Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes

by Gabriel Paulo Turato, João Gabriel Nogueira Duarte de Oliveira, Antonio Alberto S. Santos, Denis José Schiozer, Celmar Guimarães da Silva, presented at 16th International Conference and Computer Graphics, Visualization, Computer Vision and Image Processing 2022, July 2022.


Petroleum exploration activities demand the creation of lots of models to represent the uncertainties of a given reservoir, aiming to guide decision-making regarding exploration strategies. As part of these activities, a data assimilation process may refine the model set and discard models that do not honor the history data of the reservoir, given an error tolerance level. Reservoir engineers may use visualizations to analyze the partial results of iterative data assimilation processes and make decisions such as accepting the models selected in an iteration or refining the model set. Among a set of available visualizations for this process, we did not find in the literature the use of interactive heatmaps based on reorderable matrices. In this paper, we propose to add this kind of visualization to the analysis toolset for data assimilation, aiming to help engineers to better overview data assimilation datasets and how their models honor the history data of a reservoir. The proposed visual mappings use a set of heatmaps that enable the engineers to analyze a misfit measure (NQDS) for a given iteration, regarding models, wells, and their dynamic attributes. Users may reorder and filter these heatmaps according to their needs. We implemented our proposal in a web-based prototype that was submitted to a preliminary evaluation with real users in a synthetic scenario. This evaluation revealed positive opinions about the potential of the tool, which may be used to complement other approaches, and provided some opportunities for improvement.

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