Schlumberger Technology Corporation

Austin Boyd

Josselin Kherroubi

Nadege Bize-Forest

Victoria Baines

Lucas Lima De Carvalho

Peter Schlicht

Laura Lima Angelo Dos Santos

Using machine learning for sedimentary facies prediction by using one or more logs acquired in a borehole. This includes performing a petrophysical clustering of borehole depths wherein the depths of the borehole are gathered into clusters based on similarities in the one or more logs. Also performed is a log inclusion optimization, including a selection of one or more parameters of the petrophysical clustering, wherein the one or more parameters include a number and/or type of considered logs among the one or more logs and/or a clustering method. Also performed is a classification of the clusters into core depositional facies using one or more predetermined rules.

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Rio de Janeiro, Brazil

Austin Boyd