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 assumed, the campaign cost and schedule estimates may be reduced dramatically (relative to an assumption of constant performance). Many operators consider the use of learning curves a best practice, and provide procedures for estimation and implementation in their cost estimating guidelines.
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.