How to develop a chemostratigraphic correlation scheme?

Developing a chemostratigraphic correlation or zonation scheme can be a challenge. There is no standard recipe, and a range of factors come into play. Here is what this article is about:

You will learn

i) what to consider before you even start analysing rock samples,
ii) how to filter the wealth of information in the geochemical data set towards achieving your goal of the study, in
iii) a step-by-step approach.

So let’s get started!

As outlined in the ‘What is Chemostratigraphy?’ article, the geochemical compositions of the rocks are determined by their underlying lithology and ultimately mineralogy. These on the other hand, mirror the history of the sedimentary rocks:

• Provenance (‘mother’ rock type)
• Weathering/erosion (climate)
• Transport (energy; water/wind)
• Mixing (different sources)
• Temporary storage (further weathering)
• Deposition (environment; continental/marine, redox conditions)
• Diagenesis (burial depth; temperature, pressure, fluids)
• Possible uplift and repetition of the sedimentary cycle (reworking)

Therefore, a structured approach considering these factors (as far as applicable) and the objectives of the study will help get started, save time, and avoid confusion, and even misinterpretations.

As we will see, the objective of the chemostratigraphic study is the main driver on how to approach the study.
The ‘classical’ chemostratigraphy task is to establish a stratigraphic zonation and correlation, while other objectives, such as for instance determining reservoir properties, require a different approach.
This article, therefore, covers mainly how to establish a chemostratigraphic zonation and correlation, e.g., between wells. Additional information on other topics, e.g., redox environment and organic content modeling (source-rock reservoirs) will be linked to here once available,

Quick overview

For guidance, the following steps are recommended to follow

1. Clarify the objectives of the study
   • stratigraphy, specific topics
2. Geochemical analyses and data quality
   • lab-based and portable analyzers
3. Lithology differentiation
   • siliciclastics, carbonates, evaporites
4. Determining possible ‘key-elements’ and element ratios
   • statistical and graphical methods
   • choice of elements based on study objectives

Step 1 – Clarifying the objectives

The objectives of the study are the main driver on which direction the study goes (kind of self-explanatory, isn’t it?).

Possible objectives could be:

• (Chemo-) Stratigraphic zonation and correlation
• Reservoir properties (e.g., porosity/permeability, TOC modelling)
• Rock properties (e.g., rock strength, clay-mineralogy, etc.),
• Depositional environments, ‘organic preservation’ (e.g., TOC modelling, redox conditions)
• Diagenetic or hydrothermal alterations

… or a combination of these.

Step 2 – Geochemical analyses and data quality

The method of geochemical analyses should be adjusted to the desired outcome (objectives).

Lab-based analyses

For instance, a full-blown chemostratigraphy study that requires a full suite of data from major to most trace (and rare earth) elements needs to be done by laboratory instruments such as ICP-OES/MS (Inductively-Coupled Plasma Optical Emission and Mass Spectroscopy) or WD- or ED-XRF (Wavelength-Dispersive or Energy-Dispersive X-Ray Fluorescence). These instruments provide a great number of elements with high accuracy and precision. Unfortunately, this is often not applicable due to economic (time and costs) reasons.

Field laboratory, and cost-effective applications

A range of portable analyzers, such as benchtop ED-XRF instruments, provide a relatively large range of elements with good accuracy and precision. These instruments increased in their capabilities over the last decade or so and are often an alternative to the more expensive stationary ICP and XRF lab instruments, though may not deliver the same range of elements.

Nevertheless, these instruments should be able to provide a good range of elements appropriate for most applications. If these instruments go to wellsite, then there is no doubt that the pre-studies should be carried out on the same analyzers.

Field analysis, quick scan, and most ‘shale’ applications

Hand-held instruments, mainly XRF (also often labeled HH-XRF for hand-held XRF, or pXRF – portable XRF, which is not be confused with instruments from the previous point), may be sufficient if only a limited range of elements is required. This is often the case in source-rock studies, i.e., for a quick estimation of a) lithology and broad mineralogy (to estimate rock strength) via certain major elements, and b) organic-richness via redox-sensitive elements. These instruments are also common in field studies, on slapped cores (quick scan), and the mining industry for a quick first overview about element compositions, e.g., metals.

