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David Lumley - 4D uncertainty - Nov 11, 2015

The document outlines the concept of 4D seismic monitoring, defining it as incorporating time into three-dimensional seismic data to track changes in reservoir properties. It discusses various sources of uncertainty and error in 4D seismic data, emphasizing the importance of nonlinear uncertainty analysis. Additionally, the document provides examples of seismic data visualization and the implications for reservoir monitoring and management.

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11/11/2015 1 david.lumley@uwa.edu.auLumley et al., 2014 Nonlinear Uncertainty Analysis: 4D Seismic reservoir monitoring Prof David Lumley + various colleagues and students over the years... UWA School of Physics; School of Earth & Environment david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
11/11/2015 2 david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 1D = f(x1): eg. well logs f(z) f = porosity, clay content, age facies, velocity, density…
11/11/2015 3 david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 2D = f(x1,x2): eg. maps, cross-sections (x,z) f = porosity, clay content, age structural depth, facies, reflectivity… Lumley et al. david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 3D = f(x,y,z): volumes (x,y,z) f = porosity, clay content, age velocity, reflectivity… Niri & Lumley, 2013
11/11/2015 4 david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 4D = f(x,y,z,t): hyper-cubes (x,y,z,t) f = porosity, clay content, age velocity, reflectivity… Lumley, 1995 david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
11/11/2015 5 david.lumley@uwa.edu.auLumley et al., 2014 Rock properties can change over time with fluids, stress, temperature etc… Duffaut et al.2011 david.lumley@uwa.edu.auLumley et al., 2014 Seismic Image – 2D cross-section Lumley
11/11/2015 6 david.lumley@uwa.edu.auLumley et al., 2014 Seismic Image – zoom on reservoirs OWC Lumley david.lumley@uwa.edu.auLumley et al., 2014 TIME 1 amplitude map extracted along top of reservoir structure Lumley et al., 2003
11/11/2015 7 david.lumley@uwa.edu.auLumley et al., 2014 TIME 2 amplitude map extracted along top of reservoir structure Lumley et al., 2003 david.lumley@uwa.edu.auLumley et al., 2014 4D amplitude difference map extracted along top of reservoir structure Lumley et al., 2003
11/11/2015 8 david.lumley@uwa.edu.auLumley et al., 2014 4D Monitoring of Steam Injectors Sigit et al., 1999 steam costs > $2 MM / day david.lumley@uwa.edu.auLumley et al., 2014 Before… After! courtesy of Statoil Monitoring Injection
11/11/2015 9 david.lumley@uwa.edu.auLumley et al., 2014 Injection Pressure Anomaly Before After Lumley et al., 2003Lumley et al. david.lumley@uwa.edu.auLumley et al., 2014 Permanent Array 4D example Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 10
11/11/2015 10 david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty input output A B transform
11/11/2015 11 david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty various data results A B workflow david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty earth model simulated data A B Forward modeling… F
11/11/2015 12 david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data image of the earth A B Imaging… F* david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data earth model A B Inversion… F-1
11/11/2015 13 david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data earth model Inversion… F-1 A + errors B + ??? david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data earth model Inversion… F-1 A + errors B + uncertainty!
