A New Tool For Electro-Facies Analysis: Multi-Resolution Graph-Based Clustering

Log facies analysis is important for reservoir characterization, but is made particularly difficult by the problem of "dimensionality": log space is not equivalent to geological space, and two points that are close to each other in log space may not always be similar geologically. A classic approach to facies analysis, automatic clustering, requires an estimate of the number of clusters, with the results being very sensitive to this parameter. If clustering is tightly constrained, with few clusters, the analyst may find that, because of the problem of "dimensionality", the resulting clusters cannot easily be used for facies analysis. If log data is relatively unconstrained, the analyst is then faced with the daunting task of linking each cluster to a geological descriptor. Field experience shows that a two-step methodology provides a workable solution. First, one chooses a large number of clusters for automatic clustering. Second, one manually merges small clusters into electrofacies to which geological characteristics are assigned. Even with good visualization tools, performing this task manually in high-dimensional (>3) space is still difficult, slow, somewhat subjective, and requires a skill or expertise that is not always readily available. This paper proposes a novel method for electrofacies analysis, Multi-Resolution Graph-based Clustering1 (MRGC), that solves the problem of dimensionality and derives valuable information about the geological facies from the structure of the data itself. MRGC offers all the advantages, while eliminating most of the drawbacks, of the two-step method. MRGC is a multi-dimensional dot-pattern-recognition method based on non-parametric K-nearest-neighbor and graph data representation. The underlying structure of the data is analyzed, and natural data groups are formed that may have very different densities, sizes, shapes, and relative separations. MRGC automatically determines the optimal number of clusters, yet allows the geologist to control the level of detail actually needed to define the electrofacies. This new electrofacies analysis tool has been tested under real-world conditions using conventional logs and NMR T2 distributions, and results from such studies are presented in the paper. In comparison with the existing two-step tool, MRGC has been found to make the work much faster and easier, and is both more direct and more intuitive.