A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

Identifying Optimal Scales for Spatio-temporal Crime Clusters

The spatial and temporal scales are not only two essential parameters for the spatio-temporal clustering algorithm to generate the crime clusters but are significantly helpful for determining the interventive distance at space and time in place-based crime prevention. This study presents the issue of identifying the optimal spatial-temporal scale when examining the micro-level crime clusters approached by density-based spatio-temporal clustering methods. The approach comprises adopting a clustering evaluation index to examine the performance of different clustering results from a range of space and time values iteration. For this purpose, two types of density-based clustering algorithms called ST-DBSCAN and ST-OPTICS are compared to determine the optimal scales for space-time crime clusters. A case study is demonstrated using individual crime records of burglary from Vancouver, Canada in 2010. Several derived results are significant. First, appropriate scales – 500m and 3 days can be distinctively determined by clustering algorithm ST-OPTICS from our tested parameters. Second, the narrowed scales were found in this study significantly for spatio-temporal crime clusters, which can help to develop a more focused and specific policing tactics

Cluster Oriented Spatio Temporal Multidimensional Data Visualization of Earthquakes in Indonesia

Spatio temporal data clustering is challenge task. The result of clustering data are utilized to investigate the seismic parameters. Seismic parameters are used to describe the characteristics of earthquake behavior. One of the effective technique to study multidimensional spatio temporal data is visualization. But, visualization of multidimensional data is complicated problem. Because, this analysis consists of observed data cluster and seismic parameters. In this paper, we propose a visualization system, called as IES (Indonesia Earthquake System), for cluster analysis, spatio temporal analysis, and visualize the multidimensional data of seismic parameters. We analyze the cluster analysis by using automatic clustering, that consists of get optimal number of cluster and Hierarchical K-means clustering. We explore the visual cluster and multidimensional data in low dimensional space visualization. We made experiment with observed data, that consists of seismic data around Indonesian archipelago during 2004 to 2014.Keywords: Clustering, visualization, multidimensional data, seismic parameters