11/11/2015 14 david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty model simulated data A + errors B + ??? Forward modeling… F david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty model simulated data A + errors B + uncertainty! Forward modeling… F
11/11/2015 15 david.lumley@uwa.edu.auLumley et al., 2014 Errors Uncertainty Domain 1 Domain 2 A Definition of “uncertainty” david.lumley@uwa.edu.auLumley et al., 2014 Forward Modeling Model space Data space m d F
11/11/2015 16 david.lumley@uwa.edu.auLumley et al., 2014 Model space + errors Data space + uncertainty m +  d +  F +  Forward Modeling david.lumley@uwa.edu.auLumley et al., 2014 Model space + errors Data space + uncertainty m +  dmod +  F +  dobs +  Forward Modeling
11/11/2015 17 david.lumley@uwa.edu.auLumley et al., 2014 * You may have the right model, but it may not fit the data! * You may have the wrong model, but it may fit the data! Model space + errors Data space + uncertainty m +  dmod +  F +  dobs +  david.lumley@uwa.edu.auLumley et al., 2014 Non-uniqueness, null space… Model “null” space Data space m + m’ d +  F(m’)≈ 0
11/11/2015 18 david.lumley@uwa.edu.auLumley et al., 2014 Example: low resolution data Gravity data david.lumley@uwa.edu.auLumley et al., 2014 “Mickey Mouse model” Gravity data
11/11/2015 19 david.lumley@uwa.edu.auLumley et al., 2014 Model “null” space Data space m + imi’ d +  F(m’)≈ 0 * There are infinitely many models that fit the data! david.lumley@uwa.edu.auLumley et al., 2014 Uncertainty ≠ Non-uniqueness
11/11/2015 20 david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space Data space F-1 m d david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space + uncertainty Data space + errors F-1 +  m +  d + 
11/11/2015 21 david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space + uncertainty + non-uniqueness Data space + errors F-1 +  m + + m’ d +  david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space + uncertainty + non-uniqueness * Regularization * * Model-shaping * Data space + errors F-1 +  m + + imi’ d + 
11/11/2015 22 david.lumley@uwa.edu.auLumley et al., 2014 “Mickey Mouse model” Gravity data david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
11/11/2015 23 david.lumley@uwa.edu.auLumley et al., 2014 “Closing the loop… history matching” Observed Data; t++ Inversion Estimated model; t++ Simulation Predicted data david.lumley@uwa.edu.auLumley et al., 2014 4D amplitude difference map extracted along top of reservoir structure Lumley et al., 2003
11/11/2015 24 david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • 4D Seismic data errors • Non-repeatable noise • Source-receiver positioning errors • Changes in the water column / near-surface / overburden • Changes in acquisition geometry • Changes in source-receiver characteristics • Non 4D-compliant processing flow • Etcetera… david.lumley@uwa.edu.auLumley et al., 2014 3D Noise data = “signal” + noise
11/11/2015 25 david.lumley@uwa.edu.auLumley et al., 2014 4D NR Noise T1 T2 T2-T1 david.lumley@uwa.edu.auLumley et al., 2014 4D Repeatability Saul & Lumley, 2013
11/11/2015 26 david.lumley@uwa.edu.auLumley et al., 2014 after Landro 4D NRMS vs. position error david.lumley@uwa.edu.auLumley et al., 2014 image image difference Image difference after 40-60 cm tidal corrections Eiken et al., EAGE 1999 4D tidal corrections
11/11/2015 27 david.lumley@uwa.edu.auLumley et al., 2014 Line A: before the platform Line B: after the platform with Petrobras david.lumley@uwa.edu.auLumley et al., 2014 “Statistical” image processing Baseline Monitor Difference Lumley et al., SEG, 1998
11/11/2015 28 david.lumley@uwa.edu.auLumley et al., 2014 Lumley et al., SEG, 1998 Baseline Monitor Difference “Physics-based” image processing david.lumley@uwa.edu.auLumley et al., 2014 1999 Local Diff Global Diff ? 4D Local vs. Global optimization Lumley et al. 2003
11/11/2015 29 david.lumley@uwa.edu.auLumley et al., 2014 Local image difference Global image difference CO2 injectors? Lumley et al. 2003 4D Local vs. Global Optimization david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Model definition • Model parameterization (acoustic, elastic, aniso, attenuation…) • Physical property relationships (velocity-pressure…) • Model discretization/sampling (fine, coarse, up/down-scale…) • Model relationships (geology, seismic, fluid flow…) • Etcetera…
11/11/2015 30 david.lumley@uwa.edu.auLumley et al., 2014 Reservoir container david.lumley@uwa.edu.auLumley et al., 2014 Reservoir properties
