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2011 Operations Conference & Exhibition Nashville, TN, May 25 to 27.

Exploring a ‘Total Energy Concept’ (TEC) for Natural Gas Metering MARTIN BRAGG [Lead Author], Vice-President Technology (Electronic), Elster Group MIJNDERT VAN DER BEEK, Senior Scientist, NMi / VSL Dutch Metrology Institute JOS BERGERVOET, Senior Metrological Expert, Elster-Instromet BV GWILYM FOULKES, Flow Measurement Consultant, FlowMAC Ltd., UK SAUL JACOBSON, Chief Electrical Engineer, Elster-Instromet, Australia JOACHIM KASTNER, Division Director Gas Quality, Elster GmbH HENK RIEZEBOS, Senior Expert Gas Flow & Flow Acoustics, KEMA, Nederland BV JURGEN DE WIJS, Manager Project Engineering, Integrated Metering Solutions, Elster NV/SA Format:

1. Abstract 2. Introduction / Background 3. Gas Quality 4. Methods (new methods related to the concept) 5. Results 6. Summary and Conclusions 7. Acknowledgements 8. References

Copyright Statement All Rights Reserved. This document is sole property of Elster NV/SA and is subject to the conditions that it or any information contained therein will not be used in any way detrimental to our interests and that all copies will be returned immediately on demand. It is subject to change without notice. Permission to reproduce this document by any means or any medium is granted to the American Gas Association. The copyright owners shall be acknowledged within any reproduction of this document.

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1.0 ABSTRACT

In this paper, we consider the theory supporting a Total Energy Measurement Concept (TEMC, abbreviated herein as “TEC”) for Natural Gas metering systems deploying both Turbine and Ultrasonic flow meters in-series (“twin set”). The ‘first stage’ laboratory results of an installed system is discussed based on general principles, current and working standards/recommendations, and related to laboratory results that were recorded during actual calibrations. Data is shown for normal calibration conditions.

The paper provides an insight into theoretical and practical engineering resulting in experiential learning for a new ‘Total Energy Concept’ (TEC) deploying a Turbine meter, an Ultrasonic flow meter, a Gas Chromatograph, and a TEC dedicated supervisory system.

2.0 INTRODUCTION / BACKGROUND

This paper is a continuation of the work presented by Bragg et al in 2009 at the AGA meeting entitled: “Performance Analysis of an Operational Metering Station using Ultrasonic Flowmeters (May 19 - 21, 2009)”. In this latest work, it is intended to ‘explore’ a concept by providing both discrete theoretical information and practical experiences as a result of the 2009 paper in reviewing ‘Metering Stations Operational Performance.’ Discrete in that the authors do not present results from custom built fabrications of real life TEC systems, but the results from a number of installations that deploy the essential components coupled with new developments that suggest the possibility of a workable TEC. This paper is not intended to be a single (stand-alone) piece of work, but the first of a series of papers on the subject. The concept is not new, in itself, however with advancing technologies being readily deployed in such a concept there are new opportunities. The main areas of discussions are those of flow metering (Turbine and Ultrasonic), Gas Quality, and a dedicated Supervisory Suite that form a total concept solution.

A Total Energy Measurement Concept (TEC) is a concept that considers a given metering system as a whole instead of a number of discrete individual components. By deploying measurement redundancies, that are present in almost every metering installation, or by deliberately introducing additional measurement redundancies, a system is constructed that detects ‘maintenance or time induced measurement inaccuracies’ when they occur. Subsequently, the system design is guarded over the total life of the installation. Maintenance and recalibration intervals can be extended or performed only when the system itself highlight’s that there is a requirement. In essence, the TEC metering system becomes an ‘intelligent system’ that is able to determine its ‘health’ and confirm its original design intent with reference to the total measurement uncertainty. For a definition, the lead author proposes that: “An intelligent metering system learns how to react to a given circumstance so that it may maintain its original design intent/objectives (after: Bragg, 2011)”.

Firstly, let us define what a flow measurement is: “Flow measurement is the quantification of bulk fluid movement. It can be measured in a variety of ways (Wikipedia®, 2008)”.

Secondly, let us define the long-term goal of a TEC as proposed by the lead author to be: “A systematic use of tools to identify significant variations in operational performance and output quality, determine root causes, make corrections and verify results (Evans and Lindsay, 1999:345)”. This definition is deployed within one manufacturer’s ‘MeasCon Technology®’ software packages that are a number of developments, ongoing, as a direct result of this paper and that of the earlier AGA paper in 2009. Today, there are two software systems installed in the world that deploy the first beta test release of an early stage software package for Ultrasonic meters in ‘real life’ metering stations.

Thirdly, and specifically referring to a TEC supervisory suite, let us define a truth table: “A truth table is a mathematical table used in logic - specifically in connection with Boolean algebra, Boolean functions, and propositional calculus - to compute the functional values of logical expressions on each of their functional arguments, that is, on each combination of values taken by their logical variables (Enderton, 2001). In particular, truth tables can be used to tell whether a propositional expression is

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true for all legitimate input values, that is, logically valid (Wikipedia®, 2011)”. An example of a typical truth table is shown as follows.

Finally, for a basic TEC one needs to determine flow rate, composition, temperature, and pressure generally in accordance with the following ‘picture (formula).’ Such a TEC may be shown in the following Picture with a single flow meter. As a basic TEC this system is acceptable, however this paper proposes that a second meter be deployed for the reasons that shall be discussed in the following sections.

In summary, one can deploy a number of basic concepts within such a metering system with reference to its overall performance. For example, comparing the performance of the individual flow meters and further considering their combined results: comparing the measured Speed-of-sound (SOS) with that calculated to a recognised standard (AGA-10); continuous monitoring of the flow meters via a health check programme; historical mean deviation checks (flagging sudden changes); and deploying a variant of the traditional ‘truth table’ to highlight the probable source of a subsequent measurement error.

0smm0

0m0s0 HV

K

1

Tp

TpHVE

Gas Quality Measurement:

• Gas Composition: xi

• Gas Parameters: Hs0, r0, xCO2

Volume V0 at Standard Conditions

Standard State p0, T0

Metering State pm, Tm Volume Vm at Metering Conditions

Calorific Value Hs0 at Gas Law Deviation Factor K

AGA-8 DC 92: K(p,T,xi) S-GERG-88:

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A TEC with two flow meters would, ideally, be based on two differing fundamental measurement principles as shown in the following Picture 02.

Within this paper’s section ‘5.0 Results’ we shall address the question of: “Which meter is first in a series installation and why?” Traditionally, a great deal of time and effort is spent during the design phase of a metering system to ensure that the metering has an overall ‘acceptable performance.’ For the owners of gas stations it is very important to have some means of control over the performance. Performance implies various aspects, not only ‘accuracy’ but also safety, reliability, security of

Picture 01

Picture 02

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supply, control of environmental aspects (noise, emission, carbon footprint, etc.), and costs. By cost we refer not only to the costs of purchase (CAPEX) but also costs of maintenance, subsequent recalibration, and regular performance monitoring (OPEX).

In referencing the various aspects of accuracy, one can use uncertainty models to direct the effort to the largest contributors (sub-components). This helps in the design stage to find the appropriate balance between CAPEX and OPEX with the desire for an ever lower uncertainty of measurement.

In more recent publications, the authors note the question of “What recalibration and/or maintenance intervals shall be deployed in order to maintain the system’s original design uncertainties?” The authors’ noted that the balance shall be found between the individual system’s OPEX and the desired uncertainty of the final measurement. The latest trends, within our industry, appear to suggest that one should use vendor specific diagnostic and performance monitoring tools to assess the condition of the individual components within a metering system (for example, healthcare diagnostics monitoring).

It may be noted there are a number of relevant individual standards for the ‘component parts’ of a typical metering system as follows.

GAS METER STANDARD GAS PROPERTY STANDARD

Turbine Meter AGA 7 Compressibility SGERG88 ISO 9951 SGERG91 EN 12261 MGERG Ultrasonic Meter AGA 9 AGA NX 19 ISO 17089-1 AGA 8 EN 14236 Base Conditions ISO 6976 Orifice Plate AGA 3 Composition ISO 6974 ISO 5167 ISO 10723 Conversion Device EN 12405 ASTM 1945 Calorific Value ISO 6976 GPA 2145 GPA 2172 Speed-of-sound AGA 10 Specific Heat AGA 10

Table 01: Field Verification and Validation of an Energy Measurement System after: Herwijn, 2008.

In concluding this introduction and background, it can be shown that the existing manuals and standards do not totally reflect what a TEC is and how it shall be deployed within our industry.

2.0.1 Uncertainty

In this paper, we explore two areas of uncertainty being (i) traditional mathematical models, and (ii) new approaches to a TEC system where we can deploy techniques and methods to reduce uncertainty in the measurement device or devices (cross-checks). For (i) we explore the ‘long-term operational uncertainties’ plus the ‘operational philosophy component’, and for (ii) we introduce new techniques such as the TurbinScope® for Turbine meters. The TurbinScope® is to Turbine meters what the traditional Healthcare and Condition Based Monitoring diagnostics are to an Ultrasonic flow meter. For a TEC system, we have further expanded the traditional uncertainty models to address not only design time influences, but also so called “maintenance and time induced uncertainties”. When the design life of a metering system is 20-years or more the uncertainty model of such an installation shall also include effects that occur within the 20-years life span. Commonly addressed equipment failure shall be addressed, as historical practise, but one should also consider the effects such as incorrectly performed maintenance. Even with periodic maintenance, a fault can exist for several months until it is detected at the next scheduled maintenance period. This additional uncertainty of measurement exists in ‘real life’ situations and shall, therefore, be addressed in an enhanced uncertainty model.

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In this paper, we introduce the concepts of a TEC system that is able to detect such “maintenance or time induced uncertainties”. Critically, to note, is that we propose to detect ‘instantaneous faults’ that are quite likely to occur during the life time of an installation and the influence of such events on the total uncertainty of the system. Such an approach would greatly reduce the system’s total uncertainty during its operational life span.

Such a TEC System may be deployed to increase the confidence within the overall performance of a given metering installation. When confidence is increasing, maintenance and re-calibration can be performed only when the system highlight’s that there is a need to do so. Such a design will reduce the OPEX of a metering installation without negatively affecting the system’s total metering uncertainty.

From the users’ perspective, the focus is upon the individual metering station components such as: measurement of volume; gas quality; volume conversion; heating systems; pressure or flow control; liquid and dust filtering; and odorisation systems. However, there is also the possibility to cross-check the individual measurement’s to obtain an overall performance with a reduced uncertainty.

2.1 THE TURBINE FLOW METER

New technology introductions for Turbine meters are reducing pressure drop up to 50%, providing a bi-directional measurement capability, the TurbinScope® diagnostic tool is introduced for ‘diagnostics’, and the auto lubrication system are some of the latest innovations from the world of Turbine meters.

The following sections will explore these innovations in a little more detail in considering one manufacturer’s product line. Here we may note the newer advanced design offering both uni and bi-directional metering with lower pressure drop and high flow velocities being ideally suited to a TEC system deploying two meters in series. Such a recommendation, deploying two meters in series, may be noted in the standard “Gas supply systems - Natural Gas measuring stations - functional requirements; NEN-EN 1776 (en); 1999; Annex B1 (1)”. This standard is being updated and due for re-release in 2011.

A typical flow curve, for the new meter, under varying pressures is shown as follows.

Picture 03

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Note: the following text is extracted from the manufacturer’s literature.

2.1.1 Reduced pressure drop

A significant issue when deploying a Turbine meter and an Ultrasonic meter in series within a TEC system is that of the historically lower flow range of the mechanical meter. Another historical issue has been that of bi-directional flow measurement capability. New developments have started to address these issues for which there is now the SMRI model of Turbine meter that is commercially available. The pressure loss of the new generation SMRI-2 can be reduced down to 50% of that of conventional SM-RI-X models, while the flow capacity has been increased to match the existing Ultrasonic gas meter capacities for the same size. It’s also the first Turbine gas meter on the market with optional bi-directional capability. It is designed to function perfectly together with an Ultrasonic gas meter to achieve high redundancy rates with improved long-term stability. This makes it ideally suited for import and export stations, underground gas storage, and bi-directional transmission lines. The SM-RI-2 is certified according to the MID. There are uni-directional and bi-directional versions.

2.1.2 TurbinScope® diagnostic tool

This unique service is a breakthrough for examining Turbine meter performance under real operating conditions. No need to remove the meter from the process. Data is collected on-site and is analysed at the factory to determine meter and installation conditions. Possible outcomes of these analyses include discovering misreading due to installation effects, Turbine blade damage, bearing damage, flow pulsations and accuracy forecasts at minimum flow. This method has been approved by the Dutch NMi.

2.1.3 Automatic lubrication

Manual lubrication by means of hand oil pumps has always been a maintenance issue for Turbine meters. Now there is an automatic lubrication system, developed for Turbine gas meters, which is controlled by the measured gas quantity and activated by the gas pressure of the pipeline (optionally by separate nitrogen bottle, if pressure < 6 bar (g)). The amount of lubricant oil supplied is adjusted depending on the bearing size and the operating flow of the gas meter. Hence the overall efficiency of the Turbine gas meter is further enhanced. The auto lubrication system will have a positive effect on the cost of ownership, owing to the fact that the meter does not necessarily need to be maintained manually.

Deviation curve SMRI-2 Size 400 Version Qmax 4000 and 6500

-2

-1

0

1

2

0.01 0.1 1 10 100Re relative to Re_Qmax_air

dev

iati

on

[%

]

E atm E 8bar E 20 bar E 50 bar

Picture 04

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2.2 THE ULTRASONIC FLOW METER

In this section of the paper, we consider how far this metering technology has progressed by taking an historical look at the technique after Jacobson. We further expand on the subject by considering ‘by products’ of the fundamental measurement principle to yield information not previously considered/deployed within a TEC system.

In 1966 Lindsay[1] published his work on the history of acoustics including the Speed-of-sound in air in 1635. Piezoelectricity was discovered in 1880 by the Curie brothers, whose work forms the foundation for the majority of Ultrasonic sensors in today’s process control measurements. Lord Rayleigh’s first edition of ‘The Theory of Sound’ was published in 1877 before atomic theory, modern physics, quantum mechanics, and before relativity theory. Ultrasound has a long-established history that is well documented. Electronics used within Ultrasonic measuring systems realised a significant development during 1939 - 1945 as a result of both radar and sonar. Milestones in Ultrasonic flow metering can be traced back to the late 1940’s, however there were also significant shifts in technology during the 1960’s as a result of both military and space projects. Since these dates, there has been continuous development in technologies within this field of measurement.

