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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.

Intro Slide for holy grail machine learning

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.

Title slide for SEG 2018 machine learning

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.

Title slide for making sense of machine learning

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.

Title slide comparison of traditional interpretation results to SOM machine learning in paradise

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.

Title slide for Marfurt presentation

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.”

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