11/11/2015 31 david.lumley@uwa.edu.auLumley et al., 2014 Up/down scaling david.lumley@uwa.edu.auLumley et al., 2014 porosity clay fraction
11/11/2015 32 david.lumley@uwa.edu.auLumley et al., 2014 Rock physics param pdfs david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Physics (modeling/inversion operators/code) • Linear versus nonlinear • Acoustic, Elastic, Anisotropy, Attenuation… • Convolution, Raytracing, Finite Difference… • Algorithm implementations • Etcetera…
11/11/2015 33 david.lumley@uwa.edu.auLumley et al., 2014 4D FD elastic modeling david.lumley@uwa.edu.auLumley et al., 2014 1 layer CO2 Lumley et al., 2008 synth PSDM
11/11/2015 34 david.lumley@uwa.edu.auLumley et al., 2014 real PSTM 1 layer CO2 StatoilLumley et al., 2008 synth PSDM david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Optimization criteria (inversion/estimation) • Least squares (L2), Least absolute (L1), hybrid… • Optimal fit to data, amplitude, phase… • Multiple objectives, weighting schemes… • Inversion constraints (soft, hard, weighted…) • Optimization method (gradients, stochastic, MC, PSO, genetic…) • Resolution, Null space, Non-uniqueness… • Etcetera…
11/11/2015 35 david.lumley@uwa.edu.auLumley et al., 2014 Multi-objective optimisation find a reservoir model m such that: min E = (seismic)p + (logs)q + (geology)r + … david.lumley@uwa.edu.auLumley et al., 2014 13.22 13.27 13.32 13.37 13.42 13.47 13.52 13.57 36 41 46 ObjectiveFunction2 Objective Function 1 Generation 1 13.22 13.24 13.26 13.28 13.3 13.32 13.34 13.36 35 36 37 38 39 40 ObjectiveFunction2 Objective Function 1 Generation 10 13.12 13.14 13.16 13.18 13.2 13.22 13.24 13.26 30 32 34 36 38 ObjectiveFunction2 Objective Function 1 Generation 20 13 13.02 13.04 13.06 13.08 13.1 13.12 26 27 28 29 30 ObjectiveFunction2 Objective Function 1 Generation 50 12.96 12.97 12.98 12.99 13 13.01 13.02 13.03 13.04 13.05 13.06 24 25 26 27 28 29 ObjectiveFunction2 Objective Function 1 Generation 70 12.95 12.96 12.97 12.98 12.99 13 13.01 13.02 13.03 13.04 13.05 24 25 26 27 28 ObjectiveFunction2 Objective Function 1 Generation 100 Multi-objective optimization – “Pareto front” 70 Niri & Lumley 2013
11/11/2015 36 david.lumley@uwa.edu.auLumley et al., 2014 a) Reference Litho-facies model b) Before MO model updating c) After MO model updating  Average Mismatch error reduced from 36.5% to 14.6% Multi-objective optimization – “Pareto front” Niri & Lumley 2013 david.lumley@uwa.edu.auLumley et al., 2014 How to quantify 4D errors + uncertainty?
11/11/2015 37 david.lumley@uwa.edu.auLumley et al., 2014 How to quantify 4D errors + uncertainty? >> Nonlinear stochastic error propagation david.lumley@uwa.edu.auLumley et al., 2014 d1 d2 p N=1000 realizations Sw Pp 4D inversion statistics N=1000
11/11/2015 38 david.lumley@uwa.edu.auLumley et al., 2014 4D seismic image of water saturation (Sw) a b david.lumley@uwa.edu.auLumley et al., 2014 4D seismic inversion for water saturation (Sw) Sw a b
11/11/2015 39 david.lumley@uwa.edu.auLumley et al., 2014 {Sw} for 1000 realizations a b david.lumley@uwa.edu.auLumley et al., 2014 signal to noise ratio “S/N” of Sw a b
11/11/2015 40 david.lumley@uwa.edu.auLumley et al., 2014 Probability that Sw > 0.5 a b david.lumley@uwa.edu.auLumley et al., 2014 “Closing the loop… history matching” Observed Data; t++ Inversion Estimated model; t++ Simulation Predicted data
11/11/2015 41 david.lumley@uwa.edu.auLumley et al., 2014

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David Lumley - 4D uncertainty - Nov 11, 2015

  • 1.
    11/11/2015 1 david.lumley@uwa.edu.auLumley et al.,2014 Nonlinear Uncertainty Analysis: 4D Seismic reservoir monitoring Prof David Lumley + various colleagues and students over the years... UWA School of Physics; School of Earth & Environment david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
  • 2.
    11/11/2015 2 david.lumley@uwa.edu.auLumley et al.,2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 1D = f(x1): eg. well logs f(z) f = porosity, clay content, age facies, velocity, density…
  • 3.
    11/11/2015 3 david.lumley@uwa.edu.auLumley et al.,2014 Geophysics definition of “4D” 2D = f(x1,x2): eg. maps, cross-sections (x,z) f = porosity, clay content, age structural depth, facies, reflectivity… Lumley et al. david.lumley@uwa.edu.auLumley et al., 2014 Geophysics definition of “4D” 3D = f(x,y,z): volumes (x,y,z) f = porosity, clay content, age velocity, reflectivity… Niri & Lumley, 2013
  • 4.
