Big Data

Big Data and the use of AI is an ever-increasing part of the oil and gas industry. Although applications are ever-evolving, presently AI is most often used in seismic processing or maintenance scheduling. But what about for geology? While petrophysical workflows are increasingly implementing AI, ground truthing mineralogy is difficult. Drill cuttings are quite literally a mixed bag, and plug-based analysis provides a measurement only on the 1 inch plug that is sampled, disregarding the interval between sampling points. 

Vidence's approach to Automated Mineralogy of drill cuttings provides not only a bulk analysis (the average of the whole sample), but also the mineralogical breakdown of each lithotype contained. Typically over 400 variables are reported and include not only mineralogy by also porosity, grain density and size for each lithtoype.

Vidence provides an interactive platform to view and interpret results. Results can be broken down to understand not only what is the most abundant lithotype or the most porous, but what is the most significant (incorporating abundance as well as porosity).

Other data such as wireline logs, surveys, and MWD data can be integrated into the dataset to allow for the visual comparison with mineralogy at any point along the wellbore, or a high level view of log responses to certain lithotypes or minerals across the producing field. 

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