Join us for a joint Technical Luncheon with the Oklahoma City Geological Society and Geophysical Society of OKC

"Beyond Physics in Geophysics"

Speaker:  AAPG Distinguished Lecture John Castagna - Margaret S. and Robert E. Sheriff Endowed Faculty Chair in Applied Seismology at the University of Houston.

OSU Hamm Institute for American Energy, 300 NE 9th St, Oklahoma City, OK 73104

Dates: February 21, 2024
Time: 11:30 am - 1:00 pm
Cost: $25 Members $35 Non-members (lunch included)

“For tomorrow, the main entrance to building will be closed off, everyone will need to enter the garage through our 10^th street entrance. Visitors will still be able to check in an enter the building through the second floor. “

Click Here to Register

BIO: John Castagna is the Margaret S. and Robert E. Sheriff Endowed Faculty Chair in Applied Seismology at the University of Houston. He has degrees in geology, geochemistry, and geophysics and specializes in exploration geophysics. He has more than 40 years of experience in petroleum exploration including more than 20 years in academia and has published more than 100 papers in areas such as rock physics, direct hydrocarbon detection, multi-attribute analysis, and seismic inversion. Despite his ineptitude in that subject, he loves and values physics.

Abstract: Physics is an essential component of geophysics but there is much that physics cannot know or address.  For example, physics alone cannot ascertain that an inverted low seismic impedance is indicative of a coal layer in one stratigraphic interval and the same inverted impedance represents an organic shale reservoir in another.  Such geological non-uniqueness often results in the false conclusion from physics that such distinctions from seismic data are not possible. Applying physics to determine what is not possible disregards what information outside of physics can achieve. Seismology only understands relationships controlled by the wave equation and is thus incapable of predicting geological correlations between parameters and seismic attributes that are not governed by the physics of wave propagation.  Generally, the more geological information that can be properly incorporated into a geophysical prediction, the better.

For the past quarter-century, explorationists have successfully empirically used seismic multi-attribute regression analysis and neural networks to make predictions that physics alone cannot achieve. More recently, deeper neural networks are being employed to perform a variety of geophysical functions. This machine learning has great promise but can also be readily misapplied and abused when used to directly make predictions, especially given the usual paucity of training data.

An alternative approach is to use machine learning to uncover relationships that may not have been foreseen because they were not readily apparent or addressed using physics alone. Once these relationships are found and understood geologically and geophysically, real, rather than artificial, intelligence can be used to predict rock and fluid properties from seismic data. For example, a simple-minded analysis of reflectivity tells us that changing the impedance contrast