    11/11/2015 4 david.lumley@uwa.edu.auLumley et al.,2014 Geophysics definition of “4D” 4D = f(x,y,z,t): hyper-cubes (x,y,z,t) f = porosity, clay content, age velocity, reflectivity… Lumley, 1995 david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
  • 5.
    11/11/2015 5 david.lumley@uwa.edu.auLumley et al.,2014 Rock properties can change over time with fluids, stress, temperature etc… Duffaut et al.2011 david.lumley@uwa.edu.auLumley et al., 2014 Seismic Image – 2D cross-section Lumley
  • 6.
    11/11/2015 6 david.lumley@uwa.edu.auLumley et al.,2014 Seismic Image – zoom on reservoirs OWC Lumley david.lumley@uwa.edu.auLumley et al., 2014 TIME 1 amplitude map extracted along top of reservoir structure Lumley et al., 2003
  • 7.
    11/11/2015 7 david.lumley@uwa.edu.auLumley et al.,2014 TIME 2 amplitude map extracted along top of reservoir structure Lumley et al., 2003 david.lumley@uwa.edu.auLumley et al., 2014 4D amplitude difference map extracted along top of reservoir structure Lumley et al., 2003
  • 8.
    11/11/2015 8 david.lumley@uwa.edu.auLumley et al.,2014 4D Monitoring of Steam Injectors Sigit et al., 1999 steam costs > $2 MM / day david.lumley@uwa.edu.auLumley et al., 2014 Before… After! courtesy of Statoil Monitoring Injection
  • 9.
    11/11/2015 9 david.lumley@uwa.edu.auLumley et al.,2014 Injection Pressure Anomaly Before After Lumley et al., 2003Lumley et al. david.lumley@uwa.edu.auLumley et al., 2014 Permanent Array 4D example Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 1 0 Map of amplitude changes Map of compaction 2003 2004 2005 200820072006 LoFS Survey Timeline 1 2 3 4 5 6 7 8 9 10
  • 10.
    11/11/2015 10 david.lumley@uwa.edu.auLumley et al.,2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty input output A B transform
  • 11.
    11/11/2015 11 david.lumley@uwa.edu.auLumley et al.,2014 errors vs. uncertainty various data results A B workflow david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty earth model simulated data A B Forward modeling… F
  • 12.
    11/11/2015 12 david.lumley@uwa.edu.auLumley et al.,2014 errors vs. uncertainty geophysics data image of the earth A B Imaging… F* david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data earth model A B Inversion… F-1
  • 13.
    11/11/2015 13 david.lumley@uwa.edu.auLumley et al.,2014 errors vs. uncertainty geophysics data earth model Inversion… F-1 A + errors B + ??? david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty geophysics data earth model Inversion… F-1 A + errors B + uncertainty!
  • 14.
    11/11/2015 14 david.lumley@uwa.edu.auLumley et al.,2014 errors vs. uncertainty model simulated data A + errors B + ??? Forward modeling… F david.lumley@uwa.edu.auLumley et al., 2014 errors vs. uncertainty model simulated data A + errors B + uncertainty! Forward modeling… F
  • 15.
    11/11/2015 15 david.lumley@uwa.edu.auLumley et al.,2014 Errors Uncertainty Domain 1 Domain 2 A Definition of “uncertainty” david.lumley@uwa.edu.auLumley et al., 2014 Forward Modeling Model space Data space m d F
  • 16.
    11/11/2015 16 david.lumley@uwa.edu.auLumley et al.,2014 Model space + errors Data space + uncertainty m +  d +  F +  Forward Modeling david.lumley@uwa.edu.auLumley et al., 2014 Model space + errors Data space + uncertainty m +  dmod +  F +  dobs +  Forward Modeling
  • 17.
    11/11/2015 17 david.lumley@uwa.edu.auLumley et al.,2014 * You may have the right model, but it may not fit the data! * You may have the wrong model, but it may fit the data! Model space + errors Data space + uncertainty m +  dmod +  F +  dobs +  david.lumley@uwa.edu.auLumley et al., 2014 Non-uniqueness, null space… Model “null” space Data space m + m’ d +  F(m’)≈ 0
  • 18.