Data quality

Regardless of the technique used, every chemostratigraphic data interpretation should start with a data quality control (see my articles on data quality and accuracy and precision).

Step 3 – Lithology differentiation

Obviously, different lithologies have different mineralogical and hence different geochemical compositions. It is thus easy for inexperienced chemostratigrapher to fall into the trap of correlating geochemical signatures, e.g., log trends and changes, between locations (e.g., wells) which are based on different lithologies, and thus may end up correlating, for instance, sandstones with sandstones, just because they are ‘sandstones’.

This may not be entirely wrong but can result in very spurious correlations and misinterpretations. For instance, let’s have a look at a simplified stratigraphic model (Figure 1).

As Figure 1A shows, similar (geochemical) log signatures may be easy to correlate resulting in a lithostratigraphic ‘layer cake’ correlation. Figure 1B, on the other hand, highlights that the situation may be much more complex and that the geochemical log signatures are mainly lithology driven, while not necessarily correlative in a simple way, if at all.

It is thus highly recommended to compare the geochemical variations with lithology variations.

The next section gives some general advice in how to evaluate lithology – geochemistry associations.

Lithology estimations

Obviously, a short petrographic description, at least a broad lithology classification, should be recorded before a rock sample is prepared for geochemical analysis; – use a magnifying glass or microscope if possible/available.

But, a lithology description may be rather impossible for instance with drill cuttings produced by PDC (poly-diamond cutter) bits, which commonly produce paste-like samples lacking almost all texture.

In that case (or with any other reason where the lithology of the geochemical data is unknown), the chemical composition should give enough indication on the probable underlying lithology.

Siliciclastic versus non-siliciclastic rocks

A quick look at the major elements should provide an initial estimate about the underlying lithology:

1. Silicon (Si), aluminum (Al) and possibly potassium (K) concentrations are relatively high, and the sum of major elements is high (e.g., > 80% when expressed as oxides; suggests siliciclastics as the predominant lithology.
2. High calcium (Ca) and low totals of major elements indicate the presence of carbonate minerals. For instance, a relatively clean limestone should have around more than 50-56% CaO (35-40% Ca).

(see also my article about Data Quality Control – the Sum of Major Components)

Siliciclastics – sandstone versus mudstone

Some authors recommend separating the geochemical data according to their lithology. For instance, Ratcliffe et al. (2015) or Atkin et al. (2020) follow this approach in their studies and developed one chemostratigraphic scheme for sandstones and another one for mudstones, which then are combined to one comprehensive scheme and correlation.

SiO₂/Al₂O₃ ratios

To differentiate sandstone from mudstone or ‘shale’ lithologies, the SiO₂ concentrations (Table 1) and SiO₂/Al₂O₃ ratios (Table 2) are good starting points. Rules of thumb:

SiO2 [%]	Lithology
~35 to ~55	claystone and ‘shale’
~55 to ~70	siltstone and argillaceous sandstone
~70 to ~90	sandstone
~90 to 100	quartz sandstone (quartzite)

SiO2/Al2O3	Lithology
< 3	claystone
< 5	claystone and ‘shale’
5-7	siltstone and argillaceous sandstone
> 7	sandstone
> 10	quartz sandstone (quartzite)

See also Figure 2 demonstrating the distribution in an Al₂O₃ versus SiO₂ plot.

However, something to keep in mind is that silica can be enriched through secondary processes, such as silica cementation or the addition of biogenic silica.

Chemical Gamma Ray

An additional method for differentiating sandstones from mudstones is the Chemical gamma-ray.

The chemical gamma-ray is a theoretic gamma-ray (GR) response that can be calculated from K, Th, and U concentrations, and interpreted like a bore-hole tool derived GR (see my article about ‘How to use the Chemical Gamma-Ray?’). It can thus be used for a lithology type estimation.

