Modeling Natural Fracture Networks Using Coupled MultiPoint Geostatistics and Flow Simulation

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Modeling Natural Fracture Networks Using Coupled
Multi-Point Geostatistics and Flow Simulation

• Petroleum Technology Alliance of Canada
• Drs. Dale Wong Sanjay Srinivasan
• The University of Calgary

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Prof. Dale Wong Bio

• DR. WONG has 20 years of experience in reservoir
  engineering, software development and RD with a
  full range of companies from various industry
  sectors, such as exploration and production,
  service, consulting and software. He is also a
  founder of a U.S.-based reservoir simulation
  software company.
• Dr. Wong was the co-chair of the committee to
  create the new B. A. Sc. Oil Gas Engineering
  program at the Department of Chemical and
  Petroleum Engineering.
• One of the pioneers of the pressure derivative
  method now commonly used in pressure transient
  analysis methods for well tests, his areas of
  primary interest are advanced well test analysis
  methods, transient simulation, reservoir
  simulation and reservoir engineering.

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Prof. Sanjay Srinivasan Bio

• Prof. Sanjay Srinivasan received his doctorate in
  Geostatistics from Stanford University. His
  research focus is on multiple point geostatistics
  and calibration of information from geological
  models, seismic and production data.
• Dr. Srinivasan has over 7 years work experience
  at Bechtel Corporation as a senior petroleum
  engineer, working on both upstream and downstream
  aspects of petroleum field development.
• Dr. Srinivasan supervises a research team of 11
  graduate students working on characterization of
  fractured reservoirs and complex sandstone
  depositional environments, automatic history
  matching, data sufficiency in reservoir modeling
  etc

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Mission

• To fast-track integrated research into modeling
  naturally fractured reservoirs
  university-initiated and industry-focused
• Creation of a center of learning for these
  methods for CAGE (Center for the Advancement of
  Geostatistics in Engineering)

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Technology Delivery Point
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Development of 2-D, Steady-State Methods

• Target ? August, 2001
• Develop mathematical and numerical prototype for
  future development prove the technology, prove
  the people
• Develop a training-based algorithm for
  recognizing fracture patterns from analogues
  pattern-based geostatistics

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Development of Methods to Model Large Fracture
Networks

• Target ? April, 2002
• Investigate strategies for solving BEM equation
  (ex. iterative solution methods)
• Approximate far away fractures
• Integrated software for pattern recognition from
  analogues, simulation conditional to
  reservoir-specific fracture data

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Automatic History Matching

• Target ? April, 2003
• Developing iterative numerical procedures for
  creating fracture networks that match field
  behavior
• Calibration procedure for identifying fracture
  density orientation related information from
  production data

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Integration with Simulation Technologies

• Target ? August, 2003
• Coupling of Numerical Procedures (FDM, FEM or
  other BEM codes)
• Modular software
• pattern recognition from analogues
• calibration of info. from historic data
• creating history matched reservoir models

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Technology Development Timeline
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CAGE - CENTRE FOR ADVANCEMENT OF GEOSTATISTICS IN
ENGINEERING
THE CONFIGURATION OF THE MIXED UNIX/PC BASED
PLATFORM
P-270 CENTRAL SERVER HDD 128GB RAM 4GB
P-170 HDD-10GB RAM-1GB
P-170 HDD-10GB RAM-1GB
P-170 HDD-10GB RAM-1GB
P-170 HDD-10GB RAM-1GB
PIII HDD-10GB RAM-1GB
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Requested Support

• Funding, to be matched by government agencies,
  for capital and operating costs
• Company human and information resources
• 10,000 per participating company per annum

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Traditional Problem Fracture Placement in
Numerical Simulation
Vert Well
Hz Well
Vert Well
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Problems

• The location and scale of natural fractures
• The true representation is probabilistic and
  not deterministic ? Methodology?
• How can information from various sources be
  integrated into stochastic fracture
  representation?
• How does one model the fractures or,
  alternatively, history match their behavior?

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Geostatistical modeling Data Integration
Unknown true reservoir
Observed soft response
Seismic response
Flow/pressure response
time
Traditional geostatistical approach
Model and reproduce two-point correlations
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Reservoir modeling modern paradigm
True reservoir
Training reservoir
Data event
Set of data events training data set
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Integrated Reservoir Modeling
True reservoir
Soft data
Reservoir model
Pr( fracture template data ) x
Pr(soft data fracture )
Pr ( model hard soft data)
forward simulated soft data
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Goal Match Well Pressure and Rate History in
Naturally Fractured Reservoirs
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BEM For Natural Fractures
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How Fractures are Modeled
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Stochastic - BEM Method to Model Natural Fractures
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Summary

• New, breakthrough technology to model natural
  fractures in forward or inverse simulations
• In the forward case, for a particular fracture
  network density and orientation determine
  reservoir response
• In the inverse case, given a reservoir response,
  determine the fracture density and orientation