using integrated asset modelling to improve oil and gas planning decisions in a volatile market

Dr Andrew Wadsley 31 March 2015

Using Integrated Asset Modelling to Improve Oil and Gas Planning Decisions

in a Volatile Market

© 2015 Stochastic Simulation Ltd. All rights reserved.

Some Relevant Experience

40+ years in oil & gas industry

30+ years with integrated planning models

Early 1980’s: Used integrated gas simulation / network / market model (“Gasso”) for Shell’s southern North Sea gas fields

Early 1990’s: Installed integrated gas planning model (“Gasplan”) for ExxonMobil’s Gippsland Basin and Peninsula Malaysia fields

1980’s to 2015: Integrated planning models in South America, North Africa, SE Asia, ME, Europe, Scandinavia, New Zealand and Australia

© 2015 Stochastic Simulation Ltd. All rights reserved. 2

And now for something completely different …


VW Transparent Factory


The Oil and Gas Factory


Integrated Asset Modelling The Digital Oil & Gas Factory

Planning Horizon Varies from Days to Years



The Corporate Value Driver

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Modern IAM tools have been documented to increase annual revenues in excess of $100 USD million:

• Additional LNG spot cargoes 1

• Increased production of condensates and LPGs whilst fulfilling contracted and predicted gas demand 2

• Uncertainty quantification and “What If” scenario analysis

• Investigating additional Marketing opportunities

• Supply Chain Planning and Optimization

• Reliability of Supply and Emergency Hazard Management

1. Lanner.com, (2014). Shell’s Revolutionary Terminal and Logistics Planning System | Liquefied Natural Gas | WITNESS. [online] Available at: http://www.lanner.com/en/case-study.cfm?theCaseStudyID=CA0D41D1-15C5-F4C0-990E96EA8969C456. 2. Selot, A., Kuok, L. K., Robinson, M., Mason, T. L. and Barton, P. I. (2008), A short-term operational planning model for natural gas production systems. AIChE J., 54: 495–515. doi: 10.1002/aic.11385

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2001 2005 2009 2013 2017

Is IAM of Value to YOU?

Closure in 2017 due to: • Too Many Local Manufacturers • High local costs • Failure to implement best

technologies

Australian Export Gas Industry: • LNG oversupply ? • High local costs ? • Competition from new technologies ?

Brent Oil Price (US$/bbl) Australian Motor Vehicle Exports ($m)

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2001 2005 2009 2013 2017

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Queensland Gas Industry – Current Approach


Deloitte’s SEAOOC 2014

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“Australia could innovate its oil and gas business model along the lines of a factory”


Deloitte’s SEAOOC 2014

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“


Can a Spreadsheet be part of IAM?

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NO

“Spreadsheets, even after careful development, contain errors in one percent or more of all formula cells.

In large spreadsheets with thousands of formulas, there will be dozens of undetected errors”.

88% of audited spreadsheets had significant errors 1.

1. Panko, R.R, (2008), What We Know About Spreadsheet Errors, First published , Journal of End User Computing's, Volume 10, No Spring 1998, pp. 15-21 .Revised May 2008., http://panko.shidler.hawaii.edu/SSR/Mypapers/whatknow.htm. 2.See also, www.eusprig.com – European Spreadsheet Risks Interests Group

http://panko.shidler.hawaii.edu/SSR/Mypapers/whatknow.htm



http://www.eusprig.com/

No. of SSs % Errors Comment 19 21 Only serious errors 20 25

273 11 Only errors large enough to require additional tax payments

30 Errors caused by users hard-wiring numbers in formula cells. Henceforth, all future computations would be wrong.

1 100 One omission error would have caused an error of more than a billion dollars

23 91 Off by at least 5% 22 91 Only significant errors 2 100 In Model 2, the investment's value was overstated by 16%. 7 86 Only errors large enough to require additional tax payments 3 100 Computed on the basis of non-empty cells

~36 / yr 100 Approximately 5% had extremely serious errors ~36 / yr 100 Approximately 5% had extremely serious errors

30 100 30 most financially significant SSs audited by Mercer Finance & Risk Consulting in previous year.

