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.

Choosing the best projects to fund is easy. Our challenge is trying to weigh the complexities of the projects that are at the threshold for funding in a capital-constrained environment. Even though all the projects under consideration might be economically viable on a stand-alone basis, we seek to determine which suite of capital funding options best meets our long-term goals. Apache Corporation maintains a broad inventory of operating assets and investment opportunities, composed of projects with considerable variability with regard to uncertainty in potential performance and risk of financial loss. The opportunity suite consists of projects with highly varying capital investment patterns and production profiles. Further, the project portfolio faces varying exposure to commodity markets, petroleum fiscal regimes, and aboveground operational and sovereign risks. Apache has found that implementing and sustaining a portfolio process requires technical solutions and application of best practices for three critical elements: Production Forecasting, Project Modeling & Economic Evaluation, and Portfolio Management & Decision Making. A robust portfolio process for investment decision-making requires organizational alignment around a shared vision for value recognition and a rigorous, disciplined approach to capital allocation. Value recognition is critically dependent on establishing internal practices and standards for consistent application of methods and tools in characterizing cash flow potential from the suite of investment opportunities. Apache’s implementation of a portfolio process was undertaken as a sequence of initiatives with clear deliverables focused on building critical capabilities and infrastructure within key groups, while driving organizational alignment around the process. Major steps in the change management effort included: creation of a portfolio modeling groupshifting focus from well characterizations to project characterizationssoftware and systems investmentsorganizational alignmentexecutive adoption Over the past decade, Apache has undertaken a major shift in strategic focus toward organic growth, by placing significant investments in North American unconventional resource plays. The worldwide portfolio of opportunities became increasingly complex in terms of demand for capital, pattern of cash flows, and uncertainty in outcomes. Comparing and contrasting the performance potential and limitations of the opportunity set, in the context of corporate goals and constraints, became increasingly difficult with standard ranking methodologies. Apache’s portfolio reached the enviable state of possessing more projects in inventory than capital available for funding. With each business unit (BU) focused on extending and optimizing its own opportunity set the overall, integrated value for the corporation was not fully recognized. A realignment of processes and a shift in cultural perspective emphasizing an integrated whole was needed.