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Thin sub-resolution shaly-sands
Well logs Seismic Shale Cap Rock Shale Underburden Thin sub-resolution shaly-sands
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Distance based sensitivity analysis and cascaded interpretation scheme for subtle seismic signatures in thin shaly-sand reservoirs Piyapa Dejtrakulwong1, Tapan Mukerji2, and Gary Mavko1 1Stanford Rock Physics Laboratory (SRB), Department of Geophysics, 2Stanford Center for Reservoir Forecasting (SCRF), Department of Energy Resources and Engineering, Stanford University
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Motivation Limitation in seismic resolvability
Interpretations of the sub-resolution layers Goal: To infer properties and understand effects of spatial patterns of thin shaly-sand reservoirs with statistical distance-based attributes
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Distance-based seismic attributes
Seismic signatures and statistical attributes extracted from multiple realizations of thin sand-shale sequences. Distance-based feature extraction Distance based sensitivity analysis
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Thin sand-shale sequences Attributes MDS-Kernel
Workflow Markov Chains Rock Physics Sand/shale model Thin sand-shale sequences Interpretation Net-to-gross ratios Saturations Feature Extraction Attributes MDS-Kernel Seismic Responses
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Thin sand-shale sequences
Workflow Markov Chains Thin sand-shale sequences
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Markov chain for lithologies
Discrete states: sand, shaly sand, sandy shale, shale Transition probability matrix: 100 m
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Markov chain for lithologies
Discrete states: sand, shaly sand, sandy shale, shale Thin layers: thickness = 0.5m Wavelength/thickness ~ 100 (sub-resolution) 100 m
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Retrograding Prograding Aggrading
Transition Matrices Retrograding Prograding Aggrading (fining-upwards) (coarsening-upwards) Fixed
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Thin sand-shale sequences
Workflow Markov Chains Rock Physics Sand/shale model Thin sand-shale sequences
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Properties from rock physics
Sand Shaly-sand Sandy-Shale Shale Dvorkin and Gutierrez (2001)
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Properties from rock physics
Marion (1990) and Yin (1992)
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Thin sand-shale sequences
Workflow Markov Chains Rock Physics Sand/shale model Thin sand-shale sequences Seismic Responses
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Generate Seismic Response
Full waveform, normally-incident, reflected seismograms are simulated using the Kennett algorithm (Kennett, 1983) with a 30-Hz, zero-phase Ricker wavelet Standard convolution model does not handle sub-resolution layers correctly
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Generate Seismic Response
Multiple realizations (Monte Carlo simulation)
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Thin sand-shale sequences
Workflow Markov Chains Rock Physics Sand/shale model Thin sand-shale sequences Feature Extraction Attributes MDS/KPCA Seismic Responses
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Projections of seismograms
Eigen decompose Compute all pairwise distance/dissimilarity between seismograms Construct dissimilarity/kernel matrix Results: projections of input seismograms onto selected principal coordinates/components
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Projections of seismograms
Eigen decompose Compute all pairwise distance/dissimilarity between seismograms Construct dissimilarity/kernel matrix Configuration of points color-coded by net-to-gross ratios or other properties
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Projections of seismograms
Eigen decompose Compute all pairwise distance/dissimilarity between seismograms Construct dissimilarity/kernel matrix Configuration of points color-coded by net-to-gross ratios or other properties
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Projections of seismograms
? Eigen decompose Compute all pairwise distance/dissimilarity between seismograms Construct dissimilarity/kernel matrix Configuration of points color-coded by net-to-gross ratios or other properties
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Kernel functions Gaussian kernel
Dynamic similarity kernel (Yan et al., 2006) Inverse multi-quadric kernel Polynomial kernel (Li et al., 2003) and Δm = {the smallest m δ’s of (δ1,…, δn)}
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Net-to-gross ratio discrimination
Classification of 3 NTG classes: high, medium, low Stratified 10-fold cross validation
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Net-to-gross ratio discrimination
Kernel Gaussian Dynamic similarity Inverse multi-quadric Polynomial Classification success rate 81% 90% 79% 59% Classification of 3 NTG classes Stratified 10-fold cross validation
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Interpreting seismic signatures
?? Time Coordinate 2 X ?? Coordinate 1 well unknown N/G
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Interpreting seismic signatures
Good discrimination, but for fixed overburden/underburden impedance
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Seismic signatures in subresolution shaly sands
Seismic section Shale Cap Rock Shale underburden Thin sub-resolution shaly-sands Overburden and underburden impedance contrast /Overall thickness Overwhelm subtle signatures of net-to-gross/saturation
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Seismic signatures in subresolution shaly sands
Overburden impedance multiplier Underburden impedance multiplier Overall thickness
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Seismic signatures in subresolution shaly sands
NTG Vary: Overburden impedance multiplier Underburden impedance multiplier Overall thickness NTG No discrimination of net-to-gross ratio
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Distance based sensitivity analysis (dGSA) of
seismic signatures in subresolution shaly sands Parameters: Overburden impedance multiplier Underburden impedance multiplier Overall thickness NTG 3 classes in MDS space Distance between class-conditioned CDFs and prior CDF of each parameter.
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Distance based sensitivity analysis (dGSA) of
seismic signatures in subresolution shaly sands
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Distance based sensitivity analysis of
seismic signatures in subresolution shaly sands Underburden Overburden Parameters: Overburden impedance multiplier Underburden impedance multiplier Overall thickness NTG Thickness NTG
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Cascaded interpretation of
seismic signatures in subresolution shaly sands Step through sensitive parameters: For each seismic data trace: get posterior density of sensitive parameters in MDS space vary sensitive parameters within narrow posterior get density of desired subtle parameter from new MDS plot
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Cascaded interpretation of
seismic signatures in subresolution shaly sands Color: NTG Color: NTG Good discrimination Non-stationary
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Cascaded interpretation of
seismic signatures in subresolution shaly sands
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Conclusions Seismic attributes from distance-based feature extraction
Distance-based sensitivity analysis and cascaded approach can be applied to extract subtle attributes in thin shaly-sand reservoirs.
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Acknowledgements Stanford Center for Reservoir Forecasting (SCRF); Stanford Rock Physics and Borehole Geophysics project (SRB)
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Using distance-based sensitivity analysis to interpret seismic signatures of thin shaly-sand reservoirs Piyapa Dejtrakulwong1, Tapan Mukerji2, and Gary Mavko1 1Stanford Rock Physics Laboratory (SRB), Department of Geophysics, 2Stanford Center for Reservoir Forecasting (SCRF), Department of Energy Resources and Engineering, Stanford University
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