Using Machine Learning Techniques To QC Log Data Before A Study

Abstract

Summary A large part of a petrophysics project lies in sorting and tidying up the input data, trying to fix the logs where they are bad or missing. Another step is identifying where the log response is not as expected. Typically this is done by looking at log plots and crossplots and making judgements on the fly, often in individual wells. The answers are often people-dependent. The advent of machine learning techniques has the potential to change this by enabling users to incorporate large quantities of data and view differences in a more holistic way. This project involved a set of wells from the Barents Sea with the objective of calibrating the logs with geological observed depositional facies from cored wells, and then using just the logs to propagate those to uncored wells.