| dc.description.abstract | This study proposes a new, easily applied method to quantify uncertainty in
production forecasts based on reservoir simulation. The new method uses only observed
data and mismatches between simulated values and observed values as history matches of
observations progress to a final "best" match. The method is applicable even when only
limited information is available from a field. Previous methods suggested in the literature
require more information than our new method.
Quantifying uncertainty in production forecasts (i.e., reserve estimates) is
becoming increasingly important in the petroleum industry. Many current investment
opportunities in reservoir development require large investments, many in harsh
exploration environments, with intensive technology requirements and possibly marginal
investment indicators.
Our method of quantifying uncertainty uses a set of history-match runs and
includes a method to determine the probability density function (pdf) of future oil
production (reserves) while the history match is evolving. We applied our method to the
lower-Pleistocene 8-Sand reservoir in the Green Canyon 18 field, Gulf of Mexico.
This field was a challenge to model because of its complicated geometry and
stratigraphy.
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We objectively computed the mismatch between observed and simulated data
using an objective function and developed quantitative matching criteria that we used
during history matching.
We developed a method based on errors in the mismatches to assign likelihood to
each run, and from these results, we determined the pdf of reservoir reserves and thus
quantified the uncertainty in the forecast.
In our approach, we assigned no preconceived likelihoods to the distribution of
variables. Only the production data and history matching errors were used to assess
uncertainty. Thus, our simple method enabled us to estimate uncertainty during the
history-matching process using only dynamic behavior of a reservoir. | en |
