├── setup.py ├── README.md ├── plot_objective_function.py ├── plot_optim.py ├── plot_parameters.py ├── plot_data.py └── LICENSE /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | 3 | setup( 4 | name='Plotting', 5 | version='1.0', 6 | packages=['./'], 7 | url='https://github.com/Python-Ensemble-Toolbox/Plotting', 8 | license_files=('LICENSE.txt',), 9 | author_email='rolo@norceresearch.no', 10 | description='Useful plotting scripts for the Python Ensemble Toolbox', 11 | ) 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Plotting 2 | Useful plot functions for the PET toolboxes. 3 | 4 | - plot_data: production data, RFT data, and (2d or 3d) seismic data 5 | - plot_objective_function: data misfit, either all data combined or separated on well data and seismic data 6 | - plot_parameters: layers of field parameters or surfaces, vertical averages of field parameters, scalars, or export field parameters to grid (as .grdecl files that can be visualized in e.g., ResInsight) 7 | - plot_optim: objective function and state variables from popt 8 | 9 | **Installation** 10 | 11 | Inside the Plotting folder, run 12 | 13 | python3 -m pip install -e . 14 | 15 | - The dot is needed to point to the current directory. 16 | - The -e option installs PET such that changes to it take effect immediately (without re-installation). -------------------------------------------------------------------------------- /plot_objective_function.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | import os 4 | 5 | 6 | # Set paths and find results 7 | path_to_files = '.' 8 | path_to_figures = './Figures' # Save here 9 | if not os.path.exists(path_to_figures): 10 | os.mkdir(path_to_figures) 11 | files = os.listdir(path_to_files) 12 | results = [name for name in files if "debug_analysis_step" in name] 13 | num_iter = len(results) 14 | 15 | 16 | def combined(): 17 | """ 18 | Plot objective function for all data combined 19 | 20 | % Copyright (c) 2023 NORCE, All Rights Reserved. 21 | """ 22 | 23 | mm = [] 24 | for iter in range(num_iter): 25 | if iter == 0: 26 | mm.append(np.load(str(path_to_files) + '/debug_analysis_step_{}.npz'.format(iter + 1))['prev_data_misfit']) 27 | mm.append(np.load(str(path_to_files) + '/debug_analysis_step_{}.npz'.format(iter+1))['data_misfit']) 28 | 29 | f = plt.figure() 30 | plt.plot(mm, 'ko-') 31 | plt.xticks(np.arange(0, num_iter+1), np.arange(num_iter+1)) 32 | plt.xticks(fontsize=16) 33 | plt.yticks(fontsize=16) 34 | plt.xlabel('Iteration no.', size=20) 35 | plt.ylabel('Data mismatch', size=20) 36 | plt.title('Objective function', size=20) 37 | f.tight_layout(pad=2.0) 38 | plt.savefig(str(path_to_figures) + '/obj_func') 39 | plt.show() 40 | #plt.close('all') 41 | 42 | 43 | def separate(scaling=1.0): 44 | """ 45 | Plot objective function separately for well data and seismic data (bulkimp or sim2seis). 46 | Note that this function does not add noise to the data, and will therefore be different from the combined 47 | function. The data mismatch values are also divided by the number of data of each type. 48 | 49 | Input: 50 | - scaling: if scaling of seismic data is used during data assimilation, this input can be used to convert back 51 | to the original values 52 | 53 | % Copyright (c) 2023 NORCE, All Rights Reserved. 54 | 55 | """ 56 | 57 | obs = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['obs'] 58 | var = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['var'] 59 | num_step = len(obs) 60 | 61 | # check if cov_data.p exist (screening is used) 62 | seis_data = ['sim2seis', 'bulkimp'] 63 | actual_var = None 64 | if os.path.exists('cov_data.p'): 65 | with open('cov_data.p', 'rb') as f: 66 | actual_var = pickle.load(f) 67 | 68 | # get the cov data 69 | seis_obs = np.array([]) 70 | prod_obs = np.array([]) 71 | seis_cov = np.array([]) 72 | prod_cov = np.array([]) 73 | seismic_ind = 0 74 | for i in np.arange(num_step): 75 | for key in obs[i].keys(): 76 | 77 | if actual_var is not None: 78 | if obs[i][key] is not None: 79 | if my_data not in key: 80 | prod_cov = np.append(prod_cov, actual_var[seismic_ind:seismic_ind + var[i][key].shape[0]]) 81 | seismic_ind += var[i][key].shape[0] 82 | 83 | if key in seis_data and obs[i][key] is not None: 84 | seis_obs = np.append(seis_obs, obs[i][key] / scaling) 85 | if actual_var is not None: 86 | seis_cov = np.append(seis_cov, actual_var[seismic_ind:seismic_ind + len(obs[i][key])]) 87 | seismic_ind += var[i][key].shape[0] 88 | else: 89 | seis_cov = np.append(seis_cov, var[i][key]) 90 | seismic_ind += var[i][key].shape[0] 91 | elif obs[i][key] is not None: 92 | prod_obs = np.concatenate((prod_obs, obs[i][key])) 93 | if actual_var is None: 94 | prod_cov = np.concatenate((prod_cov, var[i][key])) 95 | seismic_ind += var[i][key].shape[0] 96 | 97 | num_seis = len(seis_obs) 98 | num_prod = len(prod_obs) 99 | prod_misfit = [] 100 | seismic_misfit = [] 101 | 102 | for iter in range(num_iter+1): 103 | 104 | if iter == 0: 105 | analysis = np.load(str(path_to_files) + '/prior_forecast.npz', allow_pickle=True) 106 | else: 107 | analysis = np.load(str(path_to_files) + f'/debug_analysis_step_{iter}.npz', allow_pickle=True) 108 | pred = analysis['pred_data'] 109 | seis_pred = [] 110 | prod_pred = [] 111 | for i in np.arange(num_step): 112 | for key in pred[i].keys(): 113 | if key not in seis_data and obs[i][key] is not None: 114 | prod_pred.append(pred[i][key]) 115 | elif key in seis_data and obs[i][key] is not None: 116 | seis_pred.append(pred[i][key] / scaling) 117 | 118 | prod_pred = np.vstack(prod_pred) - np.array([prod_obs]).T 119 | seis_pred = np.vstack(seis_pred) - np.array([seis_obs]).T 120 | hm = np.sum(prod_pred**2 / np.array([prod_cov]).T, axis=0) 121 | prod_misfit.append(hm) 122 | hm = np.sum(seis_pred ** 2 / np.array([seis_cov]).T, axis=0) 123 | seismic_misfit.append(hm) 124 | 125 | print(f'Number of production data: {num_prod}, number of seismic data: {num_seis}') 126 | 127 | f = plt.figure() 128 | plt.subplot(1, 2, 1) 129 | plt.boxplot(np.array(prod_misfit).T / num_prod, showfliers=False, showmeans=True) 130 | plt.xticks(np.arange(1, num_iter+2), np.arange(0, num_iter+1)) 