cumulative risk assessment model to analyze increased risk...

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Cumulative Risk Assessment Model

to Analyze Increased Risk due to

Impaired Barriers in Offshore Drilling Rigs

Syeda Zohra Halim

OESI Advisory Committee Meeting

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A Holistic Look into Risk

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Deviations can interact and sum up

Deviations plus a

dominating unacceptable

risk

Deviations leading to

unacceptable risk

Normal situation where

cumulative risk from

deviations is understood and

reduced

Unacceptable Risk

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Figure: Cumulative risk and tolerability [1]

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Past Incidents [3,4 ]

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U.S. Chemical Safety and Hazard Investigation Board, “Investigation Report: Refinery Explosion and Fire”, 2007

http://www.csb.gov/the-us-chemical-safety-boards-investigation-into-the-macondo-disaster-finds-offshore-risk-management-and-regulatory-oversight-still-inadequate-in-gulf-of-mexico/

Texas City Refinery Explosion March, 2005 15 killed, 180+ injured Fine: $87 million

Macondo Disaster April, 2010

11 killed 4.9 million barrels of oil spilled

Fine: $18.7 billion + others

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Cumulative Risk Assessment: The Challenge

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Integration of human and organizational factors with technical and operational factors

Complexity and size of system

Dependencies of components and events

Temporal aspects (Dynamic)

Uncertainties of parameter estimation

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Identify causes behind offshore

incidents and understand the

effect of impaired barriers

Conduct literature review for developing

required framework

Find applicable tool for

developing a model that can handle required

criteria

Objective

• Develop a framework for merging all factors together and

build a model based on this framework that will enable

determination of cumulative risk

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required framework



criteria





required framework



criteria

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Previous Work

Accident Evolution and Barrier Analysis

(AEB): Svensson (2001)

Barrier Analysis(BA):

Dianous and Fievez (2006)

Management Oversight and Risk

Tree (MORT): Johnson (1980)

Events and Causal Factor Charting and

Analysis (ECFCA):DOE

(1999)

Swiss Cheese Model : James Reason

(1990, 1997)

Functional Resonance Accident

Model (FRAM): Hollnagel (2004)

Human Factors Analysis and Classification

System (HFACS): FAA/NTIS (2000)

Semi-Quantitative Fault Tree Analysis

(SQUAFTA): Hauptmanns (2004)

The Shortcut Risk Analysis Method

(SCRAM): Davis et. Al (2011)

PyraMAP: Bellamy et. Al (2008)

System Theoretic Process Analysis (STPA): Leveson

(2009)

Barrier and Operational Risk

Analysis (BORA): Aven et.al (2006)

Bowtie: ICI (1979) Acci-Map:

Rasmussen et. al (2000)

Tripod BetaGroeneweg

(2008)

I-Risk: Bellamy et. Al (2003)

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… and many more

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Previous Work (2)

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APPROACH ALL FACTORS

QUANTI-TATIVE

DEPEN-DENCY

DYNAMIC OTHERS

Swiss Cheese Model: James Reason

MORT: Johnson FT becomes too large

STAMP/ STPA: Nancy Leveson

No software to identify all the loops, depends on expertise and control structure diagram

FRAM: Erik Hollnagel

Requires iteration, difficult and time consuming

BORA, Risk-OMT: Aven et. al.

Scoring depends on RIFs and hence on the expert team

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Objective

• Develop a framework for merging all factors together and

build a model based on this framework that will enable

determination of cumulative risk

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required framework



criteria

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Available Tools

• Event-Cause Trees (Includes Fault Tree, Event Tree, Bow-Tie)

• Markov Chains

• Bayesian Network

• Petri Nets

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Bayesian Networks

• It is a directed graph consisting of a set of nodes and arcs.

• Handles dynamic systems, distributions and dependencies and

allows probability updating

• Based on Bayes Theorem:

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P(pi| e) =

P(e | pi)P(p

i)

P(e | pi)P(p

i)

i=1

k

å

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Petri Net Basics

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• Petri Nets is a directed bipartite graph where system is

analyzed by movement of tokens from one place to another via

firing of transitions.

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Case Study

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Ts

V1

C2

C1

V1 C1 C2 Consequence

Open

Open Open Delayed Rupture

Close Success

Close Open Early Rupture

Close Delayed Rupture

Close

Open Open Delayed Rupture

Close Success

Close Open Early Rupture

Close Rupture V1

C1

C2

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Case Study: Event Tree

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Case Study: Bayesian Network

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Bobbio et al. (2003) Bearfield et al. (2005) Weber et al. (2012) Khakzad et al. (2013)

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Case Study: Petri Nets

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Liu et al. (1997) Labeau et al. (2000) Nyvlt et al. (2012) Pasman (2015)

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Results

ET BN PN

Successful Mitigation 36.90% 36.90% 36.89%

Early Rupture 5.90% 5.90% 5.89%

Rupture 3.69% 3.69% 3.67%

Delayed Rupture 53.51% 53.51% 53.55%

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Conclusion

• An integrated model may be required to analyze cumulative

risk

• A framework is to be used to merge technical, operational,

human and organizational factors together

• BN and PN achieve their common goal through different

approaches. Selection should depend on intended application

• Further work on development of the model is underway

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Acknowledgements

• Dr. M. Sam Mannan

• Dr. Hans Pasman

• Dr. Yogesh Koirala

• Jim Pettigrew

• All members of OESI Advisory Committee

• All members of MKOPSC

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References

1. Halim S. Z., Koirala, Y., Mannan, S. M., ‘Probabilistic Methods of Quantitative Risk Analysis: A Case Study with

Bayesian Network and Petri Net Approaches’, MKOPSC International Symposium 2016, College Station, 2016.

