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.
Tag: history matching
Model-Based Life-Cycle Optimization for Field Development and Management Integrated with Production Facilities
Reservoir simulation models often support decision making in the development and management of petroleum fields. The process is complex, sometimes treated subjectively, and many methods and parameterization techniques are available. When added to uncertainties, the lack of standardized procedures may yield largely suboptimal decisions. In this work, we present a comprehensive outline for model-based life-cycle production optimization problems, establishing guidelines to make the process less subjective. Based on several applications and a literature review, we established a consistent methodology by defining seven elements of the process: (1) the degree of fidelity of reservoir models; (2) objective function (single- or multi-objective, nominal or probabilistic); (3) integration between reservoir and production facilities (boundary conditions, IPM); (4) parametrization (design, control and revitalization optimization variables); (5) monitoring variables (for search space reduction); (6) optimization method, including optimizer/ algorithm, search space exploration, faster-objective function estimators (coarse models, emulators, others), type of ensemble-based optimization (robust or nominal based on representative models); (7) additional improvements (value of information and flexibility). With an application on a publicly available benchmark reservoir, this work shows how a model-based life-cycle optimization process can be systematically defined. In this initial work, the focus is the field development phase and some simplifications were made due to the high computational demand, but in future works we plan to address the control and revitalization variables and reduce the number of simplifications to compare. The optimization results are analyzed to understand the evolution of the objective function and the evolution of the optimization variables. We also discuss the importance of including uncertainties in the process and we discuss future work to emphasize the difference between life-cycle (control rules) and short-term (effective control) management of equipment, as well as ways to deal with the computational intensity of the problem, such as the combined use of representative models and fast simulation models.