Thomas Thai-Binh Tran1
(1) Chevron Petroleum Technology Company, San Ramon, CA
Abstract: Efficient conditioning of a 3-D fine-scale reservoir model to multiphase production data using streamline-based coarse-scale model inversion and geostatistical downscaling
In addition to seismic and well constraints, production data must be integrated into geostatistical reservoir models for reliable reservoir performance predictions. An iterative inversion algorithm is required for such integration and is usually computationally intensive since forward flow simulation must be performed at each iteration. This paper presents an efficient approach for generating fine-scale 3D reservoir models that are conditioned to multiphase production data by combining a recently developed streamline-based inversion technique with a geostatistical downscaling algorithm. Production data cannot reveal fine scale details of reservoir heterogeneity. By solving the streamline pressure solution at a coarse scale consistent with the production data we are able to invert numerous geostatistical realizations. while coarse grid pressure based streamlines do not capture the full residence time distribution as in a fine scale heterogeneous grid, they do maintain a sharp two-phase front which is the major driver for multiphase scale-up. Therefore, because the streamline method has fine resolution along the 1D streamlines independent of the coarse grid pressure solution, we do not need to explicitly address multiphase scale-up. Multiple geostatistical fine scale models are scaled up to the coarse scale used in the inversion process. These inverted models are each geostatistically downscaled to multiple fine scale realizations. These fine scale models are now preconditioned to the production data and can be scaled up to any scale for final flow simulation.
AAPG Search and Discovery Article #90914©2000 AAPG Annual Convention, New Orleans, Louisiana