Reservoir studies, including preparation of field development plan, are processes typically dominated by time constraints. In general, reservoir studies consist in multiple geoscience activities integrated to build a fine geological model that eventually leads to an upscaled numerical model suitable for history matching and forecast simulations. In the simulation stage, the quality and effectiveness of the activity is highly dependent on the computational efficiency of the numerical model. This is particularly true for complex, supergiant carbonate reservoirs. Often, even with today's simulators, upscaling is still needed and simplifications can be implemented to allow computationally intensive history matching and risk analysis workflows. This paper provides some real field examples where these issues were faced and successfully managed, without applying further simplifications to the geological concept of the model: principles of reservoir simulations and common sense reservoir engineering were used to adjust properties of the model and then speed-up numerical simulation. Tuning included a combination of various solutions, such as deactivating critical cells whenever possible, calibrating convergence and time stepping control, tweaking field management to prevent instability in the computation, optimization of number of cores and cells split among cores to optimize load balancing and scalability. These solutions were used on two super-giant carbonate fields, a triple porosity (matrix, karst and fractures) undersaturated light oil reservoir and a supercritical gas and condensate reservoir. The former field was described using an upscaled model of about 700,000 active cells and a dual porosity - dual permeability formulation; the latter was described by a relatively coarse model of about 400 thousand active cells using a single porosity formulation. Large speed-up, up to five times with respect to reference simulations, was achieved without simplifying the geology and losing accuracy perceivably. Benefits were achieved for both conventional and high-resolution simulators.
Thin turbidity siliciclastic reservoir is a challenging deep-water environment for modeling. In a deep off-shore field in West Africa, sedimentological characterization of these reservoir suggests typical turbiditic sandstones: Arenites with medium granulometry and normal gradation over imposed by plane-parallel sand laminations intercalated by shaly levels (late stage turbidity sandstone beds - characterization by Mutti, 1992). The oil bearing reservoir, is producing both from thick sands and thin layer sands. It is supposed the presence of up to 40% of sand-shale inter-bedded layers with a thickness less than 0.3m. These sands are defined in the reservoir model by curves of petrophysical properties, log facies, characterization of thin bedded intervals and a volume of seismic inversion. Tuning analysis suggest the potential seismic resolution is 16 meters. Seismic inversion was processed to generate a higher resolution driver for modelling. Formation evaluation uses high-resolution logs within "Thin Layer Analysis and Characterization" (e-tlac™) method (Galli et Al., 2002) which helps enhancing thin inter-bedded facies characterization, not captured with conventional logs. The method provides an estimate of the Sand content, Porosity and Saturation logged by the tools with thin lithology. High-resolution logs were acquired only in three wells over twelve drilled in the field. For this reason, a re-calibration of all conventional CPIs including the "e-tlac" output results was necessary to better control the reservoir property distribution all over the grid. This methodology increased capability estimating pay volume close to real value avoiding underestimation of Net Sand and Water Saturation overestimation. The solution to model thin turbiditic sands within the static 3D model is integrating all the above data inputs (stratigraphical environment, seismic inversion volume and "e-tlac" output). Reservoir cores was the input for the sedimentological study; the seismic inversion volume was background for reservoir facies distribution and "e-tlac™" formation evaluation output to assign unbiased reservoir properties to sand and thin layer facies at the well position. As lesson learned, the acquisition of triaxial induction, high-resolution dielectric or image is the key to better characterize the inter-bedded thin levels that are present in similar deep-water environment.
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