Normalization or optimization of the data to accentuate the geologic responses while minimizing the effects of well log vintage, bore hole conditions, well logging company and well log calibration must be done prior to any serious attempt at analysis.
With regard to multi-well digital well log analysis projects: proceed at your peril if you do not plan to normalize your data set. From experience in dealing with digital well log data from multiple wells, almost all interpretation busts or failures, aside from using the wrong interpretation procedures, can be linked back to poorly calibrated data and not doing a proper and consistent normalization prior to analysis.
The raw data crossplot shown below is a composite crossplot of the deep resistivity vs. gamma ray data trends for four wells over the Lodgepole interval and overlying shales from wells in North Dakota. The data trends from individual wells have been plotted in separate colors.
The data trends colored green and yellow represent the data from two wells chosen from a database of another 30 or so wells. The data for the other 30 wells are not shown for clarity but they more-or-less exactly overlay the data trends represented by the yellow and green data wells.
The data trends represented by the red and blue data show two wells where the data trends do not match the trends of the yellow and green wells.
Geologically, regionally and depositionally, there is no reason why the data trends of the blue and red data should not match the trends of the yellow and green wells as well as the 30 other wells not shown other than most probable miscalibration of the raw well log data.
Consider the well represented by the blue data trend. Water saturation calculations made using the raw data from this well would suggest more pessimistic water saturations relative to the yellow-green trend data. In fact, this well could have been abandoned based on the raw data calculations.
But, by close inspection of the trends it is obvious that the blue trend data would consistently match the yellow-green trends by simply shifting (increasing) the resistivity. Water saturation calculations generated using the modified or normalized resistivity would provide more consistent results relative to the trends to the 30+ other wells.
Consider the well represented by the red data trend. Water saturation calculations made using the raw data from this well would suggest more optimistic water saturations relative to the yellow-green trend data. In fact, this well would probably have been cased based on the optimistic water saturation calculations.
By close inspection of the trends it is obvious that the red trend data would consistently match the yellow-green trends by simply shifting (decreasing) the resistivity. Water saturation calculations generated using the modified or normalized resistivity would provide more consistent results relative to the trends to the 30+ other wells.
The normalized crossplot shows the resultant composite trends for the four example wells.
Once normalized, the analysis can proceed knowing that there is now a consistent data set to work with and that powerful interpretive strategies such as multi-well contour crossplots can be applied to the interpretation of the data.
RE-CALIBRATION OF OLDER WELL LOG DATA
The figure below shows an example of how normalization procedures can be used to recalibrate older well log data. The cross plot on the left is a cross plot of older neutron porosity data vs. older gamma ray data (circa 1956). The colored polygon outlines on the cross plot are reference outlines of the neutron-gamma trends from modern wells (circa 1985) generated using contour crossplot techniques. From inspection of the trends of the data in the left hand crossplot, it is obvious that the older data trends cannot be directly compared to the modern data trends. The older data requires re-scaling.
The cross plot on the right hand side shows the data trends of the older data after the re-scaling or normalization. A close inspection of the normalized data trends show how well the internal trends of the older data match or are at least very consistent with the modern data trends.
There is a considerable amount of older data sitting unused in well log libraries all over the world. These data can be recalibrated to match the sensitivities of modern data very successfully. The best place to find more hydrocarbons is where the hydrocarbons are known to exist. A detailed re-look at existing older data in concert with more modern data has the potential to uncover large amounts of by-passed hydrocarbon potential and may even open up older producing areas to new horizons found using normalized data.
Remember, any data that can be organized to be placed on depth with the well log data can be used to help with the normalization and the ultimate interpretation. These data could even include drilling time charts and geological descriptive data.
Now is the time to re-work the data from older fields. Normalization methods are the key to success in unlocking bypassed hydrocarbon potential.
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These normalization procedures are carried out on all combinations of raw well log data. At a minimum and when available, the bulk density vs. gamma, neutron vs. gamma, acoustic vs. gamma, resistivity vs. gamma, neutron vs. bulk density, neutron vs. acoustic are the cross plot combinations used for normalization.
Selection of the depth interval to use for reference for normalization is also crucial. Generally, two separate intervals with good ranges of data are used iteratively (test then confirm) to establish a consistent well log response from well to well.
Approximate cost: $100.00 per well per well logging run depending on log suite available, the number of logging runs to integrate and data quality
Gray is the color of truth - McGeorge Bundy
Note: All prices in US Dollars