Estimating original hydrocarbons in place (OHIP) in a reservoir is fundamentally
important to estimating reserves and potential profitability. Quantifying the uncertainties
in OHIP estimates can improve reservoir development and investment decision-making
for individual reservoirs and can lead to improved portfolio performance. Two
traditional methods for estimating OHIP are volumetric and material balance methods.
Probabilistic estimates of OHIP are commonly generated prior to significant production
from a reservoir by combining volumetric analysis with Monte Carlo methods. Material
balance is routinely used to analyze reservoir performance and estimate OHIP. Although
material balance has uncertainties due to errors in pressure and other parameters,
probabilistic estimates are seldom done.
In this thesis I use a Bayesian formulation to integrate volumetric and material balance
analyses and to quantify uncertainty in the combined OHIP estimates. Specifically, I
apply Bayes?? rule to the Havlena and Odeh material balance equation to estimate
original oil in place, N, and relative gas-cap size, m, for a gas-cap drive oil reservoir. The
paper considers uncertainty and correlation in the volumetric estimates of N and m
(reflected in the prior probability distribution), as well as uncertainty in the pressure data
(reflected in the likelihood distribution). Approximation of the covariance of the
posterior distribution allows quantification of uncertainty in the estimates of N and m
resulting from the combined volumetric and material balance analyses. Several example applications to illustrate the value of this integrated approach are
presented. 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. One
of the advantages over reservoir simulation is that, with the smaller number of
parameters in this approach, we can easily sample the entire posterior distribution,
resulting in more complete quantification of uncertainty. The approach can also detect
underestimation of uncertainty in either volumetric data or material balance data,
indicated by insufficient overlap of the prior and likelihood distributions. When this
occurs, the volumetric and material balance analyses should be revisited and the
uncertainties of each reevaluated.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2621 |
| Date | 01 November 2005 |
| Creators | Ogele, Chile |
| Contributors | McVay, Duane A. |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 3088632 bytes, electronic, application/pdf, born digital |
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