def __init__(self, simulation_time, gas_field, **kwargs):
"""
Inputs:
gas_field a gas_field object (Defined in feast_classes)
time a time object (Defined in feast_classes)
kwargs optional input dicitionary that will override default parameters
"""
super().__init__(simulation_time, gas_field)
# -------------- Hardware properties --------------
# Sensitivity is the trigger sensitivity for the sniffer
self.sensitivity = .01 # g/m^3 (multiply by 1500 to get ppm)
# x, y, and z define the positions of the sensors
# x is distance East from the center of the well pad
# y is distance North from the center of the well pad
self.x = [-3.5, 0, 3.5, 0]
self.y = [-10, -10, -10, 10]
self.z = [2, 3, 4, 4]
self.lifetime = 5 * 365 # days
# -------------- Process variables --------------
self.repair_interval = 50 # days
self.survey_speed = 500 # component/person-hour
self.drive_speed = 15 # m/s
self.setup_time = 0.5 # hours
self.work_time = 5 # hours/day on average ~= 35 hours per week.
# -------------- Financial --------------
self.sniffer_cost = 500 # $ per sniffer
self.camera_capital = 90000 # $
self.truck_capital = 30000 # $
self.labor = 100 # $/hour
self.maintenance_factor = 0.1
# -------------- Override default parameters with kwargs --------------
set_kwargs_attrs(self, kwargs)
# Calculated parameters
self.n_sniff = len(self.x) # sniffers per well
self.sniffer_capital = self.sniffer_cost * self.n_sniff # $ per well
self.location_time = (
gas_field.components_per_site / self.survey_speed / self.n_sniff +
gas_field.site_spacing / self.drive_speed / 3600 +
self.setup_time) / self.n_sniff # hours to locate one leak
self.time_factor = self.location_time * gas_field.site_count / \
(self.repair_interval * self.work_time)
self.capital_0 = self.sniffer_capital * gas_field.site_count + self.time_factor \
* (self.camera_capital + self.truck_capital) # $
self.maintenance0 = self.capital_0 * self.maintenance_factor
self.maintenance = [self.maintenance0 * simulation_time.delta_t / 365
] * simulation_time.n_timesteps # $/year
self.location_cost = self.labor * self.location_time # $/leak
# Calculate capital costs vector
self.capital = np.zeros(simulation_time.n_timesteps) # dollars
helper_functions.replacement_cap(simulation_time, self)
# The finding np.costs must be calculated as leaks are found. The vector is
# initialized here.
self.find_cost = [0] * simulation_time.n_timesteps
def __init__(self, time, gas_field, **kwargs):
"""
Inputs:
time a time object (Defined in feast_classes)
gas_field a gas_field object (Defined in feast_classes)
kwargs optional input dictionary that will override default parameters
"""
DetectionMethod.__init__(self, time, gas_field)
# -------------- Hardware variables --------------
self.lifetime = 10 * 365 # days
# -------------- Process Variables --------------
self.survey_interval = 100 # days
self.survey_speed = 150 # components/person-hour
self.drive_speed = 15 # m/s
self.setup_time = 0.1 # hours
self.work_time = 5 # hours/day on average...5 hours per day on average corresponds to 35 hours per week.
self.labor = 100 # dollars/hour
set_kwargs_attrs(self, kwargs)