An Ultrasonic Flow meter for gas has the advantages seen in its liquid counterpart: high turn-down, non-intrusive, no pressure drop, bi-directional measurement, no moving parts, maintenance free and it can be used with a wide range of pipe sizes. Ultrasonic gas meters have the advantage over most other technologies of being able to measure at low and high pressure, and of providing a composition independent flow velocity measurement (profile effects notwithstanding). Ultrasonics is also unique among flow metering methods in that the transducers may be ‘clamped on’ to the outside of the pipe wall, and no penetration of the pipe is required. Clamp-on is generally defined as being both non-invasive and non-intrusive, however designs were proposed in the past two decades that are partially invasive and non-intrusive. Originally limited to liquid applications only, in recent year’s clamp-on techniques have been applied to gas as well[2]. Applications also include temperature extremes, thermal buffers such as coiled foil or bundles of many thin rods enable transducers to be used with fluids, including gases, well above their Curie point as well as down to cryogenic temperatures[3][4][5].

Two methods have principally been used for Ultrasonic gas flow metering, namely, Transit-time and Tag Cross-correlation. Doppler methods, which have seen widespread use in liquid Ultrasonic flow meters, have not been used for gas, due to the absence in general of suitable reflectors (scatterers). The Transit-time and Tag Correlation methods are described below. Transit-time principles have been applied in gas flow measurements at audible frequencies too, as exemplified by the work of Kleppe and colleagues[6]. Frequencies on the order of 1.5 kHz were used in CEM (continuous emissions monitoring) applications starting around 1990.

The flow meter itself shall be able to derive the bulk flow rate within a closed conduit and ‘validate’ the resultant dataset. In other words, the meter shall become ‘smarter’ in its ability to determine the validity of the flow rate dataset by ‘validation through a combination of acoustic path and advanced diagnostics’ (including healthcare). Acoustic paths may be deployed in various configurations, as we have seen within commercially available meters, to achieve a given design intent. Within these path configurations, and accepting the prolific use of the gas Speed-of-sound (Sound speed or Velocity-of-Sound) as deployed in AGA-10, the lead author believe’s that there are other ‘by products’ of the operating principle (typically, Transit-Time and Tag Cross-Correlation) that may be used to determine other gas properties, such as the gas composition, molecular weight, or calorific value.

2.2.1 Speed-of-sound (Sound speed or Velocity-of-Sound)

The Speed-of-sound for an ideal gas mixture consisting of components is given by the equation:

------------(8)

------------(9)

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------------(10)

Where:

is the gas sound speed

is the universal gas constant

is the absolute temperature

is the specific heat ratio of gas component ‘I’

is the molecular weight of gas component ‘I’

... are the concentrations of the gas components respectively

In the case of a binary gas, where the ’s and molecular weights of the two gases are known, and the temperature, , and the gas sound speed, , are measured, we have sufficient information to calculate

the respective concentrations of the two gas components, and [7]. There may also be sufficient information for ‘pseudo-binary’ gases, consisting of a well defined gas mixture, for example, air, and a second component, for example, hydrogen. In the case of a gas mixture of more than two components, more information is needed to determine composition. Gases saturated with water vapour are a special case, as the concentration of one component, H2O, can be determined directly from saturated water vapour tables, if temperature and pressure are known.

Several researchers have recognised that, when a gas mixture is saturated, and the third component of a ternary gas mixture is water vapour, the volume percent of the water vapour may be determined using saturated water vapour tables. Examples where this has been exploited include an analyser intended for monitoring the generation of hydrogen by electrolysis of water, which gives a saturated mixture of H2 and N2

[8], and the analysis of breathing gas, where the gas mixture consists of air, carbon dioxide and water vapour[9].

The equations 8, 9 and 10 are accurate for ideal gases, and real gases will deviate somewhat. The gases discussed above, nitrogen, methane, carbon dioxide, hydrogen, etc., approximate ideal gases at low pressure, but the ’s do exhibit a small temperature dependence. Water vapour is less ‘ideal’. Equation 9 shows the for water vapour derived from two sources[10] and[11]. The relationship,

, was used to calculate from tables in[11]. In fact, based on empirical results for

‘simulated biogas’ consisting of a saturated mixture of methane and carbon dioxide, a good fit to equations was obtained only by adjusting the for water vapour significantly from the published values. Using the empirically determined it is possible to determine percent methane in biogas to within 1% or 2% over the range of interest.

Another successful application of gas analysis based on sound speed is the determination of molecular weight in flare gas[12]. Flare gas consists primarily of hydrocarbons, and the inventors determined that, while the ’s varied with molecular weight, an empirical relationship exists between and molecular weight:

----------------(11)

Using this equation, and the equation 8, we have sufficient information to calculate molecular weight when gas sound speed and temperature are known.

Using the viral equations, this technique was extended to cases where known quantities of non-

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hydrocarbons are included in the gas mixture[13].

2.2.2 Attenuation

Attenuation is perhaps a less robust parameter for determining gas parameters than sound speed. Attenuation requires careful calibration and can be effected by flow turbulence, entrained particles or droplets in the gas, or even transducer degradation over time. Nevertheless attenuation has been used, separately or in combination with sound speed to determine gas properties. Petculescu et al [14] describe a prototype sensor based on attenuation for multi-component gases, concluding that ‘...combining sound speed and attenuation measurement in a simple sensor offers the potential to identify and quantify the gases making up a multi-component mixture.’ However, the authors are not aware of any commercial sensors for gas based on acoustic attenuation.

2.2.3 Acoustic impedance

Acoustic impedance appears an attractive means of determining gas density, as the acoustic impedance is given by:

------------(12)

where is the gas density and is the sound speed.

Therefore, if the sound speed and acoustic impedance are measured, the density of the gas may be calculated. However, the measurement of gas acoustic impedance is extremely difficult in practice, particularly in a process environment. Mylvaganam et al [15], used the effect of gas loading on the transducer and the resulting change in its electrical impedance to develop a prototype gas density sensor. However, as in the case of acoustic attenuation, the authors are not aware of any commercial sensors for gas analysis based on this principle.

2.2.4 Ultrasonic flow meter summary

Ultrasonic flow meters for gas have been gaining acceptance in recent years, and have seen an expanding share in diverse markets, from simple process monitoring applications to multi-path systems for high accuracy custody transfer of Natural Gas. Part of this rapid growth has been due to the ‘traditional’ advantages of Ultrasonic flow metering: wide turn-down, high accuracy, no pressure drop, no moving parts, etc. Recent developments have provided additional benefits to Ultrasonic gas flow metering. These include the unique ability of Ultrasonic flow meters to measure clamp-on, without penetrating the pipe wall, and the use of Ultrasonic measurements other than flow rate, such as sound speed, to determine process gas properties. These gas properties may be considered during ‘traditional’ product development to add to the more typical data sets in providing ‘evidential learning’ for the ‘meter explorer’.

3.0 GAS QUALITY

The role of Gas Quality, particularly Gas Chromatography, within the TEC is critical to such a system. This section of the paper addresses the subjects of the tasks, technologies, and the role of Gas Quality measurement within the TEC.

3.1 Tasks of gas quality measurement

Gas Quality Measurement (GQM) has a wide range of tasks in gas metering. The basic function is fiscal metering, other measurements are related to various specific gas quality parameters. These tasks require a standard or an extended analysis of the gas composition or even of the dynamics of the gas quality parameters. These complex measurements have several links to the Total Energy Concept. Most measurement tasks are related to quantitative and qualitative metering aspects, however gas quality measurement can also provide additional information to assess the total measurement system integrity.

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3.1.1 Fiscal measurement: flow and calorific value

Gas trading is based on energy billing, since energy is the utility value for the consumer, therefore fiscal metering is also the primary task of gas quality measurement.

The energy flow is the product of volume flow at standard conditions and calorific value, as illustrated in the equation in Picture 05.

The key result of gas quality measurement is the calorific value, typically related to standard volume, denoted with Hs,0 in the equation. The calorific value is determined directly with calorimeters or indirectly by molar component analysis, typically with Gas Chromatographs and calculation, e.g. according to ISO 6976[16].

For further calculation of the total energy, volume at flowing conditions V must be converted to volume at standard conditions V0 by state conversion with pressure p and temperature T. The index 0 denotes the standard state, p0 = 1013.25 mbar and T0 = 273.15 K. Since Natural Gas is not an ideal gas, this conversion calculation needs to regard the non-ideal gas property, described by the compressibility factor K. This is calculated with the AGA-8 or SGERG equation dependent on the pressure and temperature and also on the quality of the gas. These equations require input data such as the molar gas composition for the detailed calculation or key gas parameters, e.g. superior calorific value, normal density and CO2 mole fraction for the gross calculation. Hence gas quality measurement is also essential for the volume flow determination.

Picture 05 shows some examples of gas measurement instrumentation. The leading gas analysis technology for billing is the process Gas Chromatograph. It supplies a detailed analysis based on substance quantities, relatively slow and discontinuously, but with very high accuracy and precision. Many gas parameters can then be calculated from the subsequent molar analysis[16].

Picture 05: Energy is the fundamental quantity in gas industry. It is determined by measurement of the volume under operational conditions V, calorific value at standard conditions Hs,0, operational pressure p and temperature T. Since Natural Gas is not an ideal gas, the compressibility factor K must be regarded. The Picture also shows examples of typical measurement instrumentation.

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3.1.2 Extended analysis, Speed-of-sound

Besides the key gas parameters for fiscal billing, there are further gas properties that are relevant for proper and safe gas production, transportation, trading and consumption. These gas parameters are partly specified in the various gas quality standards[17] [18], and hence are often subject to gas trade and transportation contracts. The determination of these useful quantities often demand’s an extended gas analysis or calculation.

Most gas quality parameters are related to individual substances and substance groups, e.g. oxygen O2, sulfur, particularly H2S, and water H2O that are mainly related to corrosion. Others such as hydrogen H2, carbon dioxide CO2 and hydrocarbon dewpoint temperature are more related to the utilisation properties of the gas. Further properties such as water dewpoint and formation of hydrates are significant for mechanical disturbances.

Another important gas parameter that can be derived from gas analysis by extended calculation is the Speed-of-sound. It can be determined from the gas composition according to AGA-10[19] and might be used for validation of the TEC system integrity by comparison with the SOS/VOS measurement resulting from the Ultrasonic flow meters.

The extended gas parameters are often determined using specialist measurement instruments or by sampling and laboratory analysis. Current, high performance field Gas Chromatographs, however, now makes it possible to measure the parameters of an extended gas analysis within the process.

3.1.3 Dynamics of gas quality, real-time measurement for process control & regulation

The gas quality in the transportation networks varies with time, due to blending or conditioning stations or by unintended blending situations in the transportation network; the gas quality might show significant dynamics. It is expected that these dynamics will grow in future, because a higher variety of gas qualities will be fed into the networks from international gas trade, mainly by LNG, or from novel gas sources such as shale gas, biogas and synthetic Natural Gas. In addition to the primary measures for fiscal metering, there are further gas parameters that are important for ensuring that gas can be transported and consumed safely and efficiently. Real-time measurement is often required to face gas-quality dynamics in the control of sensitive processes in industrial gas applications or in gas blending stations.

One critical gas property is the Wobbe index that correlates with the heat power of a burner nozzle. Another issue is knock resistance that is important if Natural Gas is used as a motor fuel. Its measure is the methane number (similar to the octane number for petrol). With a prompt methane number signal, engine control can be optimised, thereby improving efficiency and service life. Another gas utilisation that is strongly sensitive to gas quality dynamics are power plants, based on gas Turbines. They profit from fast and accurate gas quality measurement for adjustment of the Turbine’s operational parameters in the trade-off between high efficiency, low pollution and long service life.

These tasks often require a higher measurement dynamic than provided by Gas Chromatographs. Alternative measurement technologies are based on sensor systems with various measurement principles such as infrared absorption, thermal quantities, etc. These technologies determine the aforementioned gas parameters via a correlative evaluation. Sensor systems typically deliver a less detailed and less accurate analysis than Gas Chromatographs, but they provide the desired gas parameters faster and continuously. In addition there are some technologies that have the potential for lower capital and operational costs.

3.2 Technologies deployed within gas quality measurement

This section of the paper shall briefly address a sample of gas quality measurement technologies presently available within the market.

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3.2.1 Gas Chromatography (GC)

Process Gas Chromatographs for the Natural Gas industry have the following (typical) principal setup. A carrier gas, typically Helium, is sequentially and continuously flowing through an injector, a thin tube, the so called column, and a detector, typically a heat conductivity or flame ionisation detector. A small amount of the sample is injected as a pulse into the carrier gas stream and transported through the column and the detector. The ‘internal’ of the column is packed or coated with an active material that interacts physically with the components of the gas sample. The different components interact with different intensity with the active material of the column and hence reach the detector at different delay times after injection; this time delay is called retention time. The peak intensity corresponds to the concentration of the corresponding sample component; this correlation is represented by the so-called response factor. The detector’s signal is a function of time, while the concentration is called chromatogram. It can be evaluated quantitatively by comparing the peak intensities of the sample chromatogram with the chromatogram of a calibration gas with known composition.

Picture 06: Principle set-up of a Gas Chromatograph

Modern process GCs are based on microsystems technology with micro-electro-mechanical-systems (MEMS) technology. The key functional blocks of a GC include for its injector, column, heating, and detector that are highly miniaturised and integrated, resulting in a number of advantages. Due to their small dimensions, injector and detector fit well to capillary columns. The separation performance of Gas Chromatographic columns increases with decreasing column diameter; hence these capillary columns achieve better analytical performance than classical columns. Furthermore, thermal conductivity detectors on MEMS technology achieve very high linearity and sensitivity. Finally, the consumption of consumables like carrier, calibration and sample gas is significantly lower and allows annual maintenance intervals - an important aspect for the total cost of ownership.

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3.2.2 Sensor systems

As an established example for sensor systems, this chapter presents a correlative gas quality measurement technology based on a sensor system for optical and thermal gas properties. An example from one manufacturer of the technology enables accurate online gas quality analysis over a wide range of generic Natural Gases. The measurement device ‘gas-lab Q1’, based on this technique, has no need for additional an carrier gas; the device calibrates itself automatically with methane. The system was developed and tested for a wide variety of generic Natural Gases. The full measurement range can be covered with a single calibration data set. The new technology is more accurate and lower in total costs than present calorimeters and Wobbe meters. The current online measurement technologies typically need extensive ambient conditioning. In comparison to process Gas Chromatography, the new device is much faster and has lower operational costs, since it does not need a carrier gas and multi-component calibration gas. Present field applications range from billing measurement to control and regulation applications for fast industrial processes.

