Geophysical Insights hosting the 2018 Oil & Gas Machine Learning Symposium in Houston on September 27, 2018
Introduction to Machine Learning for Multi–Attribute Interpretation and AASPI attributes - A 1-day, DGS Continuing Education course in Denver, CO on September 18th
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
 

Interpret Results of a SOM

Once the SOM results are available, use the interactive 2D Colormap in Paradise to analyze the results.  The 2D Colormap, which is unique to Paradise, presents a logical map of the classes of data in the Universal Viewer.  Each hexagonal object (neuron) in the 2D Colormap can be selected to highlight the corresponding class of data in the Universal Viewer.  The SOM process can reveal geologic and stratigraphic features, fracture trends and sweet spots in unconventional settings, and direct hydrocarbon indicators (DHI’s) such as flat spots.  The types of features and geobodies revealed depend on the selected attributes analyzed by the SOM process.

Being able to identify/quantify the appropriate attributes for the region and highlight geobodies deserving further investigation promises to accelerate the interpretation process and reduce exploration risk.  And, quantifying the attributes that respond to a given geobody, as shown in the screen on the right, will dramatically alter the interpretation workflow altogether.