added NPV and PCA component Sens

main_DGSA_Reservoir_Sensitivity_forBEL.asv

@@ -0,0 +1,106 @@
+%% Distance based global sensitivity analysis - Application to reservoir responses
+% Part1: Compute main/conditional effects of Prior models
+
+% Author : Jihoon Park (jhpark3@stanford.edu)
+% Date : 17 May 2016
+
+clear all; close all; fclose('all'); rng('default');
+%% 0. Add directories for path
+addpath(genpath(pwd))
+
+%% 1. Specify inputs for DGSA. 
+addpath(genpath(pwd))
+
+% Loading the BEL data struct
+
+% History
+prior_path = 'Z:/Projects/BM/Code/Data/BM_NFA_first/Prior_data_OilRate.mat';
+load(prior_path);
+
+% taking history out
+Data = Prior_Property_data.data(2:end,:); % we can keep the original ordering for history
+
+DGSA.D = squareform(pdist(Data));
+
+% reading the parameters
+
+parameter_path = 'Z:/Projects/BM/Code/Data/Parameters_History.csv';
+
+Parameters = csvimport(parameter_path);
+Parameters = Parameters(:,2:end);
+
+DGSA.ParametersNames = Parameters(1,:);
+
+DGSA.ParametersValues = Parameters(2:end,11:end);
+
+DGSA.ParametersValues(:,1) = categorical(DGSA.ParametersValues(:,1));
+
+
+DGSA.N=size(DGSA.ParametersValues,1);
+
+GeomodelsParam = grp2idx(DGSA.ParametersValues(:,1));
+
+%% 4. Compute & display main effects
+
+% 4.1 Inputs for clustering and display options.
+DGSA.Nbcluster=3; % # of clusters
+DGSA.MainEffects.Display.ParetoPlotbyCluster=1; % if true, main effects over cluster will be displayed with Pareto plot.
+DGSA.MainEffects.Display.StandardizedSensitivity='CI'; 
+
+% if 'CI', the confidence interval will be overlapped on the Pareto plot
+% if 'Pareto', Pareto plots will be displayed only
+% if 'None' No plot will be generated for standardd main effects
+% (default)
+
+% 4.2 Compute main effects from DGSA.
+SigLevel_CI=[.95,.9,1]; % This is needed only when you want to display confidence intervals
+                        % If you do not need to display confidence intervals,set DGSA.MainEffects.Display.StandardizedSensitivity='Preto' or 'None'     
+
+% 4.3 Perform clustering
+DGSA.Clustering=kmedoids(DGSA.D,DGSA.Nbcluster,10); % In this example, K medoid clustering is applied.
+
+% 4.4 Compute Main Effects
+DGSA=ComputeMainEffects(DGSA,SigLevel_CI); % If you use Pareto plot or do not want to display main effects, remove SigLevel_CI, otherwise it shows an error.
+
+%% Display cdfs
+
+cdf_MainFactor(DGSA.ParametersValues, DGSA.Clustering, DGSA.ParametersNames,{'owc','oilvis'}); % In this example, CDFs for 'owc' and 'oilvis' will be displayed.
+%% 5. Compute & Display conditional effects
+
+% 5.1 Specify additional variables to estimate conditional effects.
+
+DGSA.ConditionalEffects.NbBins=3*ones(1,length(DGSA.ParametersNames));
+
+% 5.2 Compute conditional effects
+
+rng('default'); 
+DGSA=ComputeConditionalEffects(DGSA);
+
+% 5.3 Display conditional effcts
+
+% Speicify the method to display standardized conditional effects.
+DGSA.ConditionalEffects.Display.SensitivityByClusterAndBins=1; % if true, display pareto plots to visualize sensitivities by bins/clusters. 
+DGSA.ConditionalEffects.Display.StandardizedSensitivity='Hplot'; % If omitted Pareto plot will be used. However, this is not recommended when there are lots of parameters
+
+% Visualize conditional effects
+DisplayConditionalEffects(DGSA,DGSA.ConditionalEffects.Display.StandardizedSensitivity)
+
+% 5.4 Display class condtional CDFs
+cdf_ConditionalEffects('owc','kvkh',DGSA,1)
+
+%% 6. Check the response
+
+% Use MDS plot to check the result of clustering
+ClusterColor=DisplayMDSplot(DGSA.D, DGSA.Clustering);
+
+% Specify texts for plots
+PlotLabels.xlabtxt='Time (day)'; PlotLabels.ylabtxt='WWPT (stb)'; 
+PlotLabels.xlimits=[0, 7300]; PlotLabels.ylimits=[0 7E7];
+
+% Visualize dynamic response by clusters
+dynamicbycluster(DGSA.Clustering,PriorResponses, ClusterColor,PlotLabels);
+
+
+%% Save all variables for futher application
+%save('VariablesSaved/DGSA_Completed.mat');
+%save('VariablesSaved/DGSA_Spatial_Completed.mat');

