Lithology identification using semantic segmentation for well log data

Abstract

In the past decade, machine learning techniques were responsible for a revolution in classification and regression tasks, making it possible to automate some laborious activities, saving time and reducing errors. It is known that the geological logging process is one of the most time-consuming activities accomplished by mining companies. Additionally, it is a subjective activity, and changes in the staff directly affect the geological databases due to different human log interpretation. By developing an automatic log classifier, a company can avoid problems related to the turnover of the staff by standardizing the criteria used to label an interval and can save time by avoiding manual log description. The proposed solution is: given the well log data containing the coordinates, resistivity and natural gamma, the model will be able to predict the presence or absence of coal, and its lithology. The innovation of the methodology proposed, considers not only the geophysical logging values, but additionally inserts the neighbourhood of a given depth as valuable input information, using Fully Convolutional Network. It performs a semantic segmentation using the well log data, which means that model´s input is the complete well log data curve and the trained model will return an output curve giving the probability of the presence of coal, by interval. The results showed good prediction for the binary problem (F1-score 0.79). The multi-class modelling suffers from the lack of data for each class, resulting in a F1-score from 0.38 for the worst result to 0.76 for the best.

Keywords:
well log data; lithology identification; machine learning; semantic segmentation; Fully Convolutional Networks.