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Generation of a Reference Petrophysical Seismic Data Set the Stanford V reservoir

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Five steps to generate a reference data set. Simulate layer geometry: ... Obtain petrophysical properties: porosity, permeability, density, velocity, impedance ... – PowerPoint PPT presentation

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Title: Generation of a Reference Petrophysical Seismic Data Set the Stanford V reservoir


1
Generation of a Reference Petrophysical /
Seismic Data Set - the Stanford V reservoir -
  • Shuguang Mao and André G. Journel
  • Thanks to Tapan Mukerji, Geophysics Dept.
  • Stanford Center for Reservoir Forecasting
  • Stanford University
  • May 4-5, 2000

2
Outline
  • Objectives
  • Five steps to generate a reference data set
  • Simulate layer geometry top / bottom depths
  • Simulate lithofacies types
  • Obtain petrophysical properties porosity,
    permeability, density, velocity, impedance
  • Obtain approximate seismic survey result
    seismic measured impedance, travel time
  • Extract well paths and obtain well log data
  • Summary

3
Objectives
  • To allow extensive testing of any proposed
    reservoir characterization algorithm on 3D
    multi-layered fluvial channel reservoir
  • Layer surfaces top and bottom for all layers
  • Lithofacies types facies indiators
  • Petrophysical parameters ?, k, ?, v
  • Seismic data travel time, impedance
  • Extract any number of well log data along any
    deviated well path

4
Methodology
Layer geometry top and bottom depths
Petrophysical properties facies, ?, k, ?, v,
imp

Vertical strat. grid
z
high resolution
Forward modeling
Well planning
Seismic impedance
t
Seismic travel time
bilinear interpolation
low resolution
5
Step 1 simulate layer geometry
  • Bottom surface top surface thickness
  • Topmost surface
  • Thicknesses

bottom surface of layer k
top surface of layer k
thickness of layer k
Non-conditional Gaussian simulation (sgsim)
with small nugget ( 1) and relative large range.
Non-conditional Gaussian simulation (sgsim)
with small nugget ( 5), various ranges and
anisotropies.
6
Simulation results layer geometry
7
Step 2 simulate lithofacies types
  • A three-layered reservoir, deposited in a fluvial
    channel environment
  • Use object-based simulation algorithm, code
    fluvsim, see Deutsch Wang, 1996
  • Non-conditional facies simulation
  • Different channel parameters for each layer,
    e.g., orientation, width, channel number, etc.

8
Simulation results lithofacies
Channel Sand
Channel Sand
Crevasse
Crevasse
Mudstone
Layer 1
Layer 2
Layer 3
9
Step 3 simulate rock petrophysics
Permeability
3 facies indicators from 3 layers
Porosity distribution for different facies
Rock Physics
Porosity
Density
Non-conditional SGSIM
Velocity
Regression (channel, crevasse)
Regression (mudstone)
Porosity simulation results in 3 facies
Impedance
Re-assemble
10
Step 3a simulate porosity values
  • Non-conditional sequential Gaussian simulation
    (sgsim) for each lithofacies, conditional to
    reference distribution of porosity
  • Merge the above simulated results according to
    simulated lithofacies type

11
Simulation results porosity in 3 facies
Channel sand
m 0.29 cv 0.11
Crevasse
m 0.13 cv 0.37
Mudstone
m 0.08 cv 0.55
12
Simulation results porosity
Porosity
13
Step 3b simulate permeability
  • Sequential Gaussian simulation of permeability
    conditional to collocated porosity
  • Separately, (1) mudstone facies (2)
    non-mudstone facies
  • Back transform simulation results to lognormal
    distribution for each facies
  • Merge the above two permeability fields according
    to facies type

14
Simulation results permeability
Permeability
15
Step 3c obtain density and velocity
  • Rock mineral matrix fluid in void space
  • Use empirical regression models

16
Simulation results density
Density
17
Simulation results velocity, impedance
18
Step 4 Approximate seismic response
  • Recall impedance velocity x density
  • Apply a frequency-domain Born filter to mimic a
    realistic impedance as obtained from a seismic
    inversion
  • A local Backus average with vertical sliding
    window to smooth out thin layers
  • Apply normal incidence ray theory to get
  • seismic velocity
  • seismic two-way travel time

19
Approximate seismic responses
20
Seismic response travel time
true
available
x-cross sec.
y-cross sec.
21
Step 5 Extract well log data
  • Decide number of wells, locations and types of
    well (vertical, deviated, horizontal)
  • Design well paths in 3D
  • Extract well logs by interpolation
  • find layer k to which location u belongs
  • compute vertical stratigraphic coordinate
  • find the nearest 8 grid blocks from data set
    cubes, then perform bilinear interpolation

22
Results well paths in 3D
  • Inspired by real well setting of North Sea
    reservoir
  • 4 platforms
  • 2 vertical wells
  • 2 deviated wells
  • 8 horizontal wells

3D view of well paths
23
Sample well log
  • Well data include
  • horizontal location
  • true vertical depth
  • porosity
  • permeability
  • density
  • sonic log
  • lithology
  • layer indicator
  • Well markers true vertical depth when well
    intercepts a layer surface

24
Summary
  • A reference data set is generated to mimic a
    three layers fluvial channel reservoir, which
  • has realistic channel number, size, shape,
    porosity, density and velocity distributions
  • provides approximate seismic data, including
    seismic measured impedance, seismic two-way
    travel time to each layer surface
  • can provide well log data according to any user-
    designed well path
  • Future work
  • Design OWC then change brine in channel sand
    facies which are above OWC to oil.
  • Perform forward modeling for accurate seismic.
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