The presented method is based on the typical composition structure of generic Natural Gas. It consists mainly in hydrocarbons (abbreviated as “CH”), carbon dioxide (CO2) and nitrogen (N2); these

Picture 07: Modular setup of the Gas Chromatograph EnCal 3000 including 2 (two) micro-GC channels

MEMS Thermal Conductivity Detector

MEMS Injector

‘Narrow bore’ Capillary column

Picture 08: Micro-GC channel with key functional components in micro-electro-mechanical-systems technology: Injector, column, detector

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components typically make more than 99.9% of the total gas composition. Thus we approximate the molar composition of Natural Gas:

122 xCOxNxCH ,

with xCH, xN2 and xCO2 being the molar fractions of the total hydrocarbons (CH), nitrogen N2 and carbon dioxide CO2, respectively. The hydrocarbons mainly consists of alkanes (CnH2n+2), of which the concentrations steadily decrease with increasing alkane order in generic Natural Gas. The selection of the measurement values of the new technology corresponds to this typical composition pattern of Natural Gas. The measured values are as follows in Table 02:

Measurement Value Correlating Gas

Components

ACH Infrared Absorbance of the

Hydrocarbons CH

ACO2 Infrared Absorbance of

Carbon dioxide CO2

λ Heat Conductivity CH, N2, CO2

These physical data correlate directly with the molecular gas composition and thus make it possible to determine the key properties of Natural Gases. Picture 09 illustrates the measurement method. Hydrocarbons and carbon dioxide absorb infrared light by excitation of the CH- and CO2-molecules. The spectral intensity of this absorption is specific for the component type and proportional to the component concentration. Infrared absorption is measured by sending white light through the gas and detecting the transmitted intensity in different spectral regions. In order to detect non-IR-absorbing gas components such as nitrogen, another sensor is used to measure the heat conductivity that is (non-specific) sensitive to all gas components.

Picture 09: Measurement method: The absorption of infrared light correlates with the concentration and composition of the hydrocarbons and the concentration of carbon dioxide. The heat conductivity correlates (non-specifically) with all gas components, also with nitrogen, that is invisible in the infrared spectrum.

The missing information of the hydrocarbons concentration xCH is complementary to the nitrogen concentration xN2 and as such may be determined by measurement of the heat conductivity λ. Therefore, a model for the thermal conductivity λmodel is used as a function of the molar calorific value of the hydrocarbons HCHm and of the mole fraction of the hydrocarbons xCH, nitrogen N2 and carbon

PAGE: 16 of 61

dioxide xCO2:

),,,( 22model HCHmxCOxNxCHF

The missing information xCH, respectively xN2, is gained by an iterative evaluation algorithm. The unknown quantities are being varied iteratively until the measured thermal conductivity λ fits with the model thermal conductivity λmodel. Finally, the evaluation algorithm based on the measurement values and the mentioned correlation results in the relevant gas parameters:

calorific value Hs0 normal density 0

Further calculations based on fundamental laws and complex modelling of typical generic Natural Gases deliver additional values such as:

Wobbe index Methane number Modell analysis (CH4, C2H6, C3H8, nC4H10.. nC8H18, N2, CO2) Net calorific value, specific density, etc.

Picture 10 shows the core set-up of the sensor system gas-lab Q1. The gas is running through the sensor bench, continuously. Sensors detect pressure, temperature, total heat conductivity and infrared absorption of the hydrocarbons and of carbon dioxide.

Picture 10: Sensor system for the analysis of Natural Gas, based on infrared absorption and heat conductivity measurement.

4.0 METHODS

In this section of the paper we review some of the ‘new methods (techniques and/or models)’ deployed within today’s metering that may be directly related to the TEC.

4.1 Turbine meter

The Turbine meter has performed well for many years that may be noted within a proliferation of publications within the metering industry. Also, for quite some years, the Ultrasonic meter has been developing with its diagnostics by way of Healthcare and Condition Based Monitoring ‘packages’ (usually software based) that are deployed successfully within many installations. Today, the Turbine meter can be supplied with such diagnostic capability relative to its measurement principle as developed under the trademark ‘TurbinScope®’.

PAGE: 17 of 61

4.1.1 TurbinScope®

The TurbinScope® diagnostics is a measurement device that analyses the ‘soundness’ of a Turbine meter itself, and the flow conditions within the installation.

The principle of operation is based on sampling the Turbine meter’s pulse time for every pulse of a high frequency pick up on the Turbine wheel - with an extremely high resolution.

Picture 11: Measurement principle used by TurbinScope®

A low level diagnostics of the soundness, e.g., such as missing pulses, extra pulses, damaged/bent or missing blades, can be detected quite readily. Further, with sufficient analysis, overload on the meter, by too high a gas velocity, or too high acceleration / deceleration of the gas velocity may be observed.

Picture 12 (a) and (b): Footprint of pulse time, wherein damaged/bent blade can be detected

PAGE: 18 of 61

Picture 13: Flow and change of flow over time

If it possible to generate a spin down situation within the installation, e.g., by closing a valve, and it is sufficiently fast enough to get a real spin down for the last few turns, the mechanical resistance of the bearing can be determined and with it the expected deviation in reading for the different flow conditions. Any imbalance within the wheel can also be measured.

Picture 14: Spin down and expectation of deviation for air, and 8 barg Natural Gas

PAGE: 19 of 61

Picture 15: Imbalance in the deceleration diagram during spin down

In consideration of the high resolution of the pulse time signal, and a specially developed (proprietary) filtering of that signal, one can then analyse, with a Fast Fourier Transformation (FFT) technique, the filtered signal. The filtered signal yields frequencies of disturbance within the signal that can be made visible. Frequencies that are visible can be caused by bearing damage, by instability of the rotational axis, or by the flow.

By performing a FFT analysis for a particular flow range one can display the results within a 3D graph, from where one can further determine the origin of the frequencies. The straight lines parallel to the relative flow axis are proportional with the rotation frequencies and with the bearing frequencies. The frequencies lines parallel to the relative frequencies axis are probably induced by the rotational axis. Curved lines will yield constant frequencies that are largely related to installation frequencies applied to the flow.

Picture 16 (a) and (b): FFT analysis of pulse time for one flow and 3D representation for additional flows

PAGE: 20 of 61

Picture 17: Overview of what can be recognised within a 3D FFT analysis

4.2 From traditional uncertainty to long-term operational uncertainty

In the opening section of this paper, Section 2.0.1, we introduced the idea of a traditional uncertainty model being further developed to address the ‘long-term operational uncertainties’ plus an ‘operational philosophy component’. Follows is an introduction to such an approach that is based, in the first instance, on public domain literature for the Ultrasonic flow meter. However, it has not been possible to build a complete model in considering these ‘new’ sub-components due to a lack of data/information within the public domain (at this time).

4.2.1 Measurement uncertainty - generic theory (overview to be extended)

Within this work, it shall be noted that the individual contributors to the components of uncertainty were derived and where appropriate combined, in accordance with the GUM[21] and guidelines given within ISO 5168[22]. The uncertainties are expressed with a coverage factor, k = 2; thus providing a confidence level of approximately ±95%. Firstly, let us consider the structure of this model with reference to the following.

Each of these main three contributors or groups represents a number of effects which in turn contribute to the sub-components of uncertainty for each group and thus the primary uncertainty of the meter. The effects attributable to each group will, in part, be dependent upon the type of calibration performed on the meter. A definitive list of these effects and where they are addressed by static or dynamic calibration is outwith the scope of this paper (an Elster-Instromet internal report of some one-hundred-and-ten pages in length). The following review is intended to provide the reader with an appreciation of the breadth of this work, without revealing the entire model as this is considered ‘proprietary’ at this time.

USM PRIMARY UNCERTAINTY

(Uprimary)

USM BASE UNCERTAINTY (Ucal)

(POST CALIBRATION)

USM INSTALLATION UNCERTAINTY

(Uinst)

USM OPERATIONAL UNCERTAINTY

(Uopn)

PAGE: 21 of 61

Let us consider the Ultrasonic flow meter. If we continue to build the ‘traditional’ sub-components of uncertainty and further add components of ‘operation’ that include, for example, the uncertainties attributed to the geometry effects and to the effects of long-term drift on the performance of the meter whilst in service, we can start to build our new model. We also need to add sub-components for any pre-determined standard piping configuration. Translating into an Excel worksheet (to generate a useable model), and continuing to expand the components of uncertainty, we can provide the following ‘brief overview’ of our developing uncertainty model. Of course, this model is in itself to be developed for a ‘stand alone’ meter, but in a series check TEC system there is the additional data/information available from this second meter being of a differing operating principle. This shall deliver a further scenario due to the TEC system itself.

Combining the three groups on a root-sum-square basis gives the following primary uncertainty equation for the meter:

Uprimary = [ (Ucal)²+ (Uinst)² + (Uopn)² ] 0.5 (eq 9)

Where:

Uprimary = Primary uncertainty of the meter

Ucal = Uncertainty associated with the base function and calibration of the meter (static or dynamic) including applicable USM effects as defined within Appendix A

Uinst = Uncertainty attributed to the installation and operating philosophy of the meter in a service environment

Uopn = Component of uncertainty resulting from in-service operations impacting on the long-term functionality of the meter.

4.2.2 USM installation & operating philosophy uncertainty (Uinst)

The magnitude of the contributors to the USM installation component of uncertainty (Uinst) will vary

dependent upon the type of installation and the users operating philosophy for the meter.

The aim of this sub-section is to qualify, as far as practical, the USM effects from ‘the model’ which will impact on Uinst and hence Uprimary for all installation options:

Uinst = [ (UMF)² + (Urepi)² + (UWG)²

+ (Ufai)² ] 0.5 (eq 20)

Where:

UMF = Uncertainties attributed to changes in meter correction factor (MF) due to pressure and temperature differences between calibration and operating conditions

Urepi = Uncertainties attributed to the repeatability of the meter resulting from the operating philosophy adopted post calibration (static or dynamic)

UWG = Uncertainty attributed to the presence of suspended liquids in the gas (wet gas)

Ufai = Uncertainty attributed to the impact of the pipework configuration on the performance of the meter

PAGE: 22 of 61

The sub-components of uncertainty associated with the meter correction factor (UMF) are given by:

UMF = [ (U)² + (UDl)² + (UE)² + (Uth)²

+ (U∆T)² + (U∆P)² ] 0.5 (eq 21)

Where:

U = Uncertainty attributed to the temperature coefficient of expansion of the meter spool piece

UDl = Uncertainties attributable to variations in the meter spool bore at operating conditions

UE = Uncertainties attributable to the Modulus of Elasticity

Uth = Uncertainties attributable to the measurement of meter spool bore wall thickness

U∆T = Uncertainties attributable to the measurement of ∆ temperature

U∆P = Uncertainties attributable to the measurement of ∆ pressure

The sub-components of uncertainty associated with the installation repeatability of the meter resulting from the operating philosophy adopted post calibration (Urepi) are given by:

Urepi = [ (Upsi)² + (Utsi)² + (Ukre)² + (Uini)²

+ (Ucni)² ] 0.5 (eq 22)

Where:

Upsi = Uncertainty attributed to the effect of pressure shock on the transducers

Utsi = Uncertainty attributed to the effect of temperature shock on the transducers

Ukre = Uncertainty attributed to the effects on Kre of operating on different gas properties (viscosity & density) to those programmed into the meter SPU.

Uini = Uncertainty attributed to the effect of incoherent noise on the performance of the meter e.g. impact of EMC on signal to noise ratio

Ucni = Uncertainty attributed to the effect of coherent noise on the performance of the meter e.g. impact of acoustic cross-talk on signal to noise ratio

The sub-components of uncertainty attributed to the upstream and downstream pipework configuration (Ufai) are given by:

Ufai = [ (Ufpei)² + (Utei)² ] 0.5 (eq 23)

Where:

Ufpei = Uncertainty attributed to flow profile effects on the performance of the meter

PAGE: 23 of 61

Utei = Uncertainty attributed to turbulence effects on the performance of the meter

Thus the expanded USM installation and operating philosophy uncertainty (Uinst) becomes:

Uinst = [ (U)² + (UDl)² + (UE)² + (Uth)²

+ (U∆T)² + (U∆P)² + (Upsi)² + (Utsi)² + (Ukre)² + (Uini)² + (Ucni)² + (UWG)²

+ (Ufpei)² + (Utei)² ] 0.5 (eq 24)

The magnitude of the contributors to the in-service operating component of uncertainty (Uopn) will

vary dependent upon the operating environment of the meter.

The aim of this sub-section is to qualify, as far as practical, the USM effects from the full model and other sources which will impact on Uopn and hence Uprimary for all installation options.

Uopn = [ (Ugeomo)² + (Udrft)² ] 0.5 (eq 25)

Where:

Ugeomo = Uncertainty attributed to geometry effects on the performance of the meter whilst in service

Udrft = Uncertainty attributed to the effects of long-term drift on the performance of the meter whilst in service

The sub-components of uncertainty attributed to geometry effects on the meter (Ugeomo) during in-service life are given by:

Ugeomo = [ (Udepo)² + (Udept)² + (Ueroso)² ] 0.5 (eq 26)

Where:

Udepo = Uncertainty attributed to deposits in the certified meter bore after a period of in-service life.

Udept = Uncertainty attributed to deposits on the transducer faces after a period of in-service life

Ueroso = Uncertainty attributed to wear or erosion of the certified meter bore and/or transducer faces after a period of in-service life

Thus the expanded USM operating uncertainty (Uopn) becomes:

Uopn = [ (Udepo)² + (Ueroso)² + (Udrft)² ] 0.5 (eq 27)

With reference to equation 9, the USM primary uncertainty (Uprimary) is represented by:

Uprimary = [ (Ucal)²+ (Uinst)² + (Uopn)² ] 0.5

Thus, with further reference to full the model, the expanded equation for the USM primary uncertainty when the meter is only subject to a static (dry) calibration becomes:

Uprimary = [ (UDo)² + (UDl)² + (Urpa)² + (Urpl)² + (Utttd)² + (Uttdt)²

+ (Uttkt)² + (Utttmd)² + (Uimnd)² + (Uimax)² + (Uimtr)² + (Ubd)²

PAGE: 24 of 61

+ (Lund)² + (Urepcd)² + (Uresv)² + (Uresf)² + (U)²

+ (UDl)² + (UE)² + (Uth)² + (U∆T)² + (U∆P)² + (Upsi)²

+ (Utsi)² + (Ukre)² + (Uini)² + (Ucni)² + (UWG)² + (Ufpei)²

+ (Utei)² +(Udepo)² + (Udept)² + (Ueroso)² + (Udrft)²] 0.5 (eq 28A)

Again with further reference to the full model, the expanded equation for the USM primary uncertainty when the meter is subject to a dynamic calibration becomes:

Uprimary = [ (UDow)² + (UDl)² + (Utttdw)² + (Uttdtw)² + (Uttktw)² + (Utttmdw)²

+ (Uimndw)² + (Uimaxw)² + (Uimtrw)² + (Ubw)² + (Lunw)²

+ (Urepcw)² + (Uresv)² + (Uresf)² + (U)² + (UDl)² + (UE)²

+ (Uth)² + (U∆T)² + (U∆P)² + (Upsi)² + (Utsi)² + (Ukre)² + (Uini)²

+ (Ucni)² + (UWG)² + (Ufpei)² + (Utei)² + (Udepo)² + (Udept)² + (Ueroso)²

+ (Udrft)²] 0.5 (eq 28B)

The expanded USM primary uncertainty equations (28A & 28B) incorporate all the key terms associated with the determination of the corrected gross volume flow rate uncertainty (UQvg’), thus:

UQvg’ = Uprimary

With further reference to the full model, the fully expanded measured mass flow rate uncertainty when the meter is only subject to a static (dry) calibration becomes:

UQm’ = [ (UDo)² + (UDl)² + (Urpa)² + (Urpl)² + (Utttd)² + (Uttdt)²

+ (Uttkt)² + (Utttmd)² + (Uimnd)² + (Uimax)² + (Uimtr)² + (Ubd)²

+ (Lund)² + (Urepcd)² + (Uresv)² + (Uresf)² + (U)² + (UDl)²

+ (UE)² + (Uth)² + (U∆T)² + (U∆P)² + (Upsi)² + (Utsi)² + (Ukre)² + (Uini)² + (Ucni)² + (UWG)² + (Ufpei)² + (Utei)²

+ (Udepo)² + (Udept)² + (Ueroso)² + (Udrft)² +(Ucal)²+ (Uinst)²

+ (Uopn)² + (Upt)² + (Uphsc)² + (Upref)² + (Upte)² + (Upstab)²

+ (Upinst)² + (Upgp)² + (Umol)² + (Ugccg)² + (Ugcrcg)² + (Ugcrsg)²

+ (Ugcrep)² + (Ugcc6)² + (UR)² + (Utt t)² + (Utt stab)² + (Utt inst)²

+ (Utt te)² + (Utt rep)² + (Utt hys)² + (Ut rtd)² + (Utt st)² + (Uz)²] ] 0.5 (eq 37A)

4.2.3 Quantification of USM long-term operating component of uncertainty

The magnitude of the contributors to the USM long-term operating component of uncertainty (Uopn) will vary considerably dependent upon the end user’s long-term operating philosophy for the meter. Thus, the assignment of numeric values to the input uncertainties is not straightforward. This component of uncertainty is derived from the combination of sub-components (equation 25 refers). One of these sub-components is in turn derived from second level components as illustrated within section the model. Thus, the expanded USM long-term operating uncertainty incorporates a total of three terms (equation 27 refers).

