Automatic Fracture Identifications From Image Logs With Machine-Learning Approaches: A Contest Summary
Hyungjoo Lee, Helmerich & Payne, Ramin Zamani, ConocoPhillips, Lei Fu, Aramco Americas, Jaehyuk Lee, Baker Hughes, Chicheng Xu, Wen Pan, Michael Ashby, Devon Energy, Vahid Dehdari, ConocoPhillips, Saleh Alatwah, Saudi Aramco, Juntao Ma, SLB, Jiaxin Li, M. Amin Nizar C.A. Razak · Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description · 2025
Borehole image logs are essential for characterizing subsurface formations, particularly in identifying fractures that influence reservoir behavior and productivity. Manual interpretation of such logs, however, remains time consuming and susceptible to subjectivity and inconsistency. To address these challenges, the SPWLA Petrophysical Data-Driven Analytics special interest group (PDDA SIG) launched its 4th Annual Machine-Learning Competition, aimed at developing automated methods for accurate, efficient, and reproducible fracture detection. The competition utilized resistivity image logs and conventional quadruple-combo logs from eight wells in the Western Canadian Sedimentary Basin (WCSB), accompanied by expert-labeled fracture annotations for training. A separate blind test data set from two additional wells in the same basin was reserved for final evaluation. Participants were provided with a Jupyter Notebook containing preprocessed data and a baseline framework to facilitate model development. Submissions were evaluated based on F1 score and averaged root mean squared error (RMSE) on the blind test set, reflecting both classification accuracy and predictive reliability. This paper reviews the top five approaches submitted, highlighting key methodologies, feature engineering strategies, and model architectures that led to improved fracture detection performance. Our results demonstrate that advanced machine-learning techniques can substantially enhance the consistency and accuracy of fracture identification from borehole image logs. These findings support the integration of data-driven solutions into petrophysical workflows, offering scalable and objective tools to augment or replace manual interpretation, ultimately improving decision making in exploration and production operations.