BISC Seminar Announcement, Thursday, June 24th , 1999, 4-5pm, 310 Soda

Frank Hoffmann (fhoffman@cs.berkeley.edu)
Fri, 18 Jun 1999 12:02:15 +0200 (MET DST)

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Berkeley Initiative in Soft Computing (BISC)
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B I S C S e m i n a r A n n o u n c e m e n t
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Intelligent Reservoir Characterization (IRESC)

Speaker :
Masoud Nikravesh
University of Utah
Email: MNikravesh@egi.utah.edu
URL: http://cit.egi.utah.edu

Date/Location: Thursday June 24th, 1999, 4-5pm, 310 Soda

Abstract:

With oil and gas companies presently recovering, on the average, less
than a third of the oil in proven reservoirs, any means of improving
yield effectively increases the world's energy reserves. Accurate
reservoir characterization through data integration (such as seismic and
well logs) is a key step in reservoir modeling & management, and
production optimization. There are many techniques for increasing and
optimizing production from oil and gas reservoirs:

1. precisely characterizing the petroleum reservoir
2. finding the bypassed oil and gas
3. processing the huge databases such as seismic and wireline logging
data,
4. extracting knowledge from corporate databases,
5. finding relationships between many data sources with different
degrees of uncertainty,
6. optimizing a large number of parameters,
7. deriving physical models from the data
8. Optimizing oil/gas production.

This presentation address the key challenges associated with development
of oil and gas reservoirs. Given the large amount of by-passed oil and
gas and the low recovery factor in many reservoirs, it is clear that
current techniques based on conventional methodologies are not adequate
and/or efficient. We are proposing to develop the next generation of
Intelligent Reservoir Characterization (IRESC) tool, based on Soft
computing which is an ensemble of intelligent computing methodologies
using neuro computing, fuzzy reasoning, and evolutionary computing. Two
main areas to be addressed are first, data processing / fusion / mining
and second, interpretation, pattern recognition and intelligent data
analysis.

Results:

An integrated methodology has been developed to identify nonlinear
relationships and mapping between 3-D seismic and well logs data. This
methodology has been applied to a producing field. The method uses
conventional techniques such as geostatistical and classical pattern
recognition [Aminzadeh and Chatterjee, 1985] in conjunction with modern
techniques such as soft computing (neuro computing, fuzzy logic, genetic
computing, and probabilistic reasoning) [Nikravesh et al., 1998;
Nikravesh and Aminzadeh, 1997; Nikravesh, 1998a]. An important goal of
our research is to use clustering and nonlinear mapping techniques to
recognize the optimal location of a new well based on 3-D seismic and
available well logs data. The classification, clustering, and
nonlinear mapping tasks were accomplished in three ways; 1) classical
statistical techniques, 2) fuzzy reasoning, and 3) neuro computing to
recognize similarity cubes. The relationships between each cluster and
well logs were recognized around the wellbore and the results used to
reconstruct and extrapolate well logs data away from the wellbore. This
advanced 3-D seismic and log analysis and interpretation can be used to
predict: 1) mapping between production data and seismic data, 2)
reservoir connectivity based on multi-attribute analysis, 3) pay zone
estimation, and 4) optimum well placement.

-- 
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Frank Hoffmann                               UC Berkeley
Computer Science Division                    Department of EECS
Email: fhoffman@cs.berkeley.edu              phone: 1-510-642-8282
URL: http://http.cs.berkeley.edu/~fhoffman   fax:  1-510-642-5775
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