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Incorporating Uncertainty Into Learning Curves: A Case Study In Oil Drilling Estimates

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Conference

2010 Annual Conference & Exposition

Location

Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010

ISSN

2153-5965

Conference Session

Advances in Engineering Economy Pedagogy

Tagged Division

Engineering Economy

Page Count

11

Page Numbers

15.716.1 - 15.716.11

Permanent URL

https://peer.asee.org/15631

Download Count

251

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Paper Authors

biography

Christopher Jablonowski University of Texas, Austin

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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.

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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. https://peer.asee.org/15631

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