Geochemical Biodegraded Oil Classification Using a Machine Learning Approach

Abstract

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of Convolutional Neural Networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one chromatographic oil images from different worldwide basins (Brazil, the USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations. Subsequently, the recurring features are grouped into common feature groups. The training result obtained an accuracy (CA) of 96.7% and an area under the ROC (Receiver Operating Characteristic) curve (AUC) of 99.7%. In turn, the test result obtained a 97.6% CA and a 99.7% AUC. This work suggests that the processing of petroleum chromatographic images through CNN can become a new tool for the study of petroleum geochemistry since the chromatograms can be loaded, read, grouped, and classified more efficiently and quickly than the evaluations applied in classical methods.

Keywords

• convolutional neural networks
• biodegradation
• organic geochemistry
• orange data mining
• chromatogram image