Geology topics

E. Mehnert

Publications and source records attributed to E. Mehnert.

5 recordsLinked to original sources

USGS2009

Understanding CO 2 Plume Behavior and Basin-Scale Pressure Changes during Sequestration Projects through the use of Reservoir Fluid Modeling

Large scale geologic sequestration tests are in the planning stages around the world. The liability and safety issues of the migration of CO 2 away from the primary injection site and/or reservoir are of significant concerns for these sequestration tests. Reservoir models for simulating single or multi-phase fluid flow are used to understand the migration of CO 2 in the subsurface. These models can also help evaluate concerns related to brine migration and basin-scale pressure increases that occur due to the injection of additional fluid volumes into the subsurface. The current paper presents different modeling examples addressing these issues, ranging from simple geometric models to more complex reservoir fluid models with single-site and basin-scale applications. Simple geometric models assuming a homogeneous geologic reservoir and piston-like displacement have been used for understanding pressure changes and fluid migration around each CO 2 storage site. These geometric models are useful only as broad approximations because they do not account for the variation in porosity, permeability, asymmetry of the reservoir, and dip of the beds. In addition, these simple models are not capable of predicting the interference between different injection sites within the same reservoir. A more realistic model of CO 2 plume behavior can be produced using reservoir fluid models. Reservoir simulation of natural gas storage reservoirs in the Illinois Basin Cambrian-age Mt. Simon Sandstone suggest that reservoir heterogeneity will be an important factor for evaluating storage capacity. The Mt. Simon Sