Automated Depth Alignment of Well Logs Using Siamese Neural Networks

Accurate depth alignment of well logs is essential for re-liable subsurface interpretation, influencing petrophysi-cal analysis. Traditional methods like cross-correlation struggle with repetitive lithologies, often leading to false matches and requiring extensive manual corrections(Torres Caceres et al., 2022). While machine learning offers alternatives, supervised approaches demand large labeled datasets, and unsupervised clustering requires extensive post-processing, introducing potential errors(Acharya and Fabian, 2024b; Acharya et al., 2025). This study proposes a Siamese Neural Network (SNN) to au-tomate log alignment by learning geological similarity between reference and unsynchronized logs. The SNN eliminates the need for labeled data, minimizes post-processing, and effectively handles irregular depth er-rors. Tested on gamma-ray (GR) logs from different logging methods within the same well, the proposed method improves Pearson correlation from 0.003 to 0.878 and reduces Euclidean distance by 74.623%, significantly outperforming cross-correlation in non-linear alignment scenarios. A novel similarity score enhances geological plausibility, complementing statistical metrics and pro-viding a valuable validation tool, especially in complex intervals. While the SNN is computationally slower, its superior accuracy makes it a reliable choice for post-drilling workflows, ensuring more precise depth align-ment with minimal manual intervention. By propagat-ing corrections to co-acquired logs, it enhances multi-log consistency for reservoir characterization. This work provides a scalable and reliable solution to depth alignment challenges, with potential applications in other geoscientific fields requiring log matching.