in Particle Swarm and application to history Matching: Stanford VI€¦ · Optimization and...
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Advances in Particle Swarm Optimization and application to history Matching: Stanford VI
Juan Luis Fernández MartínezStanford University.
UC Berkeley‐Lawrence Berkeley Lab.Oviedo University Spain.
In collaboration withTapan Mukerji, Amit Suman
and Esperanza García‐Gonzalo (Oviedo University,Spain).
Stanford Center for Reservoir forecastingStanford Center for Reservoir forecasting
Annual Meeting 2010
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SCRF 2010 2
INDEX
• Advances in PSO design• Application of PSO to the History Matching
Problem (Uncertainty analysis)• (TIP) Preliminary results on Differential
Evolution
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SCRF 2010 3
I. Advances in PSO design
Work done in collaboration with Esperanza García-Gonzalo (University of Oviedo)
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SCRF 2010 4
The spring-mass analogy
( ) ( ) ( ) ( ) ( ) ( ) ( )φ φ φ φ+ − ⋅ + + ⋅ = ⋅ + ⋅1 2 1 2
'' 1 ' .i i i ix t w x t x t l t g t
gk
φ1
xik
lik
m=11-w
φ2
lik-xikxi
k-gk
1 2(1 (1 ) ) ( ( ) ( ),( 1) ( ) ( 1)( 1) ( ) ( ) ( ) ( )
.i i i i i
i i i
v w v x g k x lk x
tk
t tv k t
k k k k kx
φ φ= − − + + −
+ = +
∆ ∆ ∆
∆
+ −+
DISCRETIZATION
in ( ) ( )'' ', .i ix t x t
GPSO
(Fernández Martínez et al, 2008)
(Fernández Martínez and García Gonzalo, 2008)
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SCRF 2010 5
PSO Analysis & Design
Based on this mechanical analogy we have1. Shown that PSO BELONGS TO A FAMILY:
• Design and stochastic stability analysis of a whole family of PSO optimizers: PSO, CC-PSO, CP-PSO (Fernández Martínez and García Gonzalo, Swarm Int., 2009), PP-PSO, RR-PSO (García Gonzalo and Fernández Martínez,2010).
2. Shown that PSO IS NOT HEURISTIC: • Full stochastic stability of the PSO family (Fernández Martínez and García
Gonzalo, 2010).
3. Designed a PSO Cloud Algorithm with variable time step (cooling and exploration) (Fernández Martínez et al, 2009, 2010).
• Avoids tuning of the PSO parameters (automatic)
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SCRF 2010 6
Parameter tuning: the cloud of particles
ω
¹
PSO ROSENBROCK
-1 -0.5 0 0.5 10
0.5
1
1.5
2
2.5
3
3.5
4
-1
0
1
2
3
4
5
6
7
ω
¹
CC ROSENBROCK
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 10
0.5
1
1.5
2
2.5
3
3.5
4
-1
0
1
2
3
4
5
6
7
ω
¹
CP ROSENBROCK
-1 -0.5 0 0.5 10
0.5
1
1.5
2
2.5
3
3.5
4
1
2
3
4
5
6
7
PP ROSENBROCK
-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 10
0.5
1
1.5
2
2.5
3
3.5
4
1
2
3
4
5
6
7
φ φ
φ φ
ωω
ωω
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SCRF 2010 7
RR-PSO is very different
_
14 / 3( 1)φ ω= −
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SCRF 2010 8
The ∆t parameter
∆t>=1 INITIAL BIG EXPLORATION
Stability region shrinks.
∆t<1 FINAL TUNING
Stability region expands.
13
1 2(1 (1 ) ) ( ( ) ( ),( 1) ( ) ( 1)( 1) ( ) ( ) ( ) ( )
.tv w v x g k x lt t
k x k tv kk k k k k
xφ φ= − − + + −
+ = +
∆ ∆ ∆
∆
+ −+
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SCRF 2010 9
II. HISTORY MATCHING, TIME LAPSE SEISMICS AND
UNCERTAINTY ANALYSISWith the collaboration of Tapan Mukerji and Amit Suman
Acknowledgments: David Echeverría, Eduardo Santos and Grégoire Mariethoz
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SCRF 2010 10
Optimization Workflow (Echeverría and Mukerji, 2009)
facies
m**
tooptimizer
manyparameters
Few PCAparameters
mξ
PSO
DE
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SCRF 2010 11
WHY UNCERTAINTY ANALYSIS IS NEEDED IN THE HISTORY MATCHING PROBLEM?
