Efficient Conditioning of 3D Fine-Scale Reservoir Model To Multiphase Production Data Using Streamline-Based Coarse-Scale Inversion and Geostatistical Downscaling
- Thomas T. Tran (Chevron Petroleum Technology Co.) | Xian-Huan Wen (Chevron Petroleum Technology Co.) | Ronald A. Behrens (Chevron Petroleum Technology Co.)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- December 2001
- Document Type
- Journal Paper
- 364 - 374
- 2001. Society of Petroleum Engineers
- 3.3 Well & Reservoir Surveillance and Monitoring, 5.5.8 History Matching, 4.1.2 Separation and Treating, 5.1.5 Geologic Modeling, 5.5.7 Streamline Simulation, 5.6.3 Pressure Transient Testing, 5.1 Reservoir Characterisation, 5.5.2 Construction of Static Models, 5.5.3 Scaling Methods, 7.6.2 Data Integration, 5.1.1 Exploration, Development, Structural Geology, 5.6.10 Seismic (Four Dimensional) Monitoring, 5.1.9 Four-Dimensional and Four-Component Seismic, 4.3.4 Scale, 5.5 Reservoir Simulation
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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.
The reliability of geostatistical models increases as more data is included in their construction. Historically, only hard data conditioned the models. Now, soft data such as geologic maps or seismic data are included routinely. More recently, there has been a growing interest and ability to include dynamic data as a constraint during the construction of reservoir models. The matching. These early attempts suffered from poor computer power, early algorithms, and an insufficient appreciation for concept of incorporating production data into models by inversion is certainly not new and was commonly termed automatic history geologic complexity. These problems are rapidly being overcome, and we are now starting to precondition geostatistical models with dynamic data before starting rigorous flow simulation. We use the term "precondition" as a euphemism to avoid the now-bitter taste of "automatic history matching" and to more accurately reflect the less ambitious goal of improving the initial flow results as an input, rather than a replacement, to formal history matching.
Inversion is both CPU-intensive and underdetermined because we have so many parameters (cells with unknown properties) to set. Furthermore, the resolution represented by this large number of parameters is typically finer than the spatial resolution of the dynamic data available to us in producer watercut, pressure transient analysis, and pressure and/or saturation estimates from 4D seismic. All of these reasons suggest that we reduce the number of parameters before inversion.
We can easily reduce the number of parameters before inversion by upscaling, but we want our final model to have fine-scale details for flow simulation. The upscaling process that selectively refines the grid is motivated by observation that fine-scale high-permeability streaks often dominate breakthrough time and strongly affect ultimate recovery. We want our final geostatistical model to again have high resolution after the coarse-scale inversion.
The resolution of the coarse inversion grid is not necessarily the same as that of the flow simulation grid. Flow simulation grid resolution is typically set to achieve one or two history match runs overnight in a full-featured flow simulator. Inversion grid resolution should be governed by the information content of the dynamic data used as constraints and by the CPU constraints of the inversion algorithm and embedded forward model. The embedded forward model in our case is a coarse-grid 3D-streamline method, but in principle it could even be a traditional finite-difference method if we were dealing with complicated flows. There is no reason to expect these grids to have similar resolutions, so one can't avoid the final downscaling by simply upscaling once for both flow simulation and inversion.
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