Summary
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.
© 2011. Society of Petroleum Engineers
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History
- Original manuscript received:
20 September 2009
- Meeting paper published:
26 October 2009
- Revised manuscript received:
14 June 2010
- Manuscript approved:
16 June 2010
- Published online:
13 January 2011
- Version of record:
11 March 2011
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