Abstract

Engineers and leaders who must decide on development strategies for unconventional resource projects face a challenging design problem. While we must make decisions on well and completion design, including well-spacing in three-dimensions, the complexity of the physical system and the interactions between these parameters can become overwhelming. The technical optimization problem can be difficult; however, asking the right questions can make the business decision clearer than it first appears.

The typical approach to design optimization problems is to build models, with a tendency toward including an ever-increasing number of parameters to describe the system in exhaustive detail. However, our uncertainty in the model parameters often makes it impossible to identify the true optimum. In this work, we focus instead on reducing the number of model parameters and capturing the impact of these critical uncertainties on our business decisions. This allows us to answer the right questions in order to define and choose the best well-spacing strategy.

For well-spacing optimization, a critical uncertainty is the relationship between the chosen well-spacing and the potential well-performance degradation, in terms of estimated ultimate recovery (EUR) and initial production (IP). Rather than attempting to describe fracture geometry and well interference from a mechanistic standpoint, we introduce a lumped parameter, the shared reservoir (SR) factor, to account for this complex relationship. The parameter distribution may be calibrated to (a) well results in a play, (b) well results in carefully selected analogue plays, or (c) simulated well results from probabilistic analyses. An example of a Monte-Carlo simulation using the uncertainty of the SR factor, as well as the mean EUR and IP, highlights the utility of the method. We also illustrate how the spacing decision impacts key risk and financial metrics, including the expected monetary value of the project, the probability of regretting the decision, and the probability of commercial success of the project.

The shared reservoir factor is proposed to capture the complex relationships between the well-spacing decision and the EUR and IP that result from this decision. Using the shared reservoir factor, we can develop simple stochastic models to clarify an otherwise frustratingly complex optimization problem.

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