Digitizer: A Synthetic Dataset for Well-Log Analysis

Abstract

Raster well-log images are digital representations of paper copies that retain the original analog data gathered during subsurface drilling. Geologists heavily rely on these images to interpret well-log curves and gain insights into the geological formations beneath the surface. However, manually extracting and analyzing data from these images is time-consuming and demanding. To tackle these challenges, researchers increasingly turn to computer vision and machine learning techniques to assist in the analysis process. Nonetheless, developing such approaches, mainly those dependent on machine learning requires a sufficient number of accurately labelled samples for model training and fine-tuning. Unfortunately, this is not a straightforward task, as existing datasets are derived from scanned hand-compiled paper copies, resulting in digital images that suffer from noise and errors. Furthermore, these samples only represent images and not the digital signals of the measured natural phenomena. To overcome these obstacles, we present a new synthetic dataset that includes both images and digital signals of well-logs. This dataset aims to facilitate more effective and accurate analysis techniques, addressing the limitations of current methods. By utilizing this dataset, researchers and practitioners can develop solutions that mitigate the shortcomings of existing methods, ultimately leading to more reliable and precise results in interpreting well-log curves and understanding subsurface geological formations.