[go: up one dir, main page]

Alameedy et al., 2022 - Google Patents

Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir, Iraq

Alameedy et al., 2022

Document ID
12542413510669629460
Author
Alameedy U
Alhaleem A
Isah A
Al-Yaseri A
El-Husseiny A
Mahmoud M
Publication year
Publication venue
Journal of Applied Geophysics

External Links

Snippet

Shear wave slowness is needed in reservoir characterization for seismic modeling, amplitude variation analysis and determination of rock elastic properties. Conventional methods such as multi-array polarization acoustic measurements as well as Dipole Shear …
Continue reading at www.sciencedirect.com (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
    • G01V1/306—Analysis for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles
    • 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
    • G01V2210/00—Details of seismic processing or analysis
    • G01V2210/60—Analysis
    • G01V2210/66—Subsurface modeling
    • G01V2210/665—Subsurface modeling using geostatistical modeling
    • 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
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • G01V2210/62—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
    • 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
    • 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
    • G01V5/00—Prospecting or detecting by the use of nuclear radiation, e.g. of natural or induced radioactivity
    • 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
Ali et al. Classification of reservoir quality using unsupervised machine learning and cluster analysis: Example from Kadanwari gas field, SE Pakistan
Bolandi et al. Analyzing organic richness of source rocks from well log data by using SVM and ANN classifiers: a case study from the Kazhdumi formation, the Persian Gulf basin, offshore Iran
Ali et al. A novel machine learning approach for detecting outliers, rebuilding well logs, and enhancing reservoir characterization
Lee et al. Electrofacies characterization and permeability predictions in complex reservoirs
Venkataramanan et al. An unsupervised learning algorithm to compute fluid volumes from NMR T1-T2 logs in unconventional reservoirs
Hill et al. Improving automated geological logging of drill holes by incorporating multiscale spatial methods
Ali et al. Reservoir characterization through comprehensive modeling of elastic logs prediction in heterogeneous rocks using unsupervised clustering and class-based ensemble machine learning
Male et al. Lessons for machine learning from the analysis of porosity-permeability transforms for carbonate reservoirs
Scheidt et al. Probabilistic falsification of prior geologic uncertainty with seismic amplitude data: Application to a turbidite reservoir case
Szabó et al. Cluster analysis of core measurements using heterogeneous data sources: An application to complex Miocene reservoirs
Alameedy et al. Predicting dynamic shear wave slowness from well logs using machine learning methods in the Mishrif Reservoir, Iraq
Zahmatkesh et al. Integration of well log-derived facies and 3D seismic attributes for seismic facies mapping: A case study from mansuri oil field, SW Iran
Massiot et al. Statistical methods of fracture characterization using acoustic borehole televiewer log interpretation
Ali et al. Data-driven lithofacies prediction in complex tight sandstone reservoirs: a supervised workflow integrating clustering and classification models
Saporetti et al. Extreme learning machine combined with a differential evolution algorithm for lithology identification
Bhattacharya Unsupervised time series clustering, class-based ensemble machine learning, and petrophysical modeling for predicting shear sonic wave slowness in heterogeneous rocks
Mahadasu et al. Electrofacies estimation of carbonate reservoir in the Scotian Offshore Basin, Canada using the multi-resolution graph-based clustering (MRGC) to develop the rock property models
Hussain et al. Application of deep learning for reservoir porosity prediction and self organizing map for lithofacies prediction
Owusu et al. Seismic facies analysis using machine learning techniques: a review and case study
Kurniadi et al. Local mean imputation for handling missing value to provide more accurate facies classification
Hussain et al. Machine learning-driven classification of hydraulic flow units for enhanced reservoir characterization
Artun et al. An Integrated Workflow for Data Analytics-Assisted Reservoir Management with Incomplete Well Log Data
Emelyanova et al. Detecting specific facies in well-log data sets using knowledge-driven hierarchical clustering
Yazid et al. Machine learning-based techniques for reservoir characterization using rock-typing from well logs
Hussain et al. Enhancing reservoir characterization: A novel machine learning approach for automated detection and reconstruction of outliers-affected well log curves