We have used some very simple python code to view thin sections from a porosity vs. permeability cross plot using python's Altair and Pane.
Many of us make a living at characterizing reservoirs and yet sometimes we can get separated from the rock itself. Do we truly understand the textural differences in the reservoir? Do we know what is controlling reservoir quality? In the past we have used Spotfire to integrate our Routine Core Analysis (RCA) with the SCAL, but we have found new ways to use python to accomplish the same task. We are using Altair and Vega Panes for our python coding.
There is an added bonus in that we can also select the samples from the cross plot and a table to the right of the cross plot shows us the RCA data associated with the selected samples. We are searching for a method to actually show the thumbnails of the thin section image in the same table of selected data too. More to come.
This clastic example is a combination of a few samples where we had porosity, permeability and thin section photomicrographs.
The workflow is simple. Our Jupyter Notebook reads in the Excel file as shown below to create a panda DataFrame in python:
and then we prooduce the following output. To observe the representative Thin Section, we hover over each sample to observe the image of the thin section. If we select data points from the cross plot, then we see the RCA data for the select samples.
In this GitHub repository we have placed our image files in a ./data subdirectory to reduce the clutter and better organize our data.
We have also integrated Capillary Pressure data using the SCAL Thomeer Capillary Pressure parameters to calculate our Pc data:
You will find these plots at the end of the Jupyter Notebook. You can also observe the thin section from the Pc curves too.
