Improving Decision Making With Limited Data
Professionals routinely face the challenge of making informed decisions with limited data sets. Our exploitation of unconventional resource plays has exacerbated the problem. We commonly refer to these resource plays as “statistical plays,” as large programs have provided repeatable year-over-year results. Decision making that relies on limited data sets has been driven by competitive pressures and the desire to get to the right answer as soon as possible. Development decisions are often made without due consideration of how representative the data are. Similarly, we frequently test new technologies with limited samples, expecting that a simple arithmetic comparison of the average results can validate or refute their further application. This talk presents the theory and use of aggregation curves as a pragmatic, graphical approach to determining the uncertainty in the sampled mean relative to the desired average program outcome.
James Gouveia, a partner in Rose & Associates, is a professional engineer with a diverse technical, business, and operations background. He has worked in a variety of technical and managerial assignments in exploration, reservoir engineering, strategic and business process planning, and portfolio and risk management. Gouveia served as an assurance champion and asset manager for BP and previously as director of risk management at Amoco Energy Group of North America.