main_DGSA_Reservoir_Sensitivity_forBEL_History.m

@@ -14,7 +14,7 @@
 % Loading the BEL data struct
 
 % History
-prior_path = 'Z:/Projects/BM/Code/Data/BM_NFA_first/Prior_data_OilRate.mat';
+prior_path = 'Y:/mzechner/Projects/BM/Code/Data/BM_NFA_first/Prior_data_OilRate.mat';
 load(prior_path);
 
 % taking history out
@@ -35,7 +35,7 @@
 
 %% reading the parameters
 
-parameter_path = 'Z:/Projects/BM/Code/Data/Parameters_History_short.csv';
+parameter_path = 'Y:/mzechner/Projects/BM/Code/Data/Parameters_History_short.csv';
 
 Parameters = csvimport(parameter_path);
 
@@ -78,7 +78,7 @@
 
 %% Display cdfs
 
-cdf_MainFactor(DGSA.ParametersValues, DGSA.Clustering, DGSA.ParametersNames,{'owc','oilvis'}); % In this example, CDFs for 'owc' and 'oilvis' will be displayed.
+cdf_MainFactor(DGSA.ParametersValues, DGSA.Clustering, DGSA.ParametersNames,{'visoil',}); % In this example, CDFs for 'owc' and 'oilvis' will be displayed.
 %% 5. Compute & Display conditional effects
 
 % 5.1 Specify additional variables to estimate conditional effects.

