International audience

In geoscience, it is necessary to study the lithography of the Earth's subsurface, which consists of different stratified layers called geological formations.This study performs well correlation task to model and characterize reservoirs.This operation links the beginning of specific geological formations called tops using measurements from drilled wells.Although data are abundant, the traditional algorithms used for well correlation are semi-automated, requiring significant time and high computational cost.This paper introduces GeoTS, a Python library to apply cutting-edge time series classification models to perform well correlation in a completely automated setting.As input, take the drilling trajectory depth and gamma-ray well logs, which measure the natural radioactivity across the well depth trajectory.The top depths of the formations are predicted as an output.The gamma-ray signatures are extracted around the top depths assigned by geologists.Preprocessing is performed to clean and cluster these signatures using the Dynamic Time Wrapping (DTW) distance and HDBSCAN.Implementation of existing deep learning architectures (FCN, InceptionTime, XceptionTime, XCM, LSTM-FCN) and new architecture (LSTM-2dCNN, LSTM-XCM) are performed.Our experiments demonstrate faster computation with an increase in accuracy.GradCAM has also been implemented for model explainability.Experiments were performed using Colorado oil fields and deployed on Wyoming oil fields.The deployment has provided us with critical insights regarding the improvements needed.