131 | plt.xticks(fontsize=16) 132 | plt.yticks(fontsize=16) 133 | plt.xlabel('Iteration no.', size=20) 134 | plt.ylabel('Data mismatch', size=20) 135 | plt.title('Well data', size=20) 136 | 137 | plt.subplot(1, 2, 2) 138 | plt.boxplot(np.array(seismic_misfit).T / num_seis, showfliers=False, showmeans=True) 139 | plt.xticks(np.arange(1, num_iter+2), np.arange(0, num_iter+1)) 140 | plt.xticks(fontsize=16) 141 | plt.yticks(fontsize=16) 142 | plt.xlabel('Iteration no.', size=20) 143 | plt.ylabel('Data mismatch', size=20) 144 | plt.title('Seismic data', size=20) 145 | 146 | f.tight_layout(pad=2.0) 147 | plt.savefig(str(path_to_figures) + '/obj_func_sep') 148 | plt.show() 149 | plt.close('all') 150 | 151 | 152 | -------------------------------------------------------------------------------- /plot_optim.py: -------------------------------------------------------------------------------- 1 | # External imports 2 | import matplotlib.pyplot as plt # Plot functions 3 | import numpy as np # Numerical toolbox 4 | import os 5 | 6 | 7 | def plot_obj_func(obj_scaling=None): 8 | """ 9 | Plot the objective function vs. iterations. 10 | 11 | % Copyright (c) 2023 NORCE, All Rights Reserved. 12 | """ 13 | 14 | # Set paths 15 | path_to_files = './' 16 | path_to_figures = './Figures/' # Save here 17 | if not os.path.exists(path_to_figures): 18 | os.mkdir(path_to_figures) 19 | 20 | # Collect all results 21 | files = os.listdir(path_to_files) 22 | results = [name for name in files if "optimize_result" in name] 23 | if len(results) == 0: 24 | return 25 | fig, ax = plt.subplots(1, 1, figsize=(10, 10)) 26 | obj = [] 27 | ind = [i for i, ltr in enumerate(results[0]) if ltr == '_'] 28 | if len(ind) > 2: # there is an epf outer loop index in the results 29 | for f in results: 30 | ind = [i for i, ltr in enumerate(f) if ltr == '_'] 31 | ind.append(f.find('.npz')) 32 | inner_it = int(f[ind[1]+1:ind[2]]) 33 | outer_it = int(f[ind[2]+1:ind[3]]) 34 | info = np.load(str(path_to_files) + 'optimize_result_{0}_{1}.npz' 35 | .format(f[ind[1]+1:ind[2]], f[ind[2]+1:ind[3]]), allow_pickle=True) 36 | if not len(obj) > outer_it: 37 | obj.extend([] for _ in range(outer_it-len(obj)+1)) 38 | if not len(obj[outer_it]) > inner_it: 39 | obj[outer_it].extend([] for _ in range(inner_it-len(obj[outer_it])+1)) 40 | val = info['obj_func_values'] 41 | if obj_scaling is not None: 42 | val *= obj_scaling 43 | obj[outer_it][inner_it] = val 44 | leg = [] 45 | for i in range(len(obj)): 46 | if len(obj[i]) > 1: 47 | ax.plot(obj[i], linewidth=4, markersize=10) 48 | else: 49 | ax.plot(obj[i], 'd', markersize=10) 50 | leg.append('epf iter ' + str(i)) 51 | ax.tick_params(labelsize=16) 52 | ax.set_xlabel('Iteration no.', size=20) 53 | ax.set_ylabel('Value', size=20) 54 | ax.set_title('Objective function', size=20) 55 | fig.legend(leg) 56 | plt.tight_layout() 57 | fig.savefig(str(path_to_figures) + '/obj_func_epf') 58 | plt.show() 59 | else: 60 | num_iter = len(results) 61 | for it in range(num_iter): 62 | info = np.load(str(path_to_files) + 'optimize_result_{}.npz' 63 | .format(it), allow_pickle=True) 64 | val = info['obj_func_values'] 65 | if obj_scaling is not None: 66 | val *= obj_scaling 67 | obj.append(val) 68 | obj = np.squeeze(np.array(obj)) 69 | if obj.ndim > 1: # multiple models 70 | if np.min(obj.shape) == 1: 71 | ax.plot(obj, '.b') 72 | else: 73 | ax.plot(obj, 'b:') 74 | obj = np.mean(obj, axis=1) 75 | ax.plot(obj, 'rs-', linewidth=4, markersize=10) 76 | ax.set_xticks(range(num_iter), minor=True) 77 | ax.tick_params(labelsize=16) 78 | ax.set_xlabel('Iteration no.', size=20) 79 | ax.set_ylabel('Value', size=20) 80 | ax.set_title('Objective function', size=20) 81 | plt.tight_layout() 82 | 83 | fig.savefig(str(path_to_figures) + '/obj_func') 84 | plt.show() 85 | 86 | 87 | def plot_state(num_var, order = 'F'): 88 | """ 89 | Plot the initial and final state. 90 | 91 | Input: 92 | - num_var: number of variables that will be displayed separately. 93 | This can be e.g., control variables for different wells. It there 94 | is multiple variable types (e.g., for injectors and producers), 95 | then num_var can be a list with one number for each type. 96 | - order: ordering of variables. See numpy.reshape for more information. Default 'F'. 97 | 98 | % Copyright (c) 2023 NORCE, All Rights Reserved. 99 | """ 100 | 101 | # Set paths 102 | path_to_files = './' 103 | path_to_figures = './Figures' # Save here 104 | if not os.path.exists(path_to_figures): 105 | os.mkdir(path_to_figures) 106 | 107 | # Load results 108 | files = os.listdir(path_to_files) 109 | results = [name for name in files if "optimize_result" in name] 110 | ind = [i for i, ltr in enumerate(results[0]) if ltr == '_'] 111 | if len(ind) > 2: # there is an epf outer loop index in the results 112 | inner_it = 0 113 | outer_it = 0 114 | for f in results: 115 | ind = [i for i, ltr in enumerate(f) if ltr == '_'] 116 | ind.append(f.find('.npz')) 117 | outer_it = np.maximum(int(f[ind[2]+1:ind[3]]), outer_it) 118 | for f in results: 119 | if '_'+str(outer_it)+'.npz' in f: 120 | ind = [i for i, ltr in enumerate(f) if ltr == '_'] 121 | inner_it = np.maximum(int(f[ind[1]+1:ind[2]]), inner_it) 122 | state_initial = np.load('optimize_result_0_0.npz', allow_pickle=True)['x'] 123 | state_final = np.load(f'optimize_result_{inner_it}_{outer_it}.npz', allow_pickle=True)['x'] 124 | else: 125 | num_iter = len(results)-1 126 | state_initial = np.load('optimize_result_0.npz', allow_pickle=True)['x'] 127 | state_final = np.load(f'optimize_result_{num_iter}.npz', allow_pickle=True)['x'] 128 | 129 | # Loop over all state variables 130 | if type(num_var) is int: 131 | num_var = [num_var] # make sure num_var is a list 132 | tot_var = sum(num_var) 133 | len_state = len(state_initial) 134 | num_steps = int(len_state / tot_var) 135 | state_initial = np.reshape(state_initial, (tot_var, num_steps), order = order) 136 | state_final = np.reshape(state_final, (tot_var, num_steps), order=order) 137 | for i, k in enumerate(num_var): 138 | 139 | if len(num_var) >= i: 140 | num = num_var[i] 141 | else: 142 | num = num_var[0] 143 | c = int(np.ceil(np.sqrt(num))) 144 | r = int(np.ceil(num / c)) 145 | f, ax = plt.subplots(r, c, figsize=(10, 5)) 146 | ax = np.array(ax) 147 | ax = ax.flatten() 148 | for w in np.arange(num): 149 | var_ini = state_initial[sum(num_var[:i]) + w, :] 150 | var_fin = state_final[sum(num_var[:i]) + w, :] 151 | if len(var_ini) > 1: 152 | ax[w].step(var_ini, '-b') 153 | ax[w].step(var_fin, '-r') 154 | else: 155 | ax[w].plot(var_ini, 'sb') 156 | ax[w].plot(var_fin, 'sr') 157 | ax[w].tick_params(labelsize=16) 158 | ax[w].set_xlabel('Index', size=18) 159 | ax[w].set_ylabel('State', size=18) 160 | ax[w].set_title('Variable ' + str(w + 1), size=18) 161 | if w == 0: 162 | ax[w].legend(['Initial', 'Final'], fontsize=16) 163 | 164 | f.tight_layout() 165 | f.savefig(str(path_to_figures) + '/variable_' + str(i)) 166 | plt.show() 167 | -------------------------------------------------------------------------------- /plot_parameters.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import matplotlib.pyplot as plt 3 | import pickle 4 | import sys 5 | import os 6 | from scipy.stats import norm 7 | from misc import ecl, grdecl 8 | from scipy.io import loadmat 9 | 10 | 11 | # Set paths and find results 12 | path_to_files = '.' 13 | path_to_figures = './Figures' # Save here 14 | save_figure = True # Use True for saving the figures 15 | if not os.path.exists(path_to_figures): 16 | os.mkdir(path_to_figures) 17 | files = os.listdir(path_to_files) 18 | results = [name for name in files if "debug_analysis_step" in name] 19 | num_iter = len(results) 20 | 21 | 22 | def plot_layer(field, f_dim, iter=1, layer_no=1): 23 | """ 24 | Plot parameters in given layer 25 | 26 | Input: 27 | - field : string specifying the property 28 | - f_dim : dimension of the property (2d or 3d) 29 | - iter : plot results at this iteration 30 | - layer_no: plot for this layer 31 | 32 | % Copyright (c) 2023 NORCE, All Rights Reserved. 33 | """ 34 | 35 | if os.path.exists(str(path_to_files) + '/actnum.npz'): 36 | actnum = np.load(str(path_to_files) + '/actnum.npz')['actnum'] 37 | else: 38 | actnum = np.ones(np.prod(f_dim), dtype=bool) 39 | 40 | # Load debug steps 41 | field_post = np.zeros(f_dim) 42 | field_post[:] = np.nan 43 | field_post_std = np.zeros(f_dim) 44 | field_post_std[:] = np.nan 45 | post = np.load(str(path_to_files) + f'/debug_analysis_step_{iter}.npz', allow_pickle=True)['state'][()][field] 46 | if 'perm' in field: 47 | post = np.exp(post) 48 | field_post[actnum.reshape(f_dim)] = post.mean(1) 49 | field_post_layer = field_post[layer_no - 1, :, :] 50 | field_post_std[actnum.reshape(f_dim)] = post.std(axis=1, ddof=1) 51 | field_post_std_layer = field_post_std[layer_no - 1, :, :] 52 | max_post_std = np.nanmax(field_post_std_layer) 53 | min_post_std = np.nanmin(field_post_std_layer) 54 | 55 | # Load Prior field 56 | prior = np.load(str(path_to_files) + '/prior.npz')[field] 57 | field_prior = np.zeros(f_dim) 58 | field_prior_std = np.zeros(f_dim) 59 | field_prior[:] = np.nan 60 | field_prior_std[:] = np.nan 61 | if 'perm' in field: 62 | prior = np.exp(prior) 63 | field_prior[actnum.reshape(f_dim)] = prior.mean(1) 64 | field_prior_layer = field_prior[layer_no - 1, :, :] 65 | field_prior_std[actnum.reshape(f_dim)] = prior.std(axis=1, ddof=1) 66 | field_prior_std_layer = field_prior_std[layer_no - 1, :, :] 67 | max_prior_std = np.nanmax(field_prior_std_layer) 68 | min_prior_std = np.nanmin(field_prior_std_layer) 69 | 70 | # Plotting 71 | if os.path.exists('utm_res.mat'): 72 | sx = loadmat('utm_res.mat')['sx_res'] 73 | sy = loadmat('utm_res.mat')['sy_res'] 74 | else: 75 | sx = np.linspace(0, f_dim[1], num=f_dim[1]) 76 | sy = np.linspace(0, f_dim[2], num=f_dim[2]) 77 | 78 | # Load wells if present 79 | wells = None 80 | if os.path.exists('wells.npz'): 81 | wells = np.load('wells.npz')['wells'] 82 | 83 | plt.figure() 84 | plt.pcolormesh(sx, sy, field_prior_layer, cmap='jet', shading='auto') 85 | plt.colorbar() 86 | if wells: 87 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 88 | title_str = 'Prior, ' + field 89 | filename = str(path_to_figures) + '/' + field + '_prior' 90 | if f_dim[0] > 1: # 3D 91 | title_str += ' at layer ' + str(layer_no) 92 | filename += '_layer' + str(layer_no) 93 | plt.title(title_str) 94 | if save_figure is True: 95 | plt.savefig(filename) 96 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 97 | 98 | plt.figure() 99 | plt.pcolormesh(sx, sy, field_post_layer, cmap='jet', shading='auto') 100 | plt.colorbar() 101 | if wells: 102 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 103 | title_str = 'Posterior, ' + field 104 | filename = str(path_to_figures) + '/' + field + '_post' 105 | if f_dim[0] > 1: # 3D 106 | title_str += ' at layer ' + str(layer_no) 107 | filename += '_layer' + str(layer_no) 108 | plt.title(title_str) 109 | if save_figure is True: 110 | plt.savefig(filename) 111 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 112 | 113 | plt.figure() 114 | field_diff = field_post_layer - field_prior_layer 115 | plt.pcolormesh(sx, sy, field_diff, cmap='jet', shading='auto') 116 | plt.colorbar() 117 | if wells: 118 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 119 | title_str = 'Posterior - Prior, ' + field 120 | filename = str(path_to_figures) + '/' + field + '_diff' 121 | if f_dim[0] > 1: # 3D 122 | title_str += ' at layer ' + str(layer_no) 123 | filename += '_layer' + str(layer_no) 124 | plt.title(title_str) 125 | if save_figure is True: 126 | plt.savefig(filename) 127 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 128 | 129 | # std 130 | np.array([np.minimum(min_prior_std, min_post_std), np.maximum(max_prior_std, max_post_std)]) 131 | plt.figure() 132 | plt.pcolormesh(sx, sy, field_prior_std_layer, cmap='jet', shading='auto') 133 | plt.colorbar() 134 | if wells: 135 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 136 | title_str = 'Prior std ' + field 137 | filename = str(path_to_figures) + '/' + field + '_std_prior' 138 | if f_dim[0] > 1: # 3D 139 | title_str += ' at layer ' + str(layer_no) 140 | filename += '_layer' + str(layer_no) 