Summary
Training images are numerical representations of geological conceptual
models that provide prior information on reservoir architecture. A new emerging
geostatistical approach named multiple-point statistics (MPS) simulation allows
extracting multiple-point structures from such training images and anchoring
these structures to the data actually observed in the reservoir. By reproducing
multiple-point statistics inferred from training images, MPS enables the
modeling of complex curvilinear structures (e.g., sinuous channels) in a much
more geologically realistic way than traditional two-point statistics
(variogram-based) techniques. However, in the original MPS implementation, all
multiple-point statistics moments computed from the training image are exported
to the reservoir model without processing, which allows simulating only
categorical or discretized variables. This implementation is appropriate with
clastic reservoirs for which, typically, depositional facies are simulated
first using MPS, then porosity and permeability are distributed within each
simulated facies using traditional variogram-based techniques. But for
reservoirs, in particular in carbonate environments, where porosity and
permeability trends/cycles are not closely tied to any facies distribution,
simulating porosity/permeability directly using corresponding continuous
training images appears to be a more suitable approach.
In this paper, a new filter-based implementation of MPS, named
filtersim, is proposed to reproduce features from continuous variable
training images. First, a set of general filters is applied to the training
image to transform (summarize) each training pattern into a set of scores
accounting for different aspects of the pattern, such as north-south and
east-west gradients and curvatures. The training patterns are classified in the
score space and grouped into a small number of similarity classes. The
simulation consists then of visiting each grid node along a random path,
identifying the similarity class that best fits to the local conditioning data,
and patching a pattern drawn from that selected similarity class. In our study,
this new approach was applied to model the 3D porosity distribution of a
carbonate reservoir in Kazakhstan. First, the original “categorical” MPS
program snesim was used to model the two main reservoir regions,
platform and slope, where the spatial porosity distribution is characterized by
significantly different features. Interpreted well markers and seismic data
were used to condition the modeling of these two regions. Then porosity was
distributed in the platform region using the “continuous” filter-based MPS
program filtersim, as described previously. The 3D training images used
in that second step displayed porosity trends/cycles controlled by the type of
geological sedimentation process believed to have occurred in the
reservoir.
© 2006. Society of Petroleum Engineers
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History
- Original manuscript received:
27 June 2005
- Revised manuscript received:
16 May 2006
- Manuscript approved:
12 June 2006
- Version of record:
20 September 2006
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