The webinar presents the following key ideas that will benefit anyone engaged in reservoir delineation:

1. Set up petrophysical properties as a discrete categorical variable to apply bi-variate statistics.


2. Test the statistical relationship between machine learning neurons and reservoir properties.


3. Generate histrograms of machine learning neurons and petrophysics to identify reservoirs.


4. Apply the machine learning classification results to stratigraphic analysis and prediction.

Abstract
The tools of petrophysics, well logs, machine learning, and bi-variate statistics are applied in an integrated methodology to identify and discriminate reservoirs in a region of interest 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:

1. Conventional: Channel slope and fan facies environments offhore Mexico


2. 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 Vsahele is applied and a specific Effective Porosity range bounds 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 a 3D seismic volumes across a region of interest.