1. Regularity and uncertainty analysis and management for the Norwegian natural gas pipeline system RAMONA http://qp.rf.no/ramona

2. RAMONA history

3. Industry challenges Statoil Gassco UiS/IRIS UiB NTNU/ SINTEF Cognit

4. 3 MNOK 30 MNOK Flow allocation problems and computer code Broad project covering many issues

5. 20 MNOK Norwegian Research Council StatoilHydro and Gassco 10 MNOK

6. Objectives To develop new theory, methods and tools to optimise regularity in offshore gas production, processing and transportation systems. Regularity: how a system is capable of meeting demand for deliveries or performance (NORSOK Z-016).

7. Subgoals New insights Better tools for making decisions on system planning, maintenance, etc. 25 papers 6 Ph.Ds

8. If this project contributes to a few percentages increased gas export or increased regularity, the value will be in order of billions of NOK over the next 30 years.

9. Petromaks The project is directed at the PETROMAKS programme topics on gas transport and HES (regularity is part of the HES concept), and represents a contribution to meeting the goals of the PETROMAKS programme on improving the effectiveness of gas to the market, reduce the cost level on the shelf, improve the basis for HES developments, and improve the petroleum related value generating processes nationally as well as internationally.

10. Results (after 2.4 years) 20 papers in journals 20 conference papers +… 6 Ph.Ds 2 post docs.

11. New insights and improved tools Where are the tools?

12. Five subprojects 1.Foundational issues (UiS) 2.Developing methodology and tools for supporting decisions on infrastructure investments (NTNU) 3.Developing methodology and tools for supporting decisions on operational flexibility and transient optimization (NTNU) 4.Developing methodology and tools for supporting decisions on ageing, maintenance and other operational aspects (UiS) 5.Developing flow allocation models and algorithms under complex constraints (UiB)

13. 1 Foundational issues A framework for uncertainty (risk) analysis Non-probabilistic representations of uncertainty Use of cost-benefit analysis Uncertainty management and risk assessments (risk acceptance criteria)

14. G(X) Model input X Quantities of interest Z Uncertainty expressed by P(Z ≤ z) EZ, VarZ etc. Sensitivity analysis and importance ranking Measure of uncertainty P(X≤x) Uncertainty evaluation Managerial review and judgment Uncertainty assessment of uncertainty factors Uncertainty propagation Background knowledge K Decision

15. A full mathematical representation of uncertainty (Bedford & Cooke 2001 p. 20): Axioms: Specifying the formal properties. Interpretations: Connecting the primitive terms in the axioms with observable phenomena. Measurement procedures: Providing, together with supplementary assumptions, practical methods for interpreting the axiom system.

16. Probability P f : fraction of sucesses when the experiment is repeated over and over again (relative frequency interpretation) P K : Comparison with a standard P K (A) = 0.1 : the same uncertainty (degree of belief) as drawing one particular ball out of an urn of 10 balls

17. A proponent of non- probabilistic representations of uncertainty ”A probability (P f ) is inadequate for expressing uncertainties, so we need … ”

18. The need for seeing beyond computed expected values and probabilities E[NPV] P(A) < 10 -3 See presentation by Roger Flage, UiS

19. INTEGRATING SAFETY AND UNCERTAINTY MANAGEMENT CASE: OPERATION AND DESIGN OF FLARE & HIPPS SYSTEMS (GASSCO) IEC 61508. –Probability for overpressure above test pressure shall be less than 1x10-5 –Probability for overpressure above design/code pressure shall be less than 1 x 10-3 Decision problem is to decide whether or not a redesign of the flare & HIPPS system is required.

20. Probability P(A1|K) Uncertainty 1 x 10 -5 Uncertainty factorsUncertainty category Main categoriesSub categoriesHighMediu m Low Human issues (M)Competence and experiences x Living up to proceduresx Operator/simulator training x Technical issues (T)Input data in design calculations x Barrier integrityx System dynamicsx Escalation riskx Updated documentationx Organisational issues (O) Management of changex System responsiblex Proceduresx

22. Uncertainty High Medium Low Cost-effectiveness High Medium Low Very lowLowMediumHigh Expected cost High Medium Low Very low Expected saved lives 0.1 5 10 1

23. Subproject 4 Developing methodology and tools for supporting decisions on maintenance and other operational aspects

24. Activities Fundamental principles, methods and models Maintenance management (DNV has a key role) –Risk-based maintenance prioritisation –Optimal maintenance, refurbishment and replacement of ageing systems Critical systems (Proactima has a key role) –Maintenance planning for safety critical valves

25. General Aven, T. and I.T.Castro (2008) A minimal repair replacement model with two types of failure and a safety constraint. European Journal of Operations Research, 188, 506–515 Aven, T. and I.T.Castro (2008) A delay time model with safety constraint. Reliability Engineering and System Safety. To appear. Aven, T. (2008) Identification of safety and security critical systems and activities. Reliability Engineering and System Safety. To appear.

26. General Aven, T. and Vatn, J. (2007) An approach to maintenance optimisation where safety issues are important Røed, W. and Aven, T. (2009) Bayesian approaches for detecting significant deterioration. Reliability Engineering and System Safety. 94 (2009), pp. 604-610. Aven, T. (2008) Risk analysis in maintenance. Chapter in Complex System Maintenance Handbook. Kobbacy, K. and D.N. Prabhakar Murthy (eds). Springer Verlag. London. …

27. Potential for industry applications

28. Optimal maintenance, refurbishment and replacement of ageing systems (gas turbine Kårstø)

29. Ageing system Signs of ageing Operating conditions Historical data Traditional analysis Availability model Cost model Model parameters Optimisation criterion Optimal maintenance policy/decision Sensitivity analysis Importance ranking Uncertainty and manageability assessment Uncertainty factors  Sensitivity  Degree of uncertainty  Manageability  Uncertainty reduction measures Decision alternatives Presentation and evaluation of results Managerial review and judgement Decision A risk/uncertainty management approach to a maintenance decision for an ageing system (Nøkland, Flage, Aven)

30. Purpose of seminar? Stimulate and direct the future research Industry relevance Co-operation …

31. UiS/IRIS SINTEF/NTNU UIB Project goals

32. 1 Basic topics 2 3 4 5 Applications (StatoilHydro, Gassco)