Visualization of Ensembles of Oil Reservoir Models Based on Pixelization, Small Multiples and Reservoir Similarities

Abstract

Providing an overview of an ensemble of oil reservoir models could help users compare and analyze their characteristics. Approaches that show a single model at a time may hamper analysts’ understanding of the whole model set. In this paper, we propose two visualization approaches that show multiple reservoir models, simultaneously and on a single screen, with the goal of helping users to compare models and improve their understanding of ensemble characteristics. First, we calculate 2D models from the ensemble's 3D models. We then create two visualizations that represent ensembles of these 2D models. The Small Multiples approach lays out heatmaps of 2D models side-by-side on a grid. Pixelization approach shows n 2D models in a single heatmap, where each cell (i, j) contains n subcells that represent values in the coordinate (i, j) of each model. Both approaches display their elements (heatmaps and subcells) clustered by X-means, which may help analysts identify similarities and representative models in the ensemble. We used two types of distance matrices: based on Euclidean distance of models for a given property or, based on Euclidean distance of feature vectors of the 2D models. We tested our approaches within models based on Brazilian benchmark cases corresponding to a turbiditic reservoir (UNISIM-I-D/M/H) and a presalt carbonatic reservoir (UNISIM-II-D). As a result, the Small Multiples approach presented clusters of similar models for some properties of the ensembles we studied, e.g. eight clusters of porosity values in UNISIM-II-D's ensemble. This fact suggests that eight representative models can represent the ensemble, regarding porosity. Also, a Pixelization approach revealed patterns that happen in specific regions of all models of an ensemble, such as an abrupt change of porosity values in the northwest region of UNISIM-I-M's models. Both approaches have the potential to help analysts perceive situations that would be improbable to observe in a graph with only mean values for each cell. Therefore, our proposal can be helpful to users who need to deal with uncertainties and have an overview of ensembles of models for better understanding and decisionmaking, e.g. when they need to choose representative models for a process of decision analysis related to petroleum field development and management.