Collection of geophysical notes in the form of IPython/Jupyter notebooks.

We have used the new hierarchical carbonate reservoir benchmarking case study created by Costa Gomes J, Geiger S, Arnold D to be used for reservoir characterization, uncertainty quantification and history matching.

Carbonate Reservoir Characterization workflow using Clerke’s carbonate Arab D Rosetta Stone calibration data to provide for a full pore system characterization with modeled saturations using Thomeer Capillary Pressure parameters for an Arab D complex carbonate reservoir

This repository has all of the python code and methods for a Comprehensive interactive petrophysical analysis workflow using python’s Panel and Param for both Jupyter Notebooks and as python loglans in a Geolog project.

Analysis notebooks for the geolink well log dataset

This repository employs NMR log echo train inversion using Scipy optimization with Tikhonov regularization, which adds a penalty term equal to the sum of the squares of the parameters. This project has both a Jupyter Notebook as well as a Geolog project.

The notebook shows the principle behind the minimum incremental pore volume (MIPV) and estimating the effective pore-throat radius (EPTR) as outlined in steps in the paper above.

The notebook demonstrates the workflow for obtaining pore size distribution from binarized micro-CT images. The general principle involves identifying each pore, estimating the volume of each pore, and ultimately determining the radius of a sphere with an equivalent volume of each pore.