Lithology Classification Based on Well Log Data - A Benchmark for Machine Learning Models

Lithology classification based on well log data has attracted the attention of geologists, as the extraction of rock samples from the wellbore is a costly process. Although some machine learning techniques have recently been applied to the problem, the existing studies are not standardized in terms of data processing and evaluation protocols. This paper aims to bridge this gap by providing common ground for the comparison of prospective works on lithology classification from well logs. The proposed bench-mark is inspired by a thorough review of the literature, from which the main aspects of an experimental protocol are captured under the principles of simplicity, variety, impartiality, and meaningfulness.