Carbonate rocks

The calcium concentration of the rock can give a good estimate if carbonate minerals are dominant. However, please keep in mind, that calcium and magnesium concentrations can be elevated in siliciclastic rocks, due to calcite, dolomite, and/or anhydrite cementation. Here are some generalized rules:

Step 4: Investigation the data for possible ‘key-elements’

The most difficult task is to identify the chemical elements to use for generating a chemostratigraphic zonation and correlation.

As outlined above, a strong driver in the mineralogical and thus geochemical composition is the underlying lithology (Step 3 above).

It is important to understand the factors that affect the geochemical compositions and how to use them for the objectives of the chemostratigraphic study. A good starting point is to investigate the element associations to each other, i.e. do certain elements show similar behaviors through the study section or parts of it? Here, we encounter an overlap with the lithology-driven variations, as described above. It thus might be a good idea to differentiate the geochemical data based on their underlying lithology before a further, separate investigation of the data set(s).

Some basic statistics and graphic evaluations may help to get first insights:

Some statistical methods:

• Correlation coefficients
• Covariation coefficients
• Principal Component Analysis
• Hierarchical Cluster Analysis

While Principal Component Analysis and Hierarchical Cluster Analysis require statistical software packages, Correlation and Covariance Coefficients are easily calculated spreadsheet processing software, such as Microsoft^® Excel^®.

Excel^® has predefined functions for correlation and covariance coefficients, e.g., on an element-to-element basis, but also the ability to calculate these coefficients between all elements and present the results in a matrix.

Both, correlation and covariance coefficients indicate if element pairs are somehow related by either positive values (element pairs vary together, e.g., both element concentrations increase or decrease together), negative values (concentrations of element pairs vary together but in different directions, e.g., while one element concentration increases, the other one’s decreases), or no correlation/covariance.

For instance, SiO₂ and Al₂O₃ concentrations in siliciclastic sedimentary rocks show often negative correlation and covariance. This is due to that in sandstones quartz and feldspar dominate the mineralogy, and thus silica values are high, while clay minerals, and therefore Al₂O₃ values, are rather subordinate, i.e., the higher the quartz (SiO₂) content, the lower the clay minerals (Al₂O₃) contents. The opposite is true for mudstones (see also Figure 2, with correlation coefficient = -0.9051 and covariance = -22.3919).

Graphical methods

Bivariate graphs

Similar to calculated correlation and covariance coefficients (see above), bivariate graphs (or XY plots) show if element pairs covary/correlate with each other.

As stated above (statistical methods), SiO₂ and Al₂O₃ concentrations in siliciclastic sedimentary rocks show often negative correlation and covariance, due to the dominance of quartz and feldspar in sandstones and clay minerals in mudstones. This is demonstrated in Figure 2 (correlation coefficient = -0.9051 and covariance = -22.3919).

However, the SiO₂ and Al₂O₃ concentrations may be affected by a third abundant element concentration, for instance CaO from calcite. The bivariate plot may change its characteristics and rather look like that in Figure 3, which highlights the effect of the abundance of calcite and thus high CaO concentrations.

Element logs against depth

In my opinion, one of the best and easiest methods to visualize element associations to each other, as well as to the lithology (particularly when petrophysical logs are available), is to plot the geochemical data as logs (concentration versus depth or other scales). The GR, Si/Al (SiO₂/Al₂O₃), and other possible lithology indicating logs such as Ca, Mg, or S (CaO, MgO, and SO₃ or S, respectively) are simple lithology indicators and lithology (e.g., grain size) depending elements should show similar variations in their log motifs when compared.

Choise of elements based on study objectives

As mentioned at the beginning, the objective of the study (Step 1) determines to a large extent which type of elements and element ratios are likely to lead to a successful study.

(Chemo-) Stratigraphic correlation and zonation

The traditional (and more sensu stricto) approach to chemostratigraphy is to correlate sedimentary strata. In other words, we are looking for geochemical variations through the rock column that can be correlated between sections (e.g., wells).

