popt.cost_functions.npv

Net present value.

 1"""Net present value."""
 2import numpy as np
 3
 4
 5def npv(pred_data, **kwargs):
 6    """
 7    Net present value cost function
 8
 9    Parameters
10    ----------
11    pred_data : array_like
12        Ensemble of predicted data.
13
14    **kwargs : dict
15        Other arguments sent to the npv function
16
17        keys_opt : list
18            Keys with economic data.
19
20                - wop: oil price
21                - wgp: gas price
22                - wwp: water production cost
23                - wwi: water injection cost
24                - disc: discount factor
25                - obj_scaling: used to scale the objective function (negative since all methods are minimizers)
26
27        report : list
28            Report dates.
29
30    Returns
31    -------
32    objective_values : numpy.ndarray
33        Objective function values (NPV) for all ensemble members.
34    """
35
36    # Get the necessary input
37    keys_opt = kwargs.get('input_dict',{})
38    report = kwargs.get('true_order', [])
39
40    # Economic values
41    npv_const = {}
42    for name, value in keys_opt['npv_const']:
43        npv_const[name] = value
44
45    values = []
46    for i in np.arange(1, len(pred_data)):
47
48        Qop = np.squeeze(pred_data[i]['fopt']) - np.squeeze(pred_data[i - 1]['fopt'])
49        Qgp = np.squeeze(pred_data[i]['fgpt']) - np.squeeze(pred_data[i - 1]['fgpt'])
50        Qwp = np.squeeze(pred_data[i]['fwpt']) - np.squeeze(pred_data[i - 1]['fwpt'])
51        Qwi = np.squeeze(pred_data[i]['fwit']) - np.squeeze(pred_data[i - 1]['fwit'])
52        delta_days = (report[1][i] - report[1][i - 1]).days
53
54        val = (Qop * npv_const['wop'] + Qgp * npv_const['wgp'] - Qwp * npv_const['wwp'] - Qwi * npv_const['wwi']) / (
55            (1 + npv_const['disc']) ** (delta_days / 365))
56
57        values.append(val)
58
59    if 'obj_scaling' in npv_const:
60        return np.array(sum(values)) / npv_const['obj_scaling']
61    else:
62        return np.array(sum(values))
def npv(pred_data, **kwargs): 
 6def npv(pred_data, **kwargs):
 7    """
 8    Net present value cost function
 9
10    Parameters
11    ----------
12    pred_data : array_like
13        Ensemble of predicted data.
14
15    **kwargs : dict
16        Other arguments sent to the npv function
17
18        keys_opt : list
19            Keys with economic data.
20
21                - wop: oil price
22                - wgp: gas price
23                - wwp: water production cost
24                - wwi: water injection cost
25                - disc: discount factor
26                - obj_scaling: used to scale the objective function (negative since all methods are minimizers)
27
28        report : list
29            Report dates.
30
31    Returns
32    -------
33    objective_values : numpy.ndarray
34        Objective function values (NPV) for all ensemble members.
35    """
36
37    # Get the necessary input
38    keys_opt = kwargs.get('input_dict',{})
39    report = kwargs.get('true_order', [])
40
41    # Economic values
42    npv_const = {}
43    for name, value in keys_opt['npv_const']:
44        npv_const[name] = value
45
46    values = []
47    for i in np.arange(1, len(pred_data)):
48
49        Qop = np.squeeze(pred_data[i]['fopt']) - np.squeeze(pred_data[i - 1]['fopt'])
50        Qgp = np.squeeze(pred_data[i]['fgpt']) - np.squeeze(pred_data[i - 1]['fgpt'])
51        Qwp = np.squeeze(pred_data[i]['fwpt']) - np.squeeze(pred_data[i - 1]['fwpt'])
52        Qwi = np.squeeze(pred_data[i]['fwit']) - np.squeeze(pred_data[i - 1]['fwit'])
53        delta_days = (report[1][i] - report[1][i - 1]).days
54
55        val = (Qop * npv_const['wop'] + Qgp * npv_const['wgp'] - Qwp * npv_const['wwp'] - Qwi * npv_const['wwi']) / (
56            (1 + npv_const['disc']) ** (delta_days / 365))
57
58        values.append(val)
59
60    if 'obj_scaling' in npv_const:
61        return np.array(sum(values)) / npv_const['obj_scaling']
62    else:
63        return np.array(sum(values))

Net present value cost function

Parameters
  • pred_data (array_like): Ensemble of predicted data.

  • **kwargs (dict): Other arguments sent to the npv function

    keys_opt : list Keys with economic data.

        - wop: oil price
    - wgp: gas price
    - wwp: water production cost
    - wwi: water injection cost
    - disc: discount factor
    - obj_scaling: used to scale the objective function (negative since all methods are minimizers)

    report : list Report dates.

Returns
  • objective_values (numpy.ndarray): Objective function values (NPV) for all ensemble members.