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

Zhang et al., 2021 - Google Patents

Applying convolutional neural networks to identify lithofacies of large-n cores from the Permian Basin and Gulf of Mexico: The importance of the quantity and quality of …

Zhang et al., 2021

Document ID
14657837834378094968
Author
Zhang J
Ambrose W
Xie W
Publication year
Publication venue
Marine and Petroleum Geology

External Links

Snippet

Convolutional neural networks (CNNs), one of the most widely employed deep learning techniques, have achieved great success in image recognition. However, few attempts have been made in sedimentary studies, partially because it is challenging to generate a large …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/34Displaying seismic recordings or visualisation of seismic data or attributes
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/61Analysis by combining or comparing a seismic data set with other data
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • G01V1/28Processing seismic data, e.g. analysis, for interpretation, for correction
    • G01V1/282Application of seismic models, synthetic seismograms
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00Details of seismic processing or analysis
    • G01V2210/60Analysis
    • G01V2210/66Subsurface modeling
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V99/00Subject matter not provided for in other groups of this subclass
    • G01V99/005Geomodels or geomodelling, not related to particular measurements
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/38Processing data, e.g. for analysis, for interpretation, for correction
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V3/00Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V5/00Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
    • G01V5/0008Detecting hidden objects, e.g. weapons, explosives
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V11/00GEOPHYSICS; 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
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Baraboshkin et al. Deep convolutions for in-depth automated rock typing
Zhang et al. Applying convolutional neural networks to identify lithofacies of large-n cores from the Permian Basin and Gulf of Mexico: The importance of the quantity and quality of training data
Di et al. Developing a seismic texture analysis neural network for machine-aided seismic pattern recognition and classification
Ameur-Zaimeche et al. Lithofacies prediction in non-cored wells from the Sif Fatima oil field (Berkine basin, southern Algeria): a comparative study of multilayer perceptron neural network and cluster analysis-based approaches
Saxena et al. Application of deep learning for semantic segmentation of sandstone thin sections
Zhang et al. Automatic prediction of shear wave velocity using convolutional neural networks for different reservoirs in Ordos Basin
Zhang et al. Seismic facies analysis based on deep learning
Masroor et al. A multiple-input deep residual convolutional neural network for reservoir permeability prediction
Koeshidayatullah et al. Faciesvit: Vision transformer for an improved core lithofacies prediction
Jiang et al. Deep-learning-based vuggy facies identification from borehole images
Zaitouny et al. Fast automatic detection of geological boundaries from multivariate log data using recurrence
Bhattacharya Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks
Tian et al. A novel deep learning method based on 2-D CNNs and GRUs for permeability prediction of tight sandstone
Griffith et al. Deep learning applied to seismic attribute computation
Cedou et al. Preliminary geological mapping with convolution neural network using statistical data augmentation on a 3D model
Bönke et al. Data augmentation for 3D seismic fault interpretation using deep learning
Bhattacharya Summarized applications of machine learning in subsurface geosciences
Wang et al. Intelligent seismic stratigraphic modeling using temporal convolutional network
Tran et al. Deep convolutional neural networks for generating grain-size logs from core photographs
Landgrebe et al. Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach
Salazar et al. Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes
Yadav et al. Agglomerative clustering to improve the resolution of pseudo well stochastic seismic inversion: A case study
Wang et al. Enhanced seismic data segmentation using an assembled scSE-Res-UNet deep neural network
Akram et al. ResNet and CWT Fusion: A New Paradigm for Optimized Heterogeneous Thin Reservoir Evaluation
Kupssinskü et al. Hyperspectral data as a proxy for porosity estimation of carbonate rocks