Reference Class Forecasting and Machine Learning for Improved Offshore Oil and Gas Megaproject Planning: Methods and Application

Abstract

This article develops and describes rigorous oil and gas project forecasting methods. First, it builds a theoretical foundation by mapping megaproject performance literature to these projects. Second, it draws on heuristics and biases literature, using a questionnaire to demonstrate forecasting-related biases and principal-agent issues among industry project professionals. Third, it uses methodically collected project performance data to demonstrate that overrun distributions are non-normal and fat-tailed. Fourth, reference-class forecasting is demonstrated for cost and schedule uplifts. Finally, a predictive approach using machine learning (ML) considers project-specific factors to forecast the most likely cost and schedule overruns in a project.

Keywords

megaproject performance reference class forecasting oil and gas projects machine learning schedule and cost overruns megaproject performance forecasting planning heuristics biases

Get full access to this article

View all access options for this article.

References

Ackoff

R. L

. (1981). The art and science of mess management. Interfaces, 11(1), 20–26.

Ansar

Flyvbjerg

Budzier

. (2014). Should we build more large dams? The actual costs of hydropower megaproject development. Energy Policy, 69, 43–56.

Baccarini

. (1996). The concept of project complexity—A review. International Journal of Project Management, 14(4), 201–204. https://doi.org/10.1016/0263-7863(95)00093-3

Batselier

Vanhoucke

. (2016). Practical application and empirical evaluation of reference class forecasting for project management. Project Management Journal, 47(5), 36–51.

Bellman

(2013). Dynamic programming. Princeton University Press.

Birnbaum

Z. W.

Saunders

S. C.

(1968). A new family of life distributions. Boeing Scientific Research Laboratories.

Bruijn

H. D.

Leijten

. (2007). Megaprojects and contested information. Transportation Planning and Technology, 30(1), 49–69.

Budzier

Flyvbjerg

(2013). Making-sense of the impact and importance of outliers in project management through the use of power aws. Oslo, International Research Network on Organizing by Projects (IRNOP).

Bundy

Pfarrer

M. D.

Short

C. E.

Coombs

W. T

. (2017). Crises and crisis management: Integration, interpretation, and research development. Journal of Management, 43(6), 1661–1692.

10.

Caron

Ruggeri

(2016). Project management in the oil & gas industry—A Bayesian approach. In Wiley (Ed.), Wiley StatsRef: Statistics reference online. (pp. 1–14) John Wiley & Sons.

11.

Carrier

R. C.

(2012). Proving history: Bayes’s theorem and the quest for the historical jesus (1st ed.). Prometheus.

12.

Clegg

S. R.

Biesenthal

Sankaran

Pollack

(2017). Power and sensemaking in megaprojects. In Flyvbjerg

(Ed.), The Oxford handbook of megaproject management (pp. 238–258). Oxford University Press.

13.

Davies

Mackenzie

. (2014). Project complexity and systems integration: Constructing the London 2012 Olympics and paralympics games. International Journal of Project Management, 32(1), 773–790. https://doi.org/10.1016/j.ijproman.2013.10.004

14.

Denicol

Davies

Krystallis

. (2020). What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project Management Journal, 51(3), 328–345.

15.

Denyer

Tranfield

(2009). Producing a systematic review. In Buchanan

Bryman

(Eds.), The SAGE handbook of organizational research methods (pp. 671–689). SAGE.

16.

EMA. (2020). Floating production systems quarterly report. EMA.

17.

Ernst & Young (EY). (2014). Spotlight on oil and gas megaprojects. https://www.ey.com/Publication/vwLUAssets/EY-spotlight-on-oil-and-gas-megaprojects/$FILE/EY-spotlight-on-oil-and-gas-megaprojects.pdf

18.

Ernst & Young (EY). (2015). Oil and gas megaproject development. Ernst & Young.

19.

Flyvbjerg

. (2006). From nobel prize to project management: Getting risks right. Project Management Journal, 37(3), 5–15.

20.

Flyvbjerg

. (2014). What you should know about megaprojects and why: An overview. Project Management Journal, 45(2), 6–19. https://doi.org/10.1002/pmj.21409

21.

