Plug and Abandonment Decision-Making: Quality at the Right Price

doi:10.23940/ijpe.20.01.p1.19

Abstract

Abstract:

In Norway, the current regulation for permanent plugging and abandonment of offshore wells is prescriptive, where the requirements for the number and size of plugs do not consider the different types of wells. One then disregards the fact that the wells differ with respect to, for example, flow potential. A differentiation between the wells could allow for cost-saving benefits from the least critical wells, which environment. In this regard, a special challenge lies in how much weight should be given to uncertainties and, in particular, the cautionary principle. In this paper, we look more closely into this issue. We conclude that it is not appropriate to use a static approach to manage plug and abandonment operations. As different ways of plugging and abandonment may be appropriate depending on the well situation, the approach should allow different weights to be given to uncertainties and the cautionary principle. Wells with limited flow potential, for example, should not give as much weight to the cautionary principle as wells with high flow potential. We argue for the use of a dynamic approach to manage plug and abandonment operations, ranging from one extreme, where decisions are made with strong reference to expected cost for some plug and abandonment wells, to another, where the cautionary principle is adopted instead for other wells, with a strong reference to leakage consequences (such as environmental aspects).

Key words: plug and abandonment, uncertainty, cautionary principle, cost-benefit analysis, expected values, ALARP

Eirik Bjorheim Abrahamsenab, Jon Tømmerås Selvik, Hans Petter Lohne, and Øystein Arild. Plug and Abandonment Decision-Making: Quality at the Right Price [J]. Int J Performability Eng, 2020, 16(1): 1-9.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

Figures/Tables 5

Figure 1.

Table 1

Table 2

Table 3

Figure 2.

References 33.

