Jaehoon Lee Tapan Mukerji Michael Tompkins Motivation and Objective 2 Joint integration of multidisciplinary geophysical data can provide complementary information not only in reservoir characterization but also in reservoir monitoring ID: 619948
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Slide1
Statistical Integration of Time-lapse Seismic and Electromagnetic Data for Reservoir Monitoring and Management
Jaehoon
Lee,
Tapan
Mukerji
, Michael TompkinsSlide2
Motivation and Objective
2
Joint integration of multidisciplinary geophysical data can provide complementary information not only in reservoir characterization but also in reservoir monitoring.
Statistical Rock Physics Modeling (
Avseth
et al., 2001;
Mukerji
et al., 2001)
To develop a statistical workflow to integrate
time-lapse seismic and EM data with proper consideration of scale differences.
Well logs
Rock physics modeling
Multivariate PDFSlide3
Example: 2-D Cross Section
Stanford VI-E
(Lee &
Mukerji
, 2012)A 3-layer fluvial channel system.30 year simulation with 31 producers and 15 injectors .
150×200×200 cells.Dx =
Dy = 25m
, Dz =1m.Oil Saturation (30 years)
FaciesSlide4
Example: Impedances & Resistivity
Acoustic Impedance
Elastic Impedance (30°)
Electrical Resistivity
Born filtering
Geometric moving averageSlide5
Example: Well Logs
Well 1
Well 2Slide6
Example: Well Log Data Extension
Oil sand
Oil sand
Oil sand
Brine sand
Brine sand
Brine sand
Shale
Shale
ShaleSlide7
Example: Well-scale Data PDF
Well-log Data
(Training Data)
Field-scale Data (Classifying Attributes)Slide8
Example: Classification by Well-scale PDF
True
Facies
Well-scale PDF
Bad classification result caused by scale differenceSlide9
Well-scale Data
Field-scale Data
Methodology: Analogous Reservoir Generation
9
MP
Geostatistics
, SNESIM (
Strebelle, 2000)
Well-scale PDFSlide10
Example: Simulated Field-scale PDF
Simulated Field-scale Data
(Training Data)
Field-scale Data (Classifying Attributes)Slide11
Example: Classification by Field-scale PDF
True
Facies
Field-scale PDF
Well-scale PDF
Improved classificationSlide12
Example: Classification by Field-scale PDF
True
Facies
Seismic & EM
Only seismic
Only EMSlide13
Example: 2-D Cross Section (Time-lapse)
13
Oil Saturation (30 years)
Facies
(30 years)
Oil Saturation (20 years)
Facies
(20 years)Slide14
Methodology: Analogous Reservoir Generation
14
Two different saturation profiles are simulated for each realization. Seismic and EM attributes are related to each other by porosity.
Well-scale PDFSlide15
Methodology:
Hexa-variate
Gaussian mixture
Hexa-variate
Gaussian mixture models are used to incorporate time-lapse seismic and EM data into statistical integration.
Acoustic Impedance
Elastic Impedance
Electrical ResistivitySlide16
Example: Classification with Time-lapse Data
True
Facies
Non-parametric
(with
D
AI
, DEI, D
R)Gaussian mixture
(with AIt-1, EIt-1, Rt-1)
Gaussian mixture
(with DAI,
DEI, DR)Slide17
Conclusions
17
Developed analogous reservoir generation method to consider the scale differences between well logs, seismic and EM data.
Consistent way with seismic and EM forward modeling and inversion.
Filtering can be used to reduce computational time.
A few realizations are enough.
Realizations do not have to be as large as the target reservoir.
Applied hexa-variate Gaussian mixture models to incorporate time-lapse data into field-scale joint PDFs with the analogous reservoir generation method.Slide18
Future Work
18
The developed workflow will be applied to a three-dimensional synthetic reservoir and a real reservoir.
Norne
field
Stanford VI-E layer 3