Integrating Learning Curves in Probabilistic Well-Construction Estimates
- Christopher Jablonowski (University of Texas at Austin) | Amin Ettehadtavakkol (University of Texas at Austin) | Babafemi Ogunyomi (University of Texas at Austin) | Issam Srour (University of Beirut)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- March 2011
- Document Type
- Journal Paper
- 133 - 138
- 2011. Society of Petroleum Engineers
- 5.6.3 Deterministic Methods, 1.6 Drilling Operations, 4.1.2 Separation and Treating, 2 Well Completion
- Probabilistic, Cost Estimates, Learning Curves
- 1 in the last 30 days
- 1,019 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 12.00|
|SPE Non-Member Price:||USD 35.00|
For multiple-well drilling and completion campaigns, cost and schedule performance tend to improve over time. This trend in improvement is commonly referred to as a "learning curve" When a learning curve is anticipated, the campaign cost and schedule estimates may be reduced dramatically relative to an assumption of constant performance. That is, ignoring the learning curve will lead to overly pessimistic estimates. While learning curves can be observed in campaigns of various lengths and complexity, they are typically most important in large campaigns where the majority of wells are drilled after a significant portion of the learning has occurred. Conversely, they may not be appropriate in short campaigns where there is a limited time to implement learnings, or in campaigns with highly idiosyncratic wells where learning does not necessarily translate across projects.
Many operators consider the use of learning curves a best practice and provide procedures for estimation and implementation in their cost-estimating guidelines. In cases where comparison projects exist, estimating a learning curve for a prospective project can be achieved with some certainty. This form of deterministic learning is a well-established topic in the drilling-engineering literature and in practice. However, in cases where the sample of comparison projects is small, there may be significant uncertainty in the rate and magnitude of learning over time, and some form of probabilistic learning is more appropriate. This form of learning is not well established in the literature or in practice.
This paper investigates methods for systematic integration of learning curves in probabilistic estimates. Brief reviews of probabilistic estimating methods and learning curves are provided. A general method and specific procedures for integrating learning curves in probabilistic estimates are then provided. For each method, the key assumptions are itemized and discussed and a demonstration is provided. While no single procedure will fit every situation, it is concluded that the general method is straightforward, transparent, and can be implemented using off-the-shelf spreadsheet software. The proposed procedures generate results that provide engineers and decision makers with a refined representation of uncertainty and can improve capital-investment valuation and decision making.
|File Size||362 KB||Number of Pages||6|
Adams, A.J., Gibson, C., Smith, R. 2009. Probabilistic Well Time EstimationRevisited. Paper SPE 119287 presented at the SPE/IADC Drilling Conferenceand Exhibition, Amsterdam, 17-19 March. doi: 10.2118/119287-MS.
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.
Brett, J.F. and Millheim, K.K. 1986. The Drilling Performance Curve: AYardstick for Judging Drilling Performance. Paper SPE 15362 presented atthe SPE Annual Technical Conference and Exhibition, New Orleans, 5-8 October.doi: 10.2118/15362-MS.
Hariharan, P.R., Judge, R.A., and Nguyen, D.M. 2006. The Use of Probabilistic Analysis forEstimating Drilling Time and Costs While Evaluating Economic Benefits of NewTechnologies. Paper SPE 98695 presented at the IADC/SPE Drilling Conferencein Miami, Florida, USA, 21-23 February. doi: 10.2118/98695-MS.
Ikoku, C.U. 1978. Application ofLearning Curve Models to Oil and Gas Well Drilling. Paper SPE 7119presented at the SPE California Regional Meeting, San Francisco, 12-14 April.doi: 10.2118/7119-MS.
Jablonowski, C. and MacEachern, D. 2009. Using Regression Analysis toDevelop Probabilistic Well Construction Estimates. Presented at the 2009 AADENational Technical Conference and Exhibition, New Orleans, 31 March-2April.
Kaiser, M.J. and Pulsipher, A.G. 2007. Generalized Functional Models forDrilling Cost Estimation. SPE Drill & Compl 22 (2):67-73. SPE-98401-PA. doi: 10.2118/98401-PA.
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.
Murtha, J. 1997. Monte CarloSimulation: Its Status and Future. Distinguished Author Series, J PetTechnol 49 (4): 361-370. SPE-37932-MS. doi:10.2118/37932-MS.
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.
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.
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. 2004. Some Pitfalls in Well Forecasting. Paper SPE 89984 presented at the SPE Annual Technical Conference andExhibition, Houston, 26-29 October. doi: 10.2118/89984-MS.
Zoller, S.L., Graulier, J.-R., and Paterson, A.W. 2003. How Probabilistic Methods Were Usedto Generate Accurate Campaign Costs for Enterprise's Bijupirá & SalemaDevelopment. Paper SPE 79902 presented at the SPE/IADC Drilling Conference,Amsterdam, 19-21 February. doi: 10.2118/79902-MS.