flow rates through the various meters. By using this technique, the MEM canidentify any high risk metering elements in the system and establish the overallAllocation Uncertainties.

The results are typically presented as follows:

Measurement Uncertainty of each measurement point expressed as a percentageand in terms of product

Measurement Uncertainty associated with the Plant Balance expressed as apercentage and in terms of product

Measurement Uncertainty associated with the Plant Allocation expressed as apercentage and in terms of product


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Cost based Risk Analysis to contributing Fields and Owners for various operatingscenarios

Thus, once the initial uncertainty analysis has been performed on each meteringsystem to establish the Allocation measurement point Inputs, then the MEM analysis canbe performed to asses the sensitivity of these points on the Plant Balance & Allocationand the results documented.

Any trouble spots highlighted can be re-assessed in order to maximize the Plantsperformance. In this way, attention need only be focused on those elements that mostaffect the system and the reduced risk associated with the changes can be identified bythe model.

Figure 4 - Typical Input & Output Screens of MEM

As can be seen from the Mimic illustrated in Section 2.0, the whole AllocationProcess depended on the Train Inlet Meters (TIM) to enable back allocation of all theAtlantic LNG Plant OUTPUT products to the Gas Delivery Points (GDP), it follows thattheir measurement accuracy is of prime importance. The MEM demonstrated the cost ofthe measurement uncertainty to the Operators and their Partners by reference to a liveMimic responding to reported Allocation data.

5 MEASUREMENT PROJECTS JUSTIFICATION

The objectives of Capital expenditure plant projects can be generally categorizedinto two main areas:

Maintaining and Sustaining Production (Reliability Projects): Major upgradesand/or overhauls to plant equipment aimed at improving the cost of operationand for mitigating against equipment obsolescence.

Throughput and Yield Projects (Optimization Projects): Equipmentenhancements and upgrades geared towards increasing the productioncapability of the asset.

With measurement projects both of the objectives can be achieved.


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As shown in the MEM, the single biggest measurement point is the inletmeasurement to each of the LNG trains. In general, although measurement projects maynot generally increase production it is the perception of the production that is beingaddressed. This perception translates into financial exposure risk for all parties. As canbe seen from the MEM an improvement in the train inlet measurement uncertainty from5% to 1% translates to an improvement in Allocation Uncertainty of 2% or in other wordsa financial exposure benefit of 2% to the shareholders.

Therefore, using traditional techniques for project justifications such as NPV, PayPack and IRR measurement projects may be justified on this basis.

As in the case of the Train Inlet Measurement Upgrade project assuming that a 4%reduction in measurement uncertainty can be practically achieved then this projectshould be evaluated in relation to other initiatives competing for financial resources.

For example, assuming that three days of downtime on a facility are required for themeasurement upgrade and the total cost of the project is 1.2 million USD, for an LNG

complex with annual production target of 576 TBtu this gives an NPV (financial risk) of444 million USD and a payback in less than 1 year with a profitability index of 17.

Assumptions: Gas Price $ 5.00 per MMBtu,Discount Factor of 10%,Capital Outlay = Loss production + Cost of project= $25.2MMProject Life cycle 20 years.

Of interest to note, is that once other projects are undertaken which increases the576 TBtu for the facility then this project financing can be further enhanced, thereforegiving economic benefits to perpetuity. Therefore it is imperative that on an LNGcomplex inlet measurement uncertainty be optimized.

6 TRAIN INLET MEASUREMENT

As shown from the MEM an improvement in inlet measurement meant a significantreduction in LNG allocation product uncertainty. The challenge for the project teamwould be to improve the Train inlet measurement to 1% or better thus improving theallocation uncertainty by 2%. The project team chose to improve on the existing singlepath ultrasonic meters by replacing them with multi path ultrasonic meters. Since theinlet measurement to the LNG train significantly impacted on the apportioned LNG, it hadto be as close as possible to custody metering systems.

Ultrasonic meters (USM) were selected as the preferred flow measurementtechnology for the following reasons:

1. Endorsed by various regulatory bodies, standards and codes for custodymeasurement, e.g. AGA Report 9, UK Department of Trade & IndustrysPetroleum Measurement Guidelines and the Norwegian Petroleum Directorate.

2. High accuracy can be achieved in the field when properly installed andmaintained.

3. High turndown ratios available for individual meters.4. Minimum pressure drop.5. Strong diagnostic built in capability.6. Low maintenance as compared to other technologies e.g. Orifice or Turbine

meters.


