Expanding Uncertainty: Predictive Distributions for Undiscovered Oil and Gas Pools in a Play

Kaufman, Gordon M.¹, John H. Schuenemeyer² (1) MIT, Cambridge, MA (2) Southwest Statistical Consulting, LLC, Cortez, CO

The U.S. Minerals Management Service (MMS) is responsible for assessing magnitudes of undiscovered oil and gas in offshore Federal waters. They have long used modified versions of a Canadian system called PETRIMES. While this system provides critical information, a peer review suggested that PETRIMES under-represents the degree of uncertainty that should be attributed to undiscovered oil and gas pools.

In response and with the support of MMS, we outline an alternative discovery process modeling approach based on an algorithm that drives PETRIMES. A play is assumed to possess N pools whose magnitudes are generated by independent sampling from a Lognormal distribution. Then pools are discovered by sampling proportional to magnitude and without replacement from this finite population.

Given discovery data and expert probability judgments about key parameters, we compute (Bayesian) predictive probability distributions of properties of undiscovered oil and gas pools; i.e., the distributions unconditional with respect to prior uncertainty about the number N of pools in the play, pool size distributions at various fractiles, and about Lognormal (super-population) parameters.

Our approach relies heavily on computational schemes that were not in current use when PETRIMES was created such as Importance Sampling, Acceptance-Rejection Sampling and Markov Chain Monte Carlo. Discovery data either in order of discovery or unordered can be handled.

We will illustrate with an application to a typical petroleum play.