    11/11/2015 18 david.lumley@uwa.edu.auLumley et al.,2014 Example: low resolution data Gravity data david.lumley@uwa.edu.auLumley et al., 2014 “Mickey Mouse model” Gravity data
  • 19.
    11/11/2015 19 david.lumley@uwa.edu.auLumley et al.,2014 Model “null” space Data space m + imi’ d +  F(m’)≈ 0 * There are infinitely many models that fit the data! david.lumley@uwa.edu.auLumley et al., 2014 Uncertainty ≠ Non-uniqueness
  • 20.
    11/11/2015 20 david.lumley@uwa.edu.auLumley et al.,2014 Inversion …imaging, estimation Model space Data space F-1 m d david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space + uncertainty Data space + errors F-1 +  m +  d + 
  • 21.
    11/11/2015 21 david.lumley@uwa.edu.auLumley et al.,2014 Inversion …imaging, estimation Model space + uncertainty + non-uniqueness Data space + errors F-1 +  m + + m’ d +  david.lumley@uwa.edu.auLumley et al., 2014 Inversion …imaging, estimation Model space + uncertainty + non-uniqueness * Regularization * * Model-shaping * Data space + errors F-1 +  m + + imi’ d + 
  • 22.
    11/11/2015 22 david.lumley@uwa.edu.auLumley et al.,2014 “Mickey Mouse model” Gravity data david.lumley@uwa.edu.auLumley et al., 2014 Outline • Define “4D” • Examples of 4D seismic • Define “uncertainty” • Nonlinear uncertainty analysis + examples
  • 23.
    11/11/2015 23 david.lumley@uwa.edu.auLumley et al.,2014 “Closing the loop… history matching” Observed Data; t++ Inversion Estimated model; t++ Simulation Predicted data david.lumley@uwa.edu.auLumley et al., 2014 4D amplitude difference map extracted along top of reservoir structure Lumley et al., 2003
  • 24.
    11/11/2015 24 david.lumley@uwa.edu.auLumley et al.,2014 Sources of 4D error and uncertainty • 4D Seismic data errors • Non-repeatable noise • Source-receiver positioning errors • Changes in the water column / near-surface / overburden • Changes in acquisition geometry • Changes in source-receiver characteristics • Non 4D-compliant processing flow • Etcetera… david.lumley@uwa.edu.auLumley et al., 2014 3D Noise data = “signal” + noise
  • 25.
    11/11/2015 25 david.lumley@uwa.edu.auLumley et al.,2014 4D NR Noise T1 T2 T2-T1 david.lumley@uwa.edu.auLumley et al., 2014 4D Repeatability Saul & Lumley, 2013
  • 26.
    11/11/2015 26 david.lumley@uwa.edu.auLumley et al.,2014 after Landro 4D NRMS vs. position error david.lumley@uwa.edu.auLumley et al., 2014 image image difference Image difference after 40-60 cm tidal corrections Eiken et al., EAGE 1999 4D tidal corrections
  • 27.
    11/11/2015 27 david.lumley@uwa.edu.auLumley et al.,2014 Line A: before the platform Line B: after the platform with Petrobras david.lumley@uwa.edu.auLumley et al., 2014 “Statistical” image processing Baseline Monitor Difference Lumley et al., SEG, 1998
  • 28.
    11/11/2015 28 david.lumley@uwa.edu.auLumley et al.,2014 Lumley et al., SEG, 1998 Baseline Monitor Difference “Physics-based” image processing david.lumley@uwa.edu.auLumley et al., 2014 1999 Local Diff Global Diff ? 4D Local vs. Global optimization Lumley et al. 2003
  • 29.
    11/11/2015 29 david.lumley@uwa.edu.auLumley et al.,2014 Local image difference Global image difference CO2 injectors? Lumley et al. 2003 4D Local vs. Global Optimization david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Model definition • Model parameterization (acoustic, elastic, aniso, attenuation…) • Physical property relationships (velocity-pressure…) • Model discretization/sampling (fine, coarse, up/down-scale…) • Model relationships (geology, seismic, fluid flow…) • Etcetera…
  • 30.
    11/11/2015 30 david.lumley@uwa.edu.auLumley et al.,2014 Reservoir container david.lumley@uwa.edu.auLumley et al., 2014 Reservoir properties
  • 31.
