GSA Connects 2022 meeting in Denver, Colorado

Paper No. 168-1
Presentation Time: 9:00 AM-1:00 PM


GOLDBERG, Karin, Geology, Kansas State University, 108 Thompson Hall, Manhattan, KS 66506 and DA ROSA, Lucas Goldberg, Economics, Kansas State University, Manhattan, KS 66506

The complex controls on the accumulation of organic-rich rocks remain elusive, despite their economic importance as source rocks and unconventional reservoirs, partially due to the multitude of factors that may impact production and preservation of organic matter. Chemical indices that serve as proxies for primary controls such as detrital input, primary productivity, and degree of oxygenation in bottom waters have been used to infer environmental parameters, but their interpretation is complicated by the fact that some indices respond to more than one factor. For example, an increase in Mo concentration may result from increased productivity and/or bottom-water anoxia. The ability to distinguish between possible scenarios is critical to establishing more accurately what controls organic concentration in mudrocks.

The complexity of Earth systems is comparable to the intricacies of Economics. Hence, application of statistical and econometrics methods and models to analyze geological data (particularly environmental proxies) may shed light on the processes controlling accumulation of organic matter. This approach was tested in mudrock datasets, with the aim of discriminating different depositional conditions and establishing the controls on total organic carbon (TOC) in the sediments.

Chemical indices (Ti/Al, Si/Al, Ni/Al, Cu/Al, Fe/Al, Mo/Al, U/Al, P/Al) were used as proxies for detrital input, primary productivity, redox conditions, and upwelling. To test the method sensitivity, we ran cluster analysis on modern environments with known redox conditions (anoxic, hypoxic, oxic). Discrimination and classification analysis show the indices predict redox conditions correctly. Cluster analysis on data for the Woodford Formation produced three distinct clusters, independent of TOC. We also ran linear regression models to assess the relative interdependence of parameters, e.g. detrital input vs productivity or redox. Model results show not only that individual chemical indices (proxies) can reliably predict TOC but also which indices correlate with one another, providing better constraints on environmental conditions that control accumulation of organic matter in mudrocks. The next step will be to test the approach on a larger database.