Well Clustering and Reservoir Segmentation Based on Machine Learning Analysis to the Extracted Features from Multiple Well Logs

In conventional reservoir modeling, the well log curve shape information is usually lost when it is up-scaled. In this study, we implemented machine learning method to capture the well log curve shape information of each well and clustering those wells in target spatial domain. One state-of-art machine learning algorithm used in most of time series classification area is implemented for feature extraction. All the wells are grouped into different groups according to the similarity of extracted features from multiple log curves. The final spatial reservoir segmentation is obtained through spatial interpolation from the clustered well groups. The results of the 2D spatial map provides valuable insights to the depositional background analysis through providing the lateral geological heterogeneity features. One small synthetic data set is used in case study to illustrate this method as an effective way to characterize the spatial heterogeneity. As illustrated in the case study, the proposed spatial segmentation technique can be used as a fast geological modelling method to integrate geological heterogeneity features embedded in multiple well logs.