141 | plt.title(title_str) 142 | if save_figure is True: 143 | plt.savefig(filename) 144 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 145 | 146 | plt.figure() 147 | plt.pcolormesh(sx, sy, field_post_std_layer, cmap='jet', shading='auto') 148 | plt.colorbar() 149 | if wells: 150 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 151 | title_str = 'Posterior std ' + field 152 | filename = str(path_to_figures) + '/' + field + '_std_post' 153 | if f_dim[0] > 1: # 3D 154 | title_str += ' at layer ' + str(layer_no) 155 | filename += '_layer' + str(layer_no) 156 | plt.title(title_str) 157 | if save_figure is True: 158 | plt.savefig(filename) 159 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 160 | 161 | plt.show() 162 | 163 | 164 | def plot_avg_field(field, ecl_file, iter=1, trunc=None, max_depth=100): 165 | """ 166 | Plot averaged parameters 167 | 168 | Input: 169 | - field : string specifying the property 170 | - ecl_file : path to an eclipse init file 171 | - iter : plot results at this iteration 172 | - truc : when plotting differences, only show values larger than trunc 173 | - max_depth: compute the average over this depth 174 | 175 | % Copyright (c) 2023 NORCE, All Rights Reserved. 176 | 177 | """ 178 | 179 | if trunc is None: 180 | trunc = {} 181 | ecl_init = ecl.EclipseInit(ecl_file) 182 | dz = ecl_init.cell_data('DZ') 183 | porv = ecl_init.cell_data('PORV') 184 | f_dim = [ecl_init.nk, ecl_init.nj, ecl_init.ni] 185 | 186 | if os.path.exists(str(path_to_files) + '/actnum.npz'): 187 | actnum = np.load(str(path_to_files) + '/actnum.npz')['actnum'] 188 | else: 189 | actnum = np.ones(np.prod(f_dim), dtype=bool) 190 | 191 | # Load Prior field 192 | if 'swat' in field or 'pres' in field: 193 | type = field[0:4] 194 | base_ind = int(field[5]) 195 | monitor_ind = int(field[7]) 196 | data = np.load('dynamic_data_0.npz', allow_pickle=True) 197 | act = data['act'] 198 | field_prior = data[type][monitor_ind] - data[type][base_ind] 199 | for member in range(len(act)): 200 | field_prior[member, ~act[member, :]] = np.nan 201 | field_prior = np.nanmean(field_prior, axis=0) 202 | else: 203 | prior = np.load(str(path_to_files) + '/prior.npz')[field] 204 | field_prior = np.zeros(f_dim) 205 | field_prior[:] = np.nan 206 | if 'perm' in field: 207 | prior = np.exp(prior) 208 | elif 'multz' in field: 209 | prior = norm.cdf(prior) * 2 210 | field_prior[actnum.reshape(f_dim)] = prior.mean(1) 211 | 212 | # Load debug steps 213 | if 'swat' in field or 'pres' in field: 214 | if os.path.exists('dynamic_data.npz'): 215 | type = field[0:4] 216 | base_ind = int(field[5]) 217 | monitor_ind = int(field[7]) 218 | data = np.load('dynamic_data.npz', allow_pickle=True) 219 | act = data['act'] 220 | field_post = data[type][monitor_ind] - data[type][base_ind] 221 | for member in range(len(act)): 222 | field_post[member, ~act[member, :]] = np.nan 223 | field_post = np.nanmean(field_post, axis=0) 224 | else: 225 | field_post = np.zeros(field_prior.shape) 226 | else: 227 | field_post = np.zeros(f_dim) 228 | field_post[:] = np.nan 229 | post = np.load(str(path_to_files) + f'/debug_analysis_step_{iter}.npz', allow_pickle=True)['state'][()][field] 230 | if 'perm' in field: 231 | post = np.exp(post) 232 | elif 'multz' in field: 233 | post = norm.cdf(post) * 2 234 | field_post[actnum.reshape(f_dim)] = post.mean(1) 235 | 236 | # loop over all columns (dz is in shape nz,ny,nx) 237 | field_post_avg = np.zeros(f_dim[1:]) 238 | field_post_avg[:] = np.nan 239 | field_prior_avg = np.zeros(f_dim[1:]) 240 | field_prior_avg[:] = np.nan 241 | for i in range(ecl_init.ni): 242 | for j in range(ecl_init.nj): 243 | depth_column = dz[:, j, i].data 244 | porv_column = porv[:, j, i].data 245 | depth = np.cumsum(depth_column.data) 246 | depth_index = np.asarray(depth <= max_depth).nonzero()[0][-1] 247 | porv_total = np.cumsum(porv_column.data)[depth_index] 248 | vec_prior = np.multiply(field_prior[0:depth_index, j, i], porv_column[0:depth_index]) 249 | vec_post = np.multiply(field_post[0:depth_index, j, i], porv_column[0:depth_index]) 250 | if depth[depth_index] > 0: 251 | field_post_avg[j, i] = np.nansum(vec_post) / porv_total 252 | field_prior_avg[j, i] = np.nansum(vec_prior) / porv_total 253 | 254 | max_post = np.nanmax(field_post_avg) 255 | min_post = np.nanmin(field_post_avg) 256 | max_prior = np.nanmax(field_prior_avg) 257 | min_prior = np.nanmin(field_prior_avg) 258 | 259 | # Plotting 260 | if os.path.exists('utm_res.mat'): 261 | sx = loadmat('utm_res.mat')['sx_res'] 262 | sy = loadmat('utm_res.mat')['sy_res'] 263 | else: 264 | sx = np.linspace(0, f_dim[1], num=f_dim[1]) 265 | sy = np.linspace(0, f_dim[2], num=f_dim[2]) 266 | cl = np.array([np.minimum(min_prior, min_post), np.maximum(max_prior, max_post)]) 267 | prefix = '' 268 | if 'swat' in field or 'pres' in field: 269 | prefix = '$\Delta$' 270 | 271 | # Load wells if present 272 | wells = None 273 | if os.path.exists('wells.npz'): 274 | wells = np.load('wells.npz')['wells'] 275 | 276 | plt.figure() 277 | field_prior_avg = field_prior_avg 278 | im = plt.pcolormesh(sx, sy, field_prior_avg, cmap='jet', shading='auto') 279 | im.set_clim(cl) 280 | plt.colorbar() 281 | if wells: 282 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 283 | plt.title('Prior ' + prefix + field) 284 | if save_figure is True: 285 | filename = str(path_to_figures) + '/' + field + '_prior_avg' 286 | plt.savefig(filename) 287 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 288 | 289 | plt.figure() 290 | field_post_avg = field_post_avg 291 | im = plt.pcolormesh(sx, sy, field_post_avg, cmap='jet', shading='auto') 292 | im.set_clim(cl) 293 | plt.colorbar() 294 | if wells: 295 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 296 | plt.title('Posterior ' + prefix + field) 297 | if save_figure is True: 298 | filename = str(path_to_figures) + '/' + field + '_post_avg' 299 | plt.savefig(filename) 300 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 301 | 302 | plt.figure() 303 | data_diff = field_post_avg - field_prior_avg 304 | if field in trunc.keys(): 305 | data_diff[np.abs(data_diff) < trunc[field]] = np.nan 306 | im = plt.pcolormesh(sx, sy, data_diff, cmap='seismic', shading='auto') 307 | cl_value = np.nanmax(np.abs(data_diff)) 308 | cl_diff = np.array([-cl_value, cl_value]) 309 | im.set_clim(cl_diff) 310 | plt.colorbar() 311 | if wells: 312 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 313 | if field in trunc.keys(): 314 | plt.title('Posterior - Prior, ' + prefix + field + ' (trunc ' + str(trunc[field]) + ')') 315 | else: 316 | plt.title('Posterior - Prior, ' + prefix + field) 317 | if save_figure is True: 318 | filename = str(path_to_figures) + '/' + field + '_diff_avg' 319 | plt.savefig(filename) 320 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 321 | 322 | plt.show() 323 | 324 | 325 | def plot_scalar(param, iter=None): 326 | """ 327 | Plot scalar parameters 328 | 329 | Input: 330 | - param : string spesifying the property 331 | - iter : plot results at this iteration 332 | 333 | % Copyright (c) 2023 NORCE, All Rights Reserved. 334 | 335 | """ 336 | 337 | 338 | # Load debug steps 339 | post = np.load(str(path_to_files) + f'/debug_analysis_step_{iter}.npz', allow_pickle=True)['state'][()][param] 340 | post = post.flatten() 341 | post_mean = np.mean(post) 342 | 343 | # Load Prior field 344 | prior = np.load(str(path_to_files) + '/prior.npz')[param] 345 | prior = prior.flatten() 346 | prior_mean = np.mean(prior) 347 | 348 | # Plotting 349 | plt.figure() 350 | plt.hist(prior, 10, density=True, facecolor='b', alpha=0.3, label='Prior') 351 | plt.hist(post, 10, density=True, facecolor='g', alpha=0.3, label='Posterior') 352 | ylim = plt.gca().get_ylim() 353 | plt.plot(prior_mean * np.ones(2), np.array(ylim), 'b') 354 | plt.plot(post_mean * np.ones(2), np.array(ylim), 'g') 355 | plt.legend() 356 | plt.title('Distribution for ' + param) 357 | if save_figure is True: 358 | plt.savefig(str(path_to_figures) + '/' + param) 359 | 360 | plt.show() 361 | 362 | 363 | def export_to_grid(propname): 364 | """ 365 | Export a property to .grdecl file (for inspection in e.g., ResInsight) 366 | 367 | Input: 368 | - propname: string spesifying property name 369 | 370 | % Copyright (c) 2023 NORCE, All Rights Reserved. 371 | 372 | """ 373 | 374 | # Load posterior property 375 | post = np.load(str(path_to_files) + f'/debug_analysis_step_{num_iter}.npz', 376 | allow_pickle=True)['state'][()][propname] 377 | if 'perm' in propname: 378 | post = np.exp(post) 379 | 380 | # Load prior property 381 | prior = np.load(str(path_to_files) + '/prior.npz')[propname] 382 | if 'perm' in propname: 383 | prior = np.exp(prior) 384 | 385 | # Active gridcells 386 | if os.path.exists(str(path_to_files) + '/actnum.npz'): 387 | actnum = np.load(str(path_to_files) + '/actnum.npz')['actnum'] 388 | else: 389 | actnum = np.ones(prior.shape[0], dtype=bool) 390 | 391 | # Make the property on full grid 392 | field_post = np.zeros(actnum.shape) 393 | field_prior = np.zeros(actnum.shape) 394 | field_post[actnum] = post.mean(1) 395 | field_prior[actnum] = prior.mean(1) 396 | dim = len(actnum) 397 | 398 | trans_dict = {} 399 | 400 | def _lookup(kw): 401 | return trans_dict[kw] if kw in trans_dict else kw 402 | 403 | # Write a quantity to the grid as a grdecl file 404 | with open(path_to_files + '/prior_' + propname + '.grdecl', 'wb') as fileobj: 405 | grdecl._write_kw(fileobj, 'prior_'+propname, field_prior, _lookup, dim) 406 | with open(path_to_files + '/posterior_' + propname + '.grdecl', 'wb') as fileobj: 407 | grdecl._write_kw(fileobj, 'posterior_'+propname, field_post, _lookup, dim) 408 | -------------------------------------------------------------------------------- /plot_data.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | import matplotlib.patches as pat 5 | import matplotlib.collections as mcoll 6 | from matplotlib.colors import ListedColormap 7 | import pickle 8 | from scipy.interpolate import interp1d 9 | from scipy.io import loadmat 10 | import datetime as dt 11 | import cv2 12 | import os 13 | 14 | 15 | # Set paths and find results 16 | path_to_files = '.' 17 | path_to_figures = './Figures' # Save here 18 | save_figure = True # Use True for saving the figures 19 | if not os.path.exists(path_to_figures): 20 | os.mkdir(path_to_figures) 21 | files = os.listdir(path_to_files) 22 | results = [name for name in files if "debug_analysis_step" in name] 23 | num_iter = len(results) 24 | seis_data = ['sim2seis', 'bulkimp'] 25 | non_scalar = seis_data + ['rft'] 26 | 27 | 28 | def plot_prod(): 29 | """ 30 | Plot all production data 31 | 32 | % Copyright (c) 2023 NORCE, All Rights Reserved. 33 | """ 34 | 35 | obs = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['obs'] 36 | data_dates = np.genfromtxt('true_data_index.csv', delimiter=',') 37 | assim_index = np.genfromtxt('assim_index.csv', delimiter=',') 38 | assim_index = assim_index.astype(int) 39 | 40 | pred1 = np.load(str(path_to_files) + '/prior_forecast.npz', allow_pickle=True)['pred_data'] 41 | pred2 = np.load(str(path_to_files) + f'/debug_analysis_step_{num_iter}.npz', allow_pickle=True)['pred_data'] 42 | ref_data = [] 43 | if os.path.exists(str(path_to_files) + '/ref_data.p'): 44 | with open(str(path_to_files) + '/ref_data.p', 'rb') as f: 45 | ref_data = pickle.load(f) 46 | 47 | # Time_step 48 | tot_key = [el for el in obs[0].keys() if el not in non_scalar] 49 | x_days = [data_dates[i] for i in assim_index] 50 | ne = pred1[0][list(pred1[0].keys())[0]].shape[1] # get the ensemble size from here 51 | 52 | for k in tot_key: 53 | 54 | # Find a well number 55 | n = tot_key.index(k) 56 | my_data = tot_key[n] 57 | print(my_data) 58 | t1, t2 = my_data.split() 59 | 60 | data_obs = [] 61 | data1 = [] 62 | data2 = [] 63 | ref = [] 64 | for ind, i in enumerate(assim_index): 65 | data_obs.append(obs[i][my_data]) 66 | data1.append(pred1[i][my_data]) 67 | data2.append(pred2[i][my_data]) 68 | if ref_data: 69 | if my_data in ref_data[ind].keys(): 70 | ref.append(ref_data[ind][my_data]) 71 | else: 72 | ref.append(None) 73 | 74 | n_d_obs = np.empty(0) 75 | x_d = np.empty(0) 76 | n_d1 = np.empty((ne, 0)) 77 | x_d1 = np.empty(0) 78 | n_d2 = np.empty((ne, 