Identify Reservoirs by Combining Machine Learning, Petrophysics, and Bi-variate Statistics

This webinar features a 45-minute presentation by Dr. Carrie Laudon (CV below). An interactive Q&A with Dr. Laudon will follow her presentation.

The webinar will be held two times. See the time of the webinar in your region.

Title: Identify Reservoirs by Combining Machine Learning, Petrophysics, and Bi-variate Statistics
Presenter: Dr. Carrie Laudon
Date: Tuesday/Wednesday, 1/2 June 2021
What you will learn in this webinar:
  • Set up petrophysical properties as a discrete categorical variable to apply bi-variate statistics.
  • Test the statistical relationship between machine learning neurons and reservoir properties.
  • Generate histograms of machine learning neurons and petrophysics to identify reservoirs.
  • Apply the machine learning classification results to stratigraphic analysis and prediction.
The tools of machine learning, petrophysics, well logs, and bi-variate statistics are applied in an integrated methodology to identify and discriminate reservoirs with hydrocarbon storage capacity. While the use of any one of these methods is familiar, their application together is unique. The webinar presents the process and results from two different geologic settings:
  • Conventional: Channel slope and fan facies environments offshore Mexico
  • Unconventional: Niobrara chalk and shale formation in the U.S.
The webinar is based on work published initially by Leal et al. (2019), and the methodology continues to yield excellent results in conventional and unconventional geologic settings alike.
Petrophysics is used to define sedimentary facies and their Effective Porosity using well logs. Petrophysical ranges are grouped in classes and labeled as categorical variables, specifically “Net Reservoir” and “Not Reservoir.”   First, a lithology cutoff such as Vshale is applied, and a specific Effective Porosity range defines a “Net Reservoir” condition.  Neurons from machine learning are compared to the Net Reservoir condition using bi-variate statistics, determining if there is a statistical relationship between neurons and sedimentary facies. The result is a histogram that reveals which neurons are most responsive to the Net Reservoir condition, enabling a prediction of similar sedimentary facies utilizing 3D seismic volumes across a region of interest.
Carrie Laudon, Ph.D.
Senior Geophysical Consultant
Geophysical Insights
Carolan (Carrie) Laudon holds a PhD in geophysics from the University of Minnesota and a BS in geology from the University of Wisconsin Eau Claire. She has been Senior Geophysical Consultant with Geophysical Insights since 2017 working with Paradise®, the AI seismic workbench. Her prior roles include Vice President of Consulting Services and Microseismic Technology for Global Geophysical Services and 17 years with Schlumberger in technical management and sales. Dr. Laudon’s career has taken her to Alaska, Aberdeen, Scotland, Houston, Texas, Denver, Colorado and Reading, England. She spent five years early in her career with ARCO Alaska as a seismic interpreter for the Central North Slope exploration team.
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