# -------------- Set calculated parameters --------------
self.survey_time = (gas_field.components_per_site / self.survey_speed +
gas_field.site_spacing / self.drive_speed / 3600 +
self.setup_time) # hours
# time_factor accounts for the finite simulation size. The effective capital cost is
# reduced in the simulation based on the ratio of the wells in the
# simulation to the number of wells that a single FID could survey.
self.time_factor = self.survey_time * gas_field.site_count / (
self.survey_interval * self.work_time)
# -------------- Financial Properties --------------
self.capital_0 = 35000 * self.time_factor # dollars (covers 5k for FID and 30k for truck)
self.maintenance_0 = self.capital_0 * 0.1 # dollars/year
self.capital = np.zeros(time.n_timesteps)
helper_functions.replacement_cap(time, self)
# maintenance costs are estimated as 10% of capital per year
self.maintenance = [
self.maintenance_0 * time.delta_t / 365,
] * time.n_timesteps # $
# survey_cost is the cost to survey all wells in the natural gas field
self.survey_cost = self.labor * gas_field.site_count * self.survey_time
# find_cost is the cost of searching for leaks
for ind in range(0, time.n_timesteps):
curr_time = ind * time.delta_t
if curr_time % self.survey_interval < time.delta_t:
self.find_cost[ind] = self.survey_cost
def __init__(self, simulation_time, gas_field, **kwargs):
"""
AD is the model of how an airborne detector detects leaks.
Inputs:
gas_field a gas_field object (Defined in feast_classes)
time a time object (Defined in feast_classes)
kwargs optional input dicitionary that will override default parameters
"""
super().__init__(simulation_time, gas_field)
# -------------- LeakSurveyor default properties ---------------------
# Detection
self.name = "AD"
# Some generic detection probabilities, to be replaced with actual model
# used only in the "legacy" mode.
self.detection_sizes = np.array([0, 1, 2, 3, 4, 1000]) * mcfpd_to_gps
self.detection_prob = np.array([0, 0.5, 0.75, 0.9, 0.99, 1])
self.detection_sizes = np.sort(self.detection_sizes)
self.detection_prob = np.sort(self.detection_prob)
# Survey
self.survey_interval = 30 # days
# Money
self.lifetime = 10 * 365
# Detection probability models
self.detection_models = {
"exponential": self.get_exp_probabilities,
"legacy": self.get_legacy_probabilities,
"exp_erf": self.get_exp_erf_probabilities
}
if "detection_model_name" not in kwargs:
kwargs.update({"detection_model_name": "legacy"})
if "inst_params" not in kwargs:
kwargs.update({"inst_params": {}})
self.inst_params_index = None
self.survey_interval_index = None
# -------------- Override default parameters with kwargs --------------
set_kwargs_attrs(self, kwargs, only_existing=False)
# Calculate capital costs vector
self.capital_0 = None
self.capital = np.zeros(simulation_time.n_timesteps) # dollars
helper_functions.replacement_cap(simulation_time, self)
# The finding np.costs must be calculated as leaks are found. The vector is
# initialized here.
self.find_cost = [0] * simulation_time.n_timesteps
def __init__(self, simulation_time, gas_field, **kwargs):
"""
HY is a hybrid model including an airborne detector and an ogi, used together on
different revisit frequencies.
Inputs:
gas_field a gas_field object (Defined in feast_classes)
time a time object (Defined in feast_classes)
kwargs optional input dicitionary that will override default parameters
"""
super().__init__(simulation_time, gas_field)
# -------------- LeakSurveyor default properties ---------------------
# Detection
self.name = "HY"
# Survey
self.ad_survey_interval = 90 # days
self.ogi_survey_interval = 730 # days
self.ogi_distance = 10 # m
# Money
self.lifetime = 10 * 365
# Detection probability models
if "detection_model_name" not in kwargs:
kwargs.update({"detection_model_name": "legacy"})
if "inst_params" not in kwargs:
kwargs.update({"inst_params": {}})
self.inst_params_index = None
self.survey_interval_index = None
# -------------- Override default parameters with kwargs --------------
set_kwargs_attrs(self, kwargs, only_existing=False)
# Calculate capital costs vector
self.capital_0 = None
self.capital = np.zeros(simulation_time.n_timesteps) # dollars
helper_functions.replacement_cap(simulation_time, self)
# The finding np.costs must be calculated as leaks are found. The vector is
# initialized here.
self.find_cost = [0] * simulation_time.n_timesteps