Geologic models derived from static data alone typically fail to reproduce the
production history of a reservoir, thus the importance of reconciling simulation models
to the dynamic response of the reservoir. This necessity has been the motivation behind
the active research work in history matching. Traditionally, history matching is
performed manually by applying local and regional changes to reservoir properties.
While this is still in general practice, the subjective overtone of this approach, the time
and manpower requirements, and the potential loss of geologic consistency have led to
the development of a variety of alternative workflows for assisted and automatic history
matching. Automatic history matching requires the solution of an inverse problem by
minimizing an appropriately defined misfit function.
Recent advances in geostatistics have led to the building of high-resolution
geologic models consisting of millions of cells. Most of these are scaled up to the submillion
size for reservoir simulation purposes. History matching even the scaled up
models is computationally prohibitive. The associated cost in terms of time and
manpower has led to increased interest in efficient history matching techniques and in
particular, to sensitivity-based algorithms because of their rapid convergence.
Furthermore, of the sensitivity-based methods, streamline-based production data
integration has proven to be extremely efficient computationally.
In this work, we extend the history matching capability of the streamline-based
technique to three-phase production while addressing in general, pertinent issues associated with history matching. We deviate from the typical approach of formulating
the inverse problem in terms of derived quantities such as GOR and Watercut, or
measured phase rates, but concentrate on the fundamental variables that characterize
such quantities. The presented formulation is in terms of well node saturations and
pressures. Production data is transformed to composite saturation quantities, the time
variation of which is matched in the calibration exercise. The dependence of the
transformation on pressure highlights its importance and thus a need for pressure match.
To address this need, we follow a low frequency asymptotic formulation for the pressure
equation. We propose a simultaneous inversion of the saturation and pressure
components to account for the interdependence and thus, high non-linearity of three
phase inversion. We also account for global parameters through experimental design
methodology and response surface modeling. The validity of the proposed history
matching technique is demonstrated through application to both synthetic and field
cases.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/85951 |
| Date | 10 October 2008 |
| Creators | Oyerinde, Adedayo Stephen |
| Contributors | Akhil, Datta-Gupta |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, born digital |
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