Welcome to an interview with Bryan Turner, whose work at the University of Oklahoma's Institute of Reservoir Characterization focuses on chemostratigraphy. Bryan is helping lead a new AAPG Technical Interest Group (TIG) in Geochemistry. If you are interested in joining, please click here
and request membership.
What is Chemostratigraphy?
Chemostratigraphy is a method that is capable of highlighting, and simultaneously quantifying, geochemical variations within core, outcrop, plugs, and even cuttings. At its simplest, chemostratigraphy can be defined as using the variability of the chemical elements to evaluate the stratigraphic relationships within a succession due to changes in bulk chemical composition, isotopic composition, or organic geochemical composition. Where lithostratigraphy is based on petrologic variation within a succession, chemostratigraphy is based on geochemical variation within a succession.
What are the Benefits of Chemostratigraphic Analysis
As unconventional plays have become economic, there has been a general realization that the phrase “Homogenous Shale” is an oxymoron. Mudrocks in general are just as variable as their coarser-grained counterparts in conventional reservoirs, but mudrocks are considerably more subtle. As an example, Figure 1 shows a 10ft section of mudrock that could reasonably have fit the description of “Homogenous Shale”. There are a few horizons where the careful geologist would note a color change, indicative of mineralogical variation, but on the whole this core does not appear to contain any significant variation.
How does one interpret chemostratigraphic information?
“All models are wrong. Some models are useful”
– George E. P. Box
Chemostratigraphic profiles are interpreted through the application of geochemical models derived from the body of work developed by countless environmental geochemists from around the world. The resulting geochemical models are admittedly oversimplified for any one particular horizon, but when applied to a stratigraphic succession as a whole, provide a lens for interpreting changing conditions within the stratigraphic succession.
Some common components within these geochemical models are: 1) aluminum (Al), potassium (K), titanium (Ti), and zirconium (Zr) – all derived from continental sources; 2) within mudrocks Al and K are most likely associated with clay minerals; 3) calcium (Ca) and strontium (Sr) are most likely associated with carbonate minerals (though they can also be found in sulfates, phosphates, and feldspars); 4) molybdenum (Mo), nickel (Ni), and vanadium (V) are indications of restricted bottom water circulation; and 5) silicon (Si) is the second most common element in the Earth’s crust and should be normalized against another element, such as Al or Ti, in order to interpret the amount of quartz present within a sample (Table 1).
Element Proxy Reference
Titanium (Ti) Continental source and dust fraction Sageman and Lyons 2004
Zirconium (Zr) Continental source Bhatia and Crook 1986
Silicon:Aluminum ratio (Si/Al) Quartz (biogenic and detrital) Pearce and Jarvis 1992; Pearce et al. 1999; Sageman and Lyons 2004
Calcium (Ca) Carbonate source and phosphate Banner 1995; Tribovillard et al. 2006
Strontium (Sr) Carbonate source and phosphate Banner 1995; Tribovillard et al. 2006
Phosphorous (P) Phosphate accumulation Tribovillard et al. 2006
Aluminum (Al) Clay and feldspar Pearce and Jarvis 1992; Tribovillard et al. 2006
Potassium (K) Clay and feldspar Tribovillard et al. 2006
Molybdenum (Mo) Bottom water euxinia, redox sensitive Tribovillard et al. 2006; Algeo and Rowe 2012
Vanadium (V) Bottom water anoxia, redox sensitive Tribovillard et al. 2006
Table 1: A list of the principal elements used for correlation and what their primary stratigraphic proxy. Modified from Turner et al. 2016.
What do you do after you select a geochemical model? Are there any caveats?
Once a geochemical model is selected, it is possible to interpret the geochemical variation within a sequence stratigraphic framework. Increasing and decreasing trends in detrital proxies (c.f. Al, K, Ti, and Zr) define increasing and decreasing amounts of sediment reaching a particular portion of the basin. This information can be used to interpret progradation and retrogradation within an area (Turner et al., 2015; Turner et al., 2016). These patterns of sedimentation can be used to build stratigraphic frameworks across a sedimentary basin.
It is also possible to interpret changing depositional conditions that could influence subsequent hydrocarbon potential. Certain elements such as Mo, Ni, and V are soluble in the presence of oxygen within the water column. The presence of these elements have been used as an indication of conditions that were favorable to accumulate organic carbon due to anoxic or euxinic bottom water conditions. Horizons with increased concentration of these proxies for bottom water restriction may aid geologists in targeting horizons for subsequent development.
What are some of the key advances in XRF?
Do you have data, one of the more cost-effective results from advances in hand-held x-ray fluorescence (HHXRF) technology. These HHXRF units are portable, allowing workers to transport the instrument to the well-site or core facility with minimal effort. They are also efficient at collecting data, in under 2 minutes it is possible to collect data for 23 elements within a single sample (Rowe et al., 2012). However, it is also important to understand some of the key caveats associated with these data sets. Concerns involve sample contamination, signal attenuation and peak overlap, and improper calibration.
X-ray fluorescence (XRF) works by bombarding a sample with x-rays from a source. The incoming x-rays interact with electrons within the sample. Some of the electrons will absorb these incoming x-rays, these electrons are ejected from their orbitals within the electron cloud resulting in a vacancy. This vacancy will be filled by an electron from a higher energy state orbital, but in order to do so, this higher energy electron must emit a photon with a wavelength proportional to the difference in energy states between the two orbitals. Each element will emit a characteristic range of photons with specific wavelengths. Measuring these characteristic patterns of photons allow workers to identify the elements present within a sample.
