JON MOREAU Global Business Development Director, Cameron Measurement Systems

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3 Jon Moreau is Global Business Development Director for Cameron Measurement Systems. He has been with the company for nearly 20 years having joined Jiskoot in 1995 prior to its acquisition by Cameron in Jon has worked extensively with quality measurement and control systems and has worked on and led a number of major projects leveraging sampling technology to deliver a rapid return on investment in the Middle East, Asia, Europe and the Gulf of Mexico. JON MOREAU Global Business Development Director, Cameron Measurement Systems Jon is a previous presenter at Measurement conferences in the Middle East and Internationally. He is an active author of technical documents, many of which have been published internationally and is also a contributor to working committees for quality measurement and control issues including sampling and blending. Jon was instrumental in working with the technology team at Jiskoot to create many of the return on investment models that are now extensively used for quality measurement and blending.

4 Proving

5 Proving?

6 How do you manage risk? Paper Abstract The accuracy of a liquid hydrocarbon (crude oil, condensate etc.) sampling system is a vital component of the performance of a metering system and the ability to account for crude oil custody transfer or allocation. However, unlike the performance of flow meters which are validated by proving, the accuracy of a sampling system is often unknown, or not validated and is therefore not considered when calculating the measurement uncertainty calculation of the overall metering system performance. This can have a significant impact on the revenues of an organisation of the size of KOC. This papers reviews the methods for validating the performance of a crude oil or condensate sampling system, considers the effect of the performance of a sampling system on overall measurement uncertainty. It includes a relative evaluation of the performance of different types of sampling systems and considers the process variables that have the most effect on sampling system accuracy using a large data set of field performance proving results of installed sampling systems

7 Loss pa (Million $) Potential quality measurement losses for Kuwait $250 $200 $150 $100 $50 $0 % % % % % Water content?

8 QUANTITY MEASUREMENT I.E. METERING QUALITY MEASUREMENT I.E. SAMPLING VOLUME LIQUID +/- X% VOLUME OIL +/- Y% METERING UNCERTAINTY = X SAMPLING UNCERTAINTY = Y WHEN ASSESSING THE VALUE OF QUANTITY AND QUALITY SOMETIMES YOU JUST NEED BOTH

9 What is the RATIO between the volume analyser in the lab for BS&W and a VLCC cargo? A. <5,000,000:1 B. <10,000,000:1 C. >10,000,000:1 300,000,000,000:1 Crude oil cargo - 300,000 m 3 10,000 = 1cc sample grabs taken per batch 10 litre sample taken to lab for analysis 1 100cc analysed for water - depending on method

10 So how do you Prove a sampling system API and ISO define a water injection test to prove the uncertainty of the entire sampling & analysis chain Proves that the sampling system operates within an acceptable uncertainty

11 So how do you Prove a sampling system Check results using analyzed sample W lab = W inj + W baseline Two sequential tests for repeatability Tolerance per standards ISO 3171 /IP 6.2 API 8.2

12 Proving acceptance criteria API Two tests Total Water Using Tank Gauges Using Meters Proving to API standard Only complies with API not ISO or IP!

13 Proving acceptance criteria ISO/IP Proving to IP or ISO standard Complies with API, ISO and IP (2 tests) Proving to the API complies only with API

14 Sampling system certification test Test conditions Minimum Norm Maximum Flow Rate (m 3 /hr) Viscosity process) Density (kg/m 3 ) A B C?

15 Sampling system certification test Test conditions Minimum Norm Maximum Flow Rate (m 3 /hr) Viscosity process) Density (kg/m 3 ) A WHERE POSSIBLE WATER INJECTION PROVING SHOULD BE CARRIED OUT AT THE WORSE DESIGN CONDITIONS FOR SAMPLING OF MINIMUM FLOW RATE, DENSITY AND VISCOSITY

16 Sampling system certification test Test conditions Test the system using most stringent conditions : Lowest flow rate Lightest viscosity / density Things To Do First Conduct the sample receiver mixing and handling component test Conduct the sampling device component test System Test Failure! Cause of system certification failure can be any number of things Eliminate as many component variables as possible beforehand USE THE EXPERT SERVICES WHERE POSSIBLE

17 Typical Sampling System Proving Certificate

18 Typical Sampling System Proving Certificate Independently witnessed

19 Typical Sampling System Proving Certificate - America

20 Water injection tests provide the ability to compare the performance of different types of sampling systems. How do different types of sampling systems perform? Inline Samplers Fast Loop Samplers Either combined with Power Mixers (CoJetix) Historic sampling system certification data helps to answer that question..

21 In-line sampling system test data

22 Relative WC error % In-line sampling system test data all types of mixing Typical Inline Sampling Systems Error in Relative Water Cut 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% Other Mean Error ±95% confidence level Mean error of about -0.1% relative water fraction. The scatter about this mean however is relatively large being about ±0.4% meaning any one system result can have a relatively large variation from this mean.

23 Relative WC error % In-line sampling system pre 1998 JetMix test data Pre 1988 Inline JetMix Sampling Error in Relative Water Cut 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% JetMix pre 1988 Mean Error The JetMix and Inline Sampling System combination was developed during the early 1980 s, and during the early period these systems went through significant JetMix Nozzle development that culminated in new Nozzle designs. Note the scatter and the mean.

24 Relative WC error % In-line sampling system post 1998 JetMix test data Post 1988 Inline JetMix Sampling Error in Relative Water Cut 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% JetMix post 1988 Mean Error ±95% confidence level The combination of an active mixer and Inline Sampling System significantly improves the results from Inline Sampling Systems. The results are comparable to that seen in the Fast Loop Sampling Systems results (covered in the next section)

25 Fast Loop (bypass) sampling system test data ByScoop take-off with integral return

26 Relative WC error % Fast loop (bypass) sampling system test data 0.50% Fastloop Sampling System Error in Relative Water Cut 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% Fastloop Mean Error ±95% confidence level The Fast Loop Sampling System has a marked increase in accuracy over Inline Sampling Systems. Reduces the mean error to that of about -0.05% relative water fraction. Reduces scatter in the individual results to that of about ±0.15%.

27 CoJetix (combined fast loop and JetMix) sampling system test data JetMix

28 Relative WC error % CoJetix (combined fast loop and JetMix) sampling system test data CoJetix Sampling System Error in Relative Water Cut 0.50% 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% CoJetix Mean Error ±95% confidence level Mean relative error of <-0.01% / Scatter less than ±0.1%. The CoJetix Sampling System design is the most accurate despite being installed in the most challenging locations.

29 Relative WC error % Sampling systems comparison 0.50% Combined JetMix and CoJetix Error in Relative Water Cut 0.40% 0.30% 0.20% 0.10% 0.00% -0.10% -0.20% -0.30% -0.40% -0.50% JetMix before 1988 JetMix after 1988 CoJetix Mean Error ±95% confidence level CoJetix sampling technology yields higher accuracies, narrower 95% confidence interval, and average results closer to 0% relative error. Despite ISO 3171 Annex A mixing calculations showing that there is equal mixing using an Inline Sampling System with passive mixing..

30 Summary Sampling accuracy has a large effect on net oil measurement and unless you know your sampling system performance losses can be significant Not all sampling system designs have the same performance ISO & API have a method for proving sampling system performance that is international recognised and auditable Market conditions are driving buyers and sellers to invest in reducing measurement uncertainty This is driving increasing challenges and changes in crude oil contract terms Do you know the accuracy of your sampling system?

31 Thank you