Efficient Conditioning of 3D Fine-Scale Reservoir Model To Multiphase Production Data Using Streamline-Based Coarse-Scale Inversion and Geostatistical Downscaling

Summary 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 because 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 because they respond to the spatial variation of larger-scale effective properties. By solving the streamline pressure solution at a coarse scale consistent with the volume support of production data, we are able to invert numerous geostatistical realizations. Additionally, the streamline method allows fine resolution along the 1D streamlines independent of the coarse-grid pressure solution, so we do not need to explicitly address multiphase scaleup. Multiple geostatistical fine-scale models are upscaled to a coarse scale used in the inversion process. After inversion, the models are each geostatistically downscaled to multiple fine-scale realizations. These fine-scale models are now preconditioned to the production data and can be upscaled to any scale for final flow simulation. A 3D extension of the prior 2D sequential self-calibration method (SSC) is developed for the inversion step. This method updates the coarse models to match production data while preserving as much of geostatistical constraint as possible. A new geostatistical algorithm is developed for the downscaling step. We use Sequential Gaussian Simulation (SGS) with either block kriging or Bayesian updating to "downscale" the history-matched coarse scale models to fine-scale models honoring fine-scale spatial statistics. Combining these two developments we are able to efficiently generate multiple fine-scale geostatistical models constrained to well and production data.