geostatistical inversion of time-lapse seismic data inversion of...we have two ways to analyze...
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ANNUAL MEETING MASTER OF PETROLEUM ENGINEERING
Pedro Rijo
28/May/2014 Instituto Superior Técnico
Geostatistical Inversion of
Time-Lapse Seismic Data
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1. What?
2. Why?
3. How?
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1. What?
2. Why?
3. How?
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Time-lapse seismic data is nothing more than seismic data repeatedly acquired at multiple times,
in order to monitor the changes in seismic response during the production phase.
Year 0 Year 10 Year 20 Year …
4D
SEIS
MIC
Year 0
3D
SEIS
MIC
What is 4D Seismic?
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If we know the physical relations that link the measured properties (data) with the properties we
want to estimate (model)…
… then, we can estimate the model from the data using a mathematical tool called inverse
theory.
Earth Model Modelling
Algorithm Seismic Response
INPUT PROCESS OUTPUT
FO
RW
AR
D
MO
DELLIN
G
Seismic Response Inversion Algorithm Earth Model
INPUT PROCESS OUTPUT
INV
ER
SE
MO
DELLIN
G
What is Seismic Inversion?
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1. What?
2. Why?
3. How?
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We have two ways to analyze time-lapse seismic data:
Qualitatively
Analyse the differences by
direct observation
Without / with reservoir
dynamic model
Quantitatively
Calibration of the dynamic
model through the differences
of the infered seismic attributes
With reservoir dynamic model
Why do we want to perform 4D seismic inversion?
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History matching Production forecasting
Production history data
History matching Production forecasting
Production history data
Dynamic properties changes
With 4D Seismic
Without 4D Seismic
Seismic data have been mainly used as a reservoir monitoring tool, but if the quality of the
repeatability is sufficiently good it can be used to determine changes related to reservoir
production. For that reason, this data have been recently used as an additional set of data for
history matching in reservoir applications.
Why do we want to perform 4D seismic inversion?
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1. What?
2. Why?
3. How?
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Step 2: Estimation of reservoir properties
Step 1: Estimation of elastic properties
This type of problems can be divided in two smaller problems. The first comprises the estimation
of relative changes in elastic properties from seismic differences. The second comprises the
estimation and pressure changes from elastic property changes.
Seismic Response
Elastic properties
Reservoir
properties
Inversion
Rock
Physics
How do we propose to do it?
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Based on Soares (2007) work, the Global Stochastic Inversion methodology was first presented
by Caetano (2009). This iterative geostatistical seismic inversion methodology is based on a
direct sequential simulation and co-simulation approaches, multi-point simulation and global
perturbation method.
How do we propose to do it? – Global Stochastic
Inversion
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How do we propose to do it? – Global Stochastic
Inversion
However, this method has some limitations.
METHOD LIMITATIONS:
• The uncertainty is only related with the random path/seed of the stochastic simulation
– local uncertainty in the impedance models.
• A spatial continuity pattern is assumed known – no uncertainty.
• A prior AI global distribution is assumed known
– no uncertainty.
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To address these limitations, Azevedo (2014) proposed an updated algorithm to enable multi-
scale uncertainty assessment using adaptive stochastic and Bayesian inference algorithms.
How do we propose to do it? – Global Stochastic
Inversion
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AI / EI simulated
x Ns
Reflection Coefficients
Synthetic Seismic
Wavelet
Correlation Coefficient
Real Seismic
Best Correlation Cube
x Ns
x Ns
x Ns
Best AI Cube
Well Data
Variograms
START!
Prior Distribution
START!
Particle
Swarm
Optimisation
Co-Simulation
Simulation - DSS
Convolution
How do we propose to do it? – Global Stochastic
Inversion
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Hard data Hard data Hard data
AI /EI
Dynamic
properties
changes
AI /EI
Dynamic
properties
changes
DSS / Co-DSS
x Ns
x Ns x Ns
x Ns
Best AI
model
Best AI
model START! START! START!
B C D
Prior
Distribution
Variograms
A
Particle
Swarm
Optimisation
Δ Y0 – Y10 Δ Y10 – Y20
Year 20 Year 10 Year 0
How do we propose to do it?
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Year 20 Year 10 Year 0
Synt
hetic
Seis
mic
Real
Seism
ic
x Ns x q
Δ Y0 – Y10 Δ Y10 – Y20
B C D A
Correlation Coefficients
How do we propose to do it?
x Ns x q
x q x q x q x q x q
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How do we propose to do it? – Case Study
To proposed methodology will be tested in a cropped area of the synthetic seismic dataset
Stanford VI-E.
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Independent geostatistical acoustic inversion
REA
L
SY
NTH
ETIC
BASE SURVEY 10 YEARS 20 YEARS 30 YEARS
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Independent geostatistical acoustic inversion
BASE - 10 10 -20 YEARS 20 - 30 YEARS
REA
L
SY
NTH
ETIC
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Thank you!