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Speaker: Carrie Laudon, Geophysical Insights

Over the last few years, because of the increase in low cost computer power and the commercialization of new software tools, individuals and companies have stepped up investigations into the use of machine learning in many areas of E&P.  For geosciences, the emphasis has been in reservoir characterization, seismic data processing and to a lesser extent, interpretation. The benefits of using machine learning (whether supervised or unsupervised) has been demonstrated throughout the literature but, the technology is still not a standard workflow for most seismic interpreters. This lack of uptake can be attributed to several factors including: a lack of software tools, clear and well-defined case histories, and training.  Fortunately, all these factors are being mitigated as the technology matures. Rather than looking at machine learning as an adjunct to the traditional interpretation methodology, machine learning techniques could be the first step in an interpretation workflow.  
 
This talk will feature the application of Principal Component Analysis (PCA) and Self-organizing Maps (SOM) to several 3D seismic interpretation challenges.  It will examine SOM as a robust tool for improving the detection of events below tuning in both synthetic and real world examples and present SOM results from several conventional and unconventional geologic reservoirs along with techniques to calibrate unsupervised seismic classification 

Speaker Biography: Carrie Laudon, Geophysical Insights
A senior geophysical consultant with Geophysical Insights working with their Paradise AI platform since 2017 and joined the company full time in 2020.  Prior roles include VP of Consulting and Microseismic for Global Geophysical Services and 17 years with Schlumberger in various technical, management and sales roles.  She has a PhD in Geophysics from the University of Minnesota and started her career in Alaska with ARCO as a seismic interpreter covering most of the North Slope.

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