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

Maldonado-Cruz et al., 2023 - Google Patents

Sonic Well-Log Imputation Through Machine-Learning-Based Uncertainty Models

Maldonado-Cruz et al., 2023

View PDF
Document ID
7543428629967135887
Author
Maldonado-Cruz E
Foster J
Pyrcz M
Publication year
Publication venue
Petrophysics

External Links

Snippet

Sonic well logs provide critical information to calibrate seismic data and support geomechanical characterization. Advanced subsurface data analytics and machine learning enable new methods and workflows for property estimation, regression, and classification for …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • 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
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/40—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging
    • G01V1/44—Seismology; Seismic or acoustic prospecting or detecting specially adapted for well-logging using generators and receivers in the same well
    • G01V1/48—Processing data
    • G01V1/50—Analysing 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
    • G01V1/30—Analysis
    • 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
    • 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
    • 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
    • G01V11/002—Details, e.g. power supply systems for logging instruments, transmitting or recording data, specially adapted for well logging, also if the prospecting method is irrelevant
    • 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
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/18—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging
    • G01V3/30—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation specially adapted for well-logging operating with electromagnetic waves
    • 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
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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

Similar Documents

Publication Publication Date Title
Gupta et al. Looking ahead of the bit using surface drilling and petrophysical data: Machine-learning-based real-time geosteering in volve field
Li et al. Quantifying stratigraphic uncertainties by stochastic simulation techniques based on Markov random field
Syed et al. Application of ML & AI to model petrophysical and geomechanical properties of shale reservoirs–A systematic literature review
WO2021130512A1 (en) Device and method for predicting values of porosity lithofacies and permeability in a studied carbonate reservoir based on seismic data
Ali et al. Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan
Li et al. Data-driven in-situ geomechanical characterization in shale reservoirs
Wang et al. Data-driven S-wave velocity prediction method via a deep-learning-based deep convolutional gated recurrent unit fusion network
Wang et al. An improved method for lithology identification based on a hidden Markov model and random forests
Szabó et al. Cluster analysis of core measurements using heterogeneous data sources: An application to complex Miocene reservoirs
Li et al. Robust machine-learning workflow for subsurface geomechanical characterization and comparison against popular empirical correlations
He et al. Comparative study of shallow learning models for generating compressional and shear traveltime logs
Li et al. Direct estimation of discrete fluid facies and fluid indicators via a Bayesian seismic probabilistic inversion and a novel exact PP-wave reflection coefficient
Magana-Mora et al. Machine-learning model for the prediction of lithology porosity from surface drilling parameters
Bhattacharya Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks
Maldonado-Cruz et al. Sonic Well-Log Imputation Through Machine-Learning-Based Uncertainty Models
Otchere Fundamental error in tree-based machine learning model selection for reservoir characterisation
Liu et al. Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation
Mukherjee et al. Vertical lithological proxy using statistical and artificial intelligence approach: a case study from Krishna-Godavari Basin, offshore India
Kannaiah et al. Machine learning approaches for formation matrix volume prediction from well logs: insights and lessons learned
Shen et al. Data-driven interpretation of ultradeep azimuthal propagation resistivity measurements: Transdimensional stochastic inversion and uncertainty quantification
Hou et al. Identification of carbonate sedimentary facies from well logs with machine learning
Bai et al. Intelligent classification of carbonate reservoir quality using multisource geophysical logging and seismic data
Wang et al. Uncertainty and explainable analysis of machine learning model for reconstruction of sonic slowness logs
Mohammadpour et al. Effect of spatial variability of downhole geophysical logs on machine learning exercises
Al-Mudhafar et al. Integration of electromagnetic, resistivity-based and production logging data for validating lithofacies and permeability predictive models with tree ensemble algorithms in heterogeneous carbonate reservoirs