2. Alsyouf, I., "The role of maintenance in improving companies’ productivity and profitability." International Journal of

Production Economics 105.1 (2007): 70-78.

3. Neill, M., “Improving Risk-based Decision Making by Connecting PSM Systems to Day-to-Day Plant Operations”,

Global Congress on Process Safety, 397039, AIChE Spring Meeting, (2015).

4. U.S. Chemical Safety and Hazard Investigation Board, “Investigation Report: Refinery Explosion and Fire”,

http://www.csb.gov/assets/1/19/csbfinalreportbp.pdf, Accessed 10/21/16.

5. U.S. Chemical Safety and Hazard Investigation Board, “Final Report: Macondo Blowout and Explosion”,

http://www.csb.gov/macondo-blowout-and-explosion/, Accessed 10/21/16.

6. Weber, P., Medina-Oliva, G., Simon, C., and Lung, B., "Overview on Bayesian networks applications for dependability,

risk analysis and maintenance areas." Engineering Applications of Artificial Intelligence 25.4 (2012): 671-682.

7. Labeau, P.E., Smidts, C., and Swaminathan, S., "Dynamic reliability: towards an integrated platform for probabilistic

risk assessment." Reliability Engineering &System Safety 68.3 (2000): 219-254.

8. Blacklaw, A., Ward, A., and Cassidy, K., ‘The Cumulative Risk Assessment Barrier Model’, SPE 146255, (2011)

9. Dugan, J.C., Bavuso, S.J., and Boyd, M.A., "Dynamic fault-tree models for fault-tolerant computer systems." IEEE

Transactions on Reliability 41.3 (1992): 363-377.

10. Khakzad, N., Khan, F., and Amyotte, P., "Risk-based design of process systems using discrete-time Bayesian

networks." Reliability Engineering & System Safety 109 (2013): 5-17.

11. Hosseini, S.M.H., and Takahashi, M., "Combining static/dynamic fault trees and event trees using Bayesian networks."

International Conference on Computer Safety, Reliability, and Security. Springer Berlin Heidelberg, (2007).

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References (Contd.)

12. Nývlt, O., and Rausand, M., "Dependencies in event trees analyzed by Petri nets." Reliability Engineering & System Safety

104 (2012): 45-57.

13. Kulkarni, V.G., Modeling and Analysis of Stochastic Systems. CRC Press, (1996).

14. Siu, N., "Risk assessment for dynamic systems: an overview." Reliability Engineering & System Safety 43.1 (1994): 43-73.

15. Bobbio, A., Ciancamerla, E., and Franceschinis, G., "Sequential application of heterogeneous models for the safety

analysis of a control system: a case study." Reliability Engineering & System Safety 81.3 (2003): 269-280.

16. Hu, J., Zhang, L., Cai, Z., Wang, Y., and Wang, A., "Fault propagation behavior study and root cause reasoning with

dynamic Bayesian network based framework." Process Safety and Environmental Protection 97 (2015): 25-36.

17. Bobbio, A., Portinale, L., Minichino, M., and Ciancamerla, E., "Improving the analysis of dependable systems by mapping

fault trees into Bayesian networks." Reliability Engineering & System Safety 71.3 (2001): 249-260.

18. Boudali, H., and Dugan, J.B., "A discrete-time Bayesian network reliability modeling and analysis framework." Reliability

Engineering & System Safety 87.3 (2005): 337-349.

19. Bearfield, G., and Marsh, W., "Generalising event trees using Bayesian networks with a case study of train derailment."

International Conference on Computer Safety, Reliability, and Security. Springer Berlin Heidelberg, (2005).

20. Khakzad, N., Khan, F., and Amyotte, P., "Dynamic safety analysis of process systems by mapping bow-tie into Bayesian

network." Process Safety and Environmental Protection 91.1 (2013): 46-53.

21. Liu, T.S., and Chiou, S.B., "The application of Petri nets to failure analysis." Reliability Engineering & System Safety 57.2

(1997): 129-142.

22. Nývlt, O., Ferkl, L., and Haugen, S., "Stochastic Coloured Petri Nets as a modelling language for complex Event Trees."

Nutritional Care of the Patient with Gastrointestinal Disease (2015): 201.

23. Pasman, H., “Risk Analysis and Control for Industrial Processes - Gas, Oil and Chemicals: A System Perspective for

Assessing and Avoiding Low-Probability, High-Consequence Events”, Butterworth-Heinemann, (2015)

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Questions?

Comments?

[email protected]

If history repeats itself, and the unexpected

always happens, how incapable must Man

be of learning from experience.

—George Bernard Shaw

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A Simple Petri Net Example

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TFailure TRepair

µ

2,0 1,1 0,2

Pr(1) Pr(2) Pr(3)

µ µ

Reachability Graph:

P1: Operating (ON)

P2: Failed (OFF)

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A New Approach

• Use of two colored tokens to represent probability of failure

and success

• Straight forward and easy approach to obtain system’s failure

probabilities

• Reduced simulation time compared to previous approaches

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