Similarity Heuristics for Clustering Wells Based on Logging-Data

Accurate clustering of oil wells is important for lithological studies and hydrocarbon production processes. Traditional methods for solving this problem require large amount of expert work, including careful analysis of high-dimensional datasets. Modern approaches to automatizing clustering are mostly based on deep neural networks (DNN) with complex architecture, which require significant training time and lack interpretability. This paper analyses methods based on simple similarity heuristics, which stem from reasonable assumptions on data generating process and can be interpreted in terms of statistics and geometry. For dataset labeled by experts, clustering is performed by means of computed heuristics, and the quality of algorithm is measured by Adjusted Rand Index (ARI). Results thus obtained turn out to be comparable to those of modern DNN models (0.41 vs 0.37), but the computation time is reduced significantly. For some types of heuristics physical interpretation is suggested and approaches to obtaining geological insights are studied.