Flyvbjerg

. (2020). The law of regression to the tail: How to survive Covid-19, the climate crisis, and other disasters. Environmental Science & Policy, 114, 614–618.

22.

Flyvbjerg

Bruzelius

Rothengatter

(2003). Megaprojects and risk: An anatomy of ambition. Cambridge University Press.

23.

Flyvbjerg

Budzier

. (2011). Why your IT project might be riskier than you think. Harvard Business Review, 89(9), 23–25.

24.

Flyvbjerg

, & COWI. (2004). Procedures for dealing with optimism bias in transport planning: Guidance document. UK Department for Transport.

25.

Flyvbjerg

Garbuio

Lovallo

. (2009). Delusion and deception in large infrastructure projects: Two models for explaining and preventing executive disaster. California Management Review, 51(2), 170–193.

26.

Flyvbjerg

Ansar

Budzier

Buhl

Cantarelli

Garbuio

Glenting

Holm

M.S.

Lovallo

Lunn

Molin

Rønnest

Stewart

van Wee

, (2018). Five things you should know about cost overrun. Transportation Research Part A: Policy and Practice, 118, 174–190.

27.

Garthwaite

P. H.

Kadane

J. B.

O’Hagan

. (2005). Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100(470), 680–701.

28.

Gigerenzer

Brighton

(2011). Homo heuristicus: Why biased minds make better inferences. Oxford University Press.

29.

Goldratt

(1997). Critical chain (1st ed.). North River Press.

30.

Goodfellow

Bengio

Courville

(2016). Deep learning (1st ed.). MIT Press.

31.

Gyasi

E. A.

(2017). A Bayesian approach to cost estimation for offshore deepwater drilling projects. The University of Warwick.

32.

Hitchins

D. K.

(2007). Systems engineering: A 21st century systems methodology (1st ed.). Wiley.

33.

Hubbard

(2009). The failure of risk management: Why it’s broken and how to fix it (1st ed.). Wiley.

34.

Kahneman

(2012). Thinking, fast and slow. Penguin.

35.

Kahneman

Klein

. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526.

36.

Kahneman

Tversky

. (1979). Intuitive prediction: Biases and corrective procedures. TIMS Studies in the Management Sciences, 12, 313–327.

37.

Klein

G. A.

(1993). A recognition-primed decision (RPD) model of rapid decision making. In Klein

G. A.

Orasanu

Calderwood

Zsambok

C. E.

(Eds.), Decision making in action: Models and methods (pp. 138–147). Ablex Publishing.

38.

Lee

C.-Y.

(2019). Integrative trust-based functional contracting: A complementary contractual approach to BIM-enabled oil and gas EPC project delivery. Curtin University.

39.

Locatelli

Mancini

Romano

. (2014). Systems engineering to improve the governance in complex project environments. International Journal of Project Management, 32(8), 1395–1410.

40.

Lovallo

Kahneman

. (2003). Delusions of success: How optimism undermines executives’ decisions. Harvard Business Review, 81, 56–63.

41.

Lundin

Soderholm

. (1995). A theory of the temporary organization. Scandinavian Journal of Management, 11(4), 437–455.

42.

Lundrigan

C. P.

Nuno

A. G.

Puranam

(2015). The (under) performance of mega-projects: A meta-organizational perspective. Academy of Management Proceedings, 1(2015), 11299.

43.

MacNicol

. (2016). Taking the lead. Construction Journal, 1(1), 16–18.

44.

Maylor

H. R.

Turner

N. W.

Murray-Webster

. (2013). How hard can it be? Actively managing complexity in technology projects. Research Technology Management, 56(4), 45–51.

45.

Merrow

E. W.

(2011). Industrial megaprojects: Concepts, strategies, and practices for success (1st ed.). John Wiley & Sons.

46.

Merrow

E. W.

(2012). Oil and gas industry megaprojects: Our recent track record. Society of Petroleum Engineers.

47.

Mitchell

T. M.

(1997). Machine learning (1st ed.). McGraw-Hill.

48.

Müller

(2009). Project governance (1st ed.). Routledge.

49.

Offshore Magazine. (2019). 2019 Worldwide survey of floating production, storage and offloading (FPSO) units.

50.

