An Intelligent System for Multi-Label Classification Based on Particle Size and Shape Features using a Cascade Approach

Intelligent systems are becoming more and more popular in the petroleum industry. Particle Size Distribution (PSD) based on sieve size is a key signature of the unconsolidated/weakly consolidated sandstone formations and is commonly the main parameter in the sand control design. With available extensive PSD measurement techniques and a large number of measurements, especially for horizontal wells, it is necessary to classify the PSDs prior to further analysis for the sand control design. On the other hand, PSD analysis is not enough for sand control design, and particle shapes need to be taken into account as well. A successful clustering algorithm for the mentioned purposes needs to be a cascade, multi-label, unsupervised and self-adaptive approach since the particles can be assigned to more than one group and there is no prior idea on how many clusters should be formed after the clustering process. Besides, due to the differences between sieve size and shape features, they should be used separately for clustering the particles. In the current study, a cascade approach is used for clustering the particles. In the first level of the cascade, an unsupervised and self-adaptive algorithm is introduced based on the sieve size features. The algorithm optimizes the number of clusters through a self-adaptive and incremental approach. The proposed clustering method uses a minimum similarity threshold (δ) as the only input parameter to start the clustering and tries to minimize the number of clusters during the clustering. In the second level of the cascade, the similarity between all particles in each cluster with their corresponding cluster-center is measured, and those particles that do not respect the δ in terms of the shape similarity, are moved out of the cluster. The novelty of the proposed method is in three folds. The first one is to provide a particle clustering algorithm, which works based on the whole range of the sizes and shape descriptors rather than focusing on certain points in the size graph (D-values). The second one is the dynamic nature of the clustering, which tends to optimize the number of clusters during the clustering process. The third one is that we have used a cascade approach for involving both size and shape parameters for the clustering. Our proposed method can be applied in field application for downhole monitoring and sand screen design.