Reservoir Simulation Grid Compression for Fast 3D Visualization
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Solution Overview
Problem
The large volume of data generated by reservoir simulation models, particularly for giant hydrocarbon reservoirs, leads to significant memory and storage challenges due to the need for millions to billions of grid cells, causing issues with file size, memory capacity, and data processing time.
Innovation Solution
A method and system for compressing reservoir simulation data by selecting layers of a three-dimensional grid, batch compressing the data into a compressed layer file representation, and storing it for reduced memory usage, allowing for efficient analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of grid cells is increased to represent fine-scale reservoir geometry and heterogeneity, then the resolution and accuracy of the simulation model is improved, but the memory and storage requirements increase significantly
Solution Approach 1:
The patent divides the three-dimensional reservoir grid into multiple two-dimensional layers, processing and compressing each layer independently. This segmentation allows the large volumetric dataset to be broken down into manageable units that can be compressed more efficiently, reducing overall memory and storage requirements while preserving the fine-scale resolution needed for accurate simulation.
2Measurement precision
If the number of grid cells is increased to capture reservoir heterogeneity, then the accuracy of flow dynamics representation is improved, but the data processing time increases
Solution Approach 1:
By segmenting the 3D grid into 2D layers, the patent enables parallel processing of individual layers, which reduces overall data processing time while maintaining the high grid cell count necessary for accurate flow dynamics representation.
Solution Approach 2:
The patent extracts and removes redundant information from the grid data through compression algorithms, eliminating unnecessary data while preserving the essential flow dynamics information. This reduction in data volume directly decreases processing time without sacrificing the accuracy needed to represent reservoir heterogeneity.
3Loss of information
If the simulation grid data is stored in full resolution, then the complete information is available for analysis, but the file size and memory capacity requirements become prohibitive
Solution Approach 1:
The patent applies compression algorithms that extract and retain only the essential information from the full-resolution grid data, removing redundant or less critical details. This allows the system to maintain data completeness for analysis purposes while dramatically reducing the storage capacity required.
Solution Approach 2:
The patent applies different compression strategies to different regions of the reservoir grid based on their importance. Critical areas with high heterogeneity or complex flow dynamics are preserved with higher fidelity, while less critical regions undergo more aggressive compression, optimizing the balance between information retention and storage efficiency.
Data Source
AI summary
A dense volumetric grid coming from an oil/gas reservoir simulation output is translated into a compact representation that supports desired features such as interactive visualization, geometric continuity, color mapping and quad representation. A set of four control curves per layer results from processing the grid data, and a complete set of these 3-dimensional surfaces represents the complete volume data and can map reservoir properties of interest to analysts. The processing results yield a representation of reservoir simulation results which has reduced data storage requirements and permits quick performance interaction between reservoir analysts and the simulation data. The degree of reservoir grid compression can be selected according to the quality required, by adjusting for different thresholds, such as approximation error and level of detail. The processions results are of potential benefit in applications such as interactive rendering, data compression, and in-situ visualization of large-scale oil/gas reservoir simulations.