Ideally, it should be possible to attribute a value to each of the uncertainty terms identified within the expanded USM long-term installation uncertainty equation but in reality this is not always possible or practical. In most instances the values attributed to the input uncertainties can only be derived on a case be case basis and in certain instances further detailed research is required to attribute values to some of the uncertainty terms.

PAGE: 25 of 61

4.2.4 Uncertainty attributed to long-term operating effects on meter geometry (Ugeomo)

a) The input uncertainty attributed to the effects of long-term deposits within the bore of the meter spool piece (Udepo) has still to be investigated and cannot be fully quantified since the impact of contamination has to be evaluated on a case by case basis, usually as part of a dynamic recalibration programme. Thus, the input uncertainty attributed to the metering system when operating the meter where contaminants are present within the bore of the meter can only be referred to as X% of actual flow rate reading, with a rectangular probability distribution (√3) and a fixed sensitivity coefficient. Where X% signifies the % shift in meter performance attributed to the long-term build up of deposits derived from an “as found” dynamic calibration:

Udepo =

SC

3

X %

b) The input uncertainty attributed to the effects of long-term deposits on the faces of the transducers (Udept) has still to be investigated and cannot be fully quantified since the impact of contamination on SOS and transducer delay time (kt) has to be evaluated on a case by case basis, usually as part of a dynamic recalibration programme. Thus, the input uncertainty attributed to the metering system when operating the meter where contaminants are present on the transducer faces can only be referred to as X% of actual flow rate reading, with a rectangular probability distribution (√3) and a fixed sensitivity coefficient. Where X% signifies the % shift in meter performance attributed to the long-term build up of deposits derived from an “as found” dynamic calibration:

Udept =

SC

3

X %

c) The input uncertainty attributed to the effects of wear or erosion within the bore of the meter spool piece and on the faces of the transducers (Ueroso) has still to be investigated and cannot be fully quantified since the impact of wear/erosion has to be evaluated on a case by case basis, usually as part of a dynamic calibration. Thus, the input uncertainty attributed to the metering system when operating the meter where surface erosion occurs can only be referred to as X% of actual flow rate reading, with a rectangular probability distribution (√3) and a fixed sensitivity coefficient. Where X% signifies the % shift in meter performance attributed to the long-term wear/erosion derived from an “as found” dynamic calibration.

Ueroso =

SC

3

X %

Interestingly, it may be noted that a TEC system may assist in resolving some of the ‘presently unquantified sub-components’ by referencing the custom built supervisory suite of such a system.

4.2.5 Uncertainty attributed to long-term drift on meter performance (Udrft)

The input uncertainty attributed to the effects of long-term drift (Udrft) on the performance of the meter has still to be fully investigated since it is, in part, dependent upon the operating environment within which the meter is placed. A review of historic performance curves generated from repeat calibration data suggest that long-term drift is independent of meter size but does vary with meter type. For a typical Ultrasonic meter this effect is ‘estimated’ from current data to be less than 0.25% and 0.15% respectively of actual flow rate reading over a 12-month period, with a rectangular probability distribution (√3) and a fixed sensitivity coefficient.

Udrft =

SC

3

## % (Over 12-months)

PAGE: 26 of 61

Follows is an extract from the fuller model that is under development and that contains the three contributors or groups as highlighted at the start of this sub-section 4.2.1. It does not contain all the elements of the TEC as explored within this paper at this time.

*WARNING: There are a small number of values we’ve yet to determine, however these shall be populated as the project progresses. The worksheet is populated with numbers to test the formulae and the various iterations and, as such, does not constitute a valid analysis at this time.

Q.SONIC LONG TERM OPERATING COMPONENT OF UNCERTAINTY (U opn )% INPUT PROBABILITY % STANDARD SENSITIVITY OVERALL

UNCERTAINTY DISTRIBUTION UNCERTAINTY COEFFICIENT CONTRIBUTION

U depo 0.0000 Rectangular 1.732051 0.00000 0.00071 0.0000000000

U eroso 0.0000 Rectangular 1.732051 0.00000 0.00071 0.0000000000U drft 0.1500 Rectangular 1.732051 0.08660 1.00000 0.0075000000

OVERALL 0.0075U c 0.08660%

Uopn (Uc * k) 0.17321%

SYMBOLINPUT

DIVISORUNCERTAINTY

Q.SONIC INSTALLATION & OPERATING PHILOSOPHY COMPONENT OF UNCERTAINTY (U inst )% INPUT PROBABILITY % STANDARD SENSITIVITY OVERALL

UNCERTAINTY DISTRIBUTION UNCERTAINTY COEFFICIENT CONTRIBUTION

U α 1 0.0000

U Di 0.0500 Normal 2 0.02500 0.00071 0.0000000003

U E 1 0.0000

U th 0.1000 Normal 2 0.05000 0.00142 0.0000000050

U ∆T 1 0.0000

U ∆P 1 0.0000

U psi 2 0.0000

U ini 0.0500 Rectangular 1.732051 0.02887 1.00000 0.0008333333

U cni 2 0.0000

U WG 0.1500 Rectangular 1.732051 0.08660 1.00000 0.0075000000U fpei + U tei 0.0750 Rectangular 1.732051 0.04330 1.00000 0.0018750000

OVERALL 0.010211. Value deemed to be know without error or having negligible impact on sub-component of uncertainty U c 0.10104%2. Insufficient data available to allow values to be ascribed to this contributor to the sub-component of uncertainty Uinst (Uc * k) 0.20207%

SYMBOLINPUT

DIVISORUNCERTAINTY

Q.SONIC BASE COMPONENT OF UNCERTAINTY (U calw )

% INPUT PROBABILITY % STANDARD SENSITIVITY OVERALL

UNCERTAINTY DISTRIBUTION UNCERTAINTY COEFFICIENT CONTRIBUTION

U Dow 1

0.0000

Udi 2 0.0000

U tttd 1 0.0000

U ttdtw 3 5.0000 mm/s 0.0388 Rectangular 1.732051 0.02240 1.00000 0.0005016014

U ttmdw 0.0050 Rectangular 1.732051 0.00289 1.00000 0.0000083333

U imnd 4

0.0000

U imaxw + U imtrw 0.1500 Rectangular 1.732051 0.08660 1.00000 0.0075000000

U bd 0.2200 Normal 2 0.11000 1.00000 0.0121000000U linw 0.0300 Rectangular 1.732051 0.01732 1.00000 0.0003000000

U repcw 0.0500 Rectangular 1.732051 0.02887 1.00000 0.0008333333

U resv 4 0.0000

U resf 0.0500 Rectangular 1.732051 0.02887 1.00000 0.0008333333

OVERALL 0.02208

1. Value deemed to be know without error or having negligible impact on sub-component of uncertainty U c 0.14858%

2. Will be incorporated within the Meter Factor sub-component of uncertainty (U MF ) Ucalw (Uc * k) 0.29716%

3. This contributor to the sub-component of uncertainty varies with flow velocity, thus the value derived within this table is based on the "snap shot" flowrate

4. Insufficient data available to allow values to be ascribed to this contributor to the sub-component of uncertainty

SYMBOLINPUT

DIVISORUNCERTAINTY

PAGE: 27 of 61

MODEL: SIZE: 400mm

Frequency Range - Low Limit FLo 0 Hz Qv (gross) 1.6666667 m3/s

Frequency Range - High Limit FHi 6000 Hz Qv (corrected) 1.6686758 m3/s

Volume Flowrate - Low Limit QvLo 100 m3/h Area (M2) 0.1294619 m2

Volume Flowrate - High Limit QvHi 12000 m3/h Gas Velocity 12.8893 m/s

Meter Temperature Tline 25 Deg C Gas Velocity 12889.3 mm/s

Meter Pressure Pline 82.7 barg

Meter Density Rho1 69.533004 Kg/m3 Gross Volume Flowrate 6007.23300 m3/hr

Standard (Base) Density RhoRef 0.740081 Kg/m3 Gross Volume Flowrate 144173.5920 m3/d

Calorific Value CV 39.0028 MJ/m3

Meter Spool Diameter D 406 mm Mass Flowrate 116.0280434 kg/sec

K Factor KF 1800 pulses/m3 Mass Flowrate 417700.956 kg/h

USM Correction Factor CF 1 Mass Flowrate 417.700956 te/h

Dry or Wet Calibration (D/W) W Mass Flowrate 10024.82295 te/d

WME or Multi-point (W/M) M

Flow Conditioner Installed (Y/N) N Std Vol Flowrate 564.398973 ksm3/h

Snap Shot Frequency F 3000 Hz Std Vol Flowrate 13545.5753 ksm3/d

Std Volume Flowrate 486.480304 mmscf/d

Energy Flowrate 22013.134614 GJ/hr

Meter Spool Temp Coefficient Alpha 0.0000165 /Deg C

Meter Spool Calibration Temp Tcal 15.000 Deg C

Meter Spool Temperature Corr CTVspool 0.000165 Uncertainty in Meter Spool Bore (D) U Dow 0.000 %

Operational Uncertainty in Meter Spool Bore Udi 0.000 %

Modulus of Elastisity E 2000000 bar Uncertainty in SPU & Transit Time Delay U tttd 0.000 %

Wall Thickness t 18.300 mm Uncertainty in Transit Time Measurement (∆t) U ttdt 0.022 %

Meter Spool Calibration Press Pcal 40.000 barg Uncertainty in Transit Time Clock U ttmd 0.003 %

Meter Spool Pressure Corr Dcorr T&P 0.000237 Uncertainty in Spatial Sampling U imnd 0.000 %

Meter Factor MF 1.0012055 Uncertainty in ReD Correction U imaxw +U imtrw 0.087 %

Uncertainty in Dynamic (Wet) Calibration U bd 0.110 %

Uncertainty Due to Meter Non-linearity U linw 0.017 %

Uncertainty Due to Short Term Repeatability U repcw 0.029 %

Uncertainty in Axial Fluid Velocity Resolution U resv 4 0.000 %

Uncertainty in Meter Spool Bore (D) U Do 0.025 % Uncertainty in Output Resolution U resf 0.029 %

Operational Uncertainty in Meter Spool Bore Udl 0.000 %

Uncertainty in Reflective Path Angle U rpa 0.042 %

Uncertainty in Reflective Path Length U rpl 0.011 %

Uncertainty in SPU & Transit Time Delay U tttd 0.000 % Uncertainty Due to Deposits in Meter Spool Bore U depo 0.000 %

Uncertainty in Transit Time Measurement (∆t) U ttdt 0.022 % Uncertainty Due to Erosion in Meter Spool Bore U eroso 0.000 %

Uncertainty in Transit Time Clock U ttmd0.003 % Uncertainty Due to Long Term Drift U drft 0.087 %

Uncertainty in Spatial Sampling U imnd 0.000 %

Uncertainty in ReD Correction U imax + U imtr 0.173 %

Uncertainty in Static (Dry) Calibration U bd 0.075 %

Uncertainty Due to Meter Non-linearity U lind 0.115 % Uncertainty in Molar Mass Calculation U mol 0.000 %

Uncertainty Due to Short Term Repeatability U repcd 0.029 % Uncertainty in Calibration Gas Components U gccg 0.008 %

Uncertainty in Axial Fluid Velocity Resolution U resv 0.000 % Uncertainty in Process Gas Sample U gcrcg 0.010 %

Uncertainty in Output Resolution U resf 0.029 % Uncertainty in GC Functionality U gcrsg 0.011 %

Uncertainty Due to GC Repeatability U gcrep 0.001 %

Uncertainty in C6+ Characterisation U gcc6 0.011 %

Standard Uncertainty

Gas Chromatograph (Elster-Instromet EnCal 3000)

Standard Uncertainty

Standard Uncertainty

Standard Uncertainty

Conversions & Corrections

Dry Calibration - Base Component of Uncertainty (Ucald)

Wet Calibration - Base Component of Uncertainty (Ucalw)

Long Term Sub-Operation Component of Uncertainty (Uopn)

Q.SONIC SERIES ULTRASONIC FLOWMETER UNCERTAINTY BUDGET

Q-Sonic-5

Input Data Snap Shot of Results

Q.SONIC LINE DENSITY COMPONENT OF UNCERTAINTY (U ρ)

% INPUT PROBABILITY % STANDARD SENSITIVITY OVERALLUNCERTAINTY DISTRIBUTION UNCERTAINTY COEFFICIENT CONTRIBUTION

U P 0.3078 barg 0.3722 Normal 2 0.18612 1.07043 0.0396926792

U T 0.1888 Deg C 0.7552 Normal 2 0.37762 0.14674 0.0030703634

U M 0.0401 kg.kmol-1 0.2295 Normal 2 0.11475 1.00000 0.0131664194

U Z 0.1000 Normal 2 0.05000 1.00000 0.0025000000U R

10.0000

OVERALL 0.0584294621. Value deemed to be know without error or having negligible impact on sub-component of uncertainty U c 0.24172%

Uρ (Uc * k) 0.48344%

Uρ k (kg/m3) 0.33615

SYMBOLINPUT

DIVISORUNCERTAINTY

PAGE: 28 of 61

The project is developing and is based on ‘real life in operation’ metering stations (small sample), calibration sites data, and our continued CFD work as a result of this paper. There are sub-components yet to be quantified, however we believe the model can eventually be run in one of three modes:

to determine the ‘traditional’ pre and post calibration base uncertainties for a meter; to expand the above to include ‘typical’ (outwith the scope of this paper) installation sub-

components; and to expand further to derive an operational uncertainty including the series check scenario of

the TEC. The fundamental concept would be a Turbine - USM series configuration within a new and expanded model (as one scenario) as follows.

Uncertainty in Temperature Exp Coefficient U α 0.000 %

Operational Uncertainty in Meter Spool Bore U Di 0.025 % Uncertainty in Pressure Measurement U P 0.186 %

Uncertainty in Modulus of Elasticity (E) U E 1 0.000 % Uncertainty in Temperature Measurement U T 0.378 %

Uncertainty in Meter Spool Wall Thickness U th 0.050 % Uncertainty in Molar Mass U M 0.115 %

Uncertainty in ∆T Measurement U ∆T 0.000 % Uncertainty in Line Compressibility U Z 0.050 %

Uncertainty in ∆P Measurement U ∆P 0.000 % Uncertainty in Universal Gas Constant U R 0.000 %

Uncertainty Due to Transducer Pressure Shock U psi 0.000 % EXPANDED UNCERTAINTY U P k 0.4834 %

Uncertainty Due to Incoherent Noise U ini 0.029 % uP k 0.3362 kg/m3

Uncertainty Due to Coherent Noise U cni 0.000 %

Uncertainty Due to Suspended Liquids (Wet Gas) U WG 0.087 %

Uncertainty Due to Installation Effects U fpei + U tei 0.043 %

Uncertainty in Standard (Base) Pressure U P15,0 0.000 %

Uncertainty in Molar Mass U M 0.115 %

Uncertainty in Universal Gas Constant U R 0.000 %

Calibration Temperature 20.00 Deg C Uncertainty in Standard (Base) Temperature U T15,0 0.000 %

Tx Operating Temperature 10.00 Deg C Uncertainty in Standard (Base) Compressibility U Z15,0 0.050 %

Calibration Frequency 1 Months

Calibration Tolerence 0.25 %

Power Supply Variations 2.00 volts

Span 100 barg Uncertainty in Molar Mass U M 0.115 %

LRL 0 barg Uncertainty in CV Calculation U iso 0.013 %

URL 138 barg Uncertainty in Standard (Base) Compressibility U Z15,0 2 0.050 %

Acceptance Limit Uncertainty U pt 0.1443 barg

Test Equipment Uncertainty U phsc 0.0215 barg

Reference Accuracy U pref 0.0375 barg Base Component of Uncertainty Post Dry Cal U cald 0.4802 %

Temperature Effect U pte 0.0285 barg Base Component of Uncertainty Post Wet Cal U calw 0.2972 %

Drift Effect U pstab 0.0017 barg Installation & Operation Component of Uncertainty U inst 0.2021 %

Installation Uncertainty U pinst 0.0050 barg Long Term Operational Component of Uncertainty U opn 0.1732 %

Ambient Pressure Effects U pgp 0.0122 barg

EXPANDED UNCERTAINTY U P k 0.3078 barg Type Of Calibration Static or Dynamic (Dry or Wet) WETuP k 0.3722 %

USM Primary Uncertainty Uprimary 0.3989 %

Line Density Uncertainty U ρ 0.4834 %

TX Calibration Temperature 18.00 Deg C Line Pressure Uncertainty U P 0.3722 %

Tx Operating Temperature 16.00 Deg C Line Temperature Uncertainty U T 0.7552 %

Calibration Frequency 1 Months Uncertainty of Composition (Gas Chromatograph) U gc / U M 0.2295 %

Power Supply Variations 2 volts Standard (Base) Density Uncertainty Uρstd 0.1000 %

Calibrated Lower Range Value 0 Deg C CV Uncertainty U cv 0.0256 %

Calibrated Upper Range Value 100 Deg C

Calibrated Span 100 Deg C Gross Volume Flowrate Uncertainty Budget UQvg' 0.3989 %

Gross Volume Flowrate Uncertainty Budget UQvg' 575.1420 m3/d

Tx Calibration Tolerence U tt t 0.0577 Deg C

Tx Instabilty Uncertainty U tt stab 0.0577 Deg C Mass Flowrate Uncertainty Budget U Qm' 0.6268 %

Installation Uncertainty U tt inst 0.0050 Deg C Mass Flowrate Uncertainty Budget U Qm' 62.8339 te/d

Tx Temperature Effect U tt te 0.0020 Deg C

Tx Repeatability Uncertainty U tt rep 0.0289 Deg C Standard Volume Flowrate Uncertainty Budget UQvs' 0.6347 %

Tx Hysteresis Uncertainty U tt hys 0.0115 Deg C Standard Volume Flowrate Uncertainty Budget UQvs' 85.9752 ksm3/d

Test Equipment Uncertainty U tt st 0.0250 Deg C

Unc of RTD (BS EN 60751) E t rtd 0.0250 Deg C Energy Flowrate Uncertainty Budget UQe' 0.6352 %

EXPANDED UNCERTAINTY U T k 0.1888 Deg C Energy Flowrate Uncertainty Budget UQe' 3356.0078 GJ/d

uT k 0.7552 %

Standard Uncertainty

Standard Uncertainty

Standard Uncertainty

Line Density Sub-Component of Uncertainty (Uρ)

Base Density Sub-Component of Uncertainty (Uρstd)

CV Sub-Component of Uncertainty (Ucv)

Summary Of Expanded Uncertainties

Installation & Operation Component of Uncertainty (Uinst)Standard Uncertainty

Standard Uncertainty

Pressure Transmitter (Rosemount 3051CG)

Temperature TX & RTD (Rosemount 3144P)

Standard Uncertainty

Expanded Uncertainty

USM PRIMARY UNCERTAINTY_2 Considering the TEC

USM BASE UNCERTAINTY

(Ucal) (POST CALIBRATION)

USM INSTALLATION UNCERTAINTY

(Uinst_2)

USM OPERATIONAL UNCERTAINTY

(Uopn)

TEC uncertainty USM in series with a

Turbine Meter

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As a Total Energy Concept, we shall briefly review the ‘energy’ component of the existing uncertainty model, derived for a typical metering station but not specifically for the TEC, before discussing the methods of gas quality measurement to derive the TEC itself.

Corrected energy flow rate (Qe’) is derived from standard volume flow rate by the application of calorific value (CV):

Qe’ = CVFF

MFKf tpr ....

0,15

,

(eq 8)

Where:

CV = Calorific Value at agreed reference condition

With reference to equation 8, the energy flow rate through the meter is calculated as:

Qe’ = CVFF

MFKf tpr ....

0,15

,

or

Qe’ = CVQ tpgv .

.

0,15

,

= CVQ sv .

The energy flow rate uncertainty (UQe’) is therefore directly related to those key terms associated with the determination of the corrected gross volume flow rate uncertainty (UQvg’) together with those additional terms associated with the derivation of the mass and standard volume flow rate uncertainties (UQm’) and (UQvs’) plus those new terms associated with the real calorific value (CV) of the gas at the appropriate reference conditions.

Combining the groups on a root-sum-square basis gives the following standard volume flow rate uncertainty equation for the meter:

UQe’ = [ (Ucal)²+ (Uinst)² + (Uopn)²

+ (Uρ)² + (Uρstd)² + (Ucv)² ] 0.5 (eq 43)

Where:

Ucv = Uncertainty, at combustion conditions, of the real CV of the gas flowing through the meter.

4.2.6 Calorific value (real) component of uncertainty (Ucv)

The contributors to the component of uncertainty attributed to the CV of the gas flowing through the meter at standard or reference conditions will vary depending on the method employed to derive actual CV values. There are two typical methods employed to derive meter CV:

1. Calculated CV derived from ISO 6976 plus a live gas composition derived from an online Gas Chromatograph

2. Calculated CV derived from ISO 6976 plus a fixed gas composition.

For the purposes of this review it will be assumed that CV will be derived in accordance with Method one, i.e. calculated using an ISO 6976 routine and live gas component data.

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Real calorific value, on a volumetric basis, at reference (combustion) conditions of 15 oC and 1.01325 bar is calculated in accordance with ISO 6976:1995(E) using the equation:

CV = 0,15

1)0,1515(

~.

Z

HX oj

N

jj

(eq 44)

Where:

Xj = Mole fraction of component j at combustion (typically 15 oC & 1.0135 bar)

0

)0,1515(

~jH = Ideal CV on a volumetric basis of component j

Z15,0 = Calculated compressibility of the process gas passing through the meter at standard conditions (15 oC & 1.0135 bar)

The aim of this sub-section is to qualify, as far as practical, the effects which will impact on Ucv and hence UQe’. Thus the components of uncertainty attributed to the derivation of calorific value (Ucv) are given by:

Ucv = [ (UM)² + (Uiso)² + (UZ15,0)² ] 0.5 (eq 45)

Where:

UM = Uncertainty attributed to the calculated Molar Fraction of gas components

Uiso = Uncertainty attributed to the calculation of real CV on a volumetric basis in

accordance with ISO 6976

UZ15,0 = Uncertainty attributed to the gas compressibility factor at standard

conditions

The sub-components of uncertainty associated with the derivation of Molar Fraction (UM) when gas component data is derived from on-line analysis are given by equation 33) which can be found within the model and thus will not be included twice within the energy uncertainty budget. Similarly the sub-components of uncertainty associated with the derivation of the gas compressibility factor at standard conditions (UZ15,0) when gas component data is derived from on-line analysis are given by

the model and thus will not be included twice within the energy uncertainty budget.

4.2.7 Expanded energy flow rate uncertainty equation

Substituting equations 45) into equation 43) provides a semi-expanded uncertainty budget for measured standard volume flow rate:

UQe’ = [ (Ucal)²+ (Uinst)² + (Uopn)² + (Upt)² + (Uphsc)² + (Upref)²

+ (Upte)² + (Upstab)² + (Upinst)² + (Upgp)² + (Umol)²

+ (Ugccg)² + (Ugcrcg)² + (Ugcrsg)² + (Ugcrep)² + (Ugcc6)²

+ (UR)² + (Utt t)² + (Utt stab)² + (Utt inst)² + (Utt te)²

+ (Utt rep)² + (Utt hys)² + (Ut rtd)² + (Utt st)² + (Uz) + (UP15,0)²

+ (UR)² + (UT15,0)² + (UZ15,0)² + (Uiso)²] 0.5 (eq 46)

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Thus, with further reference to the model; the fully expanded measured energy flow rate uncertainty when the meter is only subject to a static (dry) calibration becomes:

UQe’ = [ (UDo)² + (UDl)² + (Urpa)² + (Urpl)² + (Utttd)² + (Uttdt)²

+ (Uttkt)² + (Utttmd)² + (Uimnd)² + (Uimax)² + (Uimtr)² + (Ubd)²

+ (Lund)² + (Urepcd)² + (Uresv)² + (Uresf)² + (U)² + (UDl)²

+ (UE)² + (Uth)² + (U∆T)² + (U∆P)² + (Upsi)² + (Utsi)² + (Ukre)² + (Uini)² + (Ucni)² + (UWG)² + (Ufpei)² + (Utei)²

+ (Udepo)² + (Udept)² + (Ueroso)² + (Udrft)² + (Ucal)²

+ (Uinst)² + (Uopn)² + (Upt)² + (Uphsc)² + (Upref)² + (Upte)²

+ (Upstab)² + (Upinst)² + (Upgp)² + (Umol)² + (Ugccg)²

+ (Ugcrcg)² + (Ugcrsg)² + (Ugcrep)² + (Ugcc6)² + (UR)²

+ (Utt t)² + (Utt stab)² + (Utt inst)² + (Utt te)² + (Utt rep)²

+ (Utt hys)² + (Ut rtd)² + (Utt st)² + (Uz)² + (UP15,0)² + (UR)²

+ (UT15,0)² + (UZ15,0)² + (Uiso)² ] 0.5 (eq 47A)

With further reference to the model; the fully expanded measured energy flow rate uncertainty when the meter is subject to a dynamic calibration becomes:

UQe’ = [ (UDow)² + (UDl)² + (Utttdw)² + (Uttktw)² + (Uttdtw)² + (Utttmdw)²

+ (Uimndw)² + (Uimaxw)² + (Uimtrw)² + (Ubw)² + (Lunw)²

+ (Urepcw)² + (Uresv)² + (Uresf)² + (U)² + (UDl)² + (UE)²

+ (Uth)² + (U∆T)² + (U∆P)² + (Upsi)² + (Utsi)² + (Ukre)²

+ (Uini)² + (Ucni)² + (UWG)² + (Ufpei)² + (Utei)² + (Udepo)²

+ (Udept)² + (Ueroso)² + (Udrft)² + (Upt)² + (Uphsc)² + (Upref)²

+ (Upte)² + (Upstab)² + (Upinst)² + (Upgp)² + (Umol)² + (Ugccg)²

+ (Ugcrcg)² + (Ugcrsg)² + (Ugcc6)² + (Ugcrep)² + (UR)² + (Utt t)²

+ (Utt stab)² + (Utt inst)² + (Utt te)² + (Utt rep)² + (Utt hys)² + (Ut rtd)²

+ (Utt st)² + (Uz)² + (UP15,0)² + (UR)² + (UT15,0)² + (UZ15,0)²

+ (Uiso)² ] 0.5 (eq 47B)

Although the model is yet to be expanded to address the TEC, it may be shown that the ‘background’ work of this past 2-years is an ideal ‘platform’ from which to develop the Total Energy Concept itself. This review is intended to provide a ‘snap shot’ of the work and is not written such that the formulas may be deployed. The work is considered ‘proprietary’ at this time, however as we progress with the project we shall provide a greater insight in subsequent papers on this subject.

4.3 Uncertainty of SOS calculated from GC gas analysis

This chapter discusses the various contributions to the uncertainty budget of the SOS calculated from the GC-analysis. The various sources of uncertainty are:

1. Uncertainty of the algorithm 2. Uncertainty of the GC analysis

2.1. Uncertainty of the calibration gas 2.2. Uncertainty of the instrument

4.3.1 Uncertainty and sensitivity of AGA-10 calculation

The targeted uncertainty for Natural Gas velocity-of-sound calculated according to AGA-10 depends on pressure, temperature and gas composition range 0. For typical Natural Gases and common operation conditions (region 1: -8 - 62°C, 0 - 120bar), the calculation has an uncertainty of 0.1%.

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This sets a principal limit for the uncertainty of the SOS calculation with AGA-10. Further contributions of uncertainty come from the input parameters of the calculation and hence from their measurement. The input parameters of the SOS calculation, pressure, temperature and composition, are determined by measurement. This introduces further uncertainty to the total uncertainty budget of the SOS calculation. The following explanation describes the sensitivity of the SOS calculation to the calculation input parameters

4.3.2 Uncertainty of the GC analysis

The uncertainty budget of the GC analysis comprises the external contribution of the calibration gas and of the internal contributions of the GC instrument itself, that are the systematic bias errors of the response function and statistical measurement errors due to noise (repeatability).

