Probabilistic Well-Time Estimation Revisited
- Authors
- Adrian Adams (Nexen Petroleum UK Limited) | Colin Gibson (Nexen Petroleum UK Limited) | Robert G. Smith (Nexen Petroleum UK Limited)
- DOI
- https://doi.org/10.2118/119287-PA
- Document ID
- SPE-119287-PA
- Publisher
- Society of Petroleum Engineers
- Source
- SPE Drilling & Completion
- Volume
- 25
- Issue
- 04
- Publication Date
- December 2010
- Document Type
- Journal Paper
- Pages
- 472 - 499
- Language
- English
- ISSN
- 1064-6671
- Copyright
- 2010. Society of Petroleum Engineers
- Disciplines
- 1.6 Drilling Operations, 6.1.5 Human Resources, Competence and Training, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 1.1.2 Authority for expenditures (AFE), 4.2.4 Risers
- Keywords
- well time estimation, probabilistic, duration
- Downloads
- 2 in the last 30 days
- 843 since 2007
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Summary
Probabilistic estimation of well duration has been common practice for more than a decade; many papers have been written on the subject, and commercial software is available for the purpose. Is the subject, therefore, mature? The authors suggest that this is not the case and show that several essential aspects of both data characterization and probabilistic analysis have been overlooked in previous contributions.
In 2007, Nexen began a study with the objective of improving our process for well-time estimation. Its workscope was to
- Assemble a statistically significant historical well database.
- Develop a consistent definition of nonproductive time (NPT) as it relates to the original approved-for-expenditure (AFE) timings.
- Reanalyze the historical well database for NPT from the original daily drilling reports (DDRs), on the basis of the definition above.
- Decide how NPT is best characterized (that is, determine the correct choice of input variables).
- Determine the associated occurrence frequencies and probability-density-function (PDF) parameters from the historical database.
- Validate the probabilistic model, by comparison of program predictions against the historical data set.
- Work with a software provider to implement any necessary changes in a commercially available product.
A database of 118 central North Sea wells was reanalyzed independently for NPT from the original DDRs. These considerably underestimated the true NPT, by 19.7% on average. Train wrecks (mechanical NPT events of more than 2.5 days) were only 4% by number but contributed 50% of NPT by duration. It was found that "ordinary" mechanical NPT, train wrecks, waiting on weather (WOW) in open water, and WOW with riser connected are all statistically distinct, with very different occurrence frequencies and PDFs. Earlier workers did not observe this distinction or properly validate their models against the well database.
Therefore, it is impossible to obtain historically accurate probabilistic well-time predictions (i.e., that are consistent with the historical database) using the previous state of the art. Moreover, such predictions will generally be underestimates. This paper describes an accurate method that overcomes previous limitations. While central North Sea data are used, the analysis techniques are not area specific, and the method may be applied easily to other areas in the oil field.
| File Size | 1 MB | Number of Pages | 28 |
References
Adams, A.J. 1997. CTR 03: Data Analysis. Project report for DEA(E)-64, NexenPetroleum UK Ltd., Aberdeen, UK (February 1997).
Adams, A.J. 2000a. Collapse: Trial Calibration. Report prepared for API/ISOTC67/SC5/WG2b, Nexen Petroleum UK Ltd., Aberdeen, UK (April 2000).
Adams, A.J. 2000b. Collapse: Trial Calibration (2). Report prepared forAPI/ISO TC67/SC5/WG2b, Nexen Petroleum UK Ltd., Aberdeen, UK (June 2000).
Adams, A.J. 2005. Collapse: Effect of Input Variable Cross-Correlation.Report prepared for API/ISO TC67/SC5/WG2b, Nexen Petroleum UK Ltd., Aberdeen,UK (January 2005).
Akins, W.M., Abell, M.P., and Diggins, E.M. 2005. Enhancing Drilling Risk andPerformance Management Through the Use of Probabilistic Time and CostEstimating. Paper SPE 92340 presented at the SPE/IADC Drilling Conference,Amsterdam, 23-25 February. doi: 10.2118/92340-MS.
Ang, A.H-S. and Tang, W.H. 1975. Probability Concepts in EngineeringPlanning and Design: Basic Principles, Vol. 1. New York: John Wiley &Sons.
Ang, A.H-S. and Tang, W.H. 1984. Probability Concepts in EngineeringPlanning and Design: Decision, Risk and Reliability, Vol. 2. New York: JohnWiley & Sons.
Capen, E.C. 1976. The Difficultyof Assessing Uncertainty. J Pet Technol 28 (8):843-850. SPE-5579-PA. doi: 10.2118/5579-PA.
Ditlevsen, O. and Madsen, H.O. 1996. Structural Reliability Methods.New York: John Wiley and Sons.
Freeman, H. 1963. Introduction to Statistical Inference. Columbus,Ohio: Addison-Wesley.
Iman, R.L. and Conover, W.J. 1982. A Distribution-Free Approach to InducingRank Correlation Among Input Variables. Communications in Statistics 11 (3): 311-334.
Kendall, M.G. and Stuart, A.S. 1958. The Advanced Theory of Statistics,Volume I: Distribution Theory. London: Charles Griffin & Co.
