TNO Intern

Commit a86b4771 authored by Hen Brett's avatar Hen Brett 🐔
Browse files

Figuring out the input limtis of the simulation

parent 1f9e131c

src/pythermogis/doublet_simulation/deterministic_doublet.py

+6 −2
Original line number Diff line number Diff line
@@ -6,8 +6,12 @@ import xarray as xr 


from pythermogis import simulate_doublet

from pythermogis.dask_utils.auto_chunk import auto_chunk_dataset

from pythermogis.physics.temperature_grid_calculation import calculate_temperature_from_gradient

from pythermogis.thermogis_classes.utc_properties import instantiate_utc_properties_builder

from pythermogis.physics.temperature_grid_calculation import (

    calculate_temperature_from_gradient,

)

from pythermogis.thermogis_classes.utc_properties import (

    instantiate_utc_properties_builder,

)





def calculate_doublet_performance(reservoir_properties: xr.Dataset, utc_properties = None, rng_seed: int = None, chunk_size: int = None, print_execution_duration: bool = False, mask_value: float = np.nan) -> xr.Dataset:

src/pythermogis/thermogis_classes/doublet.py

+9 −14
Original line number Diff line number Diff line
@@ -119,13 +119,13 @@ def calculate_performance_of_single_location(mask: float, depth: float, thicknes 
        )



    # The Java routine which calculates DoubletPerformance, for more detail on the simulation inspect the Java source code

    try:

        results = doublet.calculateDoubletPerformance(input)



    # If calculation was not successful, return mask value

    if results is None:

    except:

        return (mask_value,) * 14



    if results.utc() == -9999.0:

    # If calculation was not successful, return mask value

    if results is None or results.utc() == -9999.0:

        return (mask_value,) * 14



    # calculate net-present-value using the utc-cutoffs
@@ -167,15 +167,10 @@ def validate_input(depth: float, 
    transmissivity: float,

    transmissivity_with_ntg: float,

) -> bool:

    if np.any(np.isnan([depth,

                        ntg,

                        porosity,

                        temperature,

                        thickness,

                        transmissivity,

                        transmissivity_with_ntg])):

        return False

    return True

    """

    Check that none of the input is nan

    """

    return not np.any(np.isnan([depth, ntg, porosity, temperature, thickness, transmissivity, transmissivity_with_ntg]))



def instantiate_thermogis_doublet(utc_properties, rng_seed: int = None) -> JClass:

    """

tests/test_define_calculation_limits.py

0 → 100644
+48 −0
Original line number Diff line number Diff line 
from pathlib import Path



import numpy as np

import xarray as xr



from pythermogis import calculate_doublet_performance



test_files_path = Path(__file__).parent / "resources"



def print_min_max(variable: xr.DataArray):

    print(f"{variable.name}, {np.min(variable):.2f}, {np.max(variable):.2f}")



def test_define_calculation_limits():

    recalculate_results = True

    if recalculate_results:

        # generate simulation samples across desired reservoir properties

        Nsamples = 1000

        thickness_samples = np.random.uniform(low=1, high=10e3, size=Nsamples)

        porosity_samples = np.random.uniform(low=0.01, high=0.99, size=Nsamples)

        ntg_samples = np.random.uniform(low=0.01, high=0.99, size=Nsamples)

        depth_samples = np.random.uniform(low=1, high=10e3, size=Nsamples)

        permeability_samples = np.random.uniform(low=1, high=10e3, size=Nsamples)

        reservoir_properties = xr.Dataset(

            {

                "thickness": (["sample"], thickness_samples),

                "porosity": (["sample"], porosity_samples),

                "ntg": (["sample"], ntg_samples),

                "depth": (["sample"], depth_samples),

                "permeability": (["sample"], permeability_samples),

            },

            coords={"sample": np.arange(Nsamples)},

        )



        results = calculate_doublet_performance(

            reservoir_properties,

            chunk_size=100,

            print_execution_duration=True

        )

        results.to_netcdf(test_files_path / "calculation_limits_result.nc")

    else:

        results = xr.load_dataset(test_files_path / "calculation_limits_result.nc")



    # drop the values which returned nan:

    results = results.dropna(dim="sample", subset=["power"])



    # print the min and max values of all the inputs

    for var in results:

        print_min_max(results[var])
 
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