4.3.2.1 Uncertainty of calibration gas

Gas Chromatographs are calibrated with certified gas mixtures resulting in calibration functions for each calibrated gas component. The primary standards of calibration gas mixtures are produced by gravimetric blending. Calibration gas mixtures of 2nd order are derived from these primary standards by GC analysis. Typically, the working calibration gases of process GCs are derived from theses 2nd order gases, again by GC analysis. The uncertainty of the calibration gas is directly transferred to the calibration functions. Hence, the calibration gas uncertainty also contributes to the uncertainty budged of the calculated SOS. The following tables show examples of the specifications of calibration gas uncertainties.

Calibration Gas Supplier 1

Calibration Gas Supplier 2

Picture 18: Examples of a calibration gas certificates from two different suppliers with specified uncertainties.

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For the following discussion, we consider the uncertainty requirements for working calibration gas mixtures (3rd order) within Germany, according to PTB rule PTB-A 7.63, as a benchmark.

4.3.2.2 Uncertainty of the instrument

The functional principle of a Gas Chromatograph was described above. The quantitative analysis is done in the following way. The injected sample is being separated in the GC-column, so that the gas components subsequently reach the detector, resulting in response peaks for each detectable component. While the retention time is characteristic for the gas component, the response strength, defined as peak height or peak area, corresponds to the concentration of the various gas components. The relation between gas component concentration and response peak strength is called the Calibration Function and it is determined with calibration gas mixtures of certified composition. Current Gas Chomatographs have a quite linear response. Hence, the standard calibration procedure is a single-level-calibration (SLC) with just one calibration gas, resulting in a linear calibration function for each gas component i, characterised by the so-called response factor RFi. The linear single-level-calibration is sufficient for most standard applications. However a closer look reveals that the real detector response is not perfectly linear and hence application of a linear SLC results in a small bias error. In order to reduce these bias errors, so-called multi-level-calibrations (MLC) with several calibration gases are applied to determine higher order calibration functions (Picture 19). Most commonly, polynomials of 1st, 2nd or 3rd order are appropriate calibration functions. If more complex functions are required, to fit the set of calibration gases, this is considered as an indicator for a bad instrument or a wrong or inappropriate set of calibration gases.

Component Relative Uncertainty (k=2) Methane ≤ 0.1 % Nitrogen ≤ 0.8 %

Carbon Dioxide ≤ 1 % Ethane ≤ 1%

Propane ≤ 1% n-Butane ≤ 1% i Butane ≤ 1%

Table 03: Uncertainty requirements for working calibration gases for fiscal measurement in Germany according to PTB rule PTB-A 7.63

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The Calibration Function Fi(xi) of gas component i is a function of the gas component concentration xi. EQ 1 shows the linear function determined with SLC.

iiiii xRFxFR

with Ri is the instrument response, xi is the mole fraction of gas component i and RFi is the so-called response factor. The following equation shows a 3rd order polynomial for a calibration function for a MLC with a predefined set of calibration gases:

33,

22,1,0, iiiiiiiiii xaxaxaaxFR

with ai,n being the nth polynomial coefficient of the component calibration function i. The inverse function that determines the concentration xi from the instrument response Ri is called Analysis Function Gi(Ri). The following equation presents the linear form determined with a SLC

iiiii RrfRGx

with rfi being the inverse response factor i

i RFrf 1

Also, the Analysis Function can be expressed as a 3rd order polynomial as a result of a MLC:

33,

22,1,0, iiiiiiiiii RbRbRbbRGx

with bi,n being the nth polynomial coefficient of the component calibration function i.

Typically, process-Gas Chromatographs are calibrated in the field, on a daily basis, automatically with a single-level-calibration (SLC) resulting in an update of the response factors RFi, respectively rfi. In order to minimise bias errors, an optional multi-level-calibration with several calibration gas mixtures can be applied in the factory to determine an MLC-function. The daily calibration in the field is

Picture 19: Calibration Functions of a Gas Chromatograph. Deviation of single-level-calibration (SLC) and multi-level-calibration (MLC) from the actual calibration function causes bias errors.

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repeated with a SLC with only one gas, resulting in a stretch factor for the MLC-function. The bias errors of Gas Chromatographs have been investigated by various institutes and with different test protocols.

The following, in Picture 20, shows an investigation by the company Effectech (UK) within an SWRI study on GC test protocols[20]. The test instruments of this study were based on classical GC-technology and on state-of-the-art micro-GC-technology engaging micro-electro-mechanical-systems (MEMS) for injector and detector, as well as capillary columns. The ‘demonstrated’ results were generated in the following way. The Gas Chromatographs were calibrated with a single-level-calibration with one calibration gas mixture. Then, a set of certified calibration gases covering a wide range for each relevant gas component (N2, CH4, CO2, C2-C6) was measured with each test instrument. Based on these results, multi-level-calibration functions for each gas component were fitted as polynomials up to 3rd order. The differences between the linear SLC function and the best fitting polynomial MLC functions represent the bias error functions for each gas component. In order to investigate the overall effect of these bias error functions on the important gas parameter of calorific value, a Monte-Carlo-calculation was applied. Therefore a large test set of random gas compositions was calculated, regarding the typical composition patterns of typical Natural Gases. The Monte-Carlo test set was evaluated with the SLC and the MLC functions resulting in simulated measurements of the gas compositions. Finally, the calorific values of these simulated measurements were compared to the original gas compositions. The bias errors of each gas component calibration function summarise to an overall bias error of the calorific value. Picture 20 shows the results of this Monte-Carlo-calculation for a large test set of gas mixtures. The results of two test devices are shown here, a classical GC and a micro-GC. The graphs clearly show the lower bias errors of the micro-GC-device, due to a higher linearity of the instrument response.

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Test GC 1, classical GC technology

Test GC 2, micro-GC technology

Picture 20: Evaluation of the overall bias error of the calorific value resulting from the bias errors of the analysis functions of the various gas components. The assumed single-level calibration functions deviate from the best fit calibration functions and hence result in systematic errors for each gas component. In order to assess the overall effect of these systematic bias errors of the component measurements on the calorific value, a large random (Monte-Carlo) set of hypothetical gas compositions were evaluated. The upper graph shows the result of a test device with higher bias errors (classical GC technology); the lower graph shows the results of a very linear micro-GC.

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Another study was performed by Eon-Ruhgas (Germany). The test device, an Elster EnCal 3000 (micro-GC technology), was used to analyse a set of high precision test gas mixtures with BAM certificated gases (Bundesanstalt für Materialforschung, German Federal Institute for Materials Research and Testing). Single-level calibration functions (linear) and multi-level-calibration functions (2nd order polynomials) were determined from the resultant data by application of generalised least squares fitting. Finally, the scattering of the certificate data with the two different calibration functions was evaluated.

In Picture 21, we show the test gases and the results of the GC analysis with multi-level calibration functions; absolute values, absolute and relative deviations.

In Picture 22, we show the deviations of the calorific value and density measurements of the GC with multi-level calibration.

In Picture 23, we show the calibration functions and the absolute and relative deviations of each gas component. The left graph shows the absolute calibration function. The middle graph shows the absolute scattering of test gases with a multi-level-calibration. The right graph shows the relative scattering. Both the middle and right graphs show the deviations of the single-level-calibration from the multi-level-calibration and from the set of test gases (black line).

This study from Eon-Ruhrgas shows that the bias errors of a multi-level-calibrated gaschromatoraph EnCal 3000 are in the range of the uncertainties of the calibration mixtures.

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Picture 21: Performance evaluation of an Elster EnCal 3000 micro-GC with high precision calibration gases (BAM certificate, German Federal Institute for Materials Research and Testing) by Eon-Ruhrgas Laboratory. Certificate values, absolute GC measurement, absolute and relative deviations.

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Picture 22: Performance evaluation of an Elster EnCal 3000 micro-GC with high precision calibration gases (BAM certificate, German Federal Institute for Materials Research and Testing) by Eon-Ruhrgas Laboratory. The graphs show the relative deviation of the calorific value and density relative to the calibration gas certificates. The results are well within 0.1% over a range of typical gases.

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4.3.3 Modeling of uncertainty of SOS calculation

The method deployed for the model gave consideration to the generation of a random set of gases, the random errors on all analysis components corresponding to the uncertainty contributions from the individual calibration gas(s), and the bias error of the analysis functions including noise / precision.

4.4 Methods of gas quality measurement to derive the Total Energy Concept

The previous chapter discussed the role of GQM for the measurement task of the Total Energy Concept and gave hints to additional benefits for the measurement system integrity. The primary contribution for the measurement aspect is calorific value and real gas correction for fiscal metering. This chapter now presents the benefit for the assessment of the Total Energy Concept’s system integrity.

4.4.1 Speed-of-sound

Speed-of-sound is a valuable quantity that links flow measurement with Ultrasonic meters and gas quality measurement. The benefit for the assessment of the system integrity of the Total Energy Concept results from the possibility to link meters for different measures based on different technical principals and referenced to different traceability chains. The Ultrasonic flow meters provide a measurement of the Speed-of-sound, based on a time-of-flight measurement. By calibration, this measurement is traceable to the reference standards of time and length.

Alternatively, the Speed-of-sound can also be determined according to AGA-10 by calculation from the molar gas analysis, e.g., provided by a Gas Chromatograph. The Gas Chromatograph again is calibrated with standard gas mixtures. The working standards of calibration gases are directly or, by comparison, indirectly traceable via gravimetric gas mixtures to the reference standards of mass. Hence the cross reference of the Speed-of-sound measurements of these different independent technologies with different traceability chains is a very strong indicator for the overall system integrity. Another advantage is that GQM devices are calibrated by traceable standard gas mixtures, materialised as gas bottles. These portable traceable standards are relatively easy to transport to the gas measurement stations already installed in the field.

4.4.2 Extended gas analysis

In deploying an extended gas analysis many secondary gas parameters can be determined, such as, for example, trace component limits (H2S, O2, H2, H2O…) and water or hydrocarbon dewpoints. Indeed most of these parameters do not affect the energy measurement performance of a Total Energy

Picture 23: Performance evaluation of an Elster EnCal 3000 micro-GC with high precision calibration gases (BAM certificate, German Federal Institute for Materials Research and Testing) by Eon-Ruhrgas Laboratory. The left graph shows the absolute calibration function. The middle graph shows the absolute scattering of test gases with a multi-level-calibration. The right graph shows the relative scattering. Both, middle and right graph, also show the deviations of the single-level-calibration from the multi-level-calibration and from the set of test gases.

PAGE: 43 of 61

Concept directly. Nevertheless, an excess of the specified limits is still an indicator for an irregular operation of the whole gas transportation process. Therefore monitoring of certain quantities of the extended gas quality analysis does also, to some extent, maintain the integrity of the total energy measurement concept.

4.4.3 Redundancy of gas quality measurement (GQM)

Extensive Total Energy Concept’s could also benefit from redundancy of several GQM technologies, e.g. Gas Chromatograph plus sensor system. Indeed the accuracy of a Gas Chromatograph is much higher than that of sensor systems, therefore fiscal metering will always be based on the measurements of the Gas Chromatograph. However, in the event of a failure of the Gas Chromatograph a redundant measurement, with alternative sensor systems, shall always be better than approximation by estimated constant gas quality values.

4.4.4 Gas quality dynamics

An interesting aspect of the Total Energy Concept is the combination of real-time sensor systems with established Gas Chromatography; such combinations will provide positive redundancy. However, an additional benefit results if the sensor system is corrected by the high precision Gas Chromatograph resulting in a combination of measurement speed and accuracy. The absolute value is gained from the Gas Chromatograph with a measurement frequency of typical 3-5 minutes. The sensor system provides the relative variations within these measurement intervals in real-time.

4.5 SUPERVISORY SYSTEM

Almost all reasonably sized fiscal metering system’s contain a computer based data acquisition and control system. These systems are commonly referred to as the Supervisory Computer system or the metering database. Such systems traditionally fulfill the requirements of both visualisation and operation of the process, the consolidation of the production figures into a consolidated report, and to interface with telemetry and pipeline monitoring systems.

One manufacturer, Elster, has deployed their ‘Instromet Supervisory Suite (ISS)’ system software in many metering installations for the past 20-years. Elster developed this software in response to typical issues being realised within fiscal metering projects that could not be addressed with commercially available SCADA or DCS software systems. These software packages, deploying Elster’s proprietary ‘MeasCon Technology®’, are designed for the process control industry where the focus is on reliable control and less on achieving the highest possible absolute accuracy. These ISS systems go beyond a traditional SCADA system, or metering database for data gathering and control.

4.5.1 Background

In the past 20-years, many metering installations were installed within the transmission segment, i.e. border metering installations. The metering concept in such metering installations is generally dictated by a gas sales agreement that is drafted after extensive negotiations between the trading partners (usually national gas transportation companies). To satisfy individual country requirements it is necessary that a combination of different measurement equipment be deployed, for example:

The use of a specific gravity cell to obtain relative density as opposed to calculating it from composition. The calorific value is determined by one or more Gas Chromatographs. This methodology is used in several major European border metering installations.

Combination of an Ultrasonic and Turbine meter. The Ultrasonic meter was renowned for its performance monitoring features but was not accepted for custody transfer in certain countries. For that reason the Turbine meter is deployed as the ‘billing meter’.

Companies moving from traditional Orifice based measurement systems to Ultrasonic measurement systems have insisted on the use of line densitometers for determination of operating density as opposed to calculating it by the PTZ technique. This approach was in use by several operators in the Middle East. The base density and calorific value are determined from composition by using a Gas Chromatograph.

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The examples above are illustrative metering setups with deliberate redundancies. These were introduced to satisfy local ‘regulatory body’ requirements. Today, such deliberate redundancies are less common as they increase a system’s overall CAPEX.

Elster recognised early that such redundancies could be helpful to asses the performance of metering installations and its individual components. By setting a deviation limit on two measurements that determine the same physical property one can generate useful operational information, for example:

In several European border metering stations the specific gravity was measured by using a Solartron 3098 vibrating chamber densitometer. The resultant specific gravity was compared against the value calculated from composition using ISO-6976 as obtained by a set of redundant Gas Chromatographs. Elster has implemented a 2 out of 3 selection mechanism to determine a potential failure or mis-configuration in the Gas Chromatographs and the vibrating chamber densitometer.

In several metering stations in the Middle East a densitometer was used to obtain operating density. This value was to be used for the actual fiscal measurement. Since these metering installations were also equipped with a Gas Chromatograph the operating density was further calculated using AGA-8. This value was not used for fiscal metering purposes but was used solely for comparison purposes. A deviation was calculated and an alarm was generated when the limit was exceeded.

These metering installations within Europe and Middle East could be considered as the first step towards a Total Energy Concept (TEC) system. Measurement redundancies were available and differences were monitored. This was ‘unintentional’ by original design intent, but was deployed to satisfy local legal metrology requirements.