1.	NORSOK Standard D-010 Rev.4, “Well Integrity in Drilling and Well Operations,” Standards Norway, Lysaker, Norway, 2013
2.	Ø. Arild, H. P. Lohne, M. M. Majoumerd, E. P. Ford,F. Moeinikia.“Establishment of a Quantitative Risk-based Approach for Evaluating of Containment Performance in the Context of Permanently Plugged and Abandoned Petroleum Wells,” in Proceedings of Offshore Technology Conference, Houston, Texas, USA, May 2017
3.	J. O.Spieler and T. M Øia, “P&A - Well Statistics,”(Available from: https://www.norskoljeoggass.no/Activities/HSE-and-operation/Arrangemener/PLUG--ABANDONMENT-SEMINAR-2015/, 2015)
4.	M. Straume, “Need for New and Cost Effective P&A Technology,”(Available from: https://www.norskoljeoggass.no/Activities/HSE-and-operation/Arrangemener/Plug-and-Abandonment-seminar-2014/, 2014)
5.	DNV GL, “Risk-based Abandonment of Offshore Wells,” Report No.:2015-1021, Rev 0. Document No.: 1xx2LJN-1, 2015
6.	HSE, “Reducing Risk, Protecting People: HSE's Decision-Making,” HSE Books, London, 2001
7.	J. E. Vinnem, V. Kristiansen,E. S. Witsø, “Use of ALARP Evaluations and Risk Acceptance Criteria for Risk Informed Decision-Making in the Norwegian Offshore Petroleum Industry,” in Proceedings of ESREL 2006, Vol. 3, pp. 2567-2574, Taylor and Francis, London, 2006
8.	E. B. Abrahamsen, F. Asche,T. Aven, “To What Extent Should All the Attributes be Transformed to One Comparable Unit When Evaluating Safety Measures?” The Business Review, Cambridge, Vol. 19, No. 1, pp. 70-76, 2011
9.	H. B.Abrahamsen and E. B. Abrahamsen, “On the Appropriateness of using the ALARP Principle in Safety Management,” in Proceedings of ESREL 2015, pp. 773-777, Zürich, Switzerland, 7-10 September 2015
10.	E. B. Abrahamsen, H. B Abrahamsen, M. F. Milazzo,J. T. Selvik, “Using the ALARP Principle for Safety Management in the Energy Production Sector of Chemical Industry,” Reliability Engineering and System Safety, Vol. 169, pp. 160-165, January 2018
11.	J. E. Vinnem, “Offshore Risk Assessment. Principles, Modelling and Applications of QRA Studies,” Springer, 2007
12.	IRPCG - the Industry Radiological Protection Co-ordination Group, “The Application of ALARP to Radiological Risk - A Nuclear Industry Good Practice Guide,” (
13.	A. Hopkins, “Risk-Management and Rule-Compliance: Decision-Making in Hazardous Industries,”Safety Science, Vol. 49, pp. 110-120, 2011
14.	S. Baesi, B. Abdolhamidzadeh, C. R. C.Hassan, M. D. Hamid, and G. Reniers, “Application of a Multi-Plant QRA: A Case Study Investigating the Risk Impact of the Construction of a New Plant on an Existing Chemical Plant's Risk Levels,” Journal of Loss Prevention in the Process Industries, Vol. 26, No. 5, pp. 895-903, 2013
15.	N. J. Duijm, C. Fiévez, M. Gerbec, U. Hauptmanns,M. Konstandinidou, “Management of Health, Safety and Environment in Process Industry,”Safety Science, Vol. 46, pp. 908-920, 2008
16.	Petroleum Safety Authorities Norway (PSA), “Framework Regulations,” (
17.	R. T.Clemen and T. Reilly, “Making Hard Decisions,” Pacific Grove, CA, USA, Duxbury, 2001
18.	H. Levy and M. Sarnat, “Capital Investment and Financial Decisions. Fourth Edition,” Prentice Hall, New York, 1990
19.	H. R. Varian, “Intermediate Microeconomics: A Modern Approach 5th ed.,” W. W. Norton and Company, New York, 1999
20.	R. Brealey and S. Myers, “Principles of Corporate Finance,” McGraw-Hill International Book Company, 1996
21.	J. Baron, “Thinking and Deciding, 3rd Edition,” Cambridge University Press, Cambridge, 2002
22.	T. E.Copeland and J. F. Weston, “Financial Theory and Corporate Policy,” Addition-Wesley, Massachusetts, 1988
23.	E. B. Abrahamsen, T. Aven, J. E. Vinnem,H. Wienche, “Safety Management and the Use of Expected Values,”Risk Decision and Policy, Vol. 9, pp. 347-357, 2004
24.	E. B. Abrahamsen, A. M. Gelyani, H. B Abrahamsen, F. Asche, B. Heide,M. F. Milazzo, “Are Too Many Safety Measures Crowding Each Other Out?”Reliability Engineering and System Safety, Vol. 174, pp. 108-113, 2018
25.	T. Aven and R. Flage, “Use of Decision Criteria based on Expected Values to Support Decision-Making in a Production Assurance and Safety Setting,” Reliability Engineering and System Safety, Vol. 94, No. 9, pp. 1491-1498, 2009
26.	T. Aven and E. B. Abrahamsen, “On the Use of Cost-Benefit Analysis in ALARP Processes,” International Journal of Performability Engineering, Vol. 3, No. 3, pp. 345-353, 2007
27.	T. Aven and J. E. Vinnem, “Risk Management, with Applications from the Offshore Petroleum Industry,” Springer, London, 2007
28.	T. Aven, “Quantitative Risk Assessment. The Scientific Platform,” Cambridge University Press, 2011
29.	S. Sanni, E. Lyng,D. M. Pampanin, “III: Use of Biomarkers as Risk Indicators in Environmental Risk Assessment of Oil based Discharges Offshore,”Marine Environmental Research, Vol. 127, pp. 1-10, 2017
30.	E. B. Abrahamsen, J. T. Selvik,H. B. Abrahamsen, “A Note on the Layered Approach for Implementing ALARP and the Grossly Disproportionate Criterion,” International Journal of Business Continuity and Risk Management, Vol. 7, No. 3, pp. 204-210, 2017
31.	E. B. Abrahamsen, J. T. Selvik, B. Heide,J. E. Vinnem, “A Semi-Quantitative Approach for Assessment of Risk Trends in the Norwegian Oil and Gas Industry,” in Knowledge in Risk Assessment and Management (eds. T. Aven and E. Zio), Wiley, 2018
32.	R. Flage and T. Aven, “Expressing and Communicating Uncertainty in Relation to Quantitative Risk Analysis,” Reliability and Risk Analysis: Theory and Applications, Vol. 2, No. 13, pp. 9-18, 2009
33.	J. T.Selvik and T. Aven, “A Framework for Reliability and Risk Centered Maintenance,” Reliability Engineering and System Safety, Vol. 96, No. 2, pp. 324-331, 2011

