Comparison of Supervised and Unsupervised Machine Learning for Well-Log Depth Alignment
S. Acharya, Karl Fabian, K. Westeng · 2025
Abstract Well logs, crucial for drilling and post-drilling analysis, provide continuous measurements of subsurface formations as a function of depth. Logging while drilling (LWD) and electrical wireline logs (EWL) are commonly used techniques for well-log acquisition. Both methods are prone to depth measurement errors due to various factors, which need to be aligned to a common depth-scale for subsequent analysis. This study compares two automated machine learning approaches for aligning repeated measurements of the same parameters from LWD and EWL logs of the same well. The first approach is based on supervised learning and the second on unsupervised learning. The supervised approach trains a 1D convolutional neural network (1D CNN) classification model on actual well-log data from the Norwegian North Sea, using LWD-EWL pairs of log slices. A specific depth discrepancy is introduced for each pair, and the logs are divided into various classes based on the depth error between them. The unsupervised method combines autoencoders and K-means clustering to identify potential lithological boundaries in EWL and LWD multiparameter log data. These predicted boundaries are validated by requesting a maximal Pearson correlation. The performance of the classification model is evaluated using metrics such as accuracy, precision, and recall. The optimal number of clusters for K-means clustering is identified using silhouette scores and the elbow method. Depth alignment is verified through visual inspection, correlation analysis, and Euclidean distances between logs. Supervised and unsupervised approaches significantly improve the alignment of various logs, such as bulk density, deep resistivity, sonic compressional, and neutron porosity. Both methods outperform maximization of cross-correlation for specific logs, such as deep resistivity and neutron porosity. These results highlight the potential of machine learning for efficient and accurate depth alignment of well logs, with promising implications for enhancing drilling and post-drilling analysis in the oil and gas industry.