Minerology and Geochemistry

Mineral modelling. It is based on knowledge of the geological environment and element-to-mineral associations rather than a large dataset of computations and mathematical models

Periodic Table
 
  • Mineralogy and lithology from XRF: 

  • Mineralogy and lithology from XRF calibrated to XRD

  • Chemostratigraphic interpretation - including a zonation and interpretation with annotated plots.

  • Chemostratigraphic interpretation and correlation-  including a zonation and interpretation with annotated plots.


In general, this process is better described as mineral calculation rather than mineral modelling. It is based on knowledge of the geological environment and element-to-mineral associations rather than a large dataset of computations and mathematical models. It can be readily adjusted to better match XRD. It can also be improved with XRD results in cases where uncommon minerals are present. The process assumes the possibility of the presence of the following minerals and uses the elemental data to determine which are present and to calculate the quantities:


- Quartz

- Kspar

- Plagioclase

- Clays: 

  • illite/mica (using general formula so would be much more accurate with XRD for guidance)

  • smectite: although often present as mixed layer illite/smectite, smectite from XRF data is calculated as a separate mineral.

  • Kaolinite:

  • Chlorite (general intermediate composition assumed)

- Dolomite
- Calcite
- Siderite
- Apatite
- Pyrite
- Anatase


The process does not specify ankerite (Fe,Mg carbonate) but where both dolomite and siderite are present, ankerite is possible. This is best verified with XRD.


 

Major Elements

Major elements as measured from pXRF. Major elements are useful for determining lithology and some aspects of mineralogy. Si, Al and Ca are considered the primary rock-forming elements as they represent sand/silt, mudstones and carbonates, respectively. Mg can be used to help differentiate between limestone and dolomite.

1.png

Ternary Diagram

A simple ternary diagram using SiO2, Al2O3 and CaO can help define lithology. This shows most of this well is composed of sand/siltstones with minor mudstones. Additional ternary diagrams (not shown) can identify other lithologies and mineral phases such as dolomite, anhydrite, pyrite, etc.

2.png

Mineralogy Calculations

Mineralogy can be calculated using geologic and mineral knowledge along with element-mineral associations. This mineralogy was guided by XRD provided from a nearby well; however, the XRD was done on samples in mudstones whereas this well was predominately sandstones. These results may, therefore, overquantify clays and under quantify feldspars. The best way to validate the results would be to obtain XRD on the same samples. The model allows for simple adjustments based on additional mineral information. This particular model did not use spectral gamma or other wireline log data.

3.png

In The Absence Of XRD Results

In the absence of XRD results, element-to-element relationships and stoichiometry can be used to constrain clay and feldspar types. The plot to the left shows ideal clay and feldspar lines from stoichiometric formulas over which the data from this well has been plotted. In short, if data falls below the ideal mica line (with some exceptions because of variations in illite composition), it can be interpreted that non-K-bearing clays such as kaolinite, smectite or chlorite are present. If samples fall above the illite line, it can be interpreted that Kspar is present. This needs to be used with caution as a combination of minerals, such as glauconite and mica, can also result in samples plotting along the illite line, thus XRD for cross check is very valuable.

4.png

Ratios Of Elements

Ratios of elements and element-to-element comparisons can be useful in learning more about variations in lithology. SoO2/Al2O3 is plotted as a grainsize proxy with higher values (greater than 8) indicating sandstones and lower values (less than 5) indicating mudstones.
K/Al values also show significant variation resulting from changes in mineralogy. Higher values likely show more illite and/or Kspar, while lower values indicate non-K-bearing clays.
Al and Ti are plotted together to show their significant correlation. Because Ti is present in clays and sands, it is important to identify its association to properly interpret its meaning.
Fe is plotted with S to show the presence of pyrite where the two elements correlate.
While it is not too clear from this image, there seems to be a significant change at approximately 4117m where sediment below is more clay-rich and phosphatic (possibly biogenic phosphates) and above is more Si-rich indicating more sand and silt.

5.png

Differences In Lithology

To further outline the differences in lithology, this illustrates the differences between the three zones highlighted (Silt1, green circles; Sand1, blue circles; and Sand2, pink circles). It is clear that Sand1 and Sand2 both plot near the SiO2 axis on the ternary, and the silt zone plots between SiO2 and Al2O3. This shows the main difference between these two zones are that of variations in Si and Al. Some samples in Sand1 also show a higher Ca concentration.

6.png

K vs Al

K vs Al also shows a dramatic difference in the silt vs. sands, and it shows that Sand1 and Sand2 are similar in their K/Al composition.

7.png

S vs Fe

S vs Fe also shows a dramatic difference in the silt vs. sands, and shows that Sand1 and Sand2 are similar in their S/Fe composition. This plot also shows that the sulfur and iron in the sands are predominately in pyrite since samples fall along the ideal pyrite line, whereas samples from the silt zone (green samples) have a slightly lower S/Fe value, indicating there is likely an additional Fe mineral phase in this zone, likely an Fe-bearing clay like illite.

8.png

Fe vs Al

Fe vs Al shows the significant difference in silt (green) vs sands (blue and pink), but it also shows a subtle difference in the two sands, with Sand1 (blue circles) having a greater concentration of Fe than Sand2 (pink circles). This is even clearer when the silt samples are removed.
This illustrates why chemostratigraphy, which is the application of element ratios to characterize and correlate sediment, specifically looks at each ithology independently to see the subtle changes within a lithology. Additionally, ratios of trace elements can be used to find even more intra-lithology variations. Analysis on this well was not optimized for trace elements, so only majors are shown. 
The change in the amount of Fe from one sand to the next is less than one percent, but the precision of lab data in general and XRF data specifically allows these small variations to be relied upon and exploited.

9.png
 

Get a Quote

If you have any additional questions don't hesitate to get in touch and one of our specialists will be happy to help

Thanks for submitting! We’ll send you a price quote soon.