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ANNUAL MEETING MASTER OF PETROLEUM ENGINEERING
Ângela Pereira
PhD student
angela.pereira@tecnico.ulisboa.pt
3/May/2016 Instituto Superior Técnico
Earth models for early
exploration stages
1
Motivation
Methodology
Case Study
Seismic inversion results
Conclusions
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Outline
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How?
Combination of information from
Geological analogs and Geophysical
data in a geoestatistical seismic
inversion procedure.
ENMC ENMC
Motivation
Prospect characterization and uncertainty assessment of unexplored areas or in
early stages of exploration.
Geostatistical Seismic Inversion algorithm - GSI (Soares et al. 2007;
Caetano, 2009)).
Use of geological analogs for extraction of a priori distributions of Acoustic
Impedance (AI) (e.g. well-logs from nearby wells).
Use of a geological conceptual model based on seismic interpretation.
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Methodology
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Global approaches (Soares et al., 2007; Caetano, 2009)
i. Generation and perturbation of entire cube of parameters (acoustic properties) .
ii. Optimization method (genetic algorithms) to assure the convergence of the iterative procedure.
Geostatistical Seismic Inversion - GSI
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The distributions of AI should be representative of the expected lithofacies and also of the relation between the
different litho-stratigraphic units.
Seismic Interpretation
Geological Analogs
(wells-logs from nearby wells)
GEOLOGICAL MODEL
Acoustic Impedance distributions
extracted from geological analogs
Dt Rhob AI
Geological analogs
Regional Geology
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Zone 1
Zone 2
Zone 3
DEFINITION OF
GEOLOGICAL ZONES
SEISMIC SEISMIC INTERPRETATION GEOLOGICAL MODEL
Geological model with 3 different lithofacies
based on seismic interpretation
Grid size: 198 x 279 x 190
Geological model
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Zone 1
Zone 2
Zone 3
GEOLOGICAL MODELA PRIORI DISTRIBUTION OF ACOUSTIC IMPEDANCE (AI)
Each a priori AI distribution, for the different geological zones were extracted directly from geological
analogs (well-logs from nearby wells). They correspond to the expected lithofacies for each zone.
GEOLOGICAL
ANALOGS(nearby appraisal well)
AI
Dt Rhob AI
Geological analogs
Acoustic Impedance - AI
sands with shales marlsshales
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*
Compare
seismicConvolution
Seismic
Synthetic
Local correlation coefficient calculation and
Selection of best fit models
Best local
correlation coef.Best AI model
Use best models as secondary variable for co-simulation.
Iterate until the convergence of the method.
Stochastic
Simulation of
AI (DSS)
AI distributions extracted
from geological analogs
Geological model N(AI)
Workflow
Forward model
Methodology
The methodology proposed was applied to an unexplored area, where the potential
reservoir is expected to be in a turbidite system.
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3D post-stack seismic volume, with sampling interval 2ms
3 appraisal wells outside the area used as analogs (w1, w2, w3)
Case study
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Well-logs used as analogs
Well-logs available: Sonic,
Density, Acoustic Impedance
(computed from Sonic and
Density logs)
Dt Rhob AI Dt Rhob AI Dt Rhob AI
Case study
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Geological model with 3 lithofacies zones
Tree different a priori Acoustic Impedance (AI) distributions extracted from
the geological analogs.
Spatial continuity pattern expressed by a variogram model obtained from
seismic data.
Statistical wavelet extracted from seismic data.
The algorithm was run with 6 iterations in each one were generated 32 models
of Acoustic Impedance.
Seismic Inversion Parameterization
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Evolution of Global Correlation
Coefficient
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REAL SEISMIC SYNTHETIC it#6 CC=0.85
seis
mic
synthetic it#6
max
min
max
min
Correlation between real and synthetic seismic
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Difference in amplitude between real and synthetic seismic
REAL SEISMIC SEISMIC - SYNTHETIC
Zone 1
Zone 2
Zone 3
GEOLOGICAL MODEL
Transition zones have higher
diferences in amplitude
SYNTHETIC it#6max
min
max
min
max
min
Seismic amplitude
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LOCAL CC it#6 BEST LOCAL CC it#6REAL SEISMIC SYNTHETIC it#6max
min
max
min
1
0
Local Correlation Coefficient - CC
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Zone 2
Potential reservoir – turbidite system
CC=0.90HORIZON SLICE
SEISMIC
HORIZON SLICE
SYNTHETIC it#6REAL SEISMIC
seis
mic
synthetic it#6
max
min
max
min
max
min
Correlation between real and synthetic seismic
Zone 1
Zone 2
Zone 3
GEOLOGICAL MODEL
A PRIORI DISTRIBUTION OF ACOUSTIC IMPEDANCE
sands with
shales
marlsshales
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MEAN AI it#6HORIZON SLICE
BEST AI it#6
SIMULATED AI it#6
Zone 1
Zone 2
Zone 3
GEOLOGICAL MODEL
max
min
max
min
max
min
Acoustic Impedance model
Low acoustic impedance values correspond to high
porosity areas
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It is important that the acoustic impedance distributions extracted from
geological analogs should be representative of the expected lithofacies and
also of the relation between the different litho-stratigraphic units.
The Geostatistical Seismic Inversion methodology using geological analogs
proved to be a valuable tool to be applied on unexplored areas or in early
stage of exploration for prediction of the subsurface geology and for prospect
characterization.
Conclusions
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Partex Oil and Gas and Repsol for allowing the use of the data and give
permission to show the results.
Schlumberger for the academic licenses of Petrel®
CGG for the academic licenses of Hampson-Russel software.
Thank you
Acknowledgements
Caetano, H. [2009] Integration of Seismic Information in Reservoir Models: Global Stochastic
Inversion, PhD thesis, Instituto Superior Técnico, University of Lisbon.
Pereira A., Nunes R., Azevedo L., Guerreiro L. & Soares A. [2016] Seismic inversion using
analogs for reservoir characterization and uncertainty assessment in early stages of exploration.
Search and Discovery, Proccedings of International Conference and Exhibition AAPG/SEG 2016
(Accepted).
Pereira A., Nunes R., Azevedo L., Guerreiro L. & Pereira M.J. [2015] Global Stochastic Inversion
Using "Analogs-wells" and Zonal Distributions - Application to an Unexplored Area. Proceedings
of EAGE 2015 Conference - Petroleum Geostatistics.
Soares, A., Diet, J.D. & Guerreiro, L. [2007] Stochastic Inversion with a Global Perturbation
Method, EAGE Conference on Petroleum Geostatistics, Extended Abstracts.
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References