0)) 79 | x_d2 = np.empty(0) 80 | n_d_ref = np.empty(0) 81 | x_d_ref = np.empty(0) 82 | for ind, i in enumerate(assim_index): 83 | if data_obs[ind] is not None: 84 | n_d_obs = np.append(n_d_obs, data_obs[ind]) 85 | x_d = np.append(x_d, x_days[ind]) 86 | if ref_data and ref[ind] is not None: 87 | n_d_ref = np.append(n_d_ref, ref[ind]) 88 | x_d_ref = np.append(x_d_ref, x_days[ind]) 89 | if data_obs[ind] is not None: 90 | n_d1 = np.append(n_d1, data1[ind].transpose(), axis=1) 91 | x_d1 = np.append(x_d1, x_days[ind]) 92 | if data_obs[ind] is not None: 93 | n_d2 = np.append(n_d2, data2[ind].transpose(), axis=1) 94 | x_d2 = np.append(x_d2, x_days[ind]) 95 | 96 | f = plt.figure() 97 | ax1 = plt.subplot(2, 1, 1) 98 | plt.plot(x_d1, np.percentile(n_d1, 90, axis=0), 'k') 99 | plt.plot(x_d1, np.percentile(n_d1, 100, axis=0), ':k') 100 | plt.plot(x_d1, np.percentile(n_d1, 10, axis=0), 'k') 101 | plt.plot(x_d1, np.percentile(n_d1, 0, axis=0), ':k') 102 | p1 = plt.plot(x_d, n_d_obs, '.r') 103 | p2 = None 104 | if ref_data: 105 | p2 = plt.plot(x_d_ref, n_d_ref, 'g') 106 | ax1.fill_between(x_d1, np.percentile(n_d1, 100, axis=0), np.percentile(n_d1, 0, axis=0), facecolor='lightgrey') 107 | ax1.fill_between(x_d1, np.percentile(n_d1, 90, axis=0), np.percentile(n_d1, 10, axis=0), facecolor='grey') 108 | p3 = ax1.fill(np.nan, np.nan, 'lightgrey') 109 | p4 = ax1.fill(np.nan, np.nan, 'grey') 110 | p5 = plt.plot(x_d1, np.mean(n_d1, axis=0), 'orange') 111 | if p2: 112 | ax1.legend([(p1[0],), (p2[0],), (p5[0],), (p3[0],), (p4[0],)], 113 | ['obs', 'ref', 'mean', '0-100 pctl', '10-90 pctl'], 114 | loc=4, prop={"size": 8}, bbox_to_anchor=(1, -0.5), ncol=2) 115 | else: 116 | ax1.legend([(p1[0],), (p5[0],), (p3[0],), (p4[0],)], 117 | ['obs', 'mean', '0-100 pctl', '10-90 pctl'], 118 | loc=4, prop={"size": 8}, bbox_to_anchor=(1, -0.5), ncol=2) 119 | plt.title(str(t1) + ' initial forcast, at Well: ' + str(t2)) 120 | ylim = plt.gca().get_ylim() 121 | ax1.set_ylim(ylim) 122 | plt.xlabel('Days') 123 | if "WBHP" in my_data: 124 | plt.ylabel('Bar') 125 | else: 126 | plt.ylabel('Sm3/Day') 127 | 128 | ax2 = plt.subplot(2, 1, 2) 129 | plt.plot(x_d2, np.percentile(n_d2, 90, axis=0), 'k') 130 | plt.plot(x_d2, np.percentile(n_d2, 100, axis=0), ':k') 131 | plt.plot(x_d2, np.percentile(n_d2, 10, axis=0), 'k') 132 | plt.plot(x_d2, np.percentile(n_d2, 0, axis=0), ':k') 133 | plt.plot(x_d, n_d_obs, '.r') 134 | if ref_data: 135 | plt.plot(x_d_ref, n_d_ref, 'g') 136 | ax2.fill_between(x_d2, np.percentile(n_d2, 100, axis=0), np.percentile(n_d2, 0, axis=0), facecolor='lightgrey') 137 | ax2.fill_between(x_d2, np.percentile(n_d2, 90, axis=0), np.percentile(n_d2, 10, axis=0), facecolor='grey') 138 | plt.plot(x_d2, np.mean(n_d2, axis=0), 'orange') 139 | plt.title(str(t1) + ' final forcast, at Well: ' + str(t2)) 140 | f.tight_layout(pad=0.5) 141 | plt.xlabel('Days') 142 | if "WBHP" in my_data: 143 | plt.ylabel('Bar') 144 | else: 145 | plt.ylabel('Sm3/Day') 146 | if save_figure is True: 147 | plt.savefig(str(path_to_figures) + '/' + str(t2) + '_' + str(t1) + '.png', format='png') 148 | 149 | ############ 150 | plt.show() 151 | plt.close('all') 152 | 153 | 154 | def plot_rft(): 155 | """ 156 | Plot RFT data 157 | 158 | % Copyright (c) 2023 NORCE, All Rights Reserved. 159 | """ 160 | 161 | obs = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['obs'] 162 | assim_index = np.genfromtxt('assim_index.csv', delimiter=',') 163 | assim_index = assim_index.astype(int) 164 | 165 | pred1 = np.load(str(path_to_files) + '/prior_forecast.npz', allow_pickle=True)['pred_data'] 166 | pred2 = np.load(str(path_to_files) + f'/debug_analysis_step_{num_iter}.npz', allow_pickle=True)['pred_data'] 167 | if os.path.exists(str(path_to_files) + '/ref_rft_data.p'): 168 | with open(str(path_to_files) + '/ref_rft_data.p', 'rb') as f: 169 | ref_rft_data = pickle.load(f) 170 | else: 171 | print('RFT data not present') 172 | sys.exit() 173 | 174 | # Total number of time to collect the data 175 | tot_key = [el for el in obs[0].keys() if 'rft_' in el] 176 | 177 | for _ in tot_key: 178 | 179 | my_data = tot_key[n] 180 | type, well = my_data.split() 181 | depth = np.load(well + '_rft_ref_depth.npz')['arr_0'] 182 | 183 | print(my_data) 184 | 185 | data_obs = np.empty([]) 186 | data1 = np.empty([]) 187 | data2 = np.empty([]) 188 | for ind, i in enumerate(assim_index): 189 | if obs[i][my_data] is not None: 190 | data_obs = obs[i][my_data] 191 | data1 = pred1[i][my_data] 192 | data2 = pred2[i][my_data] 193 | if well in ref_rft_data.keys(): 194 | ref_pressure = ref_rft_data[well][:, 1] 195 | ref_depth = ref_rft_data[well][:, 0] 196 | interp = interp1d(ref_depth, ref_pressure, kind='linear', bounds_error=False, 197 | fill_value=(ref_pressure[0], ref_pressure[-1])) 198 | ref_pressure = interp(depth) 199 | else: 200 | continue 201 | 202 | f = plt.figure() 203 | plt.subplot(1, 2, 1) 204 | plt.plot(data1, depth, '.k') 205 | plt.plot(ref_pressure, depth, 'xg', markersize=12) 206 | plt.plot(data_obs, depth, '.r') 207 | plt.gca().invert_yaxis() 208 | plt.gca().ticklabel_format(useOffset=False) 209 | xlim = plt.gca().get_xlim() 210 | plt.title('Initial RFT at Well: ' + well) 211 | plt.xlabel('Pressure [Bar]') 212 | plt.ylabel('Total vertical depth [m]') 213 | 214 | plt.subplot(1, 2, 2) 215 | plt.plot(data2, depth, '.k') 216 | plt.plot(ref_pressure, depth, 'xg', markersize=12) 217 | plt.plot(data_obs, depth, '.r') 218 | plt.gca().invert_yaxis() 219 | plt.gca().set_xlim(xlim) 220 | plt.gca().ticklabel_format(useOffset=False) 221 | f.tight_layout(pad=3.0) 222 | plt.title('Final RFT at Well: ' + well) 223 | plt.xlabel('Pressure [Bar]') 224 | plt.ylabel('Total vertical depth [m]') 225 | plt.savefig(str(path_to_figures) + '/' + well + '_rft.png', format='png') 226 | 227 | ############ 228 | plt.show() 229 | plt.close('all') 230 | 231 | 232 | def plot_seis_2d(scaling=1.0, vintage=0): 233 | """ 234 | Plot seismic 2D data (e.g. amplitude maps) 235 | 236 | Input: 237 | - scaling: if scaling of seismic data is used during data assimilation, this input can be used to convert back 238 | to the original values 239 | - vintage: plot this vintage 240 | 241 | % Copyright (c) 2023 NORCE, All Rights Reserved. 