Predicted Stratigraphy: A Case Study from the Sureste Basin (Gulf of Mexico)

Biostratigraphy represents one of the key disciplines of geology by allowing the arrangement of geological formations in space and time based on fossil assemblages. Due to its significance in the oil and gas industry and the fast pace of technological innovations and developments in geosciences, the interpreted biostratigraphical data is prone to become quickly outdated and thus preventing its use in future interpretations. However, the presence of a large amount of available data provides an excellent opportunity for novel studies aiming to update species taxonomies, detect reworking specimens, train machine learning models and test prediction models in order to digitalize biostratigraphic approaches. In this study therefore, we use various data science and machine learning techniques to demonstrate the potentials of an automated biostratigraphic approach. We take advantage of legacy data collected in the Sureste Basin, Gulf of Mexico, where we transformed the original dataset into a final stratigraphic framework. Our inferences indicate that we can get an accurate first insight into the stratigraphy at the studied location within a very short timeframe. Even though inconsistencies were found, our approach proved its potential for future work, which could be improved by increasing the prediction accuracy of biostratigraphic events.