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November 28, 2001
Vol. 28, No. 13



A Class Act: Informal Classes marks 30th anniversary

Director of Health & Safety says to exercise caution, but keep things in perspective

UT Press offers scholarly books about Middle East

UT and LULAC develop Austin Youth Leadership Academy

Advancements in disease research, mathematical theory take honors at Siemens competition

$7.2 million grant funds medical research

Marketing professor's research blends expertise in music industry, electronic commerce

New division to enhance teaching effectiveness, learning opportunities

Four teams to compete in MOOT CORP® finals

Eckhardt continues to safeguard campus history six years after his death

Norma Cantú brings expertise into classroom

$2.15 NSF grant to improve production of oil, gas

Research team discovers mechanism regulating plant growth

Engineers harness "quantum dots" for neurological research

Orange Santa program makes season brighter

Readership Survey

Anti-terrorism expert calls for increased steps to combat terrorism

Arete: Jessica J. Summers

School of Social Work gets funding for substance abuse research

A salute to military veterans, POWs, MIAs




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University of Texas at Austin shares in $2.15 million NSF grant to improve production of oil, gas

The University of Texas at Austin will share in a $2.15 million National Science Foundation grant for research on ways to use complex sensor technology to monitor oil fields and produce sophisticated computer images for use by science and industry.

Researchers predict the instrumented oil field will reduce the financial cost in high-risk environments such as deepwater reservoirs. It also will optimize early field development and facilities costs and increase recoverable reserves in reservoirs that cannot be monitored with available seismic technology. Reductions in seismic acquisition, processing and decision timing-costs also are predicted.

"We believe our research is vital to the development of the instrumented oil field and will help provide more efficient, cost-effective and environmentally safer production of oil reservoirs," said Dr. Mary Wheeler of the Texas Institute for Computational and Applied Mathematics (TICAM).

Wheeler, a professor of petroleum, geosystems and aerospace engineering, is leader of the project, which involves four other universities. TICAM’s Dr. Clint Dawson and Dr. Malgorzata Peszynska, as well as Dr. Mrinal K. Sen and Dr. Paul Stoffa of the Institute for Geophysics, will participate.

The three-year study, which began in September, also involves computer scientists and applied mathematicians from Ohio State University, Rutgers University, the University of Maryland and the University of Chicago.

The purpose of the study is to improve on computational technologies that will allow industry to create a new generation of computer technologies to describe, or characterize, hydrocarbon reserves. Complex interactive visualization programs will be developed.

"Although the instrumented oil field doesn't yet exist, it is a concept we expect to become reality within the decade because of advances in seismic monitoring and engineering production technology, " said Sen.

The instrumented oil field will consist of permanently installed sensor arrays at the Earth's surface, on the seafloor and in boreholes. The sensors will be used to produce images of hydrocarbon reservoirs in near real time. Using data from these sensors and from computer-enhanced fluid-flow images, reservoir managers will be able to monitor and control wells to improve oil and gas production.

The research team, composed of geophysicists, engineers with backgrounds in reservoir simulation, applied mathematicians and computer scientists, face the significant challenge of making the large volumes of data understandable.

The team will focus on the use of parallel computational systems for obtaining reservoir models using diverse types of data. Such information would include seismics, well-logs, petrophysics, reservoir history and fluid flow data.

The researchers also will improve analytical techniques to permit rapid visualization of the large volume of time-lapse image data.

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