[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Bardy et al., 2019 - Google Patents

Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses

Bardy et al., 2019

View PDF
Document ID
15023770287422208586
Author
Bardy G
Biver P
Caumon G
Renard P
Publication year
Publication venue
Journal of Petroleum Science and Engineering

External Links

Snippet

Underground fluid flow in hydrocarbon reservoirs (or aquifers) is difficult to predict accurately due to geological and petrophysical uncertainties. To quantify that uncertainty, several spatial statistical methods are often used to generate an ensemble of subsurface models …
Continue reading at members.unine.ch (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
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • 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
    • 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
    • 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
    • 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
    • G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
    • G06F17/30994—Browsing or visualization
    • 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
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2217/00—Indexing scheme relating to computer aided design [CAD]

Similar Documents

Publication Publication Date Title
Mo et al. Integration of adversarial autoencoders with residual dense convolutional networks for estimation of non‐Gaussian hydraulic conductivities
Mariethoz et al. The direct sampling method to perform multiple‐point geostatistical simulations
Mariethoz et al. Bayesian inverse problem and optimization with iterative spatial resampling
Cui et al. Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm
Zhou et al. A pattern‐search‐based inverse method
Sun et al. Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques
Zovi et al. Identification of high-permeability subsurface structures with multiple point geostatistics and normal score ensemble Kalman filter
Arnold et al. Uncertainty quantification in reservoir prediction: Part 1—Model realism in history matching using geological prior definitions
Xiao et al. Conditioning of deep-learning surrogate models to image data with application to reservoir characterization
Al-Mudhafar Bayesian kriging for reproducing reservoir heterogeneity in a tidal depositional environment of a sandstone formation
Bardy et al. Oil production uncertainty assessment by predicting reservoir production curves and confidence intervals from arbitrary proxy responses
Xiao et al. Surrogate-assisted inversion for large-scale history matching: Comparative study between projection-based reduced-order modeling and deep neural network
Ma et al. Pilot points method for conditioning multiple-point statistical facies simulation on flow data
Brunetti et al. Hydrogeological model selection among complex spatial priors
Linde et al. Tomogram-based comparison of geostatistical models: Application to the Macrodispersion Experiment (MADE) site
McKenna et al. Efficient uncertainty quantification of reservoir properties for parameter estimation and production forecasting
Boyd et al. Quantifying spatial uncertainty in rock through geostatistical integration of borehole data and a geologist's cross-section
Thibaut et al. Comparing well and geophysical data for temperature monitoring within a Bayesian experimental design framework
Khaninezhad et al. Sparse Randomized Maximum Likelihood (SpRML) for subsurface flow model calibration and uncertainty quantification
Mohd Razak et al. Deep learning for latent space data assimilation in subsurface flow systems
Sahni et al. Generating multiple history-matched reservoir model realizations using wavelets
Song et al. GANSim-surrogate: An integrated framework for stochastic conditional geomodelling
Friedel et al. Hybrid modeling of spatial continuity for application to numerical inverse problems
Simoes et al. Comparative study of machine-learning-based methods for log prediction
Yanshu et al. A three-dimensional model of deep-water turbidity channel in Plutonio oilfield, Angola: From training image generation, optimization to multi-point geostatistical modelling