242 | 243 | """ 244 | 245 | wells = None 246 | if os.path.exists('wells.npz'): 247 | wells = np.load('wells.npz')['wells'] 248 | 249 | assim_index = np.genfromtxt('assim_index.csv', delimiter=',') 250 | assim_index = assim_index.astype(int) 251 | obs = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['obs'] 252 | obs_rec = None 253 | if os.path.exists('prior_forecast_rec.npz'): # the amplitude map is the actual data 254 | obs_rec = np.load(str(path_to_files) + f'/truedata_rec_{vintage}.npz', allow_pickle=True)['arr_0'] 255 | pred1 = np.load(str(path_to_files) + '/prior_forecast_rec.npz', allow_pickle=True)['arr_0'] 256 | pred2 = np.load(str(path_to_files) + '/rec_results.p', allow_pickle=True) 257 | else: 258 | pred1 = np.load(str(path_to_files) + '/prior_forecast.npz', allow_pickle=True)['pred_data'] 259 | pred2 = np.load(str(path_to_files) + f'/debug_analysis_step_{num_iter}.npz', allow_pickle=True)['pred_data'] 260 | 261 | # get the data 262 | data_obs = np.empty([]) 263 | data1 = np.empty([]) 264 | data2 = np.empty([]) 265 | current_vint = 0 266 | for i, key in ((i, key) for _, i in enumerate(assim_index) for key in seis_data): 267 | if key in obs[i] and obs[i][key] is not None: 268 | if current_vint < vintage: 269 | current_vint += 1 270 | continue 271 | if type(pred2) is list: 272 | data1 = pred1[current_vint, :, :] / scaling 273 | data1 = data1.T 274 | data2 = pred2[current_vint] / scaling 275 | data_obs = obs_rec / scaling 276 | else: 277 | data1 = pred1[i][key] / scaling 278 | data2 = pred2[i][key] / scaling 279 | data_obs = obs[i][key] / scaling 280 | break 281 | 282 | # map to 2D 283 | if os.path.exists(f'mask_{vintage}.npz'): 284 | mask = np.load(f'mask_{vintage}.npz', allow_pickle=True)['mask'] 285 | else: 286 | print('Mask is required to plot 2D data!') 287 | sys.exit() 288 | if os.path.exists('utm.mat'): 289 | sx = loadmat('utm.mat')['sx'] 290 | sy = loadmat('utm.mat')['sy'] 291 | else: 292 | sx = np.linspace(0, mask.shape[1], num=mask.shape[1]) 293 | sy = np.linspace(mask.shape[0], 0, num=mask.shape[0]) 294 | 295 | data = np.nan * np.ones(mask.shape) 296 | data[mask] = data_obs 297 | cl = np.array([np.min(data_obs), np.max(data_obs)]) 298 | data1_mean = np.nan * np.ones(mask.shape) 299 | data1_mean[mask] = np.mean(data1, 1) 300 | data2_mean = np.nan * np.ones(mask.shape) 301 | data2_mean[mask] = np.mean(data2, 1) 302 | data1_std = np.nan * np.ones(mask.shape) 303 | data1_std[mask] = np.std(data1, 1) 304 | data2_std = np.nan * np.ones(mask.shape) 305 | data2_std[mask] = np.std(data2, 1) 306 | data1_min = np.nan * np.ones(mask.shape) 307 | data1_min[mask] = np.min(data1, 1) 308 | data2_min = np.nan * np.ones(mask.shape) 309 | data2_min[mask] = np.min(data2, 1) 310 | data1_max = np.nan * np.ones(mask.shape) 311 | data1_max[mask] = np.max(data1, 1) 312 | data2_max = np.nan * np.ones(mask.shape) 313 | data2_max[mask] = np.max(data2, 1) 314 | data_diff = data2_mean - data1_mean 315 | data_diff[np.abs(data_diff) < 0.01] = np.nan 316 | 317 | # compute the misfit 318 | v = data1_mean.flatten() - data.flatten() 319 | n = np.count_nonzero(~np.isnan(v)) 320 | data1_misfit_mean = np.nansum(np.abs(v)) / n 321 | v = data2_mean.flatten() - data.flatten() 322 | n = np.count_nonzero(~np.isnan(v)) 323 | data2_misfit_mean = np.nansum(np.abs(v)) / n 324 | data1_misfit_mean_str = str(data1_misfit_mean) 325 | data2_misfit_mean_str = str(data2_misfit_mean) 326 | reduction_str = str((data1_misfit_mean - data2_misfit_mean) * 100 / data1_misfit_mean) 327 | print('Initial misfit: ' + data1_misfit_mean_str) 328 | print('Final misfit : ' + data2_misfit_mean_str) 329 | print('Reduction (%) : ' + reduction_str) 330 | 331 | colorm = 'viridis' 332 | plt.figure() 333 | im = plt.pcolormesh(sx, sy, data, cmap=colorm, shading='auto') 334 | im.set_clim(cl) 335 | plt.colorbar() 336 | if wells: 337 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 338 | plt.title('Average data top reservoir') 339 | filename = str(path_to_figures) + '/data_true' + '_vint' + str(vintage) 340 | plt.savefig(filename) 341 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 342 | 343 | plt.figure() 344 | im = plt.pcolormesh(sx, sy, data1_mean, cmap=colorm, shading='auto') 345 | im.set_clim(cl) 346 | plt.colorbar() 347 | if wells: 348 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 349 | plt.title('Initial simulated mean') 350 | filename = str(path_to_figures) + '/data_mean_initial' + '_vint' + str(vintage) 351 | plt.savefig(filename) 352 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 353 | 354 | plt.figure() 355 | im = plt.pcolormesh(sx, sy, data2_mean, cmap=colorm, shading='auto') 356 | im.set_clim(cl) 357 | plt.colorbar() 358 | if wells: 359 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 360 | plt.title('Final simulated mean') 361 | filename = str(path_to_figures) + '/data_mean_final' + '_vint' + str(vintage) 362 | plt.savefig(filename) 363 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 364 | 365 | plt.figure() 366 | im = plt.pcolormesh(sx, sy, data_diff, cmap='seismic', shading='auto') 367 | cl_value = np.nanmax(np.abs(data_diff)) 368 | cl_diff = np.array([-cl_value, cl_value]) 369 | im.set_clim(cl_diff) 370 | plt.colorbar() 371 | if wells: 372 | plt.plot(wells[0], wells[1], 'ws', markersize=3, mfc='black') # plot wells 373 | plt.title('Final - Initial (trunc 0.01)') 374 | filename = str(path_to_figures) + '/data_mean_diff' + '_vint' + str(vintage) 375 | plt.savefig(filename) 376 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 377 | 378 | plt.figure() 379 | plt.pcolormesh(sx, sy, data1_std, cmap=colorm, shading='auto') 380 | plt.colorbar() 381 | plt.title('Initial seismic std') 382 | filename = str(path_to_figures) + '/data_std_initial' + '_vint' + str(vintage) 383 | plt.savefig(filename) 384 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 385 | 386 | plt.figure() 387 | plt.pcolormesh(sx, sy, data2_std, cmap=colorm, shading='auto') 388 | plt.colorbar() 389 | plt.title('Final