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Research Interests: Numerical
modeling of fluid flow requires quantitative description of the
reservoir geology. Numerical models of porosity and permeability have
to be constructed using data from diverse sources, some quantitative -
core measurements and well test data, while others qualitative -
detailed geological description. Geostatistics provides a framework for
developing numerical models of the reservoir taking into account such
diverse data. Traditionally, variograms or two-point statistics are the
basis for geostatistical models for data integration. Recently, it has
been demonstrated that in order to accurately represent reservoir
heterogeneity, the models have to be anchored to more than the
traditional two-point statistics - they need to be constrained to
multiple point statistics. My current research focus is on exploring
further this exciting new paradigm for reservoir modeling and extending
it to integrate diverse types of data.
Developing an improved methodology for quantification of
geological information: The approach consists of developing a
digital repository of reservoir models classified on the basis of
reservoir depositional environments. These analog reservoir models will
be constructed using rock outcrop data interpreted by expert
geologists. The resultant 3-D analog reservoir models can be processed
through statistical pattern recognition schemes to extract the multiple
point information specific to that type of depositional environment.
The extracted information can then be applied in conjunction with
reservoir specific information for an unknown reservoir, to develop
geostatistical reservoir models for that reservoir. The constraint is
that the target reservoir exhibits similar depositional features as the
analog.
Studying the effects of reservoir heterogeneity on fluid
displacement processes: Several factors such as fluid flow regime,
boundary conditions, numerical modeling techniques, model resolution
etc. influence the manifestation of reservoir heterogeneity on fluid
flow. The relative influence of these factors on the observed flow
response has to be clearly understood prior to integrating such
information in reservoir models. This amounts to understanding the
complex relationship between fluid flow and reservoir heterogeneity and
paves the way for formulating a formal representation of the flow
response in terms of the multiple point characteristics of the
reservoir.
Improved characterization of fractured reservoirs: Fractured
reservoirs are an important class of oil and gas reservoir. Accurate
prediction of flow in such reservoirs requires an accurate depiction of
fracture networks in the reservoir as well as accurate models for
depicting flow through the fracture network. The research focuses on
developing innovative techniques for stochastic modeling of fracture
networks in porous media constrained to pattern statistics observed in
analogous systems, observations along well paths, high-resolution 3-D
seismic as well as multiphase production data. The development of
multiple-point statistics based algorithms incorporating the physics of
fracture propagation through reservoir rocks is the cornerstone of the
proposed methodology.
Recent Publications
- Srinivasan, S.: "Well test data integration - Part 1:
Calibrating a multi point proxy", submitted to Journal of
Petroleum Science and Technology.
- Srinivasan, S.: "Well test data integration - Part 2: Spatial
simulation constrained to multi point proxy", submitted to Journal
of Petroleum Science and Technology.
- Yusoff, N. and Srinivasan, S.: "Statistical Analyses of Ozone
Temporal Trends in Calgary, Alberta - An Application of Multivariate
Geostatistics ", submitted to Journal of Atmospheric Science,
Elsevier Publication.
- Merchan, S., Srinivasan, S. and Meyer, R.: "Characterization
of a estuarine shore-face type reservoir using outcrop analogues and
flow modeling using non-uniform coarsened grid", SPE 75232, SPE/DOE
Symposium on Improved Oil Recovery, April 2002.
- Liu, X., Srinivasan, S. and Wong, D.W.: "Geological
characterization of naturally fractured reservoirs using multiple point
geostatistics", SPE 75246, SPE/DOE Symposium on Improved Oil
Recovery, April 2002.
- Caers, J., and Srinivasan, S.: "Geostatistical quantification
of geological data in North Sea reservoir", SPE paper 56655, SPE
Annual Technical Conference and Exhibition, Houston, Texas 1999.
- Srinivasan, S.: "Is crisp modeling of geological objects
important for flow - When is flow convective?", Stanford Center
for Reservoir Forecasting (SCRF) Report 12, 1999
- Srinivasan, S. and Journel, A.G.: "Direct simulation of
permeability field conditioned to well test data", SPE paper
49289, SPE Annual Technical Conference and Exhibition, New Orleans,
Louisiana, 1998.
- Deutsch, C.V., Srinivasan, S. and Mo, Y.: "Geostatistical
reservoir modeling accounting for precision and scale of seismic
data", SPE paper 36497, SPE Annual Technical Conference and
Exhibition. Denver, Colorado, 1996.
- Srinivasan, S. and Deutsch, C.V.: "Improved reservoir
management through , ranking stochastic reservoir models", SPE
paper 35411, SPE/DOE Tenth Symposium on Improved Oil Recovery, Tulsa,
Oklahoma 1996.
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