Ideal would be the use of elements that are more or less independent of the underlying grain size, and thus may be correlated even between different grain sizes/lithologies (such as in Figure 1). Further they should be unaffected by postdepositional (diagenesis) processes, i.e. being immobile.

A common attempt to overcome grain size effects is to use element/element ratios. Often chemostratigrapher employed a normalization to aluminum (Al). However, there are pitfalls with a Al-normalization, as this is depending on that both elements (nominator and denominator) should be in the same grain size fraction.

A number of publications argue against element/Al normalisations, arguing that these ratios are biased and thus result in spurious correlations. (e.g. Rollinson, 1993; Lowey, 2015).

The next attempt is to use immobile or incompatible elements, which are usually part of the ‘highfield-strength elements’ group (see text box below).

Commonly used in chemostratigraphy are Ti, Nb, Ta, P, Co, Sc, Zr, Hf, Y, REE, and Th (and U, unless associated with organics). [See also Element – Mineral Associations]

Highfield-strength elements (HFSE) refer to elements, or better ions, that have a small ionic radius (r, in Å) but high ionic charge (Z) resulting in a ratio of Z/r > 2. The HFSEs encompass all trivalent and tetravalent ions including the rare earth elements (REE), the platinum group (PG), thorium (Th), and uranium (U). HFSEs are not readily incorporated in the crystal lattices of common rock-forming silicate minerals and are generally incorporated into accessory minerals, such as heavy minerals (e.g., zircon, rutile, spinel, and monazite)

These elements are mostly associated with stable heavy minerals. Heavy minerals are commonly enriched in the silt to fine-sand fractions. The elements are usually used in ratios to each other (e.g. Zr/Y, Zr/Nb, Th/Y, etc.), rather than on their own, to compensate for grain-size differences.

Systematic changes in these ratios might be related to possible changes in the heavy mineral assemblages, and thus may reflect provenance changes. This is often the case when abruptly changes between subsequent data sets occur that are not driven by lithology changes, for instance at unconformities.

Other ratios, such as between La and Th on one side, and Sc and Co on the other, may differentiate between felsic and mafic provenance (Cullers et al., 1988). [An article about provenance related elements, ratios, and indices will be linked here, once finalised.]

Finding key elements and ratios

Like mentioned above, this is probably the most daunting and challenging task. Applying geological knowledge about the investigated strata and linking this to an expected geochemical expressions is a good starting point. Knowledge about the likely mineralogy of the sediments is an advantage that cannot be overstated. But even with knowing which minerals, e.g. heavy minerals, are present the clue often lies in the trace elements.

For instance, using a ratios such as Ti/Zr or Zr/Nb probably reflect the proportions of zircon and Ti-bearing minerals, such as rutile. Zirconium (Zr) is almost exclusively associated with zircon (Zr(SiO₄), and Nb (niobium) substitutes for Ti (titanium) in minerals lattices, see below).

More specific is the use of elements that are commonly associated to the same mineral.

For example, as mentioned above, zirconium (Zr) is almost exclusively associated with zircon. Hafnium (Hf) substitutes for Zr, and changes in Zr/Hf or Hf/Zr ratios thus might indicate different zircon populations, due to different provenances.

Another example, although with some uncertainty attached to it, are Ti-minerals: the rutile/anatase/brookite group has the chemical formula of TiO₂. Niobium (Nb) and tantalum (Ta), however, substitute readily for Ti in the crystal lattice, and thus ratios of Ti/Nb, Ti/Ta, or Nb/Ta may give indications of different rutile generations, e.g. due to different provenances. However, it has to be noted that Ti, Nb, and Ta are not restricted to rutile/anatase/brookite (TiO₂), and are constituents of other Ti-bearing minerals, such as ilmenite (FeTiO₃), titanite (or sphene) (CaTiSiO₅), and titanomagnetite/ulvospinel ((FeTi)₂O₄).

Last resort:

Chemostratigraphic hierarchy

Like every stratigraphic discipline, chemostratigraphy should follow a hierarchy from broad to fine.