main_DGSA_Reservoir_Sensitivity_forBEL_forIncrementalPLY.asv

@@ -0,0 +1,127 @@
+%% Distance based global sensitivity analysis - Application to reservoir responses
+% Part1: Compute main/conditional effects of Prior models
+
+% Author : Jihoon Park (jhpark3@stanford.edu)
+% Date : 17 May 2016
+
+clear all; close all; fclose('all'); rng('default');
+%% 0. Add directories for path
+addpath(genpath(pwd))
+
+%% 1. Specify inputs for DGSA.
+addpath(genpath(pwd))
+
+% Loading the BEL data struct
+
+% History
+prior_path = 'Z:/Projects/BM/Code/Data/BM_NFA_first/Prior_prediction_IncrementalOilRatefromPolymer.mat';
+load(prior_path);
+
+% taking history out
+Data = Prior_Property_forecast_polymer.data; % we can keep the original ordering for history
+
+plot(Prior_Property_forecast_polymer.time,Data(1,:))
+
+DGSA.D = squareform(pdist(Data));
+
+% reading the parameters
+
+parameter_path = 'Z:/Projects/BM/Code/Data/Parameters_Polymer_short.csv';
+
+Parameters = csvimport(parameter_path);
+
+DGSA.ParametersNames = Parameters(1,2:end);
+
+% removing the detailed params of the geomodel (variogramm,.....)
+DGSA.ParametersValues = Parameters(2:end,2:end);
+
+DGSA.N=size(DGSA.ParametersValues,1);
+
+% converting the geomodels to a number
+GeomodelsParam = grp2idx(DGSA.ParametersValues(:,1));
+PLYvisc = grp2idx(DGSA.ParametersValues(:,7));
+PLYads = grp2idx(DGSA.ParametersValues(:,8));
+% recombining the parameters (geomodels plus others)
+
+Params = cell2mat(DGSA.ParametersValues(:,2:6));
+DGSA.ParametersValues = [GeomodelsParam, Params,PLYvisc,PLYads];
+
+% reodering the parameters so that they are based on the BASE RUNS
+
+
+NumRealizations_prediction_polymer = 600;
+Directory_prediction_polymer = 'Z:/Projects/BM/bm/MR_bm/workflows/wf01_POLYMER/runs/';
+
+[BaseCaseIndexForPolymercase M_POLYMERtoBASE]  = ReadingIndexFile( Directory_prediction_polymer, NumRealizations_prediction_polymer );
+
+
+for i=1:NumRealizations_prediction_polymer
+
+    DGSA.ParametersValuesNew(i,:) = DGSA.ParametersValues(M_POLYMERtoBASE(i),:)
+
+end
+
+%% 4. Compute & display main effects
+
+% 4.1 Inputs for clustering and display options.
+DGSA.Nbcluster=3; % # of clusters
+DGSA.MainEffects.Display.ParetoPlotbyCluster=1; % if true, main effects over cluster will be displayed with Pareto plot.
+DGSA.MainEffects.Display.StandardizedSensitivity='CI';
+
+% if 'CI', the confidence interval will be overlapped on the Pareto plot
+% if 'Pareto', Pareto plots will be displayed only
+% if 'None' No plot will be generated for standardd main effects
+% (default)
+
+% 4.2 Compute main effects from DGSA.
+SigLevel_CI=[.95,.9,1]; % This is needed only when you want to display confidence intervals
+% If you do not need to display confidence intervals,set DGSA.MainEffects.Display.StandardizedSensitivity='Preto' or 'None'
+
+% 4.3 Perform clustering
+DGSA.Clustering=kmedoids(DGSA.D,DGSA.Nbcluster,10); % In this example, K medoid clustering is applied.
+
+% 4.4 Compute Main Effects
+DGSA=ComputeMainEffects(DGSA,SigLevel_CI); % If you use Pareto plot or do not want to display main effects, remove SigLevel_CI, otherwise it shows an error.
+
+%% Display cdfs
+
+cdf_MainFactor(DGSA.ParametersValues, DGSA.Clustering, DGSA.ParametersNames,{'owc','oilvis'}); % In this example, CDFs for 'owc' and 'oilvis' will be displayed.
+%% 5. Compute & Display conditional effects
+
+% 5.1 Specify additional variables to estimate conditional effects.
+
+DGSA.ConditionalEffects.NbBins=3*ones(1,length(DGSA.ParametersNames));
+
+% 5.2 Compute conditional effects
+
+rng('default');
+DGSA=ComputeConditionalEffects(DGSA);
+
+% 5.3 Display conditional effcts
+
+% Speicify the method to display standardized conditional effects.
+DGSA.ConditionalEffects.Display.SensitivityByClusterAndBins=1; % if true, display pareto plots to visualize sensitivities by bins/clusters.
+DGSA.ConditionalEffects.Display.StandardizedSensitivity='Hplot'; % If omitted Pareto plot will be used. However, this is not recommended when there are lots of parameters
+
+% Visualize conditional effects
+DisplayConditionalEffects(DGSA,DGSA.ConditionalEffects.Display.StandardizedSensitivity)
+
+% 5.4 Display class condtional CDFs
+cdf_ConditionalEffects('owc','kvkh',DGSA,1)
+
+%% 6. Check the response
+
+% Use MDS plot to check the result of clustering
+ClusterColor=DisplayMDSplot(DGSA.D, DGSA.Clustering);
+
+% Specify texts for plots
+PlotLabels.xlabtxt='Time (day)'; PlotLabels.ylabtxt='WWPT (stb)';
+PlotLabels.xlimits=[0, 7300]; PlotLabels.ylimits=[0 7E7];
+
+% Visualize dynamic response by clusters
+dynamicbycluster(DGSA.Clustering,PriorResponses, ClusterColor,PlotLabels);
+
+
+%% Save all variables for futher application
+%save('VariablesSaved/DGSA_Completed.mat');
+%save('VariablesSaved/DGSA_Spatial_Completed.mat');

