from dataclasses import dataclass
from enum import Enum
from typing import Optional, Dict, Any, List, Union
[docs]
class IptaParameter(Enum):
C = "Wellbore storage"
Skin = "Skin"
k = "Permeability"
h = "Thickness"
phi = "Porosity"
S = "South"
E = "East"
N = "North"
W = "West"
L = "Distance"
class IrtaParameterEnum(Enum):
Skin = "-Skin"
Xmf = "-Xmf"
Pi = '-Pi'
k = "-k"
h = "-h"
phi = "-ɸ"
zone_1_d = "Zone 1-d"
zone_1_ri = "Zone 1-Ri"
zone_2_d = "Zone 2-d"
@dataclass
class ImproveTarget:
"""An improve target for an IPTA or IRTA workflow."""
target_type: str
is_selected: bool
target_well_id: str
on_log: Optional[bool] = None
global_weight: Optional[float] = None
def dump(self) -> Dict[str, Any]:
return {
"targetType": self.target_type,
"isSelected": self.is_selected,
"targetWellId": self.target_well_id,
"onLog": self.on_log,
"globalWeight": self.global_weight,
}
@dataclass
class ImproveParameter:
"""An improve parameter for an IPTA or IRTA workflow."""
name: str
id: str
is_included: bool
value: float
max_value: float
min_value: float
measure: str
category: Optional[str] = None
def dump(self) -> Dict[str, Any]:
result: Dict[str, Any] = {
"id": self.id,
"isIncluded": self.is_included,
"value": self.value,
"maxValue": self.max_value,
"minValue": self.min_value,
}
return result
[docs]
class WorkflowImproveSettings:
[docs]
def __init__(self, improve_parameters: List[ImproveParameter], use_all_parameters: bool = False, use_wide_search: bool = False,
update_values: bool = False,
update_bounds: bool = False,
min_elapsed_time: Optional[float] = None,
locate_and_ignore_wellbore_effect: bool = False,
improve_targets: Optional[List[ImproveTarget]] = None):
self.__parameters: List[ImproveParameter] = list(improve_parameters)
if use_all_parameters:
for parameter in self.__parameters:
parameter.is_included = True
self.__wide_search_settings: Dict[str, Any] = {"useWideSearch": use_wide_search, "initialPopulation": 50, "boundsStretch": 10}
self.__update_values: bool = update_values
self.__update_bounds: bool = update_bounds
self.__min_elapsed_time: Optional[float] = min_elapsed_time
self.__locate_and_ignore_wellbore_effect: bool = locate_and_ignore_wellbore_effect
self.__improve_targets: Optional[List[ImproveTarget]] = improve_targets
@property
def parameters(self) -> List[ImproveParameter]:
"""The improve parameters."""
return self.__parameters
@property
def improve_targets(self) -> Optional[List[ImproveTarget]]:
"""The improve targets, if any."""
return self.__improve_targets
@property
def min_elapsed_time(self) -> Optional[float]:
return self.__min_elapsed_time
@min_elapsed_time.setter
def min_elapsed_time(self, value: Optional[float]) -> None:
self.__min_elapsed_time = value
[docs]
def select_parameter(self, parameter: Union[IptaParameter, IrtaParameterEnum], min_value: Optional[float] = None, max_value: Optional[float] = None) -> None:
try:
selected_parameter = next(x for x in self.__parameters if x.id == parameter.value)
except StopIteration:
raise ValueError(f"Cannot find {parameter.value} in the improve parameter list, make sure you are using the right type of parameter, could be IptaParameter or IrtaParameter")
if min_value is not None:
selected_parameter.min_value = min_value
if max_value is not None:
selected_parameter.max_value = max_value
selected_parameter.is_included = True
[docs]
def remove_parameter(self, parameter: Union[IptaParameter, IrtaParameterEnum]) -> None:
try:
selected_parameter = next(x for x in self.__parameters if x.id == parameter.value)
except StopIteration:
raise ValueError(f"Cannot find {parameter.value} in the improve parameter list, make sure you are using the right type of parameter, could be IptaParameter or IrtaParameter")
selected_parameter.is_included = False
[docs]
def update_wide_search_settings(self, initial_population: float = 50, bounds_stretch: float = 10) -> None:
self.__wide_search_settings["initialPopulation"] = initial_population
self.__wide_search_settings["boundsStretch"] = bounds_stretch
[docs]
def dump(self) -> Dict[str, Any]:
result: Dict[str, Any] = {
"updateValues": self.__update_values,
"updateBounds": self.__update_bounds,
"nlrSettings": {
"locateAndIgnoreWellboreEffect": self.__locate_and_ignore_wellbore_effect,
"minElapsedTime": self.__min_elapsed_time,
"wideSearchSettings": self.__wide_search_settings,
},
"parameters": [p.dump() for p in self.__parameters],
}
if self.__improve_targets is not None:
result["improveTargets"] = [t.dump() for t in self.__improve_targets]
return result
