ITR Proposal

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ITR Proposal

• A Data Intense Challenge
• The Instrumented Oilfield of the Future
• Participants
• University of Texas at Austin
• CSM Wheeler, Dawson, Peszynska
• IG Sen, Stoffa
• PGE Torres-Verdin
• University of ChicagoCS Stevens, Papka
• University of MarylandCS Sussman
• Ohio StateCS Saltz, Kurc
• RutgersECE Parashar
• MITEngineering Haines

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ITR Proposal

• A Data Intense Challenge
• The Instrumented Oilfield of the Future
• Industrial Support (Data)
• British Petroleum (BP)
• Chevron
• International Business Machines (IBM)
• Landmark
• Shell
• Schlumberger

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Highlights of Instrumented Oilfield Proposal

• Motivation
• Field instrumentation for information
  technology and computational science essential
  for monitoring and optimizing oil and gas
  production. Integration yields

THE INSTRUMENTED OILFIELD

• Field Technologies
• Time-lapse surface and borehole seismic,
  permanent downhole sensors, intelligent well
  completions, fiber optics, and remote control
  operations

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Highlights of Instrumented Oilfield Proposal

• IT Technologies
• Data management, data visualization, parallel
  computing, and decision-making tools such as
  new wave propagation and multiphase,
  multi- component flow and transport computational
  portals, reservoir production

THE INSTRUMENTED OILFIELD

• Major Outcome of Proposed Research
• Computing portals which will enable reservoir
  simulation and geophysical calculations to
  interact dynamically with the data and with each
  other and which will provide a variety of visual
  and quantitative tools. Test data provided by
  oil and service companies

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Highlights of Instrumented Oilfield Proposal
Production Management Environment
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Highlights of Instrumented Oilfield Proposal
Proposed Simulation Framework
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Highlights of Instrumented Oilfield Proposal

• Dissemination of Information and Outreach
• Primary Funding for Support of Graduate and
  Postgraduate Students
• Planned Multidisciplinary Summer Internships
• IT Support for IG High School Outreach
• Industrial Fellows Program

• Spinoffs
• Environmental Remediation
• Hazardous Waste Storage
• Medical Applications
• IT and Computational Science

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Reservoir Characterization

• Task II Simulate Production
• For every DT convert reservoir properties to
  seismic properties
• 3D ZSR seismic model (fast)
• 3D OBC elastic model (finite difference)
• 3D Surface model (Kirchoff target based)
• Sparse IBS elastic model
• Fixed array
• Small number of shots
• Geophones downhole
• Task III Invert seismic Data for initial model
• Depth migrate seismic data
• Invert for seismic properties
• Convert seismic to reservoir properties
• Task IV Invert for changes in model no DT

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Reservoir Environment
f-k
BC detectors
equivalent medium
f-k
DATUM
f-d
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Wave Propagation Algorithms
source
record
f-k
WATER
z1
record
z2
split
SEDIMENTS
injected wave field
Reservoir example Nx200 Ny100 Nz100 Seismic
Nx400 512 Ny200 256 Nz 200
finite difference explicit
RESERVOIR
dx, dy, dz
Record every grid point
u(t) and E(t)
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Seismic Inversion for Reservoir Properties
image space x,y,z,
seismic data geometry of acquisition determine
type algorithm
migration inversion
seismic properties spacex,y,z
reservoir properties
4D Seismic
seismic image
algorithm
reservoir properties