main_DGSA_Reservoir_Sensitivity_forNPV.m

@@ -0,0 +1,94 @@
+%% Distance based global sensitivity analysis - Application to reservoir responses
+% Part1: Compute main/conditional effects of Prior models
+
+% Author : Jihoon Park (jhpark3@stanford.edu)
+% Date : 17 May 2016
+
+clear all; close all; fclose('all'); rng('default');
+%% 0. Add directories for path
+addpath(genpath(pwd))
+
+%% 1. Specify inputs for DGSA. 
+addpath(genpath(pwd))
+
+% Loading the NPV information
+save_path = 'Y:/mzechner/Projects/BM/Code/Data/PolymerPilotWC_Matlab/';
+
+FileName_dgsa = [save_path 'NPV_dGSA'];
+load(FileName_dgsa);
+
+
+
+DGSA.ParametersNames = ParameterNames_dgsa;
+DGSA.ParametersValues = ParameterMatrix_dgsa;
+
+
+DGSA.D = Distance_PP_FF;
+
+DGSA.N = size(ParameterMatrix_dgsa,1)
+
+
+%% 4. Compute & display main effects
+
+% 4.1 Inputs for clustering and display options.
+DGSA.Nbcluster=3; % # of clusters
+DGSA.MainEffects.Display.ParetoPlotbyCluster=1; % if true, main effects over cluster will be displayed with Pareto plot.
+DGSA.MainEffects.Display.StandardizedSensitivity='CI'; 
+
+% if 'CI', the confidence interval will be overlapped on the Pareto plot
+% if 'Pareto', Pareto plots will be displayed only
+% if 'None' No plot will be generated for standardd main effects
+% (default)
+
+% 4.2 Compute main effects from DGSA.
+SigLevel_CI=[.95,.9,1]; % This is needed only when you want to display confidence intervals
+                        % If you do not need to display confidence intervals,set DGSA.MainEffects.Display.StandardizedSensitivity='Preto' or 'None'     
+
+% 4.3 Perform clustering
+DGSA.Clustering=kmedoids(DGSA.D,DGSA.Nbcluster,10); % In this example, K medoid clustering is applied.
+
+% 4.4 Compute Main Effects
+DGSA=ComputeMainEffects(DGSA,SigLevel_CI); % If you use Pareto plot or do not want to display main effects, remove SigLevel_CI, otherwise it shows an error.
+
+%% Display cdfs
+
+cdf_MainFactor(DGSA.ParametersValues, DGSA.Clustering, DGSA.ParametersNames,{'visoil',}); % In this example, CDFs for 'owc' and 'oilvis' will be displayed.
+%% 5. Compute & Display conditional effects
+
+% 5.1 Specify additional variables to estimate conditional effects.
+
+DGSA.ConditionalEffects.NbBins=3*ones(1,length(DGSA.ParametersNames));
+
+% 5.2 Compute conditional effects
+
+rng('default'); 
+DGSA=ComputeConditionalEffects(DGSA);
+
+% 5.3 Display conditional effcts
+
+% Speicify the method to display standardized conditional effects.
+DGSA.ConditionalEffects.Display.SensitivityByClusterAndBins=1; % if true, display pareto plots to visualize sensitivities by bins/clusters. 
+DGSA.ConditionalEffects.Display.StandardizedSensitivity='Hplot'; % If omitted Pareto plot will be used. However, this is not recommended when there are lots of parameters
+
+% Visualize conditional effects
+DisplayConditionalEffects(DGSA,DGSA.ConditionalEffects.Display.StandardizedSensitivity)
+
+% 5.4 Display class condtional CDFs
+cdf_ConditionalEffects('owc','kvkh',DGSA,1)
+
+%% 6. Check the response
+
+% Use MDS plot to check the result of clustering
+ClusterColor=DisplayMDSplot(DGSA.D, DGSA.Clustering);
+
+% Specify texts for plots
+PlotLabels.xlabtxt='Time (day)'; PlotLabels.ylabtxt='WWPT (stb)'; 
+PlotLabels.xlimits=[0, 7300]; PlotLabels.ylimits=[0 7E7];
+
+% Visualize dynamic response by clusters
+dynamicbycluster(DGSA.Clustering,PriorResponses, ClusterColor,PlotLabels);
+
+
+%% Save all variables for futher application
+%save('VariablesSaved/DGSA_Completed.mat');
+%save('VariablesSaved/DGSA_Spatial_Completed.mat');