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13 m WATCH
Introduction to Machine Learning Essentials for Seismic Interpretation
This presentation is ideal for geoscientists, engineers, and data analysts at all experience levels. Concepts are supported with ample illustrations and case studies, complemented by mathematical rigor benefiting the subject.
42 m WATCH
Leveraging Deep Learning in Extracting Features of Interest from Seismic Data
In this webinar, using several seismic surveys acquired from different regions, Dr. Zhao discusses three CNN applications in seismic interpretation: seismic facies classification, fault detection, and channel extraction. These examples demonstrate that CNN models are capable of capturing the complex reflection patterns in seismic data, providing clean images of geologic features of interest, while also carrying a low computational cost.
50 m WATCH
Single Trace Attributes
The presentation focuses on Single trace seismic attributes, which include two general varieties: instantaneous and banded, sometimes called Wavelet attributes. The material starts with an organization of seven principal groups or types of attributes and proceeds to set out five primary groups of Single Trace attributes.
48 m WATCH
Energy Absorption and Traveling Waves
This presentation focuses on the physics of traveling waves and why energy absorption is important to an understanding of seismic data. Starting with a linear second-order vibrating system as a mathematical model, the video presents the classes of waves and their associated measurements.
1 h 13 m WATCH
Advantages of Machine Learning with Multi-Attribute Seismic Surveys
In this presentation, Dr. Smith explains the concept of 3D seismic survey for interpretation, the advantages of seismic surveys by using multi-attribute, how to use machine learning to analyze seismic surveys, and how seismic interpretation with machine learning of multi-attribute seismic surveys have been conducted successfully around the world.
50 m WATCH
Solving Interpretation Problems using Machine Learning on Multi-Attribute, Sample-Based Seismic Data
Deborah Sacrey, Owner and Geophysicist of Auburn Energy, provides a review of the various attribute categories and their possible machine learning application to solve problems in seismic interpretation.
1 h 05 m WATCH
Machine Learning Technologies for Seismic Interpretation with Case Studies
This presentation focuses on the physics of traveling waves and why energy absorption is important to an understanding of seismic data. Starting with a linear second-order vibrating system as a mathematical model, the video presents the classes of waves and their associated measurements.
43 m WATCH
Seismic Interpretation of DHI Characteristics with Machine Learning
The accurate interpretation of DHI characteristics has proven to significantly improve the success rates of drilling commercial wells. In this webinar, Rocky Roden looks at seismic multi-attribute analysis using Self-Organizing Maps (SOMs), a machine learning approach that distills information from numerous attributes to provide an accurate assessment of DHI characteristics.
1 h 12 m WATCH
Seismic Interpretation Below Tuning with Multi-Attribute Analysis
This international webinar describes how multi-attribute seismic analysis is applied using the Paradise software to visualize thin beds and facies below classical seismic tuning thickness. The material is presented by Mr. Rocky Roden, an industry thought leader and Senior Consulting Geophysicist for Geophysical Insights.
57 m WATCH
Advances of Machine Learning in Reservoir Characterization
The application of machine learning to classify seismic attributes at single sample resolution is producing results that reveal more reservoir characterization information than is available from traditional interpretation methods.
PowerPoint Presentation
The Holy Grail of Machine Learning in Seismic Interpretation
Dr. Tom Smith shares the “Holy Grail” of Machine Learning in Seismic Interpretation with the Geophysical Society of Houston.
PowerPoint Presentation
Geobodies in Paradise: a Machine Learning Application
Dr. Smith explains four geobody examples including Golden 3D Survey Geobodies, Eagle Ford Sweet Spot Predictions, Niobrara Sweet Spot Predictions and Stratton Field Strategraphic Fabric by Geobody Shape Classification.
PowerPoint Presentation
Making Sense of Machine Learning
Machine Learning is revolutionizing geoscience and the Oil and Gas industry. As an interpreter, Rocky Roden, explores how machine learning technologies is helping solve problems.
PowerPoint Presentation
Comparison of Seismic Amplitude to SOM Classification
This presentation compares traditional seismic interpretation results with SOM (Self-Organizing Maps) classification achieved with machine learning in Paradise software.
PowerPoint Presentation
Attribute Selection: Machine Learning vs. Interactive Interpretation
Dr. Kurt Marfurt, Principal Investigator at the AASPI Consortium at the University of Oklahoma, shares insights on “Attribute Selection: Machine Learning vs. Interactive Interpretation.”