Naturally Fractured Reservoirs (NFR) hold a significant fraction of remaining petroleum reserves. Recovery factors from NFR are usually less than in conventional reservoirs due to associated high uncertainty throughout the characterisation and modelling phases. This particularly includes the modelling and upscaling of the fracture domain using Discrete Fracture Networks (DFN). Computer assisted history matching and prediction is becoming increasingly popular as they help finding multiple historymatched models and probabilistic forecasts. Therefore, the associated uncertainty can now be quantified in a limited time frame. However, the results of a history match are known to depend on initial reservoir properties, including fracture permeability and matrix shape factors. Geological uncertainty in these two factors is exacerbated by the DFN upscaling errors. We show how DFN modelling can be used to increase geological prior knowledge and hence produce more geologically consistent models. To highlight DFN upscaling errors, we use a realistic dataset from an onshore fractured reservoir to show how the DFN upscaling error could propagate through to the history matching phase. We compare history matching of three models with different DFN upscaling processes. Results from state-of-the-art assisted history matching and prediction were found to depend on the static properties and particularly the computation of effective fracture permeability during DFN upscaling. This upscaling error alone leads to very different reservoir models, despite the best history matched models being of comparable quality. Hence, this leads to more uncertainty in reservoir production forecast. The identification of DFN upscaling errors is therefore crucial for better uncertainty quantification in reservoir simulation of NFR.
Nearly half of the remaining petroleum reserves are contained in naturally fractured reservoirs (NFR). An accurate estimate of the effective fracture permeability tensor is a key to the successful prediction of oil recovery from NFR. Standard workflows nowadays employ discrete fracture network (DFN) modeling and analytical or flow-based methods to upscale fracture permeabilities. However, DFN modeling imposes some important challenges, which can cause great uncertainty in the effective permeability tensor and subsequent recovery prediction: Analytical upscaling methods, which are commonly used due to computational efficiency, are inaccurate for poorly connected fracture networks. Flow-based upscaling methods depend on boundary conditions and are computationally expensive. Defining the optimum grid size for either method is also very difficult. In addition, DFN upscaling is often driven by practical issues such as time constrains and computational limitations, leaving little room to investigate the effects of upscaling methods and grid size. In this paper we utilize features in leading DFN simulators employed in standard industry workflows for computing effective permeability tensors with flow-based and analytical methods. We use two realistic dataset from fractured formations of onshore reservoirs in our assessment. Not surprisingly, there is up to three orders of magnitude variation in the effective permeability based on the chosen upscaling method and perceived optimum grid cell size. This has tremendous impact on predicted recovery rates and ultimate recovery; ultimately uncertainty in upscaling can mask uncertainty in the geological model. We hence introduce a new simulation technique, Discrete Fracture and Matrix (DFM) modeling, which accounts accurately for flow in the fractures and rock matrix as an efficient alternative for computing effective permeability tensors as it allows us to assess the accuracy of classical DFN upscaling approaches, which all help reducing uncertainty in recovery prediction.
Naturally Fractured Reservoirs (NFR) contain a significant amount of remaining petroleum reserves and are now being considered for water-alternating-gas (WAG) flooding as secondary or tertiary recovery. Reservoir simulation of WAG is very challenging even in non-fractured reservoirs because a proper set of saturation functions that describe the underlying physics is vitally important but associated with high uncertainty. For NFRs, another challenge is the upscaling of recovery processes, particularly the fracture-matrix transfer during three-phase flow, to the reservoir scale using dual-porosity or dual-permeability models. In this work, we approach a solution to this challenge by building models at various scales, starting from pore-scale to an intermediate scale then to the reservoir scale. We show how pore-network modelling and fine grid modelling where the fractures and matrix are represented explicitly can be used to increase the accuracy of numerical simulations at the field-scale in order to predict recoveries for NFR during WAG. We study the sensitivity to WAG design parameters as well as the impact of matrix wettability on recovery. We also compare the fine grid model with an equivalent dual-porosity model. Simulation at an intermediate scale showed at least 10% absolute change in recovery due to the choice of the empirical three-phase relative permeability model. In fine grid simulation with physically consistent pore-network derived three-phase relative permeability and capillary pressure, injected water and gas are predicted to displace each other, leaving oil behind, therefore reducing WAG efficiency. For this case, empirical models over-estimate recovery by 25%. Classical dual-porosity model over-estimates recovery during the early WAG cycles, and fails to adequately match recovery of the fine grid simulation. Our multi-scale simulation approach identifies important factors and uncertainties when considering WAG flooding in NFR. It provides a methodology through which WAG recovery can be estimated using available technology while preserving the pore-scale physics for three-phase flow, which are crucial to making reliable forecasts at the reservoir scale.
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