Dr. Tom Smith presenting on Machine Learning at the 3D Seismic Symposium on March 6th in Denver
What is the "holy grail" of Machine Learning in seismic interpretation? by Dr. Tom Smith, GSH Luncheon 2018
Using Attributes to Interpret the Environment of Deposition - A Video Course. Taught by Kurt Marfurt, Rocky Roden, and ChingWen Chen
Dr. Kurt Marfurt and Dr. Tom Smith featured in the July edition of AOGR on Machine Learning and Multi-Attribute Analysis
Rocky Roden and Ching Wen Chen in May edition of First Break - Interpretation of DHI Characteristics using Machine Learning

Seismic Interpretation of DHIs with Machine Learning

July 25, 2017 - Paradise® Webinar

TitleSeismic Interpretation of DHIs with Machine Learning

Presenter: Rocky Roden, Sr. Consulting Geophysicist, Geophysical Insights

Direct Hydrocarbon Indicators (DHIs) are seismic anomalies due to the presence of hydrocarbons, caused by changes in rock physic properties, typically of the hydrocarbon-filled reservoir in relation to the encasing rock or the brine portion of the reservoir.  The accurate interpretation of DHI characteristics has proven to significantly  improve the success rates of drilling commercial wells.  In this webinar, Rocky Roden will look 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.     

Topics in this webinar will cover:

  • The impact of DHI’s on success rates of commercial wells
  • Top DHI characteristics linked to well success rates
  • Seismic attribute applications to DHIs
  • Using machine learning for multi-attribute analysis
  • Case Study: Offshore Gulf of Mexico
  • Case Study: Onshore Texas

The webinar is open to those interested in learning more about how the application of multi-attribute analysis is key to interpreting Direct Hydrocarbon Indicator (DHI) characteristics.