Summary
This paper first reviews the basic concepts of the widely used object-based
Boolean model for modeling heterogeneous reservoirs. Then, we present a
methodology for calibrating Boolean simulations to dynamic production data.
This methodology is based on a generalization of the gradual deformation method
that was initially developed for calibrating pixel-based Gaussian-related
reservoir models to dynamic data. Finally, two examples are presented and the
results show the validity of the previously mentioned methodology. In
particular, this methodology is potentially applicable to history matching of
faulted and fractured reservoir models.
Introduction
In the last 2 decades, different stochastic models have been developed for
describing reservoir heterogeneities of different depositional environments and
at different scales. These models can be classified in three types: pixel-based
models (e.g., Gaussian-related stochastic models), object-based models (e.g.,
Boolean models), and process-based models. Pixel-based models are relatively
easy to be constrained by quantitative data, but they are often unable to
describe complex geological features, particularly at the field-appraisal stage
with few well data. On the contrary, process-based models can reproduce complex
geological features, but they are highly difficult to constrain by quantitative
data. In the case in which geological objects can be clearly identified
(fractures, faults, channels, and vacuoles), object-based models can be a good
compromise between pixel-based and process-based models. There are many
examples of geological modeling of fluvial-deltaic reservoirs using the
object-based approach.1–6 This approach is also used for representing fault and
fracture networks.7,8 Fig. 1 shows an object-based model of fracture swarms and
subseismic faults in a reservoir field.
Constraining object-based reservoir models to dynamic production data is of
great importance for their application in reservoir engineering. During the
last decade, the research on this problem has been oriented to parameterizing
individually each object and then calibrating these parameters (together with
all the other parameters) to production data.9–11 This approach cannot be
easily extended to field-scale models with multiple geological objects because
of the large number of parameters and the difficulty for preserving the model
consistency when changing these parameters.
© 2005. Society of Petroleum Engineers
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History
- Original manuscript received:
16 September 2003
- Revised manuscript received:
30 May 2005
- Manuscript approved:
1 June 2005
- Version of record:
15 September 2005
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