seismic std') 390 | filename = str(path_to_figures) + '/data_std_final' + '_vint' + str(vintage) 391 | plt.savefig(filename) 392 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 393 | 394 | plt.figure() 395 | im = plt.pcolormesh(sx, sy, data1_min, cmap=colorm, shading='auto') 396 | im.set_clim(cl) 397 | plt.colorbar() 398 | plt.title('Initial seismic min') 399 | filename = str(path_to_figures) + '/data_min_initial' + '_vint' + str(vintage) 400 | plt.savefig(filename) 401 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 402 | 403 | plt.figure() 404 | data2_min = data2_min 405 | im = plt.pcolormesh(sx, sy, data2_min, cmap=colorm, shading='auto') 406 | im.set_clim(cl) 407 | plt.colorbar() 408 | plt.title('Final seismic min') 409 | filename = str(path_to_figures) + '/data_min_final' + '_vint' + str(vintage) 410 | plt.savefig(filename) 411 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 412 | 413 | plt.figure() 414 | im = plt.pcolormesh(sx, sy, data1_max, cmap=colorm, shading='auto') 415 | im.set_clim(cl) 416 | plt.colorbar() 417 | plt.title('Initial seismic max') 418 | filename = str(path_to_figures) + '/data_max_initial' + '_vint' + str(vintage) 419 | plt.savefig(filename) 420 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 421 | 422 | plt.figure() 423 | im = plt.pcolormesh(sx, sy, data2_max, cmap=colorm, shading='auto') 424 | im.set_clim(cl) 425 | plt.colorbar() 426 | plt.title('Final seismic max') 427 | filename = str(path_to_figures) + '/data_max_final' + '_vint' + str(vintage) 428 | plt.savefig(filename) 429 | os.system('convert ' + filename + '.png' + ' -trim ' + filename + '.png') 430 | 431 | ############ 432 | plt.show() 433 | plt.close('all') 434 | 435 | 436 | def plot_seis_3d(scaling=1.0, vintage=0): 437 | """ 438 | Plot seismic 3D data (e.g. impedance cubes) 439 | 440 | Input: 441 | - scaling: if scaling of seismic data is used during data assimilation, this input can be used to convert back 442 | to the original values 443 | - vintage: plot this vintage 444 | 445 | % Copyright (c) 2023 NORCE, All Rights Reserved. 446 | 447 | """ 448 | 449 | # Use mayavi package 450 | from mayavi import mlab 451 | 452 | assim_index = np.genfromtxt('assim_index.csv', delimiter=',') 453 | assim_index = assim_index.astype(int) 454 | obs = np.load(str(path_to_files) + '/obs_var.npz', allow_pickle=True)['obs'] 455 | obs_rec = None 456 | if os.path.exists('prior_forecast_rec.npz'): # the amplitude map is the actual data 457 | obs_rec = np.load(str(path_to_files) + f'/truedata_rec_{vintage}.npz', allow_pickle=True)['arr_0'] 458 | pred1 = np.load(str(path_to_files) + '/prior_forecast_rec.npz', allow_pickle=True)['arr_0'] 459 | pred2 = np.load(str(path_to_files) + '/rec_results.p', allow_pickle=True) 460 | else: 461 | pred1 = np.load(str(path_to_files) + '/prior_forecast.npz', allow_pickle=True)['pred_data'] 462 | pred2 = np.load(str(path_to_files) + f'/debug_analysis_step_{num_iter}.npz', allow_pickle=True)['pred_data'] 463 | 464 | # get the data 465 | data_obs = np.empty([]) 466 | data1 = np.empty([]) 467 | data2 = np.empty([]) 468 | current_vint = 0 469 | for i, key in ((i, key) for _, i in enumerate(assim_index) for key in seis_data): 470 | if key in obs[i] and obs[i][key] is not None: 471 | if current_vint < vintage: 472 | current_vint += 1 473 | continue 474 | if type(pred2) is list: 475 | data1 = pred1[current_vint, :, :] / scaling 476 | data1 = data1.T 477 | data2 = pred2[current_vint] / scaling 478 | data_obs = obs_rec / scaling 479 | else: 480 | data1 = pred1[i][key] / scaling 481 | data2 = pred2[i][key] / scaling 482 | data_obs = obs[i][key] / scaling 483 | break 484 | 485 | # map to 2D 486 | if os.path.exists(f'mask_{vintage}.npz'): 487 | mask = np.load(f'mask_{vintage}.npz', allow_pickle=True)['mask'] 488 | else: 489 | print('Mask is required to plot 2D data!') 490 | sys.exit() 491 | 492 | data = np.zeros(mask.shape) 493 | data[mask] = data_obs / np.max(np.abs(data_obs.flatten())) 494 | data1_mean = np.zeros(mask.shape) 495 | data1_mean[mask] = np.mean(data1, 1) 496 | data1_mean = data1_mean / np.max(np.abs(data1_mean)) 497 | data2_mean = np.zeros(mask.shape) 498 | data2_mean[mask] = np.mean(data2, 1) 499 | data2_mean = data2_mean / np.max(np.abs(data2_mean)) 500 | 501 | fig = mlab.figure(size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.)) 502 | src = mlab.pipeline.scalar_field(np.abs(data)) 503 | mlab.pipeline.volume(src, vmin=.2, vmax=.8) 504 | mlab.colorbar() 505 | mlab.title('True signal (normalized)', color=(0, 0, 0), size=0.5) 506 | filename = str(path_to_figures) + '/data_true' + '_vint' + str(vintage) + '.png' 507 | mlab.savefig(filename, figure=fig) 508 | 509 | fig = mlab.figure(size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.)) 510 | src = mlab.pipeline.scalar_field(np.abs(data1_mean)) 511 | mlab.pipeline.volume(src, vmin=0.2, vmax=0.8) 512 | mlab.colorbar() 513 | mlab.title('Sim signal prior (normalized)', color=(0, 0, 0), size=0.5) 514 | filename = str(path_to_figures) + '/data_mean_initial' + '_vint' + str(vintage) + '.png' 515 | mlab.savefig(filename, figure=fig) 516 | 517 | fig = mlab.figure(size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.)) 518 | src = mlab.pipeline.scalar_field(np.abs(data2_mean)) 519 | mlab.pipeline.volume(src, vmin=0.2, vmax=0.8) 520 | mlab.colorbar() 521 | mlab.title('Sim signal posterior (normalized)', color=(0, 0, 0), size=0.5) 522 | filename = str(path_to_figures) + '/data_mean_final' + '_vint' + str(vintage) + '.png' 523 | mlab.savefig(filename, figure=fig) 524 | 525 | fig = mlab.figure(size=(800, 800), bgcolor=(1, 1, 1), fgcolor=(0., 0., 0.)) 526 | src = mlab.pipeline.scalar_field(np.abs(np.abs(data2_mean) - np.abs(data1_mean))) 527 | mlab.pipeline.volume(src, vmin=0.2, vmax=0.8) 528 | mlab.colorbar() 529 | mlab.title('Sim diff (normalized)', color=(0, 0, 0), size=0.5) 530 | filename = str(path_to_figures) + '/data_diff' + '_vint' + str(vintage) + '.png' 531 | mlab.savefig(filename, figure=fig) 532 | 533 | mlab.show() 534 | 535 | 536 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------