Deman et al., 2016 - Google Patents

Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model

Deman et al., 2016

View PDF
Document ID
5882790487829896023
Author
Deman G
Konakli K
Sudret B
Kerrou J
Perrochet P
Benabderrahmane H
Publication year
Publication venue
Reliability Engineering & System Safety

External Links

Snippet

The study makes use of polynomial chaos expansions to compute Sobol׳ indices within the frame of a global sensitivity analysis of hydro-dispersive parameters in a simplified vertical cross-section of a segment of the subsurface of the Paris Basin. Applying conservative …
Continue reading at arxiv.org (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
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • 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
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
    • G01N33/26—Investigating or analysing materials by specific methods not covered by the preceding groups oils; viscous liquids; paints; inks
    • G01N33/28—Oils, i.e. hydrocarbon liquids
    • 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
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20—Handling natural language 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
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V9/00—Prospecting or detecting by methods not provided for in groups G01V1/00 - G01V8/00
    • 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
    • G01V2210/6248—Pore pressure
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electro-chemical, or magnetic means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V5/00—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity

Similar Documents

Publication Publication Date Title
Deman et al. Using sparse polynomial chaos expansions for the global sensitivity analysis of groundwater lifetime expectancy in a multi-layered hydrogeological model
Fienen et al. An interactive Bayesian geostatistical inverse protocol for hydraulic tomography
Sun et al. Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method
Li et al. Three‐dimensional geostatistical inversion of flowmeter and pumping test data
Li et al. Geostatistical inverse modeling of transient pumping tests using temporal moments of drawdown
Ronayne et al. Identifying discrete geologic structures that produce anomalous hydraulic response: An inverse modeling approach
Sun et al. Data-space approaches for uncertainty quantification of CO2 plume location in geological carbon storage
Chen et al. Three‐dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data
Zhou et al. A pattern‐search‐based inverse method
Bellin et al. Probability density function of non-reactive solute concentration in heterogeneous porous formations
Riva et al. Probabilistic assessment of seawater intrusion under multiple sources of uncertainty
Wang et al. A hybrid inverse method for hydraulic tomography in fractured and karstic media
Beckie et al. What does a slug test measure: An investigation of instrument response and the effects of heterogeneity
Siade et al. Snapshot selection for groundwater model reduction using proper orthogonal decomposition
Josset et al. Functional error modeling for uncertainty quantification in hydrogeology
Bayer‐Raich et al. Average contaminant concentration and mass flow in aquifers from time‐dependent pumping well data: Analytical framework
Srzic et al. Impact of aquifer heterogeneity structure and local‐scale dispersion on solute concentration uncertainty
Ringel et al. Stochastic inversion of three‐dimensional discrete fracture network structure with hydraulic tomography
Porta et al. Inverse modeling of geochemical and mechanical compaction in sedimentary basins through P olynomial C haos E xpansion
Zhu et al. Improved estimation of hydraulic conductivity by combining stochastically simulated hydrofacies with geophysical data
Lu et al. Analytical sensitivity analysis of transient groundwater flow in a bounded model domain using the adjoint method
Murakami et al. Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area
Zhang et al. A novel deep learning approach for data assimilation of complex hydrological systems
Esfandiar et al. Impact of space‐time mesh adaptation on solute transport modeling in porous media
Wu et al. Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface