Geology topics

Matthias Kohler

Publications and source records attributed to Matthias Kohler.

3 recordsLinked to original sources

USGS2017

Environmental signatures and effects of an oil and gas wastewater spill in the Williston Basin, North Dakota

Wastewaters from oil and gas development pose largely unknown risks to environmental resources. In January 2015, 11.4 M L (million liters) of wastewater (300 g/L TDS) from oil production in the Williston Basin was reported to have leaked from a pipeline, spilling into Blacktail Creek, North Dakota. Geochemical and biological samples were collected in February and June 2015 to identify geochemical signatures of spilled wastewaters as well as biological responses along a 44-km river reach. February water samples had elevated chloride (1030 mg/L) and bromide (7.8 mg/L) downstream from the spill, compared to upstream levels (11 mg/L and < 0.4 mg/L, respectively). Lithium (0.25 mg/L), boron (1.75 mg/L) and strontium (7.1 mg/L) were present downstream at 5–10 times upstream concentrations. Light hydrocarbon measurements indicated a persistent thermogenic source of methane in the stream. Semi-volatile hydrocarbons indicative of oil were not detected in filtered samples but low levels, including tetramethylbenzenes and di-methylnaphthalenes, were detected in unfiltered water samples downstream from the spill. Labile sediment-bound barium and strontium concentrations (June 2015) were higher downstream from the Spill Site. Radium activities in sediment downstream from the Spill Site were up to 15 times the upstream activities and, combined with Sr isotope ratios, suggest contributions from the pipeline fluid and support the conclusion that elevated concentrations in Blacktail Creek water are from the leaking pipeline. Results from June 2015 demonstrate the persistence of wastewater effects in Blacktail Creek several months after remediation efforts started. Aquatic health effects were observed in June 2015; fish bioassays showed only 2.5% survival at 7.1 km downstream from the spill compared to 89% at the upstream reference site. Additional potential biological impacts were indicated by estrogenic inhibition in downstream waters. Our findings demonstrate that environmental signatures from wastewater spills are persistent and create the potential for long-term environmental health effects.

North Dakota↗

USGS2015

Upscaling of U (VI) desorption and transport from decimeter‐scale heterogeneity to plume‐scale modeling

Scientifically defensible predictions of field scale U(VI) transport in groundwater requires an understanding of key processes at multiple scales. These scales range from smaller than the sediment grain scale (less than 10 μm) to as large as the field scale which can extend over several kilometers. The key processes that need to be considered include both geochemical reactions in solution and at sediment surfaces as well as physical transport processes including advection, dispersion, and pore-scale diffusion. The research summarized in this report includes both experimental and modeling results in batch, column and tracer tests. The objectives of this research were to: (1) quantify the rates of U(VI) desorption from sediments acquired from a uranium contaminated aquifer in batch experiments;(2) quantify rates of U(VI) desorption in column experiments with variable chemical conditions, and(3) quantify nonreactive tracer and U(VI) transport in field tests.

Colorado↗

USGS2014

Assessment of parametric uncertainty for groundwater reactive transport modeling,

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM (zs) ) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM (ZS) -based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Water Resources Research↗