Summary
Reservoir management is based on the prediction of reservoir performance by
means of numerical-simulation models. Reliable predictions require that the
numerical model mimic the production history. Therefore, the numerical model is
modified to match the production data. This process is termed history matching
(HM).
Form a mathematical viewpoint, HM is an optimization problem, where the
target is to minimize an objective function quantifying the misfit between
observed and simulated production data. One of the main problems in HM is the
choice of an effective parameterization—a set of reservoir properties that can
be plausibly altered to get a history-matched model. This issue is known as a
parameter-identification problem, and its solution usually represents a
significant step in HM projects.
In this paper, we propose a practical implementation of a multiscale
approach aimed at identifying effective parameterizations in real-life HM
problems. The approach requires the availability of gradient simulators capable
of providing the user with derivatives of the objective function with respect
to the parameters at hand. Objective-function derivatives can then be used in a
multiscale setting to define a sequence of richer and richer parameterizations.
At each step of the sequence, the matching of the production data is improved
by means of a gradient-based optimization. The methodology was validated on a
synthetic case and was applied to history match the simulation model of a North
Sea oil reservoir.
The proposed methodology can be considered a practical solution for
parameter-identification problems in many real cases until sound methodologies
(primarily adaptive multiscale estimation of parameters) become available in
commercial software programs.
Introduction
Predictions of reservoir behavior require the definition of subsurface
properties at the scale of the simulation grid cells. At this scale, a reliable
description of the porous media requires us to build a reservoir model by
integrating all the available sources of data. By their nature, we can
categorize the data as prior and production data. Prior data can be seen as
“direct” measures or representations of the reservoir properties. Production
data include flow measures collected at wells [e.g., water cut, gas/oil ratio
(GOR) and shut-in pressure, and time-lapse seismic data].
Prior data are directly incorporated in the setup of the reservoir model,
typically in the framework of well-established reservoir-characterization
workflows.
© 2007. Society of Petroleum Engineers
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History
- Original manuscript received:
10 August 2005
- Revised manuscript received:
30 January 2007
- Manuscript approved:
2 February 2007
- Version of record:
20 June 2007
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