Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Using Self Organizing Maps to Expose Direct Hydrocarbon Indicators

Utilizing machine learning in Paradise to define and reveal features not seen in conventional interpretation in an offshore Gulf of Mexico oil/gas field. The SOM analyses using DHI characteristics and seismic attributes to reveal hydrocarbon contacts, amplify attenuation features and define ampliltude conformance in a Class 3 AVO.

Eagle Ford Case Study

Eagle Ford Case Study

Latest Technology for Seismic Interpretation: Direct detection & delineation of facies architecture in the Eagle Ford Group or How did the Eagle Ford GP get Made? A presentation by Patricia Santogrossi at the 2016 SEG Annual Convention.

Submitting....

X

What can we help you find today ?

More information about machine learning
More information about Paradise
More information on attributes
Identifying DHIs using SOM
Identifying thin beds / interpreting below tuning
Identifying geobodies using SOM
Something else...
Just looking around

Please tell us a bit about yourself so that we can provide the right information.

Your Role

Geoscience manager
Geophysicist
Geologist
IT
Senior Manager
G&G Technology
Other

And where you work in the industry?

E&P company
Consulting
Student
Technology / Equipment company
Other
X

Send Message