Subdivision	Characteristics
Well	Pressure
	Temperature
	Fluid properties
	Geological stability
	Well architecture
	Condition of existing well elements
	Shape of the well
Site (surroundings)	Marine activity
	Distance to coastal areas
	No. of wells in the area
	Ship traffic

Uncertainty score	Criteria
Low	All of the following conditions whenever relevant are met: ?The assumptions made are seen as very reasonable. ?A large amount of reliable and relevant data/information is available. ?Relevant experts are involved in the assessments. ?There is broad agreement among the experts. ?The phenomena involved are well understood; the models used are known to give predictions with the required accuracy.
High	One or more of the following conditions is true: ?The assumptions made represent strong simplifications. ?Data/information are/is non-existent or highly unreliable/irrelevant. ?There is a lack of relevant expertise within the assessment team. ?There is strong disagreement among the experts (or within the assessment team). ?The phenomena involved are poorly understood; models are non-existent or known/believed to give poor predictions.
Medium	Cases in between are classified as having a medium uncertainty score.

Sensitivity score	Criteria
Low	Very large changes in the input parameters are needed to change the conclusion achieved in the cost-benefit analysis; that is, there is low sensitivity.
High	Relatively small changes in the input parameters are needed to change the conclusion achieved in the cost-benefit analysis; that is, there is high sensitivity.
Medium	Conditions between high and weak sensitivity score.

[1]	Eirik Bjorheim Abrahamsen, Jon Tømmerås Selvik, and Håkon Bjorheim Abrahamsen. Using Cost-Effectiveness Acceptability Curves as a Basis for Prioritizing Investments in Safety Measures in the Offshore Oil and Gas Industry [J]. Int J Performability Eng, 2020, 16(2): 163-170.
[2]	Haiyue Yu and Xiaoyue Wu. Simulation Method for Mission Reliability Assessment of Space Telemetry Tracking and Command System with Dynamic Redundancy [J]. Int J Performability Eng, 2019, 15(6): 1684-1691.
[3]	Jinzhang Jia, Zhuang Li, Peng Jia, and Zhiguo Yang. Reliability Analysis of Multi-State Systems based on EUGF Method using Common Cause Failure Components [J]. Int J Performability Eng, 2019, 15(12): 3129-3138.
[4]	Lan Wu and Xiaolei Han. Novel Steganalysis Method for Unknown Embedding Rates using Transfer and Multi-Task Learning [J]. Int J Performability Eng, 2019, 15(12): 3139-3150.
[5]	Mourad Chebila. Non-Intrusive Polynomial Chaos for a Realistic Estimation of Accident Frequency [J]. Int J Performability Eng, 2019, 15(11): 2852-2859.
[6]	Yubin Qu, Xiang Chen, Ruijie Chen, Xiaolin Ju, and Jiangfeng Guo. Active Learning using Uncertainty Sampling and Query-by-Committee for Software Defect Prediction [J]. Int J Performability Eng, 2019, 15(10): 2701-2708.