Seismic Attribute Analysis for Reservoir Characterization Course

Heather Bedle

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This course delves into the theory and practical applications of 3D seismic data interpretation using seismic attributes for reservoir characterization. Students will gain a comprehensive understanding of various seismic attribute analysis techniques and their application in identifying and characterizing subsurface geological features. The course emphasizes hands-on experience through guided laboratory exercises, allowing students to develop practical skills in seismic data manipulation, attribute computation, and interpretation.Read more...

Throughout the course, students will explore a wide range of seismic attributes, including geometric, spectral, and texture attributes. They will learn how to condition seismic amplitude data, enhance attribute images, and apply advanced techniques such as multiattribute analysis, dimensionality reduction, and machine learning-based classification methods. By the end of the course, students will be equipped with the knowledge and skills to effectively use seismic attributes for improved reservoir characterization and interpretation.

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Course contents

    Chapter 1 - Introduction to Seismic Attributes

    01-01 - Why Utilize Seismic Attributes

    01-02 - Review of Seismic Fundamentals

    01-03 - Tuning and Resolution

    01-04 - Instantaneous and Complex Trace Attributes

    01-05 - Lab Software Installation and Seismic Data Loading

    Chapter 2 - Fundamental Seismic Attributes

    02-01 - Geometric Attributes and Their Applications

    02-02 - Frequency Domain Attribute Analysis

    02-03 - Multispectral Coherence

    02-04 - Lab Computing Geometric and Spectral Attributes

    02-05 - Enhancement and Filtering of Attribute Data

    02-06 - Lab Seismic Amplitude Data Conditioning

    Chapter 3 - Multi-Attribute Analysis

    03-01 - Visualization Techniques for Attribute Analysis

    03-02 - Multiattribute Integration and Interpretation

    03-03 - Interactive Multi-Attribute Analysis Using Visualization

    Chapter 4 - Machine Learning with Seismic Attributes

    04-01 - Dimension Reduction for Multi-Attribute Analysis

    04-02 - Unsupervised Clustering of Seismic Attributes

    04-03 - Lab Unsupervised Clustering Using Kmeans

    04-04 - Lab Unsupervised Clustering Using SOM and GTM