[go: up one dir, main page]

Li et al., 2019 - Google Patents

Seismic structure interpretation based on machine learning: A case study in coal mining

Li et al., 2019

Document ID
15680935669158024088
Author
Li D
Peng S
Lu Y
Guo Y
Cui X
Publication year
Publication venue
Interpretation

External Links

Snippet

Abstract Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application …
Continue reading at pubs.geoscienceworld.org (other versions)

Classifications

    • 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
    • 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
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/12—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with electromagnetic waves
    • 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
    • G01V3/00—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation
    • G01V3/08—Electric or magnetic prospecting or detecting; Measuring magnetic field characteristics of the earth, e.g. declination, deviation operating with magnetic or electric fields produced or modified by objects or geological structures or by detecting devices
    • 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
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—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
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Zhao et al. Fluid and lithofacies prediction based on integration of well-log data and seismic inversion: A machine-learning approach
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. Seismic structure interpretation based on machine learning: A case study in coal mining
Tariq et al. A new technique to develop rock strength correlation using artificial intelligence tools
Hoyes et al. A review of “global” interpretation methods for automated 3D horizon picking
Lee et al. Electrofacies characterization and permeability predictions in complex reservoirs
Roy et al. Generative topographic mapping for seismic facies estimation of a carbonate wash, Veracruz Basin, southern Mexico
Yang et al. High-fidelity permeability and porosity prediction using deep learning with the self-attention mechanism
Wang et al. A method for well log data generation based on a spatio-temporal neural network
Anifowose et al. Integrating seismic and log data for improved petroleum reservoir properties estimation using non-linear feature-selection based hybrid computational intelligence models
Liu et al. Artificial intelligence method for shear wave travel time prediction considering reservoir geological continuity
Tariq et al. A holistic approach to develop new rigorous empirical correlation for static Young's modulus
US12044123B2 (en) Hydrocarbon evaluation systems
Zhou et al. Data driven modeling and prediction for reservoir characterization using seismic attribute analyses and big data analytics
CN117075221A (en) Reservoir fluid identification method based on combination of seismic elastic properties, resistivity and polarizability
Liu et al. Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation
Li et al. Bayesian neural network and Bayesian physics-informed neural network via variational inference for seismic petrophysical inversion
Ali et al. Data-driven machine learning approaches for precise lithofacies identification in complex geological environments
Zhang et al. Convolutional neural network long short-term memory deep learning model for sonic well log generation for brittleness evaluation
US20250129701A1 (en) Determining spatial distributions of petrophysical properties in a subsurface formation
Ramu et al. Multi-attribute and artificial neural network analysis of seismic inferred chimney-like features in marine sediments: a study from KG Basin, India
Kakouei et al. Lithological facies identification in Iranian largest gas field: a comparative study of neural network methods
Mohammadi et al. Faults and fractures detection using a combination of seismic attributes by the MLP and UVQ artificial neural networks in an Iranian oilfield
Guo et al. Seismic facies-controlled porosity prediction in a tight sandstone reservoir based on the XGBoost algorithm
Tang et al. Incorporation of spatial characteristics into volcanic facies and favorable reservoir prediction