One of the main concerns in the oil and gas business is generating reliable
reservoir hydrodynamics forecasts. Such profiles are the cornerstones of
optimal technico-economical management decisions. A workflow combining
different methods to integrate and reduce most of the subsurface uncertainties
using multiple history matched models (explaining the past) to infer reasonably
reliable forecasts is proposed.
A sensitivity study is first performed using experimental design to scan the
whole range of static and dynamic uncertainty parameters using a proxy model of
the fluid-flow simulator. Only the most sensitive ones with respect to an
objective function (OF) (quantifying the mismatch between the simulation
results and the observations) are retained for subsequent steps.
Assisted history-matching tools are then used to obtain multiple
To obtain probabilistic pressure profiles, multiple history-matched models
are combined with the uncertain parameters not retained in the sensitivity
study, using the joint modeling method.
Another way to constrain uncertain parameters with observation data is to
use Bayesian framework where a posteriori distributions of the input parameters
are derived from the a priori distributions and the likelihood function. The
latter is computed through a nonlinear proxy model using experimental design,
kriging, and dynamic training techniques.
These two workflows have been applied to a real gas storage case submitted
to significant seasonal pressure variations. The obtained probabilistic
operational pressure profiles for a given period are then compared to the
actual gas storage dynamic behavior so that we can compare the two approaches
and assess the added value of both proposed workflows.
© 2009. Society of Petroleum Engineers
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- Original manuscript received:
29 February 2008
- Meeting paper published:
9 June 2008
- Revised manuscript received:
13 March 2009
- Manuscript approved:
30 March 2009
- Published online:
3 September 2009
- Version of record:
28 October 2009