1. MINIMA ARE LOCATED ALONG FLAT ELONGATED VALLEYS.
2. NOISE IN DATA INTRODUCES LOCAL MINIMA.
3. NOISE HAS ALSO A REGULARIZATION EFFECT (MAKES THE SAMPLING EASIER).
4. THE MODEL REDUCTION INTRODUCES SINGULARITIES IN THE COST FUNCTION TOPOGRAPHY (potential danger for local methods).
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SCRF 2010 12First PCA
Sec
ond
PC
A
Case 2-Stanford VI reservoir
5 6 7 8 9 101
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
First PCA
Sec
ond
PC
A
Case 1-Stanford VI reservoir. 10% Gaussian Noise
9 10 11 12 13 14-5
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
First PCA
Sec
ond
PC
A
Case 1-Stanford VI reservoir
9 10 11 12 13 14-5
-4.5
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
First PCA
Case 2-Stanford VI reservoir. 10% Gaussian Noise
Seco
ndPC
A
5 6 7 8 9 101
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
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SCRF 2010 13
PSO Results: Swarm size 20
0 10 20 30 40 50 60 70 800
0.003
0.005
0.01
0.015
0.02
0.025SWARM SIZE=20. 10 simulations
iterations
Erro
r
PSO medianPP medianCP median dt=1CP median dt=0.8CC median dt=0.8
0 10 20 25 30 40 50 60 70 800.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022SEQUENTIAL: 10PCA-20PCA
Iterations
Erro
r
PSOPPCPCCCP dt=1,0.8
Similar results are obtained for swarm sizes of 50 and 100 particles
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SCRF 2010 14
PSO as a posterior sampler(In collaboration with Gregoire Mariethoz, Stanford University)
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SCRF 2010 15
Computing uncertainty from samples
Median sample
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SCRF 2010 16
Noise Free 10%Gaussian Error
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SCRF 2010 17
Data Match: ProductionCumulated oil Injected water
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SCRF 2010 18
Data Match: Tomograms
Reference
Median for theInitial swarm
Median of lowmisfit samples
Section 1 Section 2
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SCRF 2010 19
III. DIFFERENTIAL EVOLUTIONWith the collaboration ofEsperanza García-Gonzalo (University of Oviedo)
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SCRF 2010 20
Differential Evolution(Storn and Price , 1997)
( ) ( )1( 1) ( ) ( ) ( ) ( ) ,( 1) ( ) ( 1),
i l n r s
i j i
k k k k kk k k+ = − + −
+ = + +2v x x x x
m x vF F1. MUTATION
: Crossover probabilityrC2. CROSSOVER
3. SELECTION
Rand-1, Best-1, Target-to-best, Rand-2, Best-2
GA like-mechanisms
PSO like-mechanism
3 parameters to tune: 1 2 rF , F , C
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SCRF 2010 21
RosenbrockGriewank
F F
F
Rastrigin Sphere
F
Cr
Cr
Cr
Cr
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SCRF 2010 22
DE PerformanceConvergence rate
Exploration capabilities
Iterations
Iterations
Rel
ativ
e m
isfit
Med
ian
dist
ance
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SCRF 2010 23
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SCRF 2010 24
CONCLUSIONS • PSO
– All the PSO family members are able to provide facies models from the low misfit region, and can be used with small number of particles.
– Sequential inversion allows to increase dynamically the number of PCA parameters as needed.
– The topography of the cost function corresponds to flat valleys. The seismic data helps to partially constraint the space of possible solutions.
– PSO samples can be used to provide an approximate measure of model uncertainty.
A paper has been submitted to Computational Geosciences.
• DE – Very promising results: good balance between exploration
and exploitation.
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SCRF 2010 25
Acknowledgments
• Smart Fields and SCRF Consortia.• Schlumberger-EMI.• University of California-Berkeley and Lawrence Berkeley
Lab.
• University of Oviedo and Spanish Ministry of Innovation.
• Eduardo Santos (formerly Stanford University) and David Echeverría for providing the forward programs to model the HM problem (Stanford VI), and Grégoire Mariethozfor collaboration in the posterior sampling in hydrogeology.
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SCRF 2010 26
ARE THERE ANY QUESTIONS?
THANK YOU FORYOUR ATTENTION
When you see the face of the anger,look behind it ,and it will suddenly change to the face of the pride.
Jalaluddin Rumi (1207-1273)