Comolli et al., 2019 - Google Patents
Mechanisms, upscaling, and prediction of anomalous dispersion in heterogeneous porous mediaComolli et al., 2019
View PDF- Document ID
- 5264178468674269019
- Author
- Comolli A
- Hakoun V
- Dentz M
- Publication year
- Publication venue
- Water Resources Research
External Links
Snippet
We study the upscaling and prediction of large‐scale solute dispersion in heterogeneous porous media with focus on preasymptotic or anomalous features such as tailing in breakthrough curves and spatial concentration profiles as well as nonlinear evolution of the …
- 239000006185 dispersion 0 title abstract description 43
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
- G06F17/18—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
- 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
- G06F17/11—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
- 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
- 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
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F2217/00—Indexing scheme relating to computer aided design [CAD]
- G06F2217/46—Fuselage
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Comolli et al. | Mechanisms, upscaling, and prediction of anomalous dispersion in heterogeneous porous media | |
Tartakovsky et al. | Physics‐informed deep neural networks for learning parameters and constitutive relationships in subsurface flow problems | |
Hyman et al. | Linking structural and transport properties in three‐dimensional fracture networks | |
Hakoun et al. | Upscaling and prediction of Lagrangian velocity dynamics in heterogeneous porous media | |
Bianchi et al. | Geological entropy and solute transport in heterogeneous porous media | |
Nowak | Best unbiased ensemble linearization and the quasi‐linear Kalman ensemble generator | |
Dagan et al. | Flow and transport in highly heterogeneous formations: 1. Conceptual framework and validity of first‐order approximations | |
Xue et al. | A multimodel data assimilation framework via the ensemble Kalman filter | |
Sole‐Mari et al. | A KDE‐based random walk method for modeling reactive transport with complex kinetics in porous media | |
Fiori et al. | Flow and transport in highly heterogeneous formations: 2. Semianalytical results for isotropic media | |
Cvetkovic et al. | Solute transport in aquifers of arbitrary variability: A time‐domain random walk formulation | |
Chen et al. | Three‐dimensional Bayesian geostatistical aquifer characterization at the Hanford 300 Area using tracer test data | |
De Barros et al. | The concept of comparative information yield curves and its application to risk‐based site characterization | |
Trefry et al. | Numerical simulations of preasymptotic transport in heterogeneous porous media: Departures from the Gaussian limit | |
De Barros et al. | First‐order based cumulative distribution function for solute concentration in heterogeneous aquifers: Theoretical analysis and implications for human health risk assessment | |
Valstar et al. | A representer‐based inverse method for groundwater flow and transport applications | |
Lu et al. | An improved multilevel Monte Carlo method for estimating probability distribution functions in stochastic oil reservoir simulations | |
Haslauer et al. | Effects of non‐Gaussian copula‐based hydraulic conductivity fields on macrodispersion | |
Porta et al. | Continuum‐scale characterization of solute transport based on pore‐scale velocity distributions | |
Sole‐Mari et al. | Lagrangian modeling of reactive transport in heterogeneous porous media with an automatic locally adaptive particle support volume | |
Sherman et al. | Characterizing the influence of fracture density on network scale transport | |
Massoudieh | Inference of long‐term groundwater flow transience using environmental tracers: A theoretical approach | |
Srzic et al. | Impact of aquifer heterogeneity structure and local‐scale dispersion on solute concentration uncertainty | |
Xu et al. | Characterization of non‐Gaussian conductivities and porosities with hydraulic heads, solute concentrations, and water temperatures | |
Sreekanth et al. | Design of optimal groundwater monitoring well network using stochastic modeling and reduced‐rank spatial prediction |