There is no universally accepted terminology for such a hierarchy in chemostratigraphy, yet. Nevertheless, the International Commission on Stratigraphy (ICS) Stratigraphic Guide infers (with chemostratigraphy being an informal stratigraphic category) the use the unit term ‘zone’ with an appropriate prefix, e.g., ‘chemozone’.

Adapting this terminology, a chemostratigraphy hierarchy could be chemozone > chemosubzone > chemodivision > chemosubdivision (in descending order, similar to e.g., Atkin et al. (2020) .

Other authors prefer a nomenclature of (chemo-) sequence > package > unit > subunit (in ascending order), e.g. El-Gezeery et al. (2009), Ratcliffe et al. (2012a), Tonner et al. (2012)

Whatever nomenclature you choose, it has to follow a a hierarchy from broad to fine (Figure 3).

Marker horizons

Besides distinct geochemical units (i.e., chemozones, subzones, etc.), geological events that are correlative over large distances / areas may be identified through their geochemical signatures.o name a dew, these could be:

• Provenance changes (as discussed above)
• Unconformities (erosion surfaces) expressed in:
  • provenance changes
  • paleosol horizons, e.g. developed during subaerial exposure (weathering / alteration indices may identify these)
  • karst horizons
  • lithology changes
• Maximum flooding surfaces
• Hardgrounds
• Volcanic activities leading to:
  • ash bands/horizons that may be distributed over large areas
  • basalt flows

Reservoir properties

Reservoir properties depend on porosity and permeability. These are difficult to model from the geochemical composition of the rocks alone. Physical porosity and permeability measurements are necessary to model against. But even with these parameters in place, an exact modeled number for percent porosity or milli-Darcy permeability is unlikely. The application of Artificial Intelligence (AI), for instance, Machine Learning (ML), might assist in better models in the near future.

Nevertheless, relative predictions are possible with some geological knowledge.

For instance, the reservoir properties of sandstones often depend on the clay mineral content and clay mineral type. Elements such as Al, K, Na, Rb, Ga, and Cs are often linked with clay minerals and can be used as proxies for clay mineral content and type. Al can be used as a general clay indicator and kaolinite {Al₂Si₂O₅}. K and Rb may indicate illite {(K,H₃O) (Al,Mg,Fe)₂ (Si,Al)₄O₁₀ [(OH)₂,(H₂O)]} while Na and possibly Cs could indicate the presence of smectites {(Na,Ca)_0.3 (Al,Mg)₂ Si₄O₁₀ (OH)₂ ·n(H2O)}. Ga may be more common in kaolinite as in for instance illite. Thus the Ga/Rb or Ga/K ratio may give indication for with clay mineral is more abundant. K/Rb ratios may give further insights about the dominance of K-feldspar over illite or vice versa. (This is a complex topic and I will go into more detail in another article). Keep also in mind that many of these elements can be hosted in feldspars which needs to be clarified.

Calcite cementation may be interpreted from elevated Ca, anhydrite from Ca and S, and dolomite from Ca and Mg concentrations.

Silica cementation, such as quartz overgrowth, is more difficult to pinpoint from geochemical data. However, there are successful attempts to model ‘excess’ silica (such as from quartz-overgrowth or biogenic silica).

Ratcliffe et al. (2012b) used Zr vs. SiO₂ binary plots for data from mud rocks to identify excess silica, here interpreted as being in form of biogenetic silica. Rowe et al. (2012) utilised a similar plot of Al vs. Si to identify excess silica.

The idea behind it is that stable/ insoluble elements, e.g. Zr, which show a correlation (detrital trend) with Si, can be used to identify ‘excess’ silica by diverting trends toward higher Si.

Alteration indices may give additional indications of rock/reservoir properties. The Chemical Index of Alteration CIA (Nesbitt & Young, 1982) or the Aluminum/Bases index (e.g. Retallack, 1990) indicate different stages of weathering or alteration in a wider sense. However, these indices are very sensitive to grain-size/lithology changes and thus need to be interpreted with caution.

Some reservoir properties are closely linked to their depositional environments (see next section). This is particularly true for source rock reservoirs (a.k.a. unconventional or shale reservoirs, resources, or plays). [There will be a link here to this topic once completed.]

