private boolean
testIfRegressionWasSuccessful() { // **** TODO Design a more effective scheme to test for NR
// success
// Test if the regression analysis was successful based on the LRE line R2
Double r2 = profile.getR2();
Double emaxNR = profile.getNrEmax();
// Very, very crude approach
if (r2 < 0.8 || r2.isNaN() || emaxNR < 0.3) {
// Error dialog DISABLED as it is extremely irrating
// Toolkit.getDefaultToolkit().beep();
// SimpleDateFormat sdf = new SimpleDateFormat("dMMMyy");
// JOptionPane.showMessageDialog(WindowManager.getDefault().getMainWindow(),
// "The nonlinear regression analysis has failed for well '"
// + sdf.format(profile.getRunDate()) + ": "
// + profile.getWellLabel() + "'.\n"
// + "LRE analysis will be conducted without baseline-slope correction",
// "Failed to Apply Nonlinear Regression.",
// JOptionPane.ERROR_MESSAGE);
// Reset the LRE window using Fb
profile.setWasNonlinearRegressionSuccessful(false);
profile.setNrFb(0);
profile.setNrFbSlope(0);
// Return to average Fb background subtraction
LreWindowSelector.substractBackgroundUsingAvFc(profile);
// Recalculate the LRE parameters
prfSum.update();
// Reinitialize the profile
List<LreWindowSelectionParameters> l =
prfSum.getDatabase().getAllObjects(LreWindowSelectionParameters.class);
LreWindowSelectionParameters parameters = l.get(0);
LreAnalysisService lreAnalService = Lookup.getDefault().lookup(LreAnalysisService.class);
lreAnalService.lreWindowInitialization(prfSum, parameters);
return false;
}
profile.setWasNonlinearRegressionSuccessful(true);
return true;
}
/**
* Generates an optimized working Fc dataset via nonlinear regression to derived Fb and Fb-slope
* using the current LRE window.
*
* <p>Nonlinear regression is conducted using Emax, Fmax and Fo derived from the current LRE
* window, from WHICH values for Fb and Fb-slope are determined. These are then used to calculate
* a new working Fc dataset, followed by recalculation of the LRE parameters. This process
* repeated 3 times from which an average Fb and Fb-slope are then determined and a final
* optimized working Fc dataset generated. This is followed by a final recalculation of the LRE
* parameters to determine final values for Emax, Fmax and Fo. THIS DOES THIS INCLUDE ANY
* MODIFICATION TO THE LRE WINDOW.
*
* <p>Note also that the LRE parameters are updated and the modified Profile is saved, which
* includes initialization of a new Cycle linked list, so any calling function must reset its
* runner.
*
* @param prfSum the ProfileSummary encapsulating the Profile
* @return true if nonlinear regression analysis was successful or false if it failed
*/
public boolean generateOptimizedFcDatasetUsingNonliearRegression(ProfileSummary prfSum) {
this.prfSum = prfSum;
profile = prfSum.getProfile();
// The profile must have a valid LRE window
if (!profile.hasAnLreWindowBeenFound()) {
return false;
}
// Need to trim the profile in order to avoid aberrancies within early cycles and within the
// plateau phase
// Exclude the first three cycles
int firstCycle = 4; // Start at cycle 4
// Use the top of the LRE window as the last cycle included in the regression analysis******THIS
// IS VERY IMPORTANT
double[] fcArray = profile.getRawFcReadings();
int lastCycle = 0;
if (prfSum.getLreWindowEndCycle() != null) {
lastCycle = prfSum.getLreWindowEndCycle().getCycNum();
} else {
return false;
}
int numberOfCycles = lastCycle - firstCycle + 1;
// Construct the trimmed Fc dataset TreeMap<cycle number, Fc reading>
TreeMap<Integer, Double> profileMap = new TreeMap<Integer, Double>();
for (int i = 0; i < numberOfCycles; i++) {
profileMap.put(firstCycle + i, fcArray[firstCycle - 1 + i]);
}
// Run NR once to grossly stablize the LRE-derived parameters
LreParameters lreDerivedParam = getLreParameters();
LreParameters optParam = nrService.conductNonlinearRegression(lreDerivedParam, profileMap);
// Reinitialize the LRE-derived parameters
// First Reset nonlinear regression-derived Fb and Fb-slope within the profile
profile.setNrFb(optParam.getFb());
profile.setNrFbSlope(optParam.getFbSlope());
// Generate a new optimized Fc dataset based on NR-derived Fb and Fb-slope
conductBaselineCorrection();
// Updating the ProfileSummary updates the LRE-derived parameters within the Profile with no
// change to the LRE window
// However, this assumes that the NR was successful
prfSum.update();
// Reset the LRE-derived paramaters
lreDerivedParam = getLreParameters();
// Run the regression analysis 10 times to determine the average and SD
// This is necessary due to the poor performance of Peter Abeles’s EJML implementation
int numberOfIterations = 3;
ArrayList<Double> emaxArray = new ArrayList<Double>();
ArrayList<Double> fbArray = new ArrayList<Double>();
ArrayList<Double> foArray = new ArrayList<Double>();
ArrayList<Double> fmaxArray = new ArrayList<Double>();
ArrayList<Double> fbSlopeArray = new ArrayList<Double>();
double emaxSum = 0;
double fbSum = 0;
double foSum = 0;
double fmaxSum = 0;
double fbSlopeSum = 0;
for (int i = 0; i < numberOfIterations; i++) {
optParam = nrService.conductNonlinearRegression(lreDerivedParam, profileMap);
emaxArray.add(optParam.getEmax());
emaxSum += optParam.getEmax();
fbArray.add(optParam.getFb());
fbSum += optParam.getFb();
foArray.add(optParam.getFo());
foSum += optParam.getFo();
fmaxArray.add(optParam.getFmax());
fmaxSum += optParam.getFmax();
fbSlopeArray.add(optParam.getFbSlope());
fbSlopeSum += optParam.getFbSlope();
// Reinitialize the LRE-derived parameters
// First reset nonlinear regression-derived Fb and Fb-slope within the profile
profile.setNrFb(optParam.getFb());
profile.setNrFbSlope(optParam.getFbSlope());
// Generate a new optimized Fc dataset
conductBaselineCorrection();
// Update the LRE-derived parameters within the Profile
prfSum.update();
// Retrieve the new LRE parameters
lreDerivedParam = getLreParameters();
}
// Set the average for each parameter into the Profile
// This allows the final recalculation of the LRE parameters based on the average Fb and
// Fb-slope
profile.setNrEmax(emaxSum / numberOfIterations);
profile.setNrFb(fbSum / numberOfIterations);
profile.setNrFo(foSum / numberOfIterations);
profile.setNrFmax(fmaxSum / numberOfIterations);
profile.setNrFbSlope(fbSlopeSum / numberOfIterations);
// Determine and set the parameter SD
profile.setNrEmaxSD(MathFunctions.calcStDev(emaxArray));
profile.setNrFbSD(MathFunctions.calcStDev(fbArray));
profile.setNrFoSD(MathFunctions.calcStDev(foArray));
profile.setNrFmaxSD(MathFunctions.calcStDev(fmaxArray));
profile.setNrFbSlopeSD(MathFunctions.calcStDev(fbSlopeArray));
// Recaculate the optimized Fc dataset using the average NR-derived Fb and Fb-slope
conductBaselineCorrection();
// Update the LRE parameters
prfSum.update();
return testIfRegressionWasSuccessful();
}