GeoTS: A time series classification framework to identify geological formations

Studying the lithography of the Earth's subsurface in geoscience involves analyzing different geological formations to model and characterize reservoirs. This process uses drilled well measurements to connect specific geological formations or tops. Reservoir modeling is essential in geothermal, mineral mining, oil and gas, and carbon storage. Traditional well correlation algorithms are time-consuming and costly, but Deep Learning (DL) models have shown promising results. This paper presents GeoTS, a Python library that employs advanced time series classification DL models for well correlation. It uses drilling trajectory depth and gamma-ray well logs as inputs, predicting the depths of formations' tops. Gamma-ray signatures around these depths are extracted, cleaned, and clustered using Dynamic Time Warping (DTW) and machine learning models like HDBSCAN and OPTICS. The implementation includes various deep learning architectures (FCN, InceptionTime, XceptionTime, XCM, LSTM-FCN) and new models (LSTM-2dCNN, LSTM-XCM). The results indicate faster computation and higher accuracy than industry benchmarks, making this the first open-source benchmark for the well correlation task, to our knowledge.