In immiscible displacements, lower viscosity injected fluids with higher mobility than crude oil can create viscous fingers, affecting displacement efficiency. The Buckley–Leverett approach for relative permeabilities (kr) may not represent accurately 2D features like increased water saturation in viscous fingering. Based on the physics issue, this work applies Sorbie’s 4-Steps methodology to a 3D simulation of an offshore heavy oil reservoir focusing on waterflooding and tertiary polymer flooding, assessing their impact on oil production forecasts. It also explores the application of this methodology to coarse grid simulation models, employing pseudo kr functions by data assimilation. During tertiary polymer injection, two processes were identified in oil displacement: viscous crossflow mechanism and oil bank mobilization by a second finger. This combination resulted in earlier and increased oil production. For both strategies, refining the grid increased simulation runtime from minutes to days compared to coarse grids, making it impractical for intensive processes. From data assimilation, the best solution with matched field indicators reduced runtime from days to minutes. This study expanded the 4-Steps methodology for 3D reservoir simulation, proposing kr as uncertainties. Data assimilation enhances the methodology, generating pseudo kr for coarser grid simulations, reducing computational costs, and capturing small-scale phenomena.
Tag: Data assimilation
Using Reorderable Heatmaps as a Support for Data Assimilation in Petroleum Reservoir Management and Development Processes
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.
A review on closed-loop field development and management
Closed-loop field development and management (CLFDM) is defined as a periodic update of an uncertain field model using the latest measurements (data assimilation), followed by production optimization aiming mainly at maximizing the field economic value. This paper provides a review of the concepts and methodologies in the CLFDM. We first discuss different types of uncertainty encountered in field development and management. Then, concepts, components, and elements of CLFDM are presented. We then discuss and compare different automated methodologies for data assimilation, followed by explaining a hierarchy of different decision variables for production optimization including design variables (G1), life-cycle control rules (G2L), short-term controls (G2S), and revitalization variables (G3). We continue with explanations for the use of closed-loop in both the development and management phases of a field project. We also discuss and compare different methodologies for production optimization. Afterwards, objective functions for production optimization are presented, followed by the description of concepts and different approaches for selecting representative models to speed up solutions. This paper also highlights the necessity of integrated modeling of reservoir and production systems in CLFDM, and also the need for a standardized stepwise approach to apply the CLFDM by discussing one method from the literature. Finally, we summarize all the previous CLFDM studies on the basis of aspects covered in this paper, and suggest open areas for future research to enhance the use of CLFDM.