4.5.2 Truth tables

The comparisons that were introduced in the previous section are quite useful, however they do not suggest any remedial cause of action in case a deviation limit is exceeded. The systems will only report that a discrepancy exists between two different measurements obtained for the same physical property. Skilled and experienced maintenance technicians could, however, deduce the most likely cause of the deviation by looking at various other checks and comparisons within the metering installation.

This process was first automated by Elster in 2003 for an offshore installation in the North Sea. In the ISS supervisory computer system, for this particular installation, several comparisons and deviation checks were combined into a logical truth table. Instead of simply reporting deviations, the metering supervisory computer reported the most likely cause of the malfunction within the metering system.

This process is illustrated with the following example.

This extract from an Elster ISS Supervisory System illustrates the metering configuration:

Picture 24

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An Ultrasonic metering system with a 2 x 100% configuration with Ultrasonic meters. Each metering line contains a single USM and a single pressure and temperature transmitter. The inlet header of the metering station is fitted with a pressure and temperature transmitter. The gas quality is obtained by a Gas Chromatograph (not shown in Picture). The relative density of the gas is measured by a vibrating chamber type Solartron 3098

densitometer.

In this metering installation, the following measurements redundancies can be identified and deviation checks can be established:

SOSGC vs. SOSUSM The Speed-of-sound as measured by each of the Ultrasonic meters and the Speed-of-sound calculated by AGA-10 from the composition measured by the Gas Chromatograph, the pressure Pline and the temperature Tline.

Pline vs Pheader When the full-bore inlet ball valves are open the pressure on the inlet header and the pressures as obtained from the pr-point on the Ultrasonic meters shall be almost identical. A deviation is calculated and a limit is established.

Tline vs Theader

When a metering line is in operation, i.e. flowing, one can assume that the header temperature is almost equal to the temperature measured downstream of the USMs. A deviation is calculated and a limit is established.

Rd3098 vs rdGC The relative density of the gas is continuously measured by the vibrating chamber type densitometer. The relative density is also calculated in accordance with ISO6976 from the gas composition. These values are compared and a deviation is calculated.

Each of these individual comparisons in themselves can be interesting for a system operator, but do not allow an exact identification as to the cause of the discrepancy. All deviation comparisons are listed in a binary table. A deviation limit that is exceeded is represented by a binary ‘True’ marked with “X”. A limit that is not exceeded is represented by a binary ‘False’ marked with “-“.

The following truth table can be created that shows all possible combinations of these deviations. For each combination, one can assess what the most likely cause of the problem is for a given circumstance.

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VOSGC - VOSUSM deviation

PTline – PTheader deviation

TTline – TTheader deviation

RdGC –Rd3098 Deviation

Most likely cause of the measurement anomaly

- - - - - - - - X Density analyser - - X - Header TT - - X X - X - - Header PT - X - X - X X - - X X X X - - - USM X - - X GC X - X - Line TT X - X X X X - - Line PT X X - X X X X - X X X X

This example illustrates the principle of the truth table and how it can be deployed to identify the source of the observed deviations within the metering installation. With such a system, it is possible to provide information for the operator that would otherwise not be evident.

4.5.3 Improving the concept of deviation monitoring

As highlighted in the previous chapter, the truth table concept is based on calculating deviations and comparing them against a configurable limit. The setting of this limit is important. As is illustrated earlier in this paper, the limit shall be configured such that only significant deviations are ‘alarmed’.

Every measurement has an inherent uncertainty that is composed of a base uncertainty, an installation uncertainty, and an operational uncertainty. Setting the deviation limit too small would cause it to trigger too often as a factor of the inherent base uncertainty. Setting it too wide would potentially not detect an operational uncertainty that we would otherwise like to detect. Determining these exact limits is not easy because it is based on statistics. For this reason, Elster has implemented, in cooperation with a European offshore operator, the mechanism of historical mean deviation. This methodology is known within published literature as ‘Exponentially Weighted Moving Average or EWMA’ (Roberts 1959, Steiner 1999). It is a control chart methodology that is suitable for detecting small changes in a process. The work has further been developed by Grigg and Spiegelhalter (2007) in consideration of a ‘A Simple Risk-Adjusted Exponentially Weighted Moving Average or RA-EWMA’ having a reported ‘simpler and fewer number of stages that has an intuitive appeal to a wide variety of stakeholders’.

The EWMA approach is a step wise process as illustrated by the following equations.

(1) 00, historical

(2) BValueAValueDiff t

(3)

BValueAValue

Diff tt 200

(4) offsettthistroicalthistorical X11,,

Table 04

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(5) AlarmRaiseV itthistorical lim,

Where:

tDiff = difference between 2 transmitter values

t = relative difference in [%]

thistorical , = historical mean value in [%]

offsetX = relative offset value (operator configurable)

= response factor (operator configurable)

itVlim = alarm limit (operator configurable)

The interesting concept worthy of note is that the absolute difference between the two measurands is not as important as the change in the historical behaviour of the system. In deploying this methodology one can set specific limits such that only ‘true’ random base uncertainties are ignored and do not trigger a deviation alarm. The limit is set small enough so that ‘type B’ or systematic installation or operational uncertainties will trigger the deviation.

As an example, one can look at the case of incorrect maintenance by a maintenance technician. This is a ‘type B’ or systematic uncertainty in the uncertainty model. In using historical mean deviations such as a suddenly occurring change of deviation does trigger an alarm. In this way the uncertainty can be detected and remedied. As a result of such an event the uncertainty has become certain and it is no longer needed to be included in the uncertainty model. Hence, the total measurement uncertainty is reduced.

4.5.4 Summary

The previous paragraphs described how a specific supervisory computer system can determine several comparisons within a metering station. The majority of these comparisons can only be assessed by an overall supervisory system and not by the individual components. The individual components simply lack the context and information on the metering system as a whole.

This work originally started as a ‘measurement redundancies’ requirement to fulfil legal metrology requirement, but the added value of these measurement redundancies were recognised and, subsequently, deployed.

Even in these modern times, where there is an extreme focus on CAPEX, where the trend is to reduce the measurement redundancies as much as possible, such a system would continue to provide added value in the sense that it can help to reduce the operational uncertainty, reduce maintenance, and hence reduce OPEX.

By assessing the most likely causes of relevant uncertainty, and introducing the appropriate measurement uncertainty to detect the variable, and then to apply a correct evaluation methodology one can speak about a Total Energy Concept (TEC).

5.0 RESULTS

In this section, we shall explore ‘real life’ installations deploying some of the principles discussed within this paper.

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5.1 An Ultrasonic flow meter installation deploying a SOS comparison check

A Speed-of-sound (SOS) comparison / check has been deployed within our industry for several years in monitoring the performance of Ultrasonic meters. In such a SOS check, the Speed-of-sound as measured by the Ultrasonic meter is compared against the Speed-of-sound calculated from actual gas composition, pressure, and temperature according to AGA-10.

Such a SOS check was installed at a major gas transportation company in Europe back in 2002 at four of the largest gas export stations in both uni-directional and bi-directional measurement systems, using Ultrasonic flow meters. The SOS check has been deployed for 9-years to enable the operator to have a greater confidence within the reported bulk flow measurement values.

When both measured and calculated SOS are in agreement there is an increased confidence in the ‘correctness / (validity)’ of the USM measurement including pressure, temperature, and a chromatograph reading. When there is no acceptable agreement, this comparison will not highlight what the cause of the deviation is and what the best remedial action would be for a given circumstance. Hence, the system could be extended to form the basis of a TEC allowing historical comparisons between the individual approaches.

A real-life system (example) that has deployed this ‘historical’ SOS check is presented as follows. However, the reader should note the use of both a Turbine meter and an Ultrasonic meter in a series configuration. Such a configuration yields data valuable to the TEC system as explored within this paper.

A typical field setup of a large metering station is shown in as follows.

To control the validity of measurement values in such a large gas station, with a huge financial risk, all the measurements are configured for dual redundancy. For the primary bulk flow rate a Turbine meter (TM) is deployed as the main measurement device due to its large reproducibility. An Ultrasonic flow meter (USM) is used as an on-line check of the main flow values and as a backup meter in case the TM fails. The use of both these different measurement principles is beneficial in determining common-mode failures or deterioration (e.g. by dirt) by inferential on-line comparisons.

SCRUBBERS

USM TM GC-set

Flow Direction

Picture 25

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Two meters with the same measurement principle may not provide this valuable information. As an additional cross-check, the measured SOS of the USM is used as a check on the consistency of the P, T, and gas composition via a calculated and actual SOS. It should be noted that all measurement values have a different time-base and, as such, it shall be noted that the major challenge here is to ensure to account for the different time bases. For example, the measurement and the time lapse of the on-line GC analysis and sampling period. A typical result of such a SOS-check, performed every 15-minutes, in sync with the analysis cycle of the GC, is shown in the following graph.

Following shows the sensitivity analysis deployed in this system.

After correcting for the individual time bases, it transpired that this check was / is a valuable instrument to control the consistency of the different custody transfer measurements. Over longer periods of time, and with the GC calibration values filtered out (automated daily), we were able to show valid results of a measurement line over a period of a few months (note: due to data collection in flow computer the SOS-OLC (on-line chromatograph) is only displayed as a positive number).

Picture 26

Picture 27

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The differences between the calculated and measured SOS has an associated supervisory alarm level to maintain the station well between 0.2% and 0.4% . The alarm is used to direct the operational teams to take the necessary steps to control the measurement setup.

5.2 Turbine meter and Ultrasonic meter in series

Considerable experience exists within many installations where a Turbine meter and an Ultrasonic meter are placed in series. When considering the TEC, one could use the average of the results obtained from both meters to reduce the overall uncertainty of the measurement, however this is not the focus of this paper. Comparing the readings of both the Ultrasonic and the Turbine meter provides valuable information on the performance of a metering installation. For example, meter fouling of both meters due to hydrocarbon buildup; changes of surface roughness; blade failure; and bearing damage. Bearing damage is an area where deploying both a Turbine meter and an Ultrasonic meter in series perfectly suites the TEC system concept. Since the measurement principles are fundamentally different, the chance (probability) that meter fouling goes undetected is greatly reduced. A further example of this series combination is that the Ultrasonic meter’s fouling is undetectable by traditional Ultrasonic meter diagnostics, but there is an increased sensitivity in deploying the two devices in series. The deviation between the Turbine and the Ultrasonic meter is an earlier indicator than condition based monitoring (diagnostics healthcare). Here, once again, should there be a deviation, it is not always possible with condition based monitoring alone to determine the root cause of the deviation and extend this to determine the best remedial action. This is quite possible with a suitably designed self-analysing TEC system where the advantages are deployed within a powerful and dedicated supervisory suite.

Picture 28

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5.2.1 The use of a Turbine - Ultrasonic tandem package in metrological inter-comparison campaigns.

Within this section of the paper we consider real, and operational, calibration facilities that assist us explore the TEC.

Within the international inter-comparisons space it is of crucial importance to design a robust, stable and reproducible transfer meter (set) or ‘transfer package’ for accurate comparisons of (inter) national calibration facilities. In the past decade, transfer packages consisting of USM and TM meters has become more and more popular. In the first (conducted) CIPM ‘Key Comparison on HP NG[23]’, a transfer set was designed with both one USM and one TM in a series configuration. The purpose of the ‘fraternal twin’ setup was to enable a quantification of possible installation effects from different measurement results after swapping the order (position) of the meters.

In the Picture shown above, we consider an actual calibration facility installation. Two concepts are considered. The first one is to make a ‘metrological’ robust concept to cancel out installation effects, as much as possible, by using two flow straighteners mounted upstream of both USM and TM. In this way, the base reference value of the particular test facility can be determined, i.e., reference value of volume or mass. The extra pressure drop, due to straighteners, is accepted for these ‘short term use’ packages. In the second concept, the upstream meter is not provided with a straightener. Not surprisingly, should the USM be used in this position it may not yield the best results as a factor of its upstream piping. Deployed downstream of the TM it may act as a ‘flow profile watchdog’ due to its flow diagnostic features and higher sensitivity for installation effects than the TM. It would also have quite a degree of isolation from upstream flow profile disturbances given that it is intrinsically less sensitive as a ‘reference value watchdog’ in this configuration. Picture 30 follows.

In the following graph, the so called ‘Youden’ plot of some actual results is provided. Plotted in this way the correlation between two meters can be studied. On the horizontal and vertical axis we have plotted the differences relative to the reference value (converted to 0.00%, parameter ‘df’) of the Ultrasonic and Turbine meters. In the ideal situation, the point cloud (or cluster) is small, symmetric and concentrated on the cross point at 0.00%. If the points tend to spread along the 45°diagonal, it signifies that the differences found by the Turbine, correlated with those of the Ultrasonic meter, is caused by unknown systematic effects due to the calibration facility (in this example). A spread perpendicular to the 45°diagonal is a measure for the reproducibility of the transfer meters (statistical noise).

Picture 29: Example of a representative transfer meter package used in key comparisons

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5.3 Monitoring reference values in calibration and test facilities with tandem setup

Several modern calibration loops are provided with USM as monitoring master meters at this present time. For example, EuroLoop (NMi), Pigsartm (Eon/PTB), TCC (TransCanada Calibrations Ltd.). In most cases, the Q.Sonic® meter is installed with an Elster-Instromet Turbine meter (in a suitably applicable run). We shall focus, in the following sub-sections, on some of the schematic diagrams and discuss the master meter configurations.

5.3.1 Test-facility of TCC

This test-facility is located with a bypass downstream of a gas compressor station delivering gas flows up to 3.9 *106 m3(n)/h at a pressure ranging between 6.2 MPa and 7.0 MPa. The bypass line is equipped with an 800 mm diameter control valve. Opening the station isolation valves and closing the control valve forces gas through the station. A second, 200 mm by-pass control valve serves as a vernier control.

The facility houses five runs of parallel reference meters with diameters of 400 mm encompassing up to 10000 m3/h each, paralleled with a single run of 300 mm of 4000 m3/h capacity and two runs of 200 mm with up to 1.600 m3/h each. Each reference run is configured with a matched size upstream Ultrasonic meter and a downstream Turbine meter

Picture 30

PAGE: 53 of 61

The installation has been in operation since 2001, and a complete recalibration cycle of all standards is carried out two times per year. From the process diagram above, it can be seen that the USM’s are built upstream of the TM masters. The 4-path USM was anticipated to be a monitoring meter and is not intended as a twin master meter. At the master meter runs, flow straighteners are installed to improve the insensitivity to upstream piping effects. During the 3-yearly recalibration campaign the indication of the master meters was evaluated. The USM showed a repeatability of 0.03% (2 times stdev single measurement points) whereas the TM showed 0.01%. It is not yet clear whether these differences are caused by the USM themselves, their (upstream) position, or the small variations in the actual flow rate which will be dampened out by the TM’s ‘flywheel’. Differences in reproducibility between USM and TM are also observed in the EuroLoop facility, although the USM behavior seems more comparable with the TM possibly because of the downstream position of the USM (see next section).

