Electrofacies Clustering and Classification from the Ariri Formation in Santos Basin (southeastern Offshore Brazil) Involving Unsupervised Learning Algorithms

During oil well drilling, geological knowledge of the layers to be drilled is essential to dimension the drilling parameters and plan contingency operations in case of operational failures. The Ariri Formation, lying in the Santos Basin (southeastern Brazil), can reach a thickness exceeding 3000 m and consists of salts with a complex rheology, requiring some effort to understand these minerals’ vertical and lateral distributions. This study aims to classify the electrofacies of evaporitic sequences in a semi-automated way based on unsupervised algorithms applied to geophysical well-logs and drilling parameters. The results were validated based on the previous classifications performed by interpreters, in which six types of saline minerals predominate in the wells under study: halite, anhydrite, tachyhydrite, carnallite, sylvite, and sylvinite. The database consisted of a set of fourteen wells located in the offshore portion of the Santos Basin. Unsupervised analyses are developed using the multilayer perceptron with lateral connections (MPLC), k-means, and Self-Organising Maps (SOM) algorithms. The obtained clusters are classified according to their mineralogical composition and drilling resistance. The SOM and MPLC algorithms provide the best accuracy in segmenting the main evaporite groups and highlighting possible mixtures between them. In terms of facies, the clustering provides electrofacies with different levels of drilling resistance for the same mineral. The selection of the best grouping enables a detailed subdivision for the Ariri Formation, which will serve as a basis for future stratigraphic studies in the distal setting of the Santos Basin.