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

Tang et al., 2022 - Google Patents

Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration

Tang et al., 2022

View PDF
Document ID
9126656171438280005
Author
Tang M
Ju X
Durlofsky L
Publication year
Publication venue
International Journal of Greenhouse Gas Control

External Links

Snippet

A deep-learning-based surrogate model capable of predicting flow and geomechanical responses in CO 2 storage operations is presented and applied. The 3D recurrent RU-Net model combines deep convolutional and recurrent neural networks to capture the spatial …
Continue reading at www.sciencedirect.com (PDF) (other versions)

Classifications

    • 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
    • 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
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • 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
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]
    • G06F2217/16—Numerical modeling
    • 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
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/66—Subsurface modeling
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/61—Analysis by combining or comparing a seismic data set with other data
    • 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
    • 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
Tang et al. Deep-learning-based coupled flow-geomechanics surrogate model for CO2 sequestration
Tang et al. Deep-learning-based surrogate flow modeling and geological parameterization for data assimilation in 3D subsurface flow
Yan et al. A physics-constrained deep learning model for simulating multiphase flow in 3D heterogeneous porous media
Yan et al. A gradient-based deep neural network model for simulating multiphase flow in porous media
Vo et al. A new differentiable parameterization based on principal component analysis for the low-dimensional representation of complex geological models
Castelletto et al. Scalable algorithms for three-field mixed finite element coupled poromechanics
Jeong et al. A learning-based data-driven forecast approach for predicting future reservoir performance
Iglesias et al. Evaluation of Gaussian approximations for data assimilation in reservoir models
Tang et al. A deep learning-accelerated data assimilation and forecasting workflow for commercial-scale geologic carbon storage
Azizmohammadi et al. Is the permeability of naturally fractured rocks scale dependent?
Jiang et al. Implementation and detailed assessment of a GNAT reduced-order model for subsurface flow simulation
Tang et al. Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR
He Reduced-order modeling for oil-water and compositional systems, with application to data assimilation and production optimization
Ma et al. Dynamic characterization of geologic CO2 storage aquifers from monitoring data with ensemble Kalman filter
Jiang et al. Combining autoencoder neural network and Bayesian inversion to estimate heterogeneous permeability distributions in enhanced geothermal reservoir: Model development and verification
Xiao et al. Surrogate-assisted inversion for large-scale history matching: Comparative study between projection-based reduced-order modeling and deep neural network
Yang et al. Simulation of three-dimensional random field conditioning on incomplete site data
Klie et al. Data-driven modeling of fractured shale reservoirs
Klie et al. Data-driven prediction of unconventional shale-reservoir dynamics
Xiao et al. Deep‐learning‐based adjoint state method: Methodology and preliminary application to inverse modeling
Nagao et al. An efficient deep learning-based workflow for CO2 plume imaging considering model uncertainties with distributed pressure and temperature measurements
Nagao et al. An Efficient Deep Learning-Based Workflow for CO2 Plume Imaging Using Distributed Pressure and Temperature Measurements
Tian et al. An advanced inverse modeling framework for efficient and flexible adjoint-based history matching of geothermal fields
Nagao et al. An efficient deep learning-based workflow for CO2 plume imaging with distributed pressure and temperature measurements
Fu et al. A multiscale method for subsurface inverse modeling: Single-phase transient flow