5.3.2 The EuroLoop calibration facility

This facility is a closed loop calibration centre driven by a master blower. It holds 5 identical large reference runs, each consisting of one Turbine reference meter and one Ultrasonic monitoring meter - Q max 5 x 6500 m3/h. Total flow rate adds up theoretically to 32500 m3/h, but depends on the actual pressure drop due to the meter under test. Low flow rates are handled with three small reference runs with a capacity of 5 to 3000 m3/h.

The facility is set out with a crossover, for primary calibration, enabling the operator to place the small and large reference runs in series for calibration maintenance, and cross-checking, etc. (see blue arrows).

Picture 31: Schematic overview of the general configuration of TCC

PAGE: 54 of 61

The variable speed drive, blower, and the flow fine tuning valves are fitted for flow control. Operating pressure can be controlled from 3 up to 62 Bara. Operating temperature can be set from 5 up to 35 °C. Gas quality is constant, monitored by a GQM deploying MEMS technology, while the same gas is stored inside the loop or in the external gasholders. The monitoring meters (Ultrasonic meters) are used to constantly monitor the behaviour of the reference Turbine meters but do not contribute in the determination of the reference value at the location of the meter under test. Consistency shall be met during the calibration time frame. At the time when the EuroLoop was designed it was believed that the reproducibility and repeatability of USM’s would not compete with modern precision Turbine metering and so two Turbine meters in series were preferred from the metrologists’ perspective (2006). However for the sake of the lowest possible pressure drop across the loop, eventually Ultrasonic meters were installed.

5.3.2.1 Correlation of USM related to TM master meter in EuroLoop

During the recent commissioning tests of the master and monitoring meters of EuroLoop, the master TM and USM were compared with travelling reference meters (TRM’s) traceable to several test facilities in the Netherlands (Bergum, Westerbork) and Germany (Dorsten) at several pressure stages (51, 21 and 9 Bara).

In the adjacent graph, the deviation is shown for meter deviation and Reynolds number. It shows both TM and USM and assumes that the TRM’s are reading perfectly. Note that the measurement points of the gas meters were taken in the same 60s time window. Or in other words, both meter deviations should correlate perfectly.

‐0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.00E+05 1.00E+06 1.00E+07 1.00E+08

Meter Deviation %

Reynolds number

TM1

TRM1‐50b‐5C

TRM4‐9b‐5C

TRM4‐21b‐10c

TRM 11‐21b‐10c

TRM8‐51b‐10c

Picture 33

Heat Exchanger

16"

16"

30"

30"

6"

10"

2"

10"

10"

NMi TraSys

65 m3/h

6500 m3/h

2500 m3/h

400 m3/h

10"

10"

2"

10"

10"

16"

24"

2"

8"

30"

10"

10"

10"

10"

Metering runs

Metering runs

usm

6500 m3/h

usm

30"

Gas Oil Piston Prover

Cross over line

Flow finetuning

Blower andvariable speed

drive

PAGE: 55 of 61

The meter deviations are defined according to the following formula:

In an ideal situation, all points should form a ‘Reynolds-smoothed’ trend. As a factor of non corrected or unknown pressure and temperature effects, possible installation effects, and drifts of

TRM’s, there will be a spread around the ideal curve. When only one master meter is used in the reference run, it would be almost impossible to separate the reproducibility behavior of the master meter itself from the reproducibility of the compared facilities. When combined with the test results of the USM one may draw interesting conclusions by deploying correlation graphs.

The adjacent graph derived from Youden’s method shed’s more light on the subject. The symbols refer to pressure’s at 51 (green), 21 (red) and 9 Bar(a) (yellow). For 9 Bar (a) only one test data set was available, therefore the yellow dots appear around the origin. From statistics, the equivalence of all the TRM’s (including that used in the calibration facilities), for all operating conditions, is calculated at 0.14% (main ellipse axis) and the reproducibility of the master meters at 0.07%. (small axis), both expressed at 2 times standard deviation.

A conclusion drawn in 1998 on the basis of pure Turbine metering[24] is confirmed, that the variations of reference values generated by national calibration institutes can be observed by

using both modern Turbine and Ultrasonic meters with sufficient consideration to the individual meter’s installation philosophy and overall ‘twin’ setup.

5.4 Discussion on ‘best location of USM in tandem setup’

In the previous sections, the obvious advantages in using a redundant (US) meter in series with the Turbine meter were explored. Two meters in series offer two possible configuration orders. In the past few years, several manufacturers, end users, and metrologists raised the question: ‘What is the best position of the USM related to the other type gas meter in the reference run?’ An answer to this question is dependant on the application of the twin setup. In the following section some pro’s and con‘s characterisations are provided.

‐0.40

‐0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.00E+05 1.00E+06 1.00E+07 1.00E+08

Meter deviation %

Reynolds number

US1

TRM1‐50b‐5C

TRM4‐9b‐5C

TRM4‐21b‐10c

TRM 11‐21b‐10c

TRM8‐51b‐10c

Picture 34

Picture 35

PAGE: 56 of 61

Option 01, Ultrasonic meter upstream of Turbine meter

Advantages:

-Meters can be calibrated as completely separated systems and can be used as such. Meters have an ‘individual uncertainty’ and are equivalent in custody transfer performance.

Disadvantages:

-Extra upstream length needed for USM (CAPEX) to reduce installation effects.

-If straightener is used to decrease installation sensitivity of USM, extra pressure drop may be evident.

Option 02, Turbine meter upstream of Ultrasonic meter

Advantages:

-Smaller master meter run, due to shorter inlet spool of TM, given a normal inlet spool for the USM;

-USM is isolated completely from possible meter run upstream perturbations and encounters a ‘defined flow disturber’ in the TM.

Disadvantages:

-Calibration should be done with the complete and assembled meter run (black box approach);

-‘Shake-off’ noise of TM may theoretically affect the USM reading in some cases – due to ‘interfering frequencies’ (not proven at this time, but theoretical possible)

Remarks:

* Within test facility setups, a motivation for using option 02 could be:

-Short master meter runs, reduction of dead volumes (connected pipe volumes).

-Master meters and piping are regarded as a ‘black box unit’ where master meters are mounted in the final position. Possible installation bias is ‘calibrated-in’ via travelling standards (and corrected for).

-Small pressure drop (assumed that flow straighteners are not used)

* In gas metering stations a motivation for using option one could be:

-Built-in gas meters typically cannot be recalibrated on site, so a ‘black box approach’ is not feasible here. Meter runs and / or individual gas meters are recalibrated separately on a test facility and (re)installation effects should be avoided at all times.

In conclusion, the order of the ‘twin-set’ depends on the individual application and its operating philosophy.

5.5 Reduction of common fault failure through applied redundancy in gas flow metering

In order to provide a level of protection against redundant software, or in general systems failing identically, a multiple number of diverse systems should be used. Diversity in the TEC system attempt’s to prevent redundant systems from failing identically, or from failing simultaneously[25] (applied

for TEC). Basically, using more imperfect instruments simultaneously offers the potential for construction of nearly perfect measurement systems.

PAGE: 57 of 61

The degree of protection afforded by design diversity is (usually) not quantifiable and so we shall restrict our approach to a qualitative approach (sees following table).

By definition, a common-mode failure (CMF) occurs when two or more systems fail in exactly the same way for the same input.

In the following table the CMF probability is stated for some single fault inputs (qualitative).

Fault input Effect on TFM Effect on USM CMF probability Severe contamination Medium Medium Medium

Flow pulsation Medium to high Small Small Medium (US TM )1)*) Small Swirl Medium Small (TM US)1) Small Medium (US TM ) 1) Small Asymmetric flow

profile Small

High (TMUS) 1) Small Very small (US TM ) Very small Mechanical damage of

blade High

Medium (TM US) Small Pneumatic interference

due to other gasmeter Zero Medium Small

Change pipeline roughness

Small Medium Small

1) Depending on number of paths/ type of path configuration and ‘diagnostic features’ of USM *) (USTM) means: Ultrasonic meter mounted upstream of the Turbine meter.

This Table 05 is restricted to the flow meters only (N=2 systems).

5.6 Extending recalibration terms based upon analyses on redundant systems

From a study carried out by NMi [26] a model was drafted to predict the latest date at which the uncertainty of a single gas (standard) meter would exceed specifications, given the meter error drift over the operational time of that meter (trend analyses). A helpful but rather complicated method to find the optimum recalibration date was related to a cost / quality ratio. However, in an N=2 redundancy setup it is easier to monitor for an optimum moment using a maximum permissible difference between both meters.

6.0 SUMMARY, CONCLUSIONS, and FUTURE WORK

This paper set out to explore a ‘Total Energy Concept (TEC)’ for Natural Gas metering in considering technology advances within the market, both theoretical and ‘real life’ installations with ‘promising results’, and concludes with the following summary. The summary is provided by way of a set of ‘brief summary points’ as the content of the various chapters is regarded as being ‘quite considerable’ in exploring the total concept.

6.1 Summary and Conclusions

A TEC system shall be founded upon a dedicated, while intelligent, supervisory system (suite) based on both ‘traditional analysis and the newer ‘TEC analysis’. Such a system shall be dedicated to the TEC for its functionality and reporting. It shall be founded upon a ‘twin metering’ configuration deploying two quite different metering techniques such as the Turbine meter and the Ultrasonic meter in series. Consideration may be given to deploying two Ultrasonic meters with quite different path geometries, however the effectiveness of the TEC shall be compromised as a factor of a ‘mutually exclusive events’ analysis. It is not recommended to utilise two meters deploying the same metering technique if one is to achieve the long-term goal of the TEC, as defined within the introduction of this paper.

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Consideration shall be given within the system to the individual ‘time based’ measurements being corrected to yield an ‘instance in time’ (or snapshot) on a common time scale. This is particularly important when considering the various Gas Chromatograph technologies when deployed for Speed-of-sound comparisons with an Ultrasonic meter. The comparison of SOS is limited by the different sampling timings of the different measurements devices (Ultrasonic meter, GC), which is obviously caused by the variations in gas quality. Therefore it may be considered a ‘strong’ recommendation to introduce an additional online gas analyser that is not a GC but some ‘other sensor’ (as explored within this paper). This additional analyser/sensor shall not be used for the SOS comparison itself, as this shall be achieved with the more accurate GC, but as an online analyser to determine the amplitude of gas quality variations within the sampling period of the GC.

The ‘running order’ of a Turbine and Ultrasonic meter in a series ‘twin’ configuration shall be dictated by the ‘application philosophy’ as discussed within this paper. It shall be noted that a calibration facility and a dedicated metering station may be considered as two different metering deployments; one can consider the running order to be changed in line with the measurement philosophy.

The use of both Ultrasonic and Turbine meter(s) as master meters yields a configuration that offers a reliable cross-monitoring system within calibration facilities. Due to the relatively short measurement timeslots within these facilities (from one minute to only a few minutes), the Turbine meter behaves in a more ‘steady’ fashion, a factor of its operating principle, and is, therefore, often used as the main (decisive) master meter. The additional USM is deployed as a ‘health monitor’ within the redundant master meter setup. As such, for a calibration facility operating as the philosophy within this paper, a TM to USM tandem is preferred. For Gas metering stations (long-term measurements) the USM to TM tandem yields a ‘Custody Transfer’ equivalent and has a great potential for reducing Common Mode Failure (probability). By deploying a suitable statistical evaluation (trend analyses) technique to this ‘twin system’, the recalibration time period of both meters may be further optimised.

The traditional uncertainty model was expanded to include long-term operational uncertainty and presented in the 2009 AGA paper after Bragg et al. This model was completed based on one meter in a single metering run and, as such, forms the basis of a model that may be deployed within a ‘real life’ metering station. At the time of the ‘publication’ of this paper, the model has not been fully deployed in a ‘real life’ installation to track the long-term operational condition against that of actual and modelled data.

The introduction of the TurbinScope® diagnostic for Turbine meters yields an ideal input for a TEC. The analysis package has only been deployed in an ‘off line and manual’ discrete form, but shall be rolled-out as an online package as part of a TEC design package.

MEMS deployed within a Gas Chromatograph have been shown to yield a good performance, faster, than traditional GC’s and shall be deployed within a TEC to reduce the ‘time delays’ explored within this paper. Correcting time based SOS data form an Ultrasonic meter with that of a GC and a subsequent AGA-10 calculation is essential within a metering station - even one not deploying a TEC.

By products of an Ultrasonic meter deploying the transit-time measurement principle were explored and, where appropriate, may be utilised to derive additional diagnostics. In a suitable application, the by products may prove to be essential when exploring the integrity of the meter in terms of operational performance. Comparing individual path diagnostics within a multi-path meter has been performed for some years with a high degree of ‘reported’ success within the Natural Gas metering market space.

6.2 Future work

This paper was written to ‘explore’ a concept based on ‘partial systems’. Partial because the in-field systems, with the historic data, were not originally deployed as TEC’s but as traditional metering stations. As such, the analysis of a ‘real life’ TEC system requires that further work shall be performed before one may conclude how a TEC is configured/designed for optimum performance. The authors propose that the following work be performed, and reported in ‘stages’, in moving the past work from the point of exploration and on to a proposed TEC system designed to exploit the unique data set that is inherent within the solution.

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The authors propose to develop an advanced quantitative evaluation model to determine the performance of a TEC with differing ‘redundancy types’ (gas meter types, cross checking methods, health monitoring, decision/voting systems, etc.). To further present the common mode failure (CMF) data so that one may express the appropriate confidence levels.

In a future paper, it is proposed that the advantage of the potential for a ‘twin master meter’ setup be consider in terms of determining the recalibration terms in more detail.

To consider the influence on an USM coupled with an upstream TM thus that one is able to express the ‘mechanical noise (shake-off noise)’ upon a subsequent flow rate measurement.

Extend the uncertainty models presented within this paper to include for a ‘twin set’ TM and USM/USM and TM combination. To determine the missing ‘X’ values within the present model; at least by range if not an absolute number.

Deploy a dedicated supervisory suite to run both a traditional diagnostics/health care package for an USM with that of the new TurbinScope® diagnostic for a TM to form a standard package for a TEC system. Such a system shall be based, in the case of one manufacturer, upon the new MeasCon Technology® for operational health care of TM and USM meters (as explored within this paper).

To further develop the work of the ISSplus Supervisory Suite in deploying the ‘Exponentially Weighted Moving Average or EWMA’ technique for a TEC as explored within this paper. The work shall be considered complimentary to that of the individual meters and instruments analysis. The interesting concept worthy of note is that the absolute difference between the two measurands is not as important as the change in the historical behaviour of the system. Although the TEC shall be configured such that the individual measurands’ performance may be validated, the use of the EWMA technique is an inherent validation parameter that provides additional confidence within the system.

7.0 ACKNOWLEDGEMENTS

The authors would like to thank their colleagues for their support and, also, the end users of a number of metering systems that allowed access to their data in support of this paper. For the support of the uncertainty consultant on the project and the subsequent model provided to date.

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8.0 REFERENCES

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