Incorporating Uncertainty Into Learning Curves: A Case Study In Oil Drilling Estimates
- Conference
- Location
-
Louisville, Kentucky
- Publication Date
-
June 20, 2010
- Start Date
-
June 20, 2010
- End Date
-
June 23, 2010
- ISSN
-
2153-5965
- Conference Session
- Tagged Division
-
Engineering Economy
- Page Count
-
11
- Page Numbers
-
15.716.1 - 15.716.11
- DOI
-
10.18260/1-2--15631
- Permanent URL
-
https://peer.asee.org/15631
- Download Count
-
809
Paper Authors
Christopher Jablonowski University of Texas, Austin
Christopher Jablonowski is Assistant Professor of Petroleum and Geosystems Engineering at the University of Texas at Austin where he performs research on decision-making under uncertainty, industrial organization, and safety management systems. Prior to joining the University of Texas at Austin, he worked as an upstream project analyst with IPA, Inc., an economist with the US Government, and as a drilling engineer with Shell Offshore Inc. He holds a B.S. in Civil Engineering from Virginia Tech, a M.B.A. from Tulane University, and a Ph.D. in Energy, Environmental, and Mineral Economics from the Pennsylvania State University.
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Incorporating Uncertainty Into Learning Curves: A Case Study in Oil Drilling Estimates
Abstract
In capital projects that contain repeated activities, and in manufacturing processes, it is common for cost and schedule performance to improve over time. This trend in improvement is commonly referred to as a “learning curve.” When learning is anticipated, cost and schedule estimates may be reduced dramatically relative to an assumption of no learning. Therefore, it is a best practice to account for a learning effect.
In cases where comparison projects exist, estimating a learning curve for a prospective project can be done with some certainty. This form of deterministic learning is a well established topic in engineering education. 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 research literature nor in the educational domain.
This paper employs a case study approach to investigate options for systematic integration of learning curves in cost and schedule estimates. Deterministic and probabilistic learning are discussed and demonstrated. All computations are made using off-the-shelf spreadsheet software. The results provide engineers and decision-makers with a refined representation of uncertainty, and can improve capital investment valuation and decision-making.
This case study is intended to be used in an undergraduate course in engineering economy or project economics and addresses several educational objectives: it introduces the basic concept of a learning curve, it provides an opportunity to reinforce basic curve fitting methods, and it highlights the value of a probabilistic approach to engineering and economic problems.
Introduction
In capital projects that contain repeated activities, and in manufacturing processes, it is common for cost and schedule performance to improve over time. This trend in improvement is commonly referred to as a “learning curve.” In the oil drilling industry many projects contain multiple well drilling campaigns, and learning curves are prevalent.3 When learning is anticipated, cost and schedule estimates may be reduced dramatically relative to an assumption of no learning. Many operators consider the use of learning curves a best practice, and provide procedures for estimation and implementation in their estimating guidelines.
Jablonowski, C. (2010, June), Incorporating Uncertainty Into Learning Curves: A Case Study In Oil Drilling Estimates Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. 10.18260/1-2--15631
ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2010 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015