    11/11/2015 31 david.lumley@uwa.edu.auLumley et al.,2014 Up/down scaling david.lumley@uwa.edu.auLumley et al., 2014 porosity clay fraction
  • 32.
    11/11/2015 32 david.lumley@uwa.edu.auLumley et al.,2014 Rock physics param pdfs david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Physics (modeling/inversion operators/code) • Linear versus nonlinear • Acoustic, Elastic, Anisotropy, Attenuation… • Convolution, Raytracing, Finite Difference… • Algorithm implementations • Etcetera…
  • 33.
    11/11/2015 33 david.lumley@uwa.edu.auLumley et al.,2014 4D FD elastic modeling david.lumley@uwa.edu.auLumley et al., 2014 1 layer CO2 Lumley et al., 2008 synth PSDM
  • 34.
    11/11/2015 34 david.lumley@uwa.edu.auLumley et al.,2014 real PSTM 1 layer CO2 StatoilLumley et al., 2008 synth PSDM david.lumley@uwa.edu.auLumley et al., 2014 Sources of 4D error and uncertainty • Optimization criteria (inversion/estimation) • Least squares (L2), Least absolute (L1), hybrid… • Optimal fit to data, amplitude, phase… • Multiple objectives, weighting schemes… • Inversion constraints (soft, hard, weighted…) • Optimization method (gradients, stochastic, MC, PSO, genetic…) • Resolution, Null space, Non-uniqueness… • Etcetera…
  • 35.
    11/11/2015 35 david.lumley@uwa.edu.auLumley et al.,2014 Multi-objective optimisation find a reservoir model m such that: min E = (seismic)p + (logs)q + (geology)r + … david.lumley@uwa.edu.auLumley et al., 2014 13.22 13.27 13.32 13.37 13.42 13.47 13.52 13.57 36 41 46 ObjectiveFunction2 Objective Function 1 Generation 1 13.22 13.24 13.26 13.28 13.3 13.32 13.34 13.36 35 36 37 38 39 40 ObjectiveFunction2 Objective Function 1 Generation 10 13.12 13.14 13.16 13.18 13.2 13.22 13.24 13.26 30 32 34 36 38 ObjectiveFunction2 Objective Function 1 Generation 20 13 13.02 13.04 13.06 13.08 13.1 13.12 26 27 28 29 30 ObjectiveFunction2 Objective Function 1 Generation 50 12.96 12.97 12.98 12.99 13 13.01 13.02 13.03 13.04 13.05 13.06 24 25 26 27 28 29 ObjectiveFunction2 Objective Function 1 Generation 70 12.95 12.96 12.97 12.98 12.99 13 13.01 13.02 13.03 13.04 13.05 24 25 26 27 28 ObjectiveFunction2 Objective Function 1 Generation 100 Multi-objective optimization – “Pareto front” 70 Niri & Lumley 2013
  • 36.
    11/11/2015 36 david.lumley@uwa.edu.auLumley et al.,2014 a) Reference Litho-facies model b) Before MO model updating c) After MO model updating  Average Mismatch error reduced from 36.5% to 14.6% Multi-objective optimization – “Pareto front” Niri & Lumley 2013 david.lumley@uwa.edu.auLumley et al., 2014 How to quantify 4D errors + uncertainty?
  • 37.
    11/11/2015 37 david.lumley@uwa.edu.auLumley et al.,2014 How to quantify 4D errors + uncertainty? >> Nonlinear stochastic error propagation david.lumley@uwa.edu.auLumley et al., 2014 d1 d2 p N=1000 realizations Sw Pp 4D inversion statistics N=1000
  • 38.
    11/11/2015 38 david.lumley@uwa.edu.auLumley et al.,2014 4D seismic image of water saturation (Sw) a b david.lumley@uwa.edu.auLumley et al., 2014 4D seismic inversion for water saturation (Sw) Sw a b
  • 39.
    11/11/2015 39 david.lumley@uwa.edu.auLumley et al.,2014 {Sw} for 1000 realizations a b david.lumley@uwa.edu.auLumley et al., 2014 signal to noise ratio “S/N” of Sw a b
  • 40.
    11/11/2015 40 david.lumley@uwa.edu.auLumley et al.,2014 Probability that Sw > 0.5 a b david.lumley@uwa.edu.auLumley et al., 2014 “Closing the loop… history matching” Observed Data; t++ Inversion Estimated model; t++ Simulation Predicted data
  • 41.