Dynamic Time Warping algorithm: A geoscience aware AI for automatic interpretation in lithostratigraphy? Insights from an application to the Gulf of Corinth (Greece)

Stratigraphic correlation of well log data is a fundamental step in geosciences. It involves correlating stratigraphic units across multiple wells to build a comprehensive understanding of subsurface geology. Currently, stratigraphic correlation is predominantly performed “manually” by geoscientists. The process is labor-intensive and time-consuming, and interpretations may vary among interpreters due to differences in expertise, experience, and perspective.

Recent advancements in the application of the Dynamic Time Warping (DTW) algorithm have demonstrated its potential to automate and enhance the stratigraphic correlation of well logs. DTW can generate multiple correlation scenarios highlighting different interpretations of subsurface continuity. Thus, the aim of this work is to explore the potential of DTW as a supporting tool in the standard workflows of geoscientists and test it on well log data from IODP Expedition 381 from the Gulf of Corinth. We automatically correlate lithostratigraphic subunits within a 700 m thick stratigraphic unit across two wells, using Natural Gamma Ray (NGR) and Magnetic Susceptibility (MAGS) logs. We selected this dataset because it illustrates the evolution of geological interpretations over time. Between the first version of the IODP data interpretations and a second version published a few years later, significant differences in interpretation were proposed. These differences highlight the critical role of geological expertise in refining subsurface data interpretations and correlations.

The automatic correlations interpreted by DTW showed a minimal average absolute difference with the most recent and updated published correlation, making the human and the machine correlation almost identical. By applying DTW to this dataset, we demonstrate it would have been possible to identify discrepancies and challenges in the interpretations of subunits at the initial stages after data acquisition. This approach could have flagged potential issues even before the IODP data were made available on the public site. Such early identification highlights the potential of DTW as a valuable tool for providing immediate feedback and guiding more accurate stratigraphic interpretations faster.

While DTW significantly reduces the time required for the correlation phase, the time investment needed for data formatting upstream should not be underestimated. Future work on larger datasets will be crucial to better quantify and validate the overall time savings provided by DTW, as well as to optimize the preparatory steps to ensure efficiency in broader applications.

In conclusion, we show that DTW can offer innovative approaches to enhance geological investigations and speed up interpretations. More generally, we consider this work illustrates how data science methods can be leveraged to assist geologists in routine tasks, with our Corinth case study highlighting both the promises and current limitations of digital transformation in well correlations.