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Description:
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Accurately estimating hydrocarbon reserves is important , because it affects every phase
of the oil and gas business . Unfortunately , reserves estimation is always uncertain , since
perfect information is seldom available from the reservoir , and uncertainty can
complicate the decision -making process . Many important decisions have to be made
without knowing exactly what the ultimate outcome will be from a decision made today .
Thus , quantifying the uncertainty is extremely important .
Two methods for estimating original hydrocarbons in place (OHIP ) are volumetric and
material balance methods . The volumetric method is convenient to calculate OHIP
during the early development period , while the material balance method can be used
later , after performance data , such as pressure and production data , are available .
In this work , I propose a methodology for using a Bayesian approach to quantify the
uncertainty of original gas in place (G ) , aquifer productivity index (J ) , and the volume of
the aquifer (Wi ) as a result of combining volumetric and material balance analysis in a
water -driven gas reservoir .
The results show that we potentially have significant non -uniqueness (i .e . , large
uncertainty ) when we consider only volumetric analyses or material balance analyses . By combining the results from both analyses , the non -uniqueness can be reduced ,
resulting in OGIP and aquifer parameter estimates with lower uncertainty . By
understanding the uncertainty , we can expect better management decision making . |