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SEG 2019 – San Antonio

SEG 2019 – San Antonio

Geophysical Insights is featuring two new off-the-shelf, deep learning applications in the Paradise® AI workbench

Seismic Facies Classification and Fault Detection

STOP BY BOOTH #1038 FOR A DEMONSTRATION

SEG 2019, 15-20 September
Booth #1038

Solving interpretation problems with deep learning and machine learning technologies

Visit booth #1038 to hear industry thought leaders present applications of machine learning and deep learning technologies to seismic interpretation in different geologic settings.  Learn how technologies like GPU processing will change and enable geoscience workflows.  Here are a few of the topics:

  • Seismic facies classification using deep learning
  • Net reservoir discrimination through multi-attribute analysis
  • Thin bed detection with unsupervised machine learning
  • Case studies of machine learning in geologic settings
  • GPU processing for attribute generation

 

 

Geophysical Insights will be hosting daily lunch & learns at booth 1038 with featured talks by Fabian Rada, Dr. Tom Smith, and Dustin Dewett. Register for the lunch & Learns with the form on this page.

Gain Access to 2019 SEG Booth Presentations or Register for a Lunch & Learn


Speakers & Presentations

Dustin Dewett

Product Manager, Geophysical Insights
Seismic Facies Classification and Fault Detection using Deep Learning

Mike Dunn

SVP of Business Development, Geophysical Insights
Generating Attributes on GPUs

Reynaldo Gomez

Energy Account Manager, NVIDIA
Generating Attributes on GPUs

Dr. Bob Hardage

Geoscience Adviser, Geophysical Insights
Machine Learning Geobodies in the Wolfberry Play of the Permian Basin

Exposing Karst Topography in the Deep Ellenberger of the Permian Basin through Machine Learning

Dr. Carrie Laudon

Senior Geoscientist, Geophysical Insights
Machine Learning Improves Stratigraphic Resolution in the Niobrara

Dr. Ivan Marroquin

Senior Research Geophysicist, Geophysical Insights
Studies in Automated Optimization for Multi-Attribute Classification

Fabian Rada

Senior Adviser to PEMEX, Petroleum Oil & Gas Services
Net Reservoir Discrimination through Multi-Attribute Analysis at Single Sample Scale

Rocky Roden

Senior Geophysicist, Geophysical Insights
Comparing Machine Learning Methods and the Black Box Perception

Machine Learning on the Geoscience Technology Adoption Cycle

Deborah Sacrey

Senior Geoscientist, Geophysical Insights
A Journey through Paradise – Case Histories in Different Depositional Environments

Dr. Tom Smith

President & CEO, Geophysical Insights
Thin Bed Detection with Unsupervised Machine Learning

Mathematical Foundation for Machine Learning of Multi-Attribute Seismic Surveys

For a printable version of the schedule, click here

Presentation Schedule

Time Monday, 16 Sep Tuesday, 17 Sep Wednesday, 18 Sep
9:30-10:00 Exhibition opens at 10 AM Deborah Sacrey: A Journey through Paradise – Case Histories in Different Depositional Environments Deborah Sacrey: A Journey through Paradise – Case Histories in Different Depositional Environments
10:30-11:00 Bob Hardage: Machine Learning Geobodies in the Wolfberry Play of the Permian Basin

Rocky Roden: Comparing Machine Learning Methods and the Black Box Connotation

Carrie Laudon: Machine Learning Improves Stratigraphic Resolution in the Niobrara
12:00-1:00 Fabian Rada: Net Reservoir Discrimination through Multi-Attribute  Analysis at Single Sample Scale Tom Smith: Thin Bed Detection with Unsupervised Machine Learning Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning
1:30-2:30 Mike Dunn and Reynaldo Gomez: Generating Attributes on GPUs Carrie Laudon: Machine Learning Improves Stratigraphic Resolution in the Niobrara Rocky Roden: Machine Learning on the Geoscience Technology Adoption Cycle
3:00-4:00 Tom Smith:  Mathematical Foundation for Machine Learning of Multi-Attribute Seismic Surveys Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning Bob Hardage: Exposing Karst Topography in the Deep Ellenberger of the Permian Basin through Machine Learning
4:30-5:30 Dustin Dewett: Seismic Facies Classification and Fault Detection using Deep Learning Ivan Marroquin: Studies in Automated Optimization for Multi-Attribute Classification Exhibition closes at 4:30 PM

EAGE 2019 – London

EAGE 2019 – London

EAGE 2019, 3-6 June
Booth #740

Solving interpretation problems with deep learning and machine learning

Visit booth #740 to hear industry thought leaders present findings from applying machine learning and deep learning technologies to seismic interpretation in different geologic settings.  Learn how technologies like GPU processing will change and enable geoscience workflows.  Here are a few of the topics:

  • Seismic facies classification using deep learning
  • Applications of machine learning on North Sea data
  • Comparing machine learning methods
  • Multi-spectral fault enhancement using deep learning
  • GPU processing for attribute generation
  • Case studies in different depositional environments

For a printable version of the booth schedule, click here.

Time Tuesday, 4 June   Wednesday, 5 June   Thursday, 6 June  
9:30 – 10:30

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

Tim Gibbons

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

Tim Gibbons

Multiple Seismic Attributes and Machine Learning: North Sea Examples

Tim Gibbons

10:30 – 11:30

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

A Journey through Paradise – Case Histories in Different Depositional Environments

Deborah Sacrey

13:30 – 14:30

Generating Attributes on GPU

Paul Holzhauer and Mike Dunn

Finding the Best Attribute Combination for Seismic Facies Classification

Kurt Marfurt

Machine Learning on the Geoscience Technology Adoption Cycle

Rocky Roden

14:30 – 15:30

Accelerate Seismic Interpretation with Deep Learning

Dustin Dewett

Accelerate Seismic Interpretation with Deep Learning

Dustin Dewett

Introduction to Multi-Spectral Fault Enhancement with Case Studies

Dustin Dewett

15:30 – 16:30

Comparing Machine Learning Methods and the Black Box Perception

Rocky Roden

Machine Learning Applied to 3D Seismic Data from the Denver Julesburg Basin –  Improved Stratigraphic Resolution in the Niobrara

Mike Dunn

 

Access to 2019 EAGE Booth Presentations


Speakers & Presentations

Dr. Kurt Marfurt

Guest Speaker – The University of Oklahoma
Principal Investigator, AASPI Consortium

Finding the Best Attribute Combination for Seismic Facies Classification

Paul Holzhauer

Guest Speaker – NVIDIA
Director of Oil and Gas

Generating Attributes on GPU

Rocky Roden

Senior Geophysicist

Comparing Machine Learning Methods and the Black Box Perception

Machine Learning on the Geoscience Technology Adoption Cycle

Dustin Dewett

Product Manager

Accelerate Seismic Interpretation with Deep Learning

Introduction to Multi-Spectral Fault Enhancement with Case Studies

Deborah Sacrey

Senior Geoscientist

A Journey through Paradise – Case Histories in Different Depositional Environments

Mike Dunn

Senior Vice President, Business Development

Machine Learning Applied to 3D Seismic Data from the Denver Julesburg Basin –  Improved Stratigraphic Resolution in the Niobrara

Hal Green

Director – Marketing & Business Development

For more information, contact Hal at (M) +1 (713) 480-2260

Tim Gibbons

Geoscience Consultant

Multiple Seismic Attributes and Machine Learning: North Sea Examples 

Access to 2019 EAGE Booth Presentations