pyopmspe11.utils.mapproperties module
Utility function for the grid and locations in the geological models.
- pyopmspe11.utils.mapproperties.add_pv_fipnum_front_back(cfg: Config, fipnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], fluxnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], porv: list, d_x: ndarray[tuple[Any, ...], dtype[_ScalarT]], d_z: ndarray[tuple[Any, ...], dtype[_ScalarT]], xcent: ndarray[tuple[Any, ...], dtype[_ScalarT]], zcent: ndarray[tuple[Any, ...], dtype[_ScalarT]], lowpoly: Polygon) None
Buffer pore volume and bc labels also on front and back boundaries.
- pyopmspe11.utils.mapproperties.boxes(cfg: Config, x_c: float, z_c: float, idx: int, fluxnum: float) int
Find the global indices for the different boxes for the report data
- pyopmspe11.utils.mapproperties.check_facie1(fluxnum: float, numa: int, numb: int) int
Handle the overlaping with facie 1
- pyopmspe11.utils.mapproperties.corner(cfg: Config, points: list[Point]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Create a SPE11 corner-point grid
- pyopmspe11.utils.mapproperties.corner_point_handling_spe11a(cfg: Config, polygons: list[Polygon], facies: list, xc: ndarray[tuple[Any, ...], dtype[_ScalarT]], zc: ndarray[tuple[Any, ...], dtype[_ScalarT]]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Locate the geological positions in the corner-point grid for the spe11a
- pyopmspe11.utils.mapproperties.corner_point_handling_spe11bc(cfg: Config, polygons: list[Polygon], facies: list, xc: ndarray[tuple[Any, ...], dtype[_ScalarT]], zc: ndarray[tuple[Any, ...], dtype[_ScalarT]], ymy: ndarray[tuple[Any, ...], dtype[_ScalarT]], ycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], d_x: ndarray[tuple[Any, ...], dtype[_ScalarT]], d_y: ndarray[tuple[Any, ...], dtype[_ScalarT]], d_z: ndarray[tuple[Any, ...], dtype[_ScalarT]]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], list]
Locate the geological positions in the corner-point grid for the spe11b/c
- pyopmspe11.utils.mapproperties.generate_files(cfg: Config) None
Handle the deck and input files generation
- pyopmspe11.utils.mapproperties.get_lines(cfg: Config, points: list[Point])
Read the points in the z-surface lines
- pyopmspe11.utils.mapproperties.get_lower_polygon(cfg: Config) Polygon
Get the polygon for the lower active cells
- pyopmspe11.utils.mapproperties.getpolygons(cfg: Config) tuple[list[Polygon], list, list[Point]]
Function to create the polygons from the benchmark geo file
- pyopmspe11.utils.mapproperties.locate_wells_sensors_cp_spe11bc(cfg: Config, fipnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], zc: ndarray[tuple[Any, ...], dtype[_ScalarT]], ymy: ndarray[tuple[Any, ...], dtype[_ScalarT]], pop1: int, pop2: int, well1: int, well2: int) None
Find wells/sources and sensors ijk positions in the corner-point spe11bc
- pyopmspe11.utils.mapproperties.map_z(cfg: Config, ycent: ndarray[tuple[Any, ...], dtype[_ScalarT]]) ndarray[tuple[Any, ...], dtype[_ScalarT]]
Mapping z for spe11c as funtion of the y coordinate
- pyopmspe11.utils.mapproperties.polygon_search_order(z: float, zmidbot: float, ztopbot: float) list
Speed up by giving the polygon order according to the search region
- pyopmspe11.utils.mapproperties.prepare_structured_grid(cfg: Config) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Set the regular grid parameters
- pyopmspe11.utils.mapproperties.refinement_z(xci: ndarray[tuple[Any, ...], dtype[_ScalarT]], zci: ndarray[tuple[Any, ...], dtype[_ScalarT]], ncz: int, znr: list[int]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], int, int]
Refine grid vertically according to znr refinement factors.
- pyopmspe11.utils.mapproperties.sensors_structured_spe11abc(cfg: Config, fipnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], xcent: ndarray[tuple[Any, ...], dtype[_ScalarT]], ycent: ndarray[tuple[Any, ...], dtype[_ScalarT]], zcent: ndarray[tuple[Any, ...], dtype[_ScalarT]]) None
Find the i,j,k sensor indices
- pyopmspe11.utils.mapproperties.set_back_front_fipnums(cfg: Config, fipnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], fluxnum: ndarray[tuple[Any, ...], dtype[_ScalarT]], ind: int) None
For the front and back boundaries in spe11c:
Box A: Fipnum 13
Facie 1 and Box A: Fipnum 14
Box B: Fipnum 15
Facie 1 and Box B: Fipnum 16
Box C: Fipnum 17
Facie 1 and Box C: Fipnum 18
- pyopmspe11.utils.mapproperties.structured_handling_spe11a(cfg: Config, polygons: list, facies: list) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Geological positions in the tensor/cartesian grid for spe11a
- pyopmspe11.utils.mapproperties.structured_handling_spe11bc(cfg: Config, polygons: list[Polygon], facies: list) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], list, ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Geological positions in the tensor/cartesian grid for the spe11b/c
- pyopmspe11.utils.mapproperties.vertices_centers(xmx: ndarray[tuple[Any, ...], dtype[_ScalarT]], ymy: ndarray[tuple[Any, ...], dtype[_ScalarT]], zmz: ndarray[tuple[Any, ...], dtype[_ScalarT]]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Get the axis centers and sizes for a regular grid
- pyopmspe11.utils.mapproperties.vertices_sizes(xmx: ndarray[tuple[Any, ...], dtype[_ScalarT]], ymy: ndarray[tuple[Any, ...], dtype[_ScalarT]], zmz: ndarray[tuple[Any, ...], dtype[_ScalarT]]) tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]
Get the axis centers and sizes for a regular grid