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

Chen et al., 2006 - Google Patents

A committee machine with empirical formulas for permeability prediction

Chen et al., 2006

Document ID
8795357239748107372
Author
Chen C
Lin Z
Publication year
Publication venue
Computers & Geosciences

External Links

Snippet

This study integrates log-derived empirical formulas and the concept of the committee machine to develop an improved model for predicting permeability. A set of three empirical formulas, such as the Wyllie–Rose, Coates–Dumanoir, and porosity models to correlate …
Continue reading at www.sciencedirect.com (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
    • G06F17/18—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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
Chen et al. A committee machine with empirical formulas for permeability prediction
Caers Geostatistical reservoir modelling using statistical pattern recognition
Evensen et al. Using the EnKF for assisted history matching of a North Sea reservoir model
Gaoming et al. An iterative ensemble Kalman filter for data assimilation
Urang et al. A new approach for porosity and permeability prediction from well logs using artificial neural network and curve fitting techniques: A case study of Niger Delta, Nigeria
Yeten et al. A comparison study on experimental design and response surface methodologies
Khan et al. Machine learning derived correlation to determine water saturation in complex lithologies
Subbey et al. Prediction under uncertainty in reservoir modeling
Zabalza-Mezghani et al. Uncertainty management: From geological scenarios to production scheme optimization
Erbas et al. Effect of sampling strategies on prediction uncertainty estimation
Jamshidi Chenari et al. Non-stationary realisation of CPT data: considering lithological and inherent heterogeneity
Wong et al. Multiple permeability predictions using an observational learning algorithm
Li et al. Novel multiple resolutions design of experiment/response surface methodology for uncertainty analysis of reservoir simulation forecasts
Mantica et al. Combining global and local optimization techniques for automatic history matching production and seismic data
Grose et al. Inversion of structural geology data for fold geometry
Gervais et al. Probability maps of reservoir presence and sensitivity analysis in stratigraphic forward modeling
Ishitsuka et al. Bayesian and neural network approaches to estimate deep temperature distribution for assessing a supercritical geothermal system: evaluation using a numerical model
Rotondi et al. Hydrocarbon production forecast and uncertainty quantification: A field application
Chaikine et al. A new machine learning procedure to generate highly accurate synthetic shear sonic logs in unconventional reservoirs
Santhosh et al. A hybrid differential evolution algorithm approach towards assisted history matching and uncertainty quantification for reservoir models
Kashib et al. A probabilistic approach to integrating dynamic data in reservoir models
Ja’fari et al. Integration of ANFIS, NN and GA to determine core porosity and permeability from conventional well log data
Fetel et al. Reservoir flow uncertainty assessment using response surface constrained by secondary information
Le Ravalec Optimizing well placement with quality maps derived from multi-fidelity meta-models
CN116911216B (en) Reservoir oil well productivity factor assessment and prediction method