Choosing an appropriate technique for upscaling the permeability of the discreet fracture network (DFN) model is vital for maximizing recovery from naturally fractured reservoirs (NFR). Flow -based upscaling is accurate, yet it is computationally expensive. Analytical (i.e. Oda method) upscaling is computationally efficient; however, it is only accurate for well-connected fractures. The objective of this paper is to analyze the performance of the newly developed Oda corrected method which addresses issues associated with previous upscaling methods. This research was executed by using a commercial numerical simulator and by using data set from the Teapot Dome Reservoir. Furthermore, DFN modeling was used to generate different realizations for the fracture network. Consequently, sensitivity analysis was performed through a realisti c uncertainty quantification to generate a base case and 6 different DFN realizations. The main parameters used for this study are fracture length and intensity. Afterwards, the fracture permeability corresponding to each DFN realization was upscaled using the above-mentioned methods. Finally, differences between the upscaling methods were evaluated and analyzed using flow-based upscaling as the criterion. Indeed, the analysis revealed that the new Oda corrected method can calculate the equivalent permeability tensor with adequate accuracy. However, it overestimates the permeability when fracture networks are below the percolation threshold and/or when fractures length is large. Hence, this method is recommended for networks with moderate to high intensity. Furthermore, it has been deduced that fracture length has a great impact on the connectivity of fractures, albeit its effect on permeability is limited by fracturing density. Additionally, it has been found that the length of fractures has an immense impact on the anisotropy ratio and control the occurrence of water bypassing, which were not captured by the Oda method.
Seismic history matching is the process of modifying a reservoir simulation model to reproduce the observed production data in addition to information gained through time-lapse (4D) seismic data. The search for good predictions requires that many models be generated, particularly if there is an interaction between the properties that we change and their effect on the misfit to observed data. In this paper, we introduce a method of improving search efficiency by estimating such interactions and partitioning the set of unknowns into noninteracting subspaces. We use regression analysis to identify the subspaces, which are then searched separately but simultaneously with an adapted version of the quasiglobal stochastic neighborhood algorithm. We have applied this approach to the Schiehallion field, located on the UK continental shelf. The field model, supplied by the operator, contains a large number of barriers that affect flow at different times during production, and their transmissibilities are highly uncertain. We find that we can successfully represent the misfit function as a second-order polynomial dependent on changes in barrier transmissibility. First, this enables us to identify the most important barriers, and, second, we can modify their transmissibilities efficiently by searching subgroups of the parameter space. Once the regression analysis has been performed, we reduce the number of models required to find a good match by an order of magnitude. By using 4D seismic data to condition saturation and pressure changes in history matching effectively, we have gained a greater insight into reservoir behavior and have been able to predict flow more accurately with an efficient inversion tool. We can now determine unswept areas and make better business decisions.
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