--> --> Quantifying Uncertainty in Original-Oil-In-Place Estimates from Volumetric and Material Balance Methods, by C. Ogele, D.A. McVay, and W.J. Lee; #90052 (2006)

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Quantifying Uncertainty in Original-Oil-In-Place Estimates from Volumetric and Material Balance Methods

C. Ogele, D.A. McVay, and W.J. Lee
Texas A&M University, College Station, TX

Volumetric and material balance methods are commonly used to estimate original hydrocarbons in place (OHIP). Geoscientists frequently generate probabilistic estimates of OHIP prior to significant production from a reservoir by combining volumetric analysis with Monte Carlo methods. Engineers routinely use material balance methods to analyze reservoir performance and estimate OHIP, although they seldom generate probabilistic estimates even though pressures and other parameters may include significant errors. Reconciliation of volumetric and material balance estimates provides a valuable point of contact between geoscientists and engineers. In this paper we use statistical techniques (Bayes' rule) to integrate volumetric and material balance analyses and to quantify uncertainty in the combined OHIP estimates. Specifically, we estimate original oil in place, N, and relative gas-cap size, m, for a gas-cap drive oil reservoir. We consider uncertainty and correlation in the volumetric estimates of N and m as well as uncertainty in pressure data in our analysis. We present several example applications to illustrate the value of this integrated approach. The examples show that material balance data reduce the uncertainty in the volumetric estimate, and the volumetric data reduce the considerable non-uniqueness of the material balance solution, resulting in more accurate OHIP estimates than from the separate analyses. Estimates of OHIP, and their uncertainties, from this integrated approach can supplement and even replace more detailed reservoir simulation studies in many reservoirs. The proposed approach has the advantage of being able to quantify uncertainty more completely because of the much smaller number of parameters compared to simulation.