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However, the USM can be susceptible to noise generated by control valves. Thisnoise, typically referred to as white noise affects the performance of the meter. Also, thelevel of noise generated by a control valve is a function of the flow-rate, differentialpressure drop, pressure and valve trim characteristics.

7 USM NOISE REDUCTION STRATEGIES

Most control valve manufacturers achieve their low noise levels by pushing theiraudible (20Hz-20kHz) emissions out of the audible range and into a range (20-200kHz)which typically affect USM detection signals. This was illustrated during our flowcalibration of the meters where a control valve similar to one used at the inlet of thefacility was installed to determine the extent of noise generated, and to see how themeter tuning (noise reduction techniques) would impact the performance.

To combat noise, most USM manufacturers will have a combination or all of thefollowing noise reduction technologies embedded in the meters electronics:

Signal Stacking Digital Filtering Correlation Techniques

Statistical Methods

Additionally installing blind tees on the piping system, locating the meters far fromnoise generating equipment and installation of silencers downstream will mitigate theimpact of noise on the meter.

8 FLOW TESTING RESULTS

As a requirement by AGA Report 9, USM being used for custody transfer should becalibrated at a Flow Test Facility. Although the Train Inlet Meters (TIM) were not usedfor custody transfer they have significant impact to the allocation of LNG on a multi-traincomplex, thus the meters were calibrated by placing a laboratory Flow Standard MeterBank in line with the USM being calibrated. The Flow Standard Meter Bank being usedto accurately measure flow, had been calibrated using standards that are traceable toNIST.

The newly purchased 24 4 path USM were calibrated at the following points asrecommended by AGA Report 9. qmin, 0.10qmax, 0.25qmex, 0.40qmax, 0.70qmax, andqmax, together with two additional points. The meters performance was checked withthe control valve at 5 times the normal pressure drop in operation. The following is the

sequence of the testing performed and results from Colorado Engineering ExperimentStation Inc (CEESI):

Calibration of the meter using the Flow standard as a referenced in Fig. 5 belowshows the results (out of the box meter) in relation to the flow standard. A meter factorper test point is applied as a piece-wise (multi-point) linearization.


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Figure 5 - Initial Calibration Results with USM and Flow Standard

Figure 6 USM Flow Testing at CEESI

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Figure 7 - USM and Control Valve at Flow Test Facility (CEESI)

The above picture (Fig. 7) shows the valve and meter being prepared for noiseverification checks by comparing the performance of the meters with the Flow StandardMeter while throttling the control valve at various differential pressures. Fig. 8 shows theUSM signal detection waveform with no noise present. Fig. 9 shows the USM signal

waveform distorted by noise generated by the control valve. Fig. 10 and Fig. 11 show theapplication of Stacking and Digital Filtering (Noise reduction techniques) respectivelywhich were required to achieve a good reading on the USM.

Figure 8 - Typical detection used by the USM for flow calculations

(No Noise)

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Figure 9 - Noise as seen by the USM (Noise distorts the USM detection waveform)

Figure 10 - USM Noise seen wi th the Application of Stacking

With Stacking Applied only, the USM was still unable to register a good reading.

Chord A Upstream Raw Waveform

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USM Detection waveform Distorted byNoise from Control Valve

High Frequency Noiseinterference

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USM Detection waveformDistorted by Noise from

Control Valve


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Figure 11 - USM with Digital Filtering and Signal StackingThe USM was able to register good readings

Fig 12 - USM with Nose algorithms and control valve in line

(Calibration Results )Figure 12 USM Calibration with Flow Standard and Control valve

The illustration above (Fig.12) shows the improvement of the USM Calibrationagainst the Flow Standard Meter Bank when the control valve was throttled at variousdifferential pressures.

9 PERFORMANCE OF USM VS CUSTODY ORIFICE MEASUREMENT SYSTEM

After the flow calibration of the ultrasonic meters at CEESI the next step was toexecute the installation and commissioning of the meters. The meters were shipped andstored until an opportune time for installation. Having the control valve available atCEESI proved invaluable, since the noise characteristics generated by this particular

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valve were analyzed and this significantly minimized the commissioning time.Furthermore this addressed the concern as to whether the valve and meter in closeproximity could fulfil plant flow regulation and product allocation. It must be notedhowever, that in some of the installations, further noise tuning (custom data filtering) wasrequired. This was readily observed on installations where the pressure drop wassignificantly greater than that tested at the facility.

