Profile Colorlith Plot Tracks
Peter L. Briggs(1) identified a method to assist geologist in well log interpretation by creating a color log presentation. His method provided a means to combine three different log curves from one well into one image track that varies along the depth by assigning each curve to a specific primary color of red, green and blue. His method assumed that the primary colors would appear to the human eye as orthogonal and miscible where the resulting color image would preserve all the information of the original log curves. The color image presentation of well log data reduced the number of displays competing for the interpreterís attentions by uniting the three log curves into one display and relieved the burden of mentally combining data from several separate displays. Overall the color log images presented log data to the user in a form that differs from the conventional log presentation and relied on the human perceptions and pattern recognition skills.
David R. Collins & John H. Doveton(2) extended this technique to identify lithology
by using the neutron & density porosity and gamma ray logs, by noting that several
porosity log readings gave direct indications of the rock mineralogy. The neutron
and density logs were first overlaid on a common scale of equivalent limestone
porosity units and their relative disposition considered together with the gamma ray
log. Shales have high neutron porosity (due to bound water), relatively low density r
eading and a high gamma ray value. Limestones have low gamma ray value, and a coincident
neutron and density porosity. Dolomites have a low gamma ray value, a relatively low
density reading and a relatively high neutron reading. Sandstones have a low gamma ray
value, a relatively high density reading and relatively low neutron reading.
They based the lithology identification on three decision rules:
They extended the 27 possible responses to a color cube whose orthogonal axes match the rules. The design rules of the cube were geared to a basic discrimination between the potential reservoir lithology of sandstone, limestone, and dolomite, and their distinction from shale. They also noted that other lithologies have characteristic log responses which made additional identifications possible.
|David Collins (3) in a USGS paper discusses the visualization of subsurface geology as achieved by COLORLITH, a software system developed at the Kansas Geological Survey in an effort to provide low-cost, high- resolution, interpretation, and visualization of well log data from a single well or multiple wells. The COLORLITH system was designed to run on UNIX workstation environments written in FORTRAN77. The input was digital LAS log data and the output was written to a PostScript file. COLORLITH was specifically designed to analyze and display gamma ray and lithodensity logs. The COLORLITH computes the apparent density (Rhomaa) and the apparent photoelectric factor (Umaa) from the lithodensity logs, Photoelectric factor, Bulk Density and the Neutron Porosity log curves.|
|KIMELEON extends both the Porosity Difference Method and the Rhomaa-Umaa Method that is computed in COLORLITH system. Both of the earlier versions done by Doveton & Collins(2,3) use a 3 X 3 X 3 color cube to create a color image track. KIMELEON extends the color cube to 256 X 256 X 256 colors to create the color image track which increases the sensitivity to the log responses and will bring out more subtle changes in the lithology over the depth. This new system is web based and is written in JAVA and allows the user to directly interact with the data in real time. The digital LAS files can be retrieved from the userís PC or from the KGS server and plotted to a well profile plot.|
Colorlith has been developed over the past 20 years from Briggs to Doveton &
Collins and has been defined by Schlumberger as "A system for color-coding three-
dimensional information. This system is used in wireline log analysis to provide
color shading in which the final color is determined by the values of three curves.
One curve dictates the intensity of red, a second the intensity of green, and the
third the intensity of blue. The final resulting color is the result of the three
input curves. The input curves may be raw curves from the field or computed curves.
When used for correlation work on cross sections, the curves must have been normalized
to remove the effects of incorrect calibrations and borehole problems." The colorlith
or Briggs Cube is considered an excellent method for condensing the wire line log
curves into one track to visualize the lithologies of a well. Another method used and
developed by Doveton (4) is the spectral gamma ray or Th/U and Th/K ratio cross plots
introduced with the KGS #1 Braun in Ellis County Kansas.
The spectral gamma ray log can be used to estimate volumes and types of clay minerals, and is also useful for identifying fractures that have uranium salts precipitated in them by ground-water. Significant potassium and thorium concentrations in carbonates are also found in clay minerals and may show on porosity logs as shales. The spectral gamma-ray log helps to differentiate the radioactive carbonates from shales and clays. Several case studies of the use of spectral gamma-ray logs in Kansas show how the data can be cross-plotted using digital recording applications to identify facies. Natural gamma radiation in rocks is almost entirely attributable to potassium-40 and the radioactive isotopes of the uranium and thorium families. The concentrations of the three main radioactive elements Potassium, Uranium and Thorium in the formation can often be used to give an indication of the mineralogy and/or geochemistry. Thorium may be associated with an increase of terrigenous clays. Uranium is frequently associated with the presence of organic matter in black shale deposits. In sandstone, high Potassium may be caused by the presence of potassium feldspars or micas.
|Another colorlith technique proposed by Matt Hall5 uses the Spectral Gamma- Ray logs, Potassium (red), Thorium (green) and Uranium (blue). His method maps the gamma ray spectrum to the visible end of the electromagnetic spectrum. The gamma-ray emissions from the decay of potassium-40 nuclei have the lowest energy, the potassium log is represented by shades of red, thorium by green, and uranium by blue. He noted that logs are represented by monochrome variable density displays. Low relative amplitudes translate to low color values and are displayed as dark colors, and high amplitudes are displayed as bright, saturated colors. The colors are then combined additively in a RGB spectrum producing a composit image.|
|The "micro-electrical imaging" technique replaces the original current measurement of the electrodes from a high precision form to normalized, quantization form with the measurments being significantly reduced. The purpose is to produce an image with color levels and contrasts that make it easier for the interpreter to analyze. Colors are represented as dark have been chosen to represent high conductivities because shales are often more conductive and darker-colored than other lithologies.|
|Normalization of the image over a given depth range can enhance the contrast of the image. The number of data points over the given depth range is subdivided equally into each color bin. This is the non-linear process and a great degree contrast that helps spotting image details in areas where sample values differ little from each other. These images often appear some what harsh in contrast.|
|Quantization procedure is very similar to dynamic normalization, but generally the assignments of colors is made using the dynamics of the entire length of the log. The number of data points are distributed by magnitude into equal bins. This often results in an image where intermediate colors abound and few extreme colors such as as dark brown or cream occur, except for unusual distribution of the input data. The image is often pleasant to the eye but may lack details in certain area.|
Author: John R. Victorine firstname.lastname@example.org
(1) Color display of well logs, Peter L. Briggs, Mathematical Geology, Volume 17, Number 4, May 1985
(2) Color Images of Kansas Subsurface Geology from Well Logs, D. R. Collins and J. H. Doveton, Computer & Geosciences, Vol. 12, No. 4B, pp.519-526 1986
(3) Visualization of Subsurface Geology from Wireline Logs, David R. Collins, Digital Mapping Techniques Ď98-Workshop Proceedings U. S. Geological Survey Open-File Report 98-487
(4) The Dakota Aquifer Program Annual Report, FY89 Kansas Geological Survey, Open-File Rept. 90-27 Annual Report, FY89-Appendix B ( http://www.kgs.ku.edu/Dakota/vol3/fy89/app_b.htm )
(5) Composite Colour Display of Spectral Gamma-Ray Logs, by Matt Hall, Canadian Well Logging Society, Dec 2005, v.24
(6) Geological Well Logs Their use in Reservoir Modeling, by Stefan M. Luthi, pg 84-86, ISBN 3-540-67840-9, © 2001 Springer-Verlag Berlin