Generalized prism grid: a pillar-based unstructured grid for simulation of reservoirs with complicated geological geometries

Li, Xin; Li, Xiang; Zhang, Dongxiao

doi:10.1007/s10596-018-9774-0

Cited by 4 publications

(1 citation statement)

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“…This simple approach is easy to parse and supports a wide range of structured grid variations, making it a common choice for geological model representation in various simulation architectures. Generalized prism grids (GPGs) [10] can represent pillarbased unstructured grids with arbitrary polyhedron cells, faults, degenerate layers, and overlapping pillars. GPGs focus on compatibility with different grid types, while supporting efficient simulation.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Visualization of Large-Scale Geological Models With Nonlinear Feature-Preserving Levels of Detail

Sicat

Ibrahim

Ageeli

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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The rapidly growing size and complexity of 3D geological models has increased the need for level-of-detail techniques and compact encodings to facilitate interactive visualization. For large-scale hexahedral meshes, state-of-the-art approaches often employ wavelet schemes for level of detail as well as for data compression. Here, wavelet transforms serve two purposes: (1) they achieve substantial compression for data reduction; and (2) the multiresolution encoding provides levels of detail for visualization. However, in coarser detail levels, important geometric features, such as geological faults, often get too smoothed out or lost, due to linear translationinvariant filtering. The same is true for attribute features, such as discontinuities in porosity or permeability. We present a novel, integrated approach addressing both purposes above, while preserving critical data features of both model geometry and its attributes. Our first major contribution is that we completely decouple the computation of levels of detail from data compression, and perform nonlinear filtering in a high-dimensional data space jointly representing the geological model geometry with its attributes. Computing detail levels in this space enables us to jointly preserve features in both geometry and attributes. While designed in a general way, our framework specifically employs joint bilateral filters, computed efficiently on a high-dimensional permutohedral grid. For data compression, after the computation of all detail levels, each level is separately encoded with a standard wavelet transform. Our second major contribution is a compact GPU data structure for the encoded mesh and attributes that enables direct real-time GPU visualization without prior decoding.

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Section: Related Workmentioning

confidence: 99%