Depositional environments

Depositional environments of coarser siliciclastic sediments, e.g. sandstones a siltstones, are difficult to identify by their geochemical composition itself. Again, some geological, sedimentological, and mineralogical knowledge will be of great benefits.

For instance, El-Gezeery & Scheibe (2010), differentiated fluvial and shore-face sands to chemosteer wells into the better reservoir sands, based on their geochemical signals derived from heavy minerals and sabhka environments. This was achieved through detailed research on petrography and sedimentology reports.

With the turn of the Oil & Gas Exploring industry towards unconventional resources in the mid- 2000s, characterising the depositional environment in terms of organic enrichment, redox-conditions, and rock ‘fracability’ became the next frontier in chemostratigraphy.

Redox conditions

A requirement of characterizing source rock reservoirs (a.k.a. unconventional or shale reservoirs, resources, or plays) is to pin-point the section with high organic content and the redox conditions during deposition.

Certain elements can be used as proxies for organic enrichments (e.g., Mo, Ni, V, U, etc., though they are not applicable everywhere). Pioneering work on differentiating (paleo-)redox conditions has been done by Tribovillard et al. (2006), Algeo & Tribolillard (2009), Scott & Lyons (2012). 

Closely related in these studies of redox-conditions and organic matter enrichments is the estimation of rock-strength or ‘fracablility’. This is ofter referred to ‘brittleness’ (a term highly discussed). The initial formula after Jarvis et al. (2007) uses mineralogy to estimate rock ‘brittleness’ or fracability, where the mineralogy can be computed from geochemical data. These approaches are heavily discussed in the industry (e.g., Mathia et al. 2016).

[There will be a link to a detailed article here once completed.]

References

Algeo, T.J and Tribovillard, N. (2009): Environmental analysis of paleoceanographic systems based on molybdenum-uranium covariation. Chemical Geology, Vol. 268, No. 3-4. 211-225. [Link]

Ainsworth, R.B., Sanlung, M., and Duivenvoorden (1999). Correlation Techniques, Perforation Strategies, and Recovery Factors: An Integrated 3-D Reservoir Modeling Study, Sirikit Field, Thailand. AAPG Bulletin, v. 83, no. 10, 1535-15551. [Link]

Atkin, D., Viera, M., and Taylor, B. (2020). Application of real-time chemical stratigraphy in support of the safe drilling of HPHT wells: examples from the Shearwater Field, Central North Sea, UK. Petroleum Geoscience, Vol. 27, 1-17. [Link]

Cullers, R.L., Basu, A., and Suttner, I.J. (1988). Geochemical signatures of provenance in sand-mixed material in soils and stream sediments near the Tobacco Root batholith, Montana. U.S.A., Chemical Geology, 70, 335-348. [Link]

El-Gezeery, T., Hamim, A., Zereik, R., Hughes, S.N., and Scheibe, C. (2009). A high-resolution chemostratigraphyapplication in carbonates – a case study in the Cretaceous Mishrif Reservoir, Minagish Field, Kuwait. International Petroleum Technology Conference, IPTC, Doha, Qatar, 7-9 December 2009. ITPC #13875. [Link]

El-Gezeery, T. and Scheibe, C. (2010). Chemostratigraphic differentiation between fluvial and shore-face sands as a real-time geosteering tool in the Albian Upper Burgan Formation, Minagish Field, West Kuwait. AAPG GEO Middle East Conference & Exhibition 2010, Manama, Bahrain, 7-10 March 2010. [Link]

El-Gezeery, T., Ismael, A.A., Al Anezi, K., Al Jeaan, M., Silambuchelvan, J., Padhy, G.S., Wasnik, A., and Al Shoeibi, A. (2013a). Drilling of multilateral wells aided with geochemical analysis, Kuwait. Drilling of multilateral wells aided with geochemical analysis, Kuwait. International Petroleum Technology Conference, IPTC, Beijing, China, 26-28 March 2013. ITPC #6617. [Link]

