Petroleum engineers usually create a set of hundreds of models of a given oil reservoir under analysis to represent its uncertainties. Assisted optimization approaches may help engineers to select a subset of these models (a.k.a. representative models, or RMs for short), which is used in computational flow simulations in replacement of the original set, aiming to reduce the total simulation runtime without changing the quality of these results. Despite the power of visualization techniques to help people to understand multidimensional datasets like the ones provided in this scenario, we noted a few efforts that use these techniques to help petroleum engineers to assist the selection of RMs. In this context, our research aims to test the hypothesis that it is possible to improve how interactive visualization resources are currently used to aid decision-making regarding the selection of RMs, mainly in the presence of multiple sets of RMs or multiple variables (obtained from running model simulations). This work presents our first steps towards this goal: (a) literature review and (b) definition of visualization prototypes that aim to help the analysis of RMs regarding the values of the variables provided by simulation outputs, and the risk curves associated with these variables. As preliminary results, we present our proposed interactive visualizations and briefly point out the design rationale behind these prototypes.
Tag: Risk Curves
Using reorderable matrices to compare risk curves of representative models in oil reservoir development and management activities
Methodologies of oil reservoir development and management demand the creation of a set of reservoir models that represents the uncertainties of a reservoir. This set of uncertainties is often simplified by the use of Representative Models (RMs), i.e., models that represent the full set. Comparison of risk curves (a.k.a. complementary cumulative distribution functions) of reservoir variables is an approach used for helping oil engineers to select a set of RMs. A typical comparison chart of a given variable of interest superposes two risk curves: one of the entire model set, and another from the set of RMs. The level of similarity between the curves in this chart indicates the representativeness of the set of RMs regarding the entire model set. A visualization with some of these charts may help to compare the representativeness of the set of RMs regarding more variables (reservoir properties, production data etc.) or distinct sets of RMs. However, this kind of chart is not enough to provide an overview of these comparisons if the number of variables or sets of RMs increase. This paper shows how the use of reorderable matrices, depicted as heatmaps, can provide an overview of this dataset that can be helpful to engineers to make decisions. We propose to represent the dissimilarity of pairs of risk curves instead of the curves themselves. This solution enables our visualization to increase the number of sets of RMs and the number of variables to represent. We show the usefulness of our proposal in three case studies of oil reservoir benchmarks, and discuss the pattern we found in these cases.