Pillar Gridding

Corner-Point grids rely on pillars as their fundamental structural framework. In Petres, pillars are modeled separately, similar to how Petrel handles them, allowing for precise control over the geometry of the grid independent of the cells themselves. If you are unfamiliar with pillars, see the Pillar Geometry section for an introduction.

Creating a Pillar Grid

Pillar grids can currently be created in two ways. Regular pillar grids are defined with uniform spacing, while rectilinear pillar grids are defined from explicitly provided coordinate arrays.

Note

At this stage, Petres supports only vertical pillars. Skewed or faulted pillar geometries are not yet supported.

Regular Pillar Grids

A regular pillar grid is defined by specifying the model extent in the x and y directions together with the number of cells along each axis. This is conceptually similar to creating a regular grid (see Creating a Regular Grid).

from petres.grids import PillarGrid

pillars = PillarGrid.from_regular(
    xlim=(0, 100),
    ylim=(0, 100),
    ni=50,
    nj=50,
)

Here, xlim and ylim define the spatial extent of the model in the x and y directions, respectively, while ni and nj control the number of cells along each axis.

Alternatively, it can be defined using cell sizes instead of cell counts.

pillars = PillarGrid.from_regular(
    xlim=(0, 100),
    ylim=(0, 100),
    dx=2,
    dy=2,
)

Here, dx=2 and dy=2 specify the cell size in the x and y directions, respectively.

Rectilinear Pillar Grids

A rectilinear pillar grid allows non-uniform spacing along each axis, making it suitable for local refinement in specific regions of the model. This follows the same principles as rectilinear grid generation (see Creating a Rectilinear Grid).

pillars = PillarGrid.from_rectilinear(
    x=[0, 10, 50, 70, 100],
    y=np.linspace(0, 100, 50),
)

In this example, the x coordinates are defined explicitly, resulting in non-uniform spacing, while the y coordinates are generated using numpy.linspace(), producing evenly spaced values.

Specifying Top and Base Depths

Pillar top and base values can be defined explicitly when creating a rectilinear grid, allowing precise placement of the pillar geometry within the model.

pillars = PillarGrid.from_rectilinear(
    x=[0, 10, 50, 70, 100],
    y=np.linspace(0, 100, 50),
    top=1000,
    base=1500,
)

In this example, the pillars are positioned with tops at 1000 and bases at 1500. These values may represent either elevation or depth, depending on the conventions used in your workflow.

Note

By default, top=0.0 and base=1.0. If you are concerned about numerical precision or want to adjust the vertical scale of your grid, you can modify these values when creating the pillar grid.

Visualize the Pillar Grid

The grid can be visualized interactively using the show() method.

pillars.show()

This provides a quick way to inspect the geometry of the generated pillar grid.

Next Steps