With HHXRF, it is possible to analyze outcrop, core, and cuttings and correlate these different sources of data. Outcrop samples should be collected from sufficiently beneath the surface to minimize the impact of surface weathering on the sample. These outcrop samples should also be cut with a rock saw to ensure a flat surface. These smooth surfaces can be difficult to achieve on an outcrop, which is why it is easier to collect specimens than measure the outcrop in the field. If the surface is not smooth, there is the possibility for non-linear signal attenuation due to the atmosphere absorbing the photon resulting from the interaction between the x-rays and the sample. This signal attenuation is more likely to affect lighter elements within the sample than heavier elements. Slabbed core is generally sufficiently smooth for analysis. Depending on the cuttings available, it may be preferable to press the cuttings samples into a pellet prior to analysis.
What are the ideal conditions of the samples used for geochemical analysis?
When using XRF for geochemical analysis, it is absolutely critical to have a clean sample. Barium (Ba) rich drilling mud is a common contaminant for well core and cuttings analysis. Due to the comparatively high number of electrons present within a Ba atom, there is a high likelihood that one of these electrons will absorb the incoming x-rays before the excited volume intersects the actual sample, resulting in an analysis of the drilling mud, rather than the sample.
In order to determine if a sample has been contaminated, it is a good idea to plot the chemostratigraphic profile for Ba. If there is drilling mud contamination, the Ba levels within a sample will commonly exceed 10,000ppm. Zones that exceed these levels may have potentially attenuated the XRF signal for lighter elements, including those of critical concern such as Si and Al.
A final general guideline for checking the validity of data set is to look through the total amount of different elements and evaluate how the measured values compare to the likely mineralogy within the samples. For example, in a pure sample of calcite (CaCO3) the Ca content is ~40wt%. This means that the measured Ca within a carbonate-rich interval should not exceed ~40wt%.
What are some recent overall trends?
Chemostratigraphic data sets are becoming an increasingly common tool for characterization and correlation of mudrocks. Using these data, it is possible to build robust stratigraphic frameworks for units that may be difficult to correlate using conventional means. Chemostratigraphy also enables workers to target the potential “sweet spots” within target zones.
Algeo, T.J. and Rowe, H. (2012) Paleoceanographic applications of trace-metal concentration data: Chemical Geology, 324-325, p.6-18
Banner, J.L. (1995) Application of the trace element and isotope geochemistry of strontium to studies of carbonate diagenesis: Sedimentology, 42, p.805-824
Bhatia, M.R. and Crook, K.A.W (1986) Trace element characteristics of graywackes and tectonic setting discrimination of sedimentary basins. Contributions to Mineralogy and Petrology, 92, p.181-193
Güler, C., Thyne, G.D., McCray, J.E., & Turner, A.K. (2002) Evaluation of graphical and multivariate statistical methods for classification of water chemistry data. Hydrogeology
Pearce, T.J., Besly, B.M., Wray, D.S., Wright, D.K. (1999) Chemostratigraphy: a method to improve interwell correlation in barren sequences – a case study using onshore Duckmantian/Stephanian sequences (West Midlands, U.K.): Sedimentary Geology, 124, p.197-220
Pearce, T.J. and Jarvis, I. (1992) Applications of geochemical data to modelling sediment dispersal patterns in distal turbidites: Late Quaternary of the Madeira Abyssal Plain: Journal of Sedimentary Petrology, 62, p.1112-1129
Rowe, H., Hughes, N., & 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, p.122-131.
Sageman, B. and T. Lyons (2004) Geochemistry of Fine-grained Sediments and Sedimentary Rocks in MacKenszie, F. ed. Sediments, Diagenesis, and Sedimentary Rocks, Treatise on Geochemistry, 7, p.115-158
Tribovillard, N., Algeo, T.J., Lyons, T., and Riboulleau A. (2006) Trace metals as paleoredox and paleoproductivity proxies: An update. Chem. Geo., 232, p.12-32
Turner, B.W., Molinares-Blanco, C.E., and Slatt, R.M. (2015) Chemostratigraphic, palynostratigraphic, and sequence stratigraphic analysis of the Woodford Shale, Wyche Farm Quarry, Pontotoc County, Oklahoma: Interpretation, 3, p.SH1-SH9
Turner, B.W., Tréanton, J.A., and Slatt, R.M. (2016) The use of chemostratigraphy to refine ambiguous sequence stratigraphic correlations in marine mudrocks. An example from the Woodford Shale, Oklahoma, USA: Journal of the Geological Society of London, In Press. First published online March 24, 2016, http://dx.doi.org/10.1144/jgs2015-125
OPPORTUNITY! We are looking to solicit presentations from industry as well as from surveys and academia. Abstract submission is open until July 12th. We've heard back from two of the sessions keynote speakers as well: Stan Paxton (USGS) has confirmed he will be giving a talk on a high quality exposure of the Woodford Shale that has only recently been described by geologists. I've also gotten confirmation that members of the British Geologic Survey will be giving a presentation on shale plays in the UK.
The session title is: T191-Mud, Mud, Glorious Mud: Advances in Stratigraphic, Sedimentologic, Geochemical, and Geomechanical Analyses of Fine-Grained Lithologies. Sponsored by SEPM, GSA Energy Geology Division, and GSA Sedimentary Geology Division.
Session Description: Recent advances in technology have enabled workers to efficiently and accurately analyze mudrock properties with increasingly fine-scale resolution. This session highlights new avenues of research that further demonstrate the inherent variability within so-called “homogenous” mudrocks.
Bryan W. Turner is a doctoral candidate and is working with the Institute of Reservoir Characterization, The University of Oklahoma