El-Gezeery, T., Ismael, A.A., Al Anezi, K., Padhy, G.S., Al Saqran, F., Abdul-Lafif, A., Silambuchelvan, J., Estarabadi, J., and Ferroni, G. (2013b). Innovative geosteering technology utilized in drilling smart multi-lateral wells, Kuwait. Offshore Technology Conference, OTC, Houston, Texas, U.S.A., 6-9- May 2013. OTC #24002. [Link]

Forbes, A., Munday, S., and Revie, D. (2020). An elemental chemostratigraphic study, Onshore Otway Basin, Victoria. Victorian Gas Program (VGP) Technical Report 12. Geological Survey of Victoria. [Link]

Gani, M.R. and Bhattacharya, J.P. (2005). Lithostratigraphy versus Chronostratigraphy in Facies Correlations of Quaternary Deltas: Application of Bedding Correlation. In: River Deltas – Concepts, Models, and Examples. SEPM Special Publication No 83, SEPM (Society for Sedimentary Geology), 31-48. [Link]

Jarvis, I., Pearce, T., and Higgs, N. (1988). Early diagenetic geochemical trends in quaternary distal turbidites. Chemical Geology, Vol. 70, Issues 1–2, 10. [Link]

Lowey, G.W. (2015). Element/aluminum ratios in chemostratigraphy: a dubious normalisation resulting in spurious correlations. GeoConvention 2015, Telus Conventional Centre, 4-8 May 2015, Calgary, AB Canada. [Link]

Mathia, E., Ratcliffe, K., and Wright, M. (2016). Brittleness Index – A Parameter to Embrace or Avoid? Unconventional Resources Technology Conference, San Antonio, Texas, 1-3 August 2016. [Link]

Nesbitt, H.W. and Young, G.M. (1982). Early Proterozoic climates and plate motions inferred from major element chemistry of lutes. Nature, v. 299, 715-717. [Link]

Ratcliffe, K.T., Woods, J., and Rice, C. (2012a). Determining well-bore pathways during multilateral drilling campaigns in shale resource plays: an example using chemostratigraphy from the Horn River Formation, British Columbia, Canada. Eastern Australian Symposium IV, Brisbane, Queensland, Australia, 10-14 September 2012. [Link]

Ratcliffe, K.T., Wright, M., and Spain, D. (2012b). Unconventional methods for unconventional plays: using elemental data to understand shale resources plays – Part 2. PESA News Resources, April/May 2012. [Link]

Ratcliffe, K.T., Wilson, A., Payenberg, T., Rittersbacher, A., Hildret, G.V., and Flint, S.S. (2015). Ground truthing chemostratigraphic correlations in fluvial systems. AAPG Bulletin, 99, 1, 155-180. [Link]

Retallack, G.J. (1990). Soils of the Past: an Introduction to Paleopedology. Unwin-Hyman, London. [Link]

Rollinson, H. (1993). Using Geochemical Data. Cambridge University Press. [Link]

Rowe, H., Hughes, N., and Robinson, K. (2012). The quantification and application of handheld energy-dispersive X-Ray Fluorescence (ED-XRF) in mudrock chemostratigraphy and geochemistry. Chemical Geology, 324-325, 122-131. [Link]

Scott, C. and Lyons, T.W. (2012): Contrasting molybdenum cycling and isotopic properties in euxinic versus non-euxinic sediments and sedimentary rocks: Refining the paleoproxies. Chemical Geology, Vol. 324-325, 19-27. [Link]

Tonner, D., Hughes, S., and Dix, M. (2012). Wellsite chemostratigraphy – new analytical tools used to evaluate unconventional reservoirs to assist in well construction and smart completions. Society of Petrophysisists and Well Log Analysts (SPWLA), SPWLA 53rd Annual Logging Symposium, Cartagena, Columbia, 16-20 June 2012. [Link]

Tribovillard, N., Algeo, T.J., Lyons, T., and Riboulleau, A. (2006): Trace metals as paleoredox and paleoproductivity proxies: An update. Chemical Geology, Vol. 232, No. 1-2, 12-32. [Link]