Geophysics Python sprint 2018 – day 2 and beyond, part II

In the last post I wrote about what Volodymyr and I worked on during a good portion of day two of the sprint in October, and continued to work on upon our return to Calgary.

In addition to that I also continued to work on a notebook example, started in day one, demonstrating on how to upscale sonic and density logs from more than one log at a time using Bruges ‘ backusand Panda’s groupby. This will be the focus of a future post.

The final thing I did was to add an error_flag functionto Bruge ‘ Petrophysics. The function calculates the difference between a predicted and real curve and flags errors if either the difference between the curves exceeds a user-defined distance (in standard deviation units) from the mean difference (default method), or if the curves have opposite slope. The result is a binary error log that can then be used to generate QC plots, like the one below,  to evaluate the performance of the prediction processes in a more (it is my hope) insightful way.

The inspiration for this stems from a discussion over coffee I had 5 or 6 years ago with Glenn Larson, a Geophysicist at Devon Energy, about the limitations of (and alternatives to) using a single global score when evaluating the result of seismic inversion against wireline well logs (the ground truth). I’d been holding that in the back of my mind for years, then finally got to it last Fall.


Summary statistics can also be calculated by stratigraphic unit, as demonstrated in the accompanying Jupyter Notebook.

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