Nothing Special   »   [go: up one dir, main page]

Vo et al., 2014 - Google Patents

A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models

Vo et al., 2014

View PDF
Document ID
11600510895171380100
Author
Vo H
Durlofsky L
Publication year
Publication venue
Mathematical Geosciences

External Links

Snippet

A new approach based on principal component analysis (PCA) for the representation of complex geological models in terms of a small number of parameters is presented. The basis matrix required by the method is constructed from a set of prior geological realizations …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/66—Subsurface modeling
    • G01V2210/665—Subsurface modeling using geostatistical modeling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/282—Application of seismic models, synthetic seismograms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/30—Analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00—Subject matter not provided for in other groups of this subclass
    • G01V99/005—Geomodels or geomodelling, not related to particular measurements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/62—Physical property of subsurface
    • G01V2210/624—Reservoir parameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/67—Wave propagation modeling
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38—Processing data, e.g. for analysis, for interpretation, for correction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Vo et al. A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models
Liu et al. A deep-learning-based geological parameterization for history matching complex models
Tang et al. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems
Tang et al. Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
Pham et al. Missing well log prediction using convolutional long short-term memory network
US11693139B2 (en) Automated seismic interpretation-guided inversion
Vo et al. Data assimilation and uncertainty assessment for complex geological models using a new PCA-based parameterization
Liu et al. 3D CNN-PCA: A deep-learning-based parameterization for complex geomodels
US11435498B2 (en) Subsurface models with uncertainty quantification
Vo et al. Regularized kernel PCA for the efficient parameterization of complex geological models
Lorentzen et al. History matching channelized reservoirs using the ensemble Kalman filter
Chen et al. Assisted history matching of channelized models by use of pluri-principal-component analysis
Jafarpour et al. Compressed history matching: exploiting transform-domain sparsity for regularization of nonlinear dynamic data integration problems
Pan et al. Stochastic pix2pix: A new machine learning method for geophysical and well conditioning of rule-based channel reservoir models
Sarma et al. A new approach to automatic history matching using kernel PCA
Xiao et al. Conditioning of deep-learning surrogate models to image data with application to reservoir characterization
Chen et al. Integration of principal-component-analysis and streamline information for the history matching of channelized reservoirs
Rimstad et al. Approximate posterior distributions for convolutional two-level hidden Markov models
Barnett et al. The projection-pursuit multivariate transform for improved continuous variable modeling
Jiang et al. Deep convolutional autoencoders for robust flow model calibration under uncertainty in geologic continuity
McKenna et al. Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting
Honorio et al. Integration of PCA with a novel machine learning method for reparameterization and assisted history matching geologically complex reservoirs
Khaninezhad et al. Sparse Randomized Maximum Likelihood (SpRML) for subsurface flow model calibration and uncertainty quantification
Chen et al. Integration of cumulative-distribution-function mapping with principal-component analysis for the history matching of channelized reservoirs
Jahanbakhshi et al. Joint estimation of facies boundaries and petrophysical properties in multi-facies channelized reservoirs through ensemble-based Kalman filter and level set parametrization