lecture 4 objects
TRANSCRIPT
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Geological Modeling:Deterministic and Stochastic Models
Irina Overeem
Community Surface Dynamics Modeling System
University of Colorado at Boulder
September 2008
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Course outline 1
Lectures by Irina Overeem:
Introduction and overview Deterministic and geometric models Sedimentary process models I Sedimentary process models II
Uncertainty in modeling
Lecture byOvereem & Teyukhina : Synthetic migrated data
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Geological Modeling: different tracks
Static
Reservoir Model
Reservoir Data
Seismic, borehole and wirelogs
Sedimentary
Process ModelStochastic ModelDeterministic
Model
Data-driven modeling Process modeling
Flow Model
Upscaling
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Deterministic and Stochastic Models
Deterministic model- A mathematical model which contains norandom components; consequently, each component and input isdetermined exactly.
Stochastic model -A mathematical model that includes some
sort of random forcing.
In many cases, stochastic models are used to simulatedeterministic systems that include smaller- scale phenomena thatcannot be accurately observed or modeled. A good stochasticmodel manages to represent the average effect of unresolved
phenomena on larger-scale phenomena in terms of a randomforcing.
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Deterministic geometric models
Two classes:
Faults (planes)
Sediment bodies (volumes)
Geometric models conditioned to seismic
QC from geological knowledge
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Direct mapping of faults andsedimentary units from seismic data
Good quality 3D seismic data allows recognition of subtle faults and sedimentarystructures directly.
Even more so, if (post-migration) specific seismic volume attributesarecalculated.
Geophysics Group at DUT worked on methodology to extract 3-D geometricalsignal characteristics directly from the data.
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09 June 2014 7
L08 Block, Southern North Sea
Seismic volume attribute analysisof the Cenozoic succession in theL08 block, Southern North Sea.Steeghs, Overeem, Tigrek, 2000.Global and Planetary Change, 27,245262.
Cenozoic succession in the
Southern North Sea consistsof shallow marine, delta andfluvial deposits.
Target for gas exploration?
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Cross-line through 3D seismic amplitude data,
with horizon interpretations (Data courtesy Steeghs et al, 2000)
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Combined volume dip/azimuth display at T = 1188 ms.Volume dip is represented by shades of grey. Shades of blue indicate theazimuth (the direction of dip with respect to the cross-line direction).
The numerous faults have been interpreted as synsedimentarydeformation, resulting from the load of the overlying sediments.Pressure release contributed to fault initiation and subsequent fluidescape caused the polygonal fault pattern.
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from retrodeformation (geometries of
restored depositional surfaces)
Fault modellingFault surfaces
Example from PETREL COURSE NOTES
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More faultmodelling
in Petrel
Check plausibility of implied stressand strain fields
Example from PETREL COURSE NOTES
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Combined volume dip / reflection strength slice at T=724 ms
Fan
Fan Feederchannel
Delta Foresets
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Combined volume dip / reflection strength slice at T= 600 ms
Delta Foresets
Delta front slump channels
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Combined volume dip / reflection strength slice at T= 92 ms
Gas-filled meandering channel
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Deterministic sedimentary modelfrom seismic attributes
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Object-based Stochastic Models
Point process: spatial distribution of points (object centroids) inspace according to some probability law
Marked point process: a point process attached to (marked with)
random processes defining type, shape, and size of objects
Marked point processes are used to supply inter-well objectdistributions in sedimentary environments with clearly definedobjects:
sand bodies encased in mud shales encased in sand
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Ingredients of marked point process
Spatial distribution (degree of
clustering, trends) Object properties (size, shape,
orientation)Multi-storey sandbodies
1
10
100
1.000
10.000
100.000
1,0 10,0 100,0 1.000,0
Thickness(m)
Width(m)
Single storey sandbodies
1
10
100
1.000
10.000
100.000
1,0 10,0 100,0
Thickness(m)
Width(m)
Object-based stochastic
geological model conditioned towells, based on outcropanalogues
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An example: fluvial channel-fill sands
Geometries have become more sophisticated, but conceptualbasis has not changed: attempt to capture geological knowledgeof spatial lithology distribution by probability laws
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Examples of shape characterisation:
Channel dimensions (L, W) andorientation
Overbank deposits
Crevasse channels
Levees
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Exploring uncertainty of objectproperties (channel width)
W = 100 m
W = 800 m
W = 800 m
R = 800 m
How can one quantify the differences between different realizations?
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Major step forward:object-based model ofchannel belt generatedby random avulsion atfixed point
Series of realisationsconditioned to wells(equiprobable)
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Stochastic Model constrained bymultiple analogue data
Extract as much information as possible from logs andcores (Tilje Fm. Haltenbanken area, offshore Norway).
Use outcrop or modern analogue data sets for faciescomparison and definition of geometries
Only then Stochastic modeling will begin
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09 June 2014 23
Lithofacies types from coreExample: Holocene Holland Tidal Basin
Tidal Channel Tidal Flat Interchannel
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#
#
#
#
#
Diamond
harbour
Kulpi
Kakdwip
RaidighiHaldia
MatlaRiv
er
Th
ak
uran
S
aptamukhiRiver
Muri
HugliRiv
er
River
Ganga
SELECTED WINDOW
FOR STUDY
Modern Ganges tidaldelta, India
distance50km
Channel width
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Tidal channels
Conceptual model of tidal basin
(aerial photos, detailed maps)
Interchannel
heterolithics
Branching
main tidal
channels
Tidal flats Fractal
pattern of
tidal creeks
Growth of fractal channels is governed by a branching rule
Q tif th l d t i t l t
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Quantify the analogue data into relevantproperties for reservoir model
Channel width vs distance to shoreline
Tidal channel width vs distance to shoreline
0
200
400
600
800
1000
1200
1400
1600
1800
2000
10 15 20 25 30 35
distance to shoreline [km]
Width[m]
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The resulting stochastical model
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Some final remarks onstochastic/deterministic models
Stochastic Modeling should be data-driven modeling
Both outcrop and modern systems play an importantrole in aiding this kind of modeling.
Deterministic models are driven by seismic data.
The better the seismic data acquisition techniquesbecome, the more accurate the resulting model.
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References
Steeghs, P., Overeem, I., Tigrek, S., 2000. SeismicVolume Attribute Analysis of the Cenozoic Successionin the L08 Block (Southern North Sea). Global and
Planetary Change 27, 245-262.
C.R. Geel, M.E. Donselaar. 2007. Reservoir modellingof heterolithic tidal deposits: sensitivity analysis of an
object-based stochastic model, Netherlands Journal ofGeosciences, 86,4.
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