Figure 13 - The performance of the ul trasonic metersand the custody metering system on the facility

The diagram shows that percentage deviation between the USM and the orificemeasurement system is of 0.16%-0.6% with the USM reading marginally higher than theorifice measurement.

10 OTHER LNG TRAIN MEASUREMENT ISSUES

Although, the inlet measurement had the single largest impact on the LNG trainthere are other areas where measurement improvements can be made to the facility.However, these must be done at the design stage thereby improving on the overallallocation process. These areas include:

1. Fuel Measurement. Installing a good single point of measurement for fuelenhances the quality of measurement that can be installed and simplifies thenumber of allocation points. For example on a facility with 3 LNG trains, having 1

meter per train for the fuel can replace 10 or more metering points per train. Thisimprovement reduces the cost of operation and maintenance.

2. Marine Flare Measurement. There are a number of practical challenges withinstalling good flare measurement during plant operation (availability issues).Having marine flare measurement implies that losses can be applied to individualshippers accordingly if agreed commercially.

3. Vapour recovery measurement will aid in the traceability of LNG production pertrain.

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11 MAINTENANCE OF MEASUREMENT AND ALLOCATION UNCERTAINTY

With the inlet measurement being so critical to the allocation of LNG and NGL on amulti train complex, this point of measurement must be effectively maintained. Ourexperience to date has shown that the USM probes must be kept clean for good meterperformance.

The piping system should be designed such that there is no accumulation of liquidupstream of the USM. Additionally, if the USM are in series with an orifice measurementsystem (custody), the similarity of the analytical data in the USM and the custody is ofgreat importance to the flow registration alignment. Operating the orifice custodymetering skids at higher differentials per stream also helps with the alignment of thereadings registered by both systems.

The USM should also be installed with double block and bleed isolation valves sothat the meter probes can be easily accessed for maintenance. Effective isolation alsofacilitates the easy removal of the meters for re-calibration exercises, typically every six

years.

Maintenance of secondary instrumentation, temperature and pressure transmittersmust also be done at regular intervals.

12 CONCLUSION

Maintenance of measurement uncertainty greatly affects the allocation of productson a multi train LNG facility. It is therefore important that all plant measurement systemsbe optimized for minimum uncertainty. Building of a MEM can help technical personnelon the facility quickly diagnose mis-measurement issues and plan for future capitalexpenditure. Design engineers would also benefit from using the MEM at the conceptualstage of a project to plan out the specifications for the measurement system.

Train inlet measurement, which is of great concern to all parties, must be properlydesigned, operated and maintained to ensure equity in the allocation. This paperhighlighted the issues on inlet USM measurement and control valves. Although USMhave gained custody appeal, their use must be planned and coordinated with theflow/pressure regulation valve into the facility, since USM are susceptible to noiseinduced by valves.


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REFERENCES:

Allen Fagerlund et al, Identification and Prediction of Piping System Noise, NoiseConference Oct 2005.

Bill J ohansen & J oel Clancy, CEESI Flow Calibrating Ultrasonic Gas MetersInternational School of Hydrocarbon Measurement May 2003

Charles Derr, Daniel Measurement and Control Energy Measurement usingUltrasonic Meters & Gas Chromatography International School of HydrocarbonMeasurement May 2003

Gerrit Vermeiren and Sven Lataire, SGS How Accurate is the Shipboard CustodyTransfer Measurement system? LNG Journal J uly/August 2005

J ames E Gallagher, Savant Measurement Corporation, Orifice Flowmeters and theEstimated Uncertainties in Natural Gas Service, International School of

Hydrocarbon Measurement May 2003

Kevin Warner and Klaus Zanker, Daniel Industries, Inc Noise Reduction in UltrasonicGas Flow Measurement 4th International Symposium on Fluid Measurement J une1999.

Lars Farestvedt, FMC Multipath Ultrasonic Flow Meters for Gas MeasurementInternational School of Hydrocarbon Measurement May 2003

Ronald H. Dieck, Measurement Uncertainty Methods and Applications ISA 4thEdition 2007

STANDARDS:

AGA Report No 9 Measurement of Gas by Multipath Ultrasonic Meters (1998)

ISO/TR 5168 Measurement of fluid flow: Evaluation of uncertainties

ISO/TR 7066-1 Assessment of uncertainty in calibration and use of flowmeasurement devices

ISO 13686 Natural Gas Quality designation

ISO 14111 Natural Gas Guidelines to traceability in analysis

ISO 14532 Natural Gas Terminology

modelling lng train design

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