25 64

11 of 25 spreadsheets contained errors with non-zero impacts: 10 had an error that exceeded $100,000, 6 had errors exceeding $10 million, and 1 had an error exceeding $100 million.

113 88%



Is this an Integrated Asset Modelling Workflow?

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NO - This is a flow assurance model which is part, but not the whole, of Asset Modelling

The Productivity Gain achieved by reducing simulation time for an integrated model from 8 hours to 8 minutes is at least a factor of 10.

Is this an Integrated Asset Modelling Workflow?

Integrated Asset Modelling must be able to effectively and efficiently carry out a Cycle of Inference with a feedback cycle taking minutes, at most hours, not days.



Reservoir Modelling

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Why are large 3D finite-difference models often worse for planning purposes than simple decline curves ?

Dimirmen (2005) SPE 95680 Dromgoole and Speers (2008) Petroleum Geoscience, 3

Long-term field outcomes are usually significantly different

to early “best case” models

• Too much detail leads to excessive development times and unnecessary complication, without increasing the reliability of forecasts 1.

• … the model may become overly complicated and actually preclude the development of understanding 2.

1.McHaney, R., Computer Simulation: A Practical Perspective, Academic Press, 1991. 2. http://www.systems-thinking.org/modsim/modsim.htm [accessed March 2015]


Chaining the Workflow With Too Much Detail

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http://www.systems-thinking.org/modsim/modsim.htm






The Oil and Gas Factory Assembly Line

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Assembly Line Components

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Reservoir Modelling o Simulation – precise, but usually wrong without good

history-match. o Matbal Tank – good on reserves range, poor on water

displacement; needs calibration to test data. o Type Curve – good for scaling recovery and deliverability,

systematically biased.

Well-bore Modelling o Thermodynamic – slow, requires calibration for

deviated multi-phase flow. o Multiphase correlation – fast, requires calibration for

deviated multi-phase flow. o Type Curves – as good as the program that produced

them.


Assembly Line Components

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Facilities Modelling o Thermodynamic – slow and difficult to calibrate.

o Linear program – fast and accurate, relies on consistent and feasible (sales gas) contract specification.

o Yield tables – fast and reasonably accurate, requires calibration to measured data or detailed thermodynamic model.

Compression o Numerical Modelling – slow and difficult to calibrate.

o Compressor curves – fast, accurate if operation of compressor consistent with manufacturers’ guidelines.

o Polytropic – fast, reasonably accurate if operating efficiency reliably known.


Examples Assembly Line Templates

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1. Exploration – Project Feasibility

2. Pre-Development – Planning

3. Sales and Markets – Optimisation

4. Flow Assurance – Reservoir and Network Deliverability

5. LNG Portfolio Management – Optimisation


Exploration – Project Feasibility Digital Assembly Line Template

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Time-frame – Project Life, Years


Pre-Development Planning Digital Assembly Line Template

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Time-frame – Project Life, Years

Market Optimisation Digital Assembly Line Template

Time-frame – Project Life, Year, Months, Days



Flow Assurance Digital Assembly Line Template

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Snap-shots at Key Points in Project Life


LNG Portfolio Optimisation Digital Assembly Line Template

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Long-term Contracts, Years; Short-term + Spot Sales, Months


The Oil & Gas Factory Digital Assembly Line

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Lessons from Volkswagen

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What have we learnt from the Transparent Factory ? • Take an Assembly Line Approach • Best Practice – computer assisted but hand-built

How do We Cope with the Different Project Time-Scales Required or Integrated Asset Modelling ?

• Fit for purpose (understand model accuracy) • Modular (plug and play) • Fast enough for timely and effective decision

making


Conclusion

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• “Australia should innovate its oil and gas business along the lines of a factory” - Deloitte

• Investment in best technology and best practice is not a luxury

• Investment in modern Integrated Asset

Modelling will ensure a sustainable future for the oil and gas industry in Australia

using integrated asset modelling to improve oil and gas planning decisions in a volatile market

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