plopm.utils.readers module

Utiliy functions to read the OPM Flow simulator type output files

plopm.utils.readers.compute_distance(cfg: ConfigPlopm, read: ReadData, quans: list, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Get the required variables from the simulation files

plopm.utils.readers.do_read_variables(cfg: ConfigPlopm, read: ReadData, quans: list, n: int, ntot: list) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Get the required variables from the simulation files

plopm.utils.readers.get_csvs(cfg: ConfigPlopm, deck: str, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], int, int, str, str]

Read the csv quantities

plopm.utils.readers.get_faults(cfg: ConfigPlopm, n: int) → tuple[list, list]

Using the input deck (.DATA) to read the i,j fault locations

plopm.utils.readers.get_histogram(cfg: ConfigPlopm, read: ReadData, quans: list, nrst: int) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Get the required variables from the histogram

plopm.utils.readers.get_indices(name: str, nx: int, ny: int, nz: int) → list

Compute the i, j, or k indices

plopm.utils.readers.get_quantity(deck: str, read: ReadData, name: str, nrst: int, skl: float, mass: list[str], mass_all: list[str], caprock: list[str], stress: float, filters: str, isgif: bool, vmin: str, vmax: str, cvs: list) → tuple[str, ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Handle the quantity from the OPM output files

plopm.utils.readers.get_readers(deck: str, gif: bool, vtk: bool, vrs: list, restart: list, filters: list, n: int = 0) → ReadData

Load the opm parsing methods

plopm.utils.readers.get_unit(name: str) → str

Get the variable unit

plopm.utils.readers.get_wells(cfg: ConfigPlopm, n: int) → tuple[list, list]

Using the input deck (.DATA) to read the i,j well locations

plopm.utils.readers.get_xycoords(cfg: ConfigPlopm, read: ReadData, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Handle the coordinates from the OPM Grid to the 2D xy-mesh

plopm.utils.readers.get_xzcoords(cfg: ConfigPlopm, read: ReadData, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Handle the coordinates from the OPM Grid to the 2D xz-mesh using opm

plopm.utils.readers.get_yzcoords(cfg: ConfigPlopm, read: ReadData, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]]]

Handle the coordinates from the OPM Grid to the 2D yz-mesh using opm

plopm.utils.readers.handle_caprock(read: ReadData, name: str, nrst: int, stress: float) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], str]

Compute quantities related to the caprock integrity

plopm.utils.readers.handle_filter(porvs: ndarray[tuple[Any, ...], dtype[_ScalarT]], quan1: ndarray[tuple[Any, ...], dtype[_ScalarT]], oper: str, value: float) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Apply the requested filter

plopm.utils.readers.handle_mass(read: ReadData, name: str, nrst: int) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Compute the mass (intensive quantities)

plopm.utils.readers.handle_saturation(unrst: ERst, name: str, nrst: int) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Compute the oil saturation

plopm.utils.readers.initialize_time(times: str) → tuple[float, str]

Handle the time units for the x axis in the summary

plopm.utils.readers.operate(var: ndarray[tuple[Any, ...], dtype[float64]], quan1: ndarray[tuple[Any, ...], dtype[float64]], oper: str) → ndarray[tuple[Any, ...], dtype[float64]]

Applied the requested operation

plopm.utils.readers.project(var: ndarray[tuple[Any, ...], dtype[_ScalarT]], oper: str, porv: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

Applied the requested projection

plopm.utils.readers.read_oned(cfg: ConfigPlopm, case: str, quan: str, tunit: str, qskl: float, n: int) → tuple[ndarray[tuple[Any, ...], dtype[_ScalarT]], ndarray[tuple[Any, ...], dtype[_ScalarT]], str, str]

Handle the oned vectors

plopm.utils.readers.resolve_variable(cfg: ConfigPlopm, read: ReadData, key_up: str, key_low: str, nrst: int, init: EclFile, unrst: ERst, mass_all: list, caprock_list: list)

Handle the variable

plopm.utils.readers.type_of_mass(name: str, co2_g: ndarray[tuple[Any, ...], dtype[_ScalarT]], co2_d: ndarray[tuple[Any, ...], dtype[_ScalarT]], h2o_l: ndarray[tuple[Any, ...], dtype[_ScalarT]], h2o_v: ndarray[tuple[Any, ...], dtype[_ScalarT]], x_l_co2: ndarray[tuple[Any, ...], dtype[_ScalarT]], x_g_h2o: ndarray[tuple[Any, ...], dtype[_ScalarT]]) → ndarray[tuple[Any, ...], dtype[_ScalarT]]

From the given variable return the associated mass

plopm.utils.readers.where_at(case: str, vec: str) → str

Using the input deck (.DATA) to read the i,j fault locations