Geophysical Insights hosting the 2018 OIl & Gas Machine Learning Symposium in Houston on September 27, 2018
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

Read More Paradise success stories

From scanning volumes for reconnaissance to visualizing features below seismic resolution, these case studies demonstrate broad application of multi-attribute seismic analysis. For example, the first set of four case studies shows how Paradise is used to identify geologic features and stratigraphy well below typical seismic resolution or tuning thickness (1/4 wavelength thickness) with multi-attribute SOM analysis. In addition to conventional seismic amplitude data, seismic attributes enable interpreters to view their seismic data in other planes, measure the rate of change and how fast the rate of change is for certain attributes, and identify phenomena in their data like frequency, amplitude and phase that often reveal information below conventional seismic resolution.  

A multi-attribute analysis combines attributes that reveal features below tuning, thereby identifying geology difficult to visualize by any other method.  Events have even been identified that approach the detection limit (approximately 1/30 of the dominant frequency – typically 5-15 feet in thickness). This thin bed interpretation approach enables a more accurate delineation of reservoir extents, possible permeability barriers, variations in stratigraphy, and subtle structural features not identified with conventional seismic amplitude data.