Kitchel, B.G., Moore, S.O., Banks, W.H., and Borland, B.M. 1997. Probabilistic Drilling-CostEstimating. SPE Comp App 12 (4): 121-125. SPE-35990-PA.doi: 10.2118/35990-PA.
Liu, P-L. and Der Kiureghian, A. 1986. Multivariate distributionmodels with prescribed marginals and covariances. ProbabilisticEngineering Mechanics 1 (2) : 105-112.doi:10.1016/0266-8920(86)90033-0.
Løberg, T., Arild, Ø., Merlo, A., and D'Alesio, P. 2008. The How's and Why's of ProbabilisticWell Cost Estimation. Paper SPE 114696 presented at the IADC/SPE AsiaPacific Drilling Technology Conference and Exhibition, Jakarta, 25-27 August.doi : 10.2118/114696-MS.
Madsen, H.O., Krenk, S., and Lind, N.C. 1986. Methods of StructuralSafety. Upper Saddle River, New Jersey: International Series in CivilEngineering and Engineering Mechanics, Prentice-Hall.
Mann, N.R., Schafer, R.E., and Singpurwalla, N.D. 1974. Methods forStatistical Analysis of Reliability and Life Data. New York: Wiley Seriesin Probability and Statistics—Applied Probability and Statistics Section, JohnWiley & Sons.
McIntosh, J. 2004. Probabilistic Modeling for Well-Construction PerformanceManagement. J Pet Technol 56 (11): 36-39.
Melchers, R.E. 1999. Structural Reliability Analysis and Prediction,second edition. West Sussex, England: John Wiley & Sons.
Merlo, A., D'Alesio, P., Løberg, T., and Arild, Ø. 2009. An Innovative Tool on aProbabilistic Approach Related to the Well Construction Costs and TimesEstimation. Paper SPE 121837 presented at the EUROPEC/EAGE Conference andExhibition, Amsterdam, 8-11 June. doi: 10.2118/121837-MS.
Mildenhall, S.J. 2005. Correlation and Aggregate Loss Distributions With AnEmphasis On The Iman-Conover Method. Correlation Working Party ChapterContribution, Casualty Actuarial Society Forum (27 November 2005), http://www.mynl.com/wp/ic.pdf.
Nataf, A. 1962. Détermination des distributions de probabilités dont lesmarges sont données. Comptes Rendus de l'Académie des Sciences 225: 42-43.
Noerager, J.A., Norge, E., White, J.P., Floetra, A., and Dawson, R. 1987. Drilling Time Predictions FromStatistical Analysis. Paper SPE 16164 presented at the SPE/IADC DrillingConference, New Orleans, 15-18 March. doi: 10.2118/16164-MS.
Pearson, E.S. and Hartley, H.O. ed. 1958. Biometrika Tables forStatisticians. Cambridge, UK: Cambridge University Press.
Peterson, S.K., De Wardt, J., and Murtha, J.A. 2005. Risk and Uncertainty Management-BestPractices and Misapplications for Cost and Schedule Estimates. Paper SPE97269 presented at the SPE Annual Technical Conference, Dallas, 9-12 October.doi: 10.2118/97269-MS.
Peterson, S.K., Murtha, J.A., and Roberts, R.W. 1995. Drilling Performance Predictions:Case Studies Illustrating the Use of Risk Analysis. Paper SPE 29364presented at the SPE/IADC Drilling Conference, Amsterdam, 28 February -2 March.doi: 10.2118/29364-MS.
Peterson, S.K., Murtha, J.A., and Schneider, F.F. 1993. Risk Analysis and Monte CarloSimulation Applied to the Generation of Drilling AFE Estimates. Paper SPE26339 presented at the SPE Annual Technical Conference, Houston, 3-6 October.doi: 10.2118/26339-MS.
Rosenblatt, M. 1952. Remarks on a multivariate transformation. Ann. Math.Stat. 23: 470-472.
Ross, S.M. 1987. Introduction to Probability and Statistics for Engineersand Scientists. New York: John Wiley & Sons.
Rubinstein, R.Y. 1981. Simulation and the Monte Carlo Method. NewYork: Wiley Series in Probability and Mathematical Statistics, John Wiley &Sons.
Shilling, R.B. and Lowe, D.E. 1990. Systems for Automated Drilling AFECost Estimating and Tracking. Paper SPE 20331 presented at the PetroleumComputer Conference, Denver, 25-28 June. doi: 10.2118/20331-MS.
Thoft-Christensen, P. and Baker, M.J. 1982. Structural Reliability Theoryand Its Applications. New York: Springer-Verlag.
Thorogood, J.L. 1987. AMathematical Model for Analysing Drilling Performance and Estimating WellTimes. Paper SPE 16524 presented at Offshore Europe, Aberdeen, 8-11September. doi: 10.2118/16524-MS.
Whelehan, O.P. and Thorogood, J.L. 1994. An Automated System for PredictingDrilling Performance. Paper SPE 27487 presented at the SPE/IADC DrillingConference, Dallas, 15-18 February. doi: 10.2118/27487-MS.
Williamson, H.S., Sawaryn, S.J., and Morrison, J.W. 2006. Monte Carlo Techniques Applied toWell Forecasting: Some Pitfalls. SPE Drill & Compl 21 (3): 216-227. SPE-89984-PA. doi: 10.2118/89984-PA.
