Attention-driven convolutional neural network for automated well log depth alignment

• Proposes an Attention-Driven Convolutional Neural Network (AD-CNN) integrating 1D CNNs and multi-head cross-attention for automated well-log depth alignment. • Achieves competitive accuracy with lower computational cost than dynamic time warping (DTW), while outperforming maximization of cross-correlation (MaxCC) and standard CNNs. • Improves alignment stability and accuracy on the evaluated wells by adaptively weighting informative intervals; attention is enriched near independently interpreted lithostratigraphic tops. Well logs often exhibit depth discrepancies between electrical-wireline logging (EWL) and logging-while-drilling (LWD) recordings due to systematic errors, such as cable stretch in EWL and variations in drilling conditions in LWD. These discrepancies can lead to misinterpretations during reservoir analysis. While manual alignment via maximization of cross-correlation (MaxCC) is common, this study proposes an automated Attention-Driven Convolutional Neural Network (AD-CNN). The hybrid architecture incorporates a 1D convolutional neural network (CNN) to extract local spatial features, such as log curve shapes, along with a multi-head cross-attention mechanism that prioritizes critical depth intervals. This design is intended to improve robustness to variable signal quality and depth offsets within the evaluated range. The model is trained on 934,561 standardized gamma-ray (GR) log pairs (151-sample slices) derived from three wells in the Norwegian North Sea, validated on one well, and tested on an independent well. Evaluation also includes four unseen case-study wells, with depth-matching assessed using Pearson correlation, Euclidean distance, and qualitative review of alignment and attention heatmaps. The proposed AD-CNN achieves an average final Pearson correlation of 0.85 across four wells, improving over CNN and MaxCC, and approaching the accuracy of dynamic time warping (DTW) (Pearson correlation = 0.93) while running faster (0.006 s vs. 0.051s per slice). In case studies across four wells spanning different fields, lithologies, and inclinations (up to 36.7°), AD-CNN improves alignment in over 98% of slices, suggesting stable performance across the evaluated conditions. This automated and scalable approach can reduce manual interpretation and support scalable reservoir analysis workflows.