Olaniran

O. J.

Love

P. E. D.

Edwards

D. J.

Olatunji

Matthews

, (2015). Chaotic dynamics of cost overruns in oil and gas megaprojects: A review. 17th International Conference on Oil, Gas and Coal Technology, 17(7).

51.

Parkinson

C. N.

(1955). Parkinson’s law. The Economist, 19 November.

52.

Raval

(2020). The last frontier: Oil industry scales back exploration. Financial Times, 21 July.

53.

Remington

Pollack

(2008). What is a complex project? In Tools for complex projects (pp. 1–12). Taylor & Francis Ltd.

54.

Ruggeri

, Alí, S., Berge, M. L., Bertoldo, G., Bjørndal, L. D., Cortijos-Bernabeu, A., Davison, C., Demić, E., Esteban-Serna, C., Friedemann, M., Gibson, S. P., Jarke, H., Karakasheva, R., Khorrami, P. R., Kveder, J., Andersen, T. L., Lofthus, I. S., McGill, L., Nieto, A. E., … Folke, T. (2020). Replicating patterns of prospect theory for decision under risk. Nature Human Behaviour, 4(6), 622–633.

55.

Rui

Peng

Ling

Chang

Chen

Zhou

, (2017). Investigation into the performance of oil and gas projects. Journal of Natural Gas Science and Engineering, 1(38), 12–20.

56.

Rystad Energy. (2020). A new offshore investment cycle is in the making. Rystad Energy.

57.

Simon

H. A

. (1956). Rational choice and the structure of the environment. Psychological Review, 63(2), 129–138.

58.

Singh

(2010). Strategies for oil and gas companies to remain competitive in the coming decades of energy challenges. MIT Sloan School of Management.

59.

Sornette

. (2009). Dragon-kings, black swans and the prediction of crises. International Journal of Terraspace Science and Engineering, 2, 1–18.

60.

Steen

Ford

J. A.

Verreynne

M.-L

. (2017). Symbols, sublimes, solutions, and problems: A garbage can model of megaprojects. Project Management Journal, 48(6), 117–131.

61.

Sydow

Braun

. (2018). Projects as temporary organizations: An agenda for further theorizing the interorganizational dimension. International Journal of Project Management, 36(1), 4–11.

62.

Taleb

(2008). The black swan: The impact of the highly improbable. Penguin.

63.

Themsen

T. N

. (2019). The processes of public megaproject cost estimation: The inaccuracy of reference class forecasting. Financial Accountability & Management, 35(4), 337–352.

64.

Todd

P. M.

Gigerenzer

. (2000). Précis of simple heuristics that make us smart. Behavioral and Brain Sciences, 23(1), 727–780.

65.

Todd

P. M.

Gigerenzer

(2012). What is ecological rationality? In Todd

P. M.

Gigerenzer

(Eds.), Ecological rationality (pp. 3–30). Oxford University Press.

66.

Tversky

Kahneman

. (1974). Judgment under uncertainty: Heuristics and biases. Science (New York, N.Y.), 185(4157), 1124–1131.

67.

Tversky

Kahneman

. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment. Psychological Review, 90(4), 293–315.

68.

Upstream. (2000). Kvaerner licks its Asgard wounds. Upstream, 22 September.

69.

Upstream. (2002). Spar losses spur McDermott exit. Upstream, 11 October.

70.

Walls

Quigley

. (2001). Building prior distributions to support Bayesian reliability growth modelling using expert judgement. Reliability Engineering and System Safety, 74(2), 117–128.

71.

Warren

(2008). Chaos theory and complexity theory. In Mizrahi

Davis

L. E.

(Eds.), Encyclopedia of social work (1st ed., pp. 227–233). NASW Press.

72.

Werndl

. (2009). What are the new implications of chaos for unpredictability? The British Journal for the Philosophy of Science, 60, 195–220.

73.

Whyte

. (2016). The future of systems integration within civil infrastructure: A review and directions for research. Edinburgh, 26th Annual INCOSE International Symposium (IS 2016).

74.

Whyte

. (2019). How digital information transforms project delivery models. Project Management Journal, 50(2), 177–194.

75.

Wright

(2009). Design error that led to listing the first of many hurdles for project. Upstream, 24 September.