pipt.misc_tools.qaqc_tools
Quality Assurance of the forecast (QA) and analysis (QC) step.
1"""Quality Assurance of the forecast (QA) and analysis (QC) step.""" 2import numpy as np 3import os 4# import matplotlib as mpl 5# mpl.use('Qt5Agg') 6import matplotlib.pyplot as plt 7import matplotlib.patches as pat 8import matplotlib.collections as mcoll 9from matplotlib.colors import ListedColormap 10import itertools 11import logging 12from pipt.misc_tools import cov_regularization 13from scipy.interpolate import interp1d 14from scipy.io import loadmat 15# import cv2 16 17 18# Define the class for qa/qc tools. 19class QAQC: 20 """ 21 Perform Quality Assurance of the forecast (QA) and analysis (QC) step. 22 Available functions (developed in 4DSEIS project and not available yet): 23 24 - `calc_coverage`: check forecast data coverage 25 - `calc_mahalanobis`: evaluate "higher-order" data coverage 26 - `calc_kg`: check/write individual gain for parameters; 27 - flag data which have conflicting updates 28 - `calc_da_stat`: compute statistics for updated parameters 29 30 Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 31 """ 32 33 # Initialize 34 def __init__(self, keys, obs_data, datavar, logger=None, prior_info=None, sim=None, ini_state=None): 35 self.keys = keys # input info for the case 36 self.obs_data = obs_data # observed (real) data 37 self.datavar = datavar # data variance 38 if logger is None: # define a logger to print ouput 39 logging.basicConfig(level=logging.INFO, 40 filename='qaqc_logger.log', 41 filemode='a', 42 format='%(asctime)s : %(levelname)s : %(name)s : %(message)s') 43 self.logger = logging.getLogger('QAQC') 44 else: 45 self.logger = logging.getLogger('PET.PIPT.QCQA') 46 self.prior_info = prior_info # prior info for the different parameter types 47 # this class contains potential writing functions (this class can be saved to debug_analysis) 48 self.sim = sim 49 self.ini_state = ini_state # the first state; used to compute statistics 50 self.ne = 0 51 if self.ini_state is not None: 52 # get the ensemble size from here 53 self.ne = self.ini_state[list(self.ini_state.keys())[0]].shape[1] 54 55 assim_step = 0 # Assume simultaneous assimiation 56 assim_ind = [keys['obsname'], keys['assimindex'][assim_step]] 57 if isinstance(assim_ind[1], list): # Check if prim. ind. is a list 58 self.l_prim = [int(x) for x in assim_ind[1]] 59 else: # Float 60 self.l_prim = [int(assim_ind[1])] 61 62 self.data_types = list(obs_data[0].keys()) # All data types 63 self.en_obs = {} 64 self.en_obs_vec = {} 65 self.en_time = {} 66 self.en_time_vec = {} 67 for typ in self.data_types: 68 self.en_obs[typ] = np.array( 69 [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 70 is not None and self.obs_data[ind][typ].shape == (1,)]) 71 l = [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] is not None 72 and self.obs_data[ind][typ].shape[0] > 1] 73 if l: 74 self.en_obs_vec[typ] = np.expand_dims(np.concatenate(l), 1) 75 self.en_time[typ] = [ind for ind in self.l_prim if self.obs_data[ind][typ] 76 is not None and self.obs_data[ind][typ].shape == (1,)] 77 l = [ind for ind in self.l_prim if self.obs_data[ind][typ] 78 is not None and self.obs_data[ind][typ].shape[0] > 1] 79 if l: 80 self.en_time_vec[typ] = l 81 82 # Check if the QA folder is generated 83 self.folder = 'QAQC' + os.sep 84 if not os.path.exists(self.folder): 85 os.mkdir(self.folder) # if not generate 86 87 if 'localization' in self.keys: 88 self.localization = cov_regularization.localization(self.keys['localization'], 89 self.keys['truedataindex'], 90 self.keys['datatype'], 91 self.keys['staticvar'], 92 self.ne) 93 self.pred_data = None 94 self.state = None 95 self.en_fcst = {} 96 self.en_fcst_vec = {} 97 self.lam = None 98 99 # Set the predicted data and current state 100 def set(self, pred_data, state=None, lam=None): 101 self.pred_data = pred_data 102 for typ in self.data_types: 103 self.en_fcst[typ] = np.array( 104 [self.pred_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 105 is not None and self.obs_data[ind][typ].shape == (1,)]) 106 l = [self.pred_data[ind][typ] for ind in self.l_prim if self.obs_data[ind][typ] is not None 107 and self.obs_data[ind][typ].shape[0] > 1] 108 if l: 109 self.en_fcst_vec[typ] = np.concatenate(l) 110 self.state = state 111 self.lam = lam 112 113 def calc_coverage(self, line=None): 114 """ 115 Calculate the Data coverage for production and seismic data. For seismic data the plotting is based on the 116 importance-scaled coverage developed by Espen O. Lie from GeoCore. 117 118 Parameters 119 ---------- 120 line : array-like, optional 121 If not None, plot 1D coverage. 122 123 Notes 124 ----- 125 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 126 - NOTE: Not available in current version of PIPT 127 """ 128 129 def calc_kg(self, options=None): 130 """ 131 Check/write individual gain for parameters. 132 Note form ES gain with an identity Cd... This can be improved 133 134 Visualization of the many of these parameters is problem-specific. In reservoir simulation cases, it is necessary 135 to write this to the simulation grid. While for other applications, one might want other visualization. Hence, 136 the method also depends on a simulator specific writer. 137 138 Parameters 139 ---------- 140 options : dict 141 Settings for the Kalman gain computations. 142 - 'num_store' : int, optional 143 Number of elements to store. Default is 10. 144 - 'unique_time' : bool, optional 145 Calculate for each time instance. Default is False. 146 - 'plot_all_kg' : bool, optional 147 Plot all the Kalman gains for the field parameters. If False, plot the num_store. Default is False. 148 - 'only_log' : bool, optional 149 Only write to the logger; no plotting. Default is True. 150 - 'auto_ada_loc' : bool, optional 151 Use localization in computations. Default is True. 152 - 'write_to_resinsight' : bool, optional 153 Pipe results to ResInsight. Default is False. (Note: this requires that ResInsight is open on the computer.) 154 155 Notes 156 ----- 157 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 158 - NOTE: Not available in current version of PIPT 159 """ 160 161 def calc_mahalanobis(self, combi_list=(1, None)): 162 """ 163 Calculate the mahalanobis distance as described in "Oliver, D. S. (2020). Diagnosing reservoir model deficiency 164 for model improvement. Journal of Petroleum Science and Engineering, 193(February). 165 https://doi.org/10.1016/j.petrol.2020.107367" 166 167 Parameters 168 ---------- 169 combi_list : list 170 List of levels and possible combinations of datatypes. The list must be given as a tuple with pairs: 171 - level : int 172 Defines which level. Default is 1. 173 - combi_typ : str 174 Defines how data are combined. Default is no combine. 175 176 Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 177 NOTE: Not available in current version of PIPT 178 """ 179 180 def calc_da_stat(self, options=None): 181 """ 182 Calculate statistics for the updated parameters. The persentage of parameters that have updates larger than one, 183 two and three standard deviations (calculated from the initial ensemble) are flagged. 184 185 Parameters 186 ---------- 187 options : dict 188 Settings for statistics. 189 write_to_file : bool, optional 190 Whether to write results to a .grdecl file. Defaults to False. 191 192 Notes 193 ----- 194 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 195 - NOTE: Not available in current version of PIPT 196 """
class
QAQC:
20class QAQC: 21 """ 22 Perform Quality Assurance of the forecast (QA) and analysis (QC) step. 23 Available functions (developed in 4DSEIS project and not available yet): 24 25 - `calc_coverage`: check forecast data coverage 26 - `calc_mahalanobis`: evaluate "higher-order" data coverage 27 - `calc_kg`: check/write individual gain for parameters; 28 - flag data which have conflicting updates 29 - `calc_da_stat`: compute statistics for updated parameters 30 31 Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 32 """ 33 34 # Initialize 35 def __init__(self, keys, obs_data, datavar, logger=None, prior_info=None, sim=None, ini_state=None): 36 self.keys = keys # input info for the case 37 self.obs_data = obs_data # observed (real) data 38 self.datavar = datavar # data variance 39 if logger is None: # define a logger to print ouput 40 logging.basicConfig(level=logging.INFO, 41 filename='qaqc_logger.log', 42 filemode='a', 43 format='%(asctime)s : %(levelname)s : %(name)s : %(message)s') 44 self.logger = logging.getLogger('QAQC') 45 else: 46 self.logger = logging.getLogger('PET.PIPT.QCQA') 47 self.prior_info = prior_info # prior info for the different parameter types 48 # this class contains potential writing functions (this class can be saved to debug_analysis) 49 self.sim = sim 50 self.ini_state = ini_state # the first state; used to compute statistics 51 self.ne = 0 52 if self.ini_state is not None: 53 # get the ensemble size from here 54 self.ne = self.ini_state[list(self.ini_state.keys())[0]].shape[1] 55 56 assim_step = 0 # Assume simultaneous assimiation 57 assim_ind = [keys['obsname'], keys['assimindex'][assim_step]] 58 if isinstance(assim_ind[1], list): # Check if prim. ind. is a list 59 self.l_prim = [int(x) for x in assim_ind[1]] 60 else: # Float 61 self.l_prim = [int(assim_ind[1])] 62 63 self.data_types = list(obs_data[0].keys()) # All data types 64 self.en_obs = {} 65 self.en_obs_vec = {} 66 self.en_time = {} 67 self.en_time_vec = {} 68 for typ in self.data_types: 69 self.en_obs[typ] = np.array( 70 [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 71 is not None and self.obs_data[ind][typ].shape == (1,)]) 72 l = [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] is not None 73 and self.obs_data[ind][typ].shape[0] > 1] 74 if l: 75 self.en_obs_vec[typ] = np.expand_dims(np.concatenate(l), 1) 76 self.en_time[typ] = [ind for ind in self.l_prim if self.obs_data[ind][typ] 77 is not None and self.obs_data[ind][typ].shape == (1,)] 78 l = [ind for ind in self.l_prim if self.obs_data[ind][typ] 79 is not None and self.obs_data[ind][typ].shape[0] > 1] 80 if l: 81 self.en_time_vec[typ] = l 82 83 # Check if the QA folder is generated 84 self.folder = 'QAQC' + os.sep 85 if not os.path.exists(self.folder): 86 os.mkdir(self.folder) # if not generate 87 88 if 'localization' in self.keys: 89 self.localization = cov_regularization.localization(self.keys['localization'], 90 self.keys['truedataindex'], 91 self.keys['datatype'], 92 self.keys['staticvar'], 93 self.ne) 94 self.pred_data = None 95 self.state = None 96 self.en_fcst = {} 97 self.en_fcst_vec = {} 98 self.lam = None 99 100 # Set the predicted data and current state 101 def set(self, pred_data, state=None, lam=None): 102 self.pred_data = pred_data 103 for typ in self.data_types: 104 self.en_fcst[typ] = np.array( 105 [self.pred_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 106 is not None and self.obs_data[ind][typ].shape == (1,)]) 107 l = [self.pred_data[ind][typ] for ind in self.l_prim if self.obs_data[ind][typ] is not None 108 and self.obs_data[ind][typ].shape[0] > 1] 109 if l: 110 self.en_fcst_vec[typ] = np.concatenate(l) 111 self.state = state 112 self.lam = lam 113 114 def calc_coverage(self, line=None): 115 """ 116 Calculate the Data coverage for production and seismic data. For seismic data the plotting is based on the 117 importance-scaled coverage developed by Espen O. Lie from GeoCore. 118 119 Parameters 120 ---------- 121 line : array-like, optional 122 If not None, plot 1D coverage. 123 124 Notes 125 ----- 126 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 127 - NOTE: Not available in current version of PIPT 128 """ 129 130 def calc_kg(self, options=None): 131 """ 132 Check/write individual gain for parameters. 133 Note form ES gain with an identity Cd... This can be improved 134 135 Visualization of the many of these parameters is problem-specific. In reservoir simulation cases, it is necessary 136 to write this to the simulation grid. While for other applications, one might want other visualization. Hence, 137 the method also depends on a simulator specific writer. 138 139 Parameters 140 ---------- 141 options : dict 142 Settings for the Kalman gain computations. 143 - 'num_store' : int, optional 144 Number of elements to store. Default is 10. 145 - 'unique_time' : bool, optional 146 Calculate for each time instance. Default is False. 147 - 'plot_all_kg' : bool, optional 148 Plot all the Kalman gains for the field parameters. If False, plot the num_store. Default is False. 149 - 'only_log' : bool, optional 150 Only write to the logger; no plotting. Default is True. 151 - 'auto_ada_loc' : bool, optional 152 Use localization in computations. Default is True. 153 - 'write_to_resinsight' : bool, optional 154 Pipe results to ResInsight. Default is False. (Note: this requires that ResInsight is open on the computer.) 155 156 Notes 157 ----- 158 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 159 - NOTE: Not available in current version of PIPT 160 """ 161 162 def calc_mahalanobis(self, combi_list=(1, None)): 163 """ 164 Calculate the mahalanobis distance as described in "Oliver, D. S. (2020). Diagnosing reservoir model deficiency 165 for model improvement. Journal of Petroleum Science and Engineering, 193(February). 166 https://doi.org/10.1016/j.petrol.2020.107367" 167 168 Parameters 169 ---------- 170 combi_list : list 171 List of levels and possible combinations of datatypes. The list must be given as a tuple with pairs: 172 - level : int 173 Defines which level. Default is 1. 174 - combi_typ : str 175 Defines how data are combined. Default is no combine. 176 177 Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 178 NOTE: Not available in current version of PIPT 179 """ 180 181 def calc_da_stat(self, options=None): 182 """ 183 Calculate statistics for the updated parameters. The persentage of parameters that have updates larger than one, 184 two and three standard deviations (calculated from the initial ensemble) are flagged. 185 186 Parameters 187 ---------- 188 options : dict 189 Settings for statistics. 190 write_to_file : bool, optional 191 Whether to write results to a .grdecl file. Defaults to False. 192 193 Notes 194 ----- 195 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 196 - NOTE: Not available in current version of PIPT 197 """
Perform Quality Assurance of the forecast (QA) and analysis (QC) step. Available functions (developed in 4DSEIS project and not available yet):
calc_coverage: check forecast data coveragecalc_mahalanobis: evaluate "higher-order" data coveragecalc_kg: check/write individual gain for parameters;- flag data which have conflicting updates
calc_da_stat: compute statistics for updated parameters
Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS
QAQC( keys, obs_data, datavar, logger=None, prior_info=None, sim=None, ini_state=None)
35 def __init__(self, keys, obs_data, datavar, logger=None, prior_info=None, sim=None, ini_state=None): 36 self.keys = keys # input info for the case 37 self.obs_data = obs_data # observed (real) data 38 self.datavar = datavar # data variance 39 if logger is None: # define a logger to print ouput 40 logging.basicConfig(level=logging.INFO, 41 filename='qaqc_logger.log', 42 filemode='a', 43 format='%(asctime)s : %(levelname)s : %(name)s : %(message)s') 44 self.logger = logging.getLogger('QAQC') 45 else: 46 self.logger = logging.getLogger('PET.PIPT.QCQA') 47 self.prior_info = prior_info # prior info for the different parameter types 48 # this class contains potential writing functions (this class can be saved to debug_analysis) 49 self.sim = sim 50 self.ini_state = ini_state # the first state; used to compute statistics 51 self.ne = 0 52 if self.ini_state is not None: 53 # get the ensemble size from here 54 self.ne = self.ini_state[list(self.ini_state.keys())[0]].shape[1] 55 56 assim_step = 0 # Assume simultaneous assimiation 57 assim_ind = [keys['obsname'], keys['assimindex'][assim_step]] 58 if isinstance(assim_ind[1], list): # Check if prim. ind. is a list 59 self.l_prim = [int(x) for x in assim_ind[1]] 60 else: # Float 61 self.l_prim = [int(assim_ind[1])] 62 63 self.data_types = list(obs_data[0].keys()) # All data types 64 self.en_obs = {} 65 self.en_obs_vec = {} 66 self.en_time = {} 67 self.en_time_vec = {} 68 for typ in self.data_types: 69 self.en_obs[typ] = np.array( 70 [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 71 is not None and self.obs_data[ind][typ].shape == (1,)]) 72 l = [self.obs_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] is not None 73 and self.obs_data[ind][typ].shape[0] > 1] 74 if l: 75 self.en_obs_vec[typ] = np.expand_dims(np.concatenate(l), 1) 76 self.en_time[typ] = [ind for ind in self.l_prim if self.obs_data[ind][typ] 77 is not None and self.obs_data[ind][typ].shape == (1,)] 78 l = [ind for ind in self.l_prim if self.obs_data[ind][typ] 79 is not None and self.obs_data[ind][typ].shape[0] > 1] 80 if l: 81 self.en_time_vec[typ] = l 82 83 # Check if the QA folder is generated 84 self.folder = 'QAQC' + os.sep 85 if not os.path.exists(self.folder): 86 os.mkdir(self.folder) # if not generate 87 88 if 'localization' in self.keys: 89 self.localization = cov_regularization.localization(self.keys['localization'], 90 self.keys['truedataindex'], 91 self.keys['datatype'], 92 self.keys['staticvar'], 93 self.ne) 94 self.pred_data = None 95 self.state = None 96 self.en_fcst = {} 97 self.en_fcst_vec = {} 98 self.lam = None
def
set(self, pred_data, state=None, lam=None):
101 def set(self, pred_data, state=None, lam=None): 102 self.pred_data = pred_data 103 for typ in self.data_types: 104 self.en_fcst[typ] = np.array( 105 [self.pred_data[ind][typ].flatten() for ind in self.l_prim if self.obs_data[ind][typ] 106 is not None and self.obs_data[ind][typ].shape == (1,)]) 107 l = [self.pred_data[ind][typ] for ind in self.l_prim if self.obs_data[ind][typ] is not None 108 and self.obs_data[ind][typ].shape[0] > 1] 109 if l: 110 self.en_fcst_vec[typ] = np.concatenate(l) 111 self.state = state 112 self.lam = lam
def
calc_coverage(self, line=None):
114 def calc_coverage(self, line=None): 115 """ 116 Calculate the Data coverage for production and seismic data. For seismic data the plotting is based on the 117 importance-scaled coverage developed by Espen O. Lie from GeoCore. 118 119 Parameters 120 ---------- 121 line : array-like, optional 122 If not None, plot 1D coverage. 123 124 Notes 125 ----- 126 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 127 - NOTE: Not available in current version of PIPT 128 """
Calculate the Data coverage for production and seismic data. For seismic data the plotting is based on the importance-scaled coverage developed by Espen O. Lie from GeoCore.
Parameters
- line (array-like, optional): If not None, plot 1D coverage.
Notes
- Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS
- NOTE: Not available in current version of PIPT
def
calc_kg(self, options=None):
130 def calc_kg(self, options=None): 131 """ 132 Check/write individual gain for parameters. 133 Note form ES gain with an identity Cd... This can be improved 134 135 Visualization of the many of these parameters is problem-specific. In reservoir simulation cases, it is necessary 136 to write this to the simulation grid. While for other applications, one might want other visualization. Hence, 137 the method also depends on a simulator specific writer. 138 139 Parameters 140 ---------- 141 options : dict 142 Settings for the Kalman gain computations. 143 - 'num_store' : int, optional 144 Number of elements to store. Default is 10. 145 - 'unique_time' : bool, optional 146 Calculate for each time instance. Default is False. 147 - 'plot_all_kg' : bool, optional 148 Plot all the Kalman gains for the field parameters. If False, plot the num_store. Default is False. 149 - 'only_log' : bool, optional 150 Only write to the logger; no plotting. Default is True. 151 - 'auto_ada_loc' : bool, optional 152 Use localization in computations. Default is True. 153 - 'write_to_resinsight' : bool, optional 154 Pipe results to ResInsight. Default is False. (Note: this requires that ResInsight is open on the computer.) 155 156 Notes 157 ----- 158 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 159 - NOTE: Not available in current version of PIPT 160 """
Check/write individual gain for parameters. Note form ES gain with an identity Cd... This can be improved
Visualization of the many of these parameters is problem-specific. In reservoir simulation cases, it is necessary to write this to the simulation grid. While for other applications, one might want other visualization. Hence, the method also depends on a simulator specific writer.
Parameters
- options (dict):
Settings for the Kalman gain computations.
- 'num_store' : int, optional Number of elements to store. Default is 10.
- 'unique_time' : bool, optional Calculate for each time instance. Default is False.
- 'plot_all_kg' : bool, optional Plot all the Kalman gains for the field parameters. If False, plot the num_store. Default is False.
- 'only_log' : bool, optional Only write to the logger; no plotting. Default is True.
- 'auto_ada_loc' : bool, optional Use localization in computations. Default is True.
- 'write_to_resinsight' : bool, optional Pipe results to ResInsight. Default is False. (Note: this requires that ResInsight is open on the computer.)
Notes
- Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS
- NOTE: Not available in current version of PIPT
def
calc_mahalanobis(self, combi_list=(1, None)):
162 def calc_mahalanobis(self, combi_list=(1, None)): 163 """ 164 Calculate the mahalanobis distance as described in "Oliver, D. S. (2020). Diagnosing reservoir model deficiency 165 for model improvement. Journal of Petroleum Science and Engineering, 193(February). 166 https://doi.org/10.1016/j.petrol.2020.107367" 167 168 Parameters 169 ---------- 170 combi_list : list 171 List of levels and possible combinations of datatypes. The list must be given as a tuple with pairs: 172 - level : int 173 Defines which level. Default is 1. 174 - combi_typ : str 175 Defines how data are combined. Default is no combine. 176 177 Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 178 NOTE: Not available in current version of PIPT 179 """
Calculate the mahalanobis distance as described in "Oliver, D. S. (2020). Diagnosing reservoir model deficiency for model improvement. Journal of Petroleum Science and Engineering, 193(February). https://doi.org/10.1016/j.petrol.2020.107367"
Parameters
- combi_list (list):
List of levels and possible combinations of datatypes. The list must be given as a tuple with pairs:
- level : int Defines which level. Default is 1.
- combi_typ : str Defines how data are combined. Default is no combine.
- Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS
- NOTE (Not available in current version of PIPT):
def
calc_da_stat(self, options=None):
181 def calc_da_stat(self, options=None): 182 """ 183 Calculate statistics for the updated parameters. The persentage of parameters that have updates larger than one, 184 two and three standard deviations (calculated from the initial ensemble) are flagged. 185 186 Parameters 187 ---------- 188 options : dict 189 Settings for statistics. 190 write_to_file : bool, optional 191 Whether to write results to a .grdecl file. Defaults to False. 192 193 Notes 194 ----- 195 - Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS 196 - NOTE: Not available in current version of PIPT 197 """
Calculate statistics for the updated parameters. The persentage of parameters that have updates larger than one, two and three standard deviations (calculated from the initial ensemble) are flagged.
Parameters
- options (dict): Settings for statistics. write_to_file : bool, optional Whether to write results to a .grdecl file. Defaults to False.
Notes
- Copyright (c) 2019-2022 NORCE, All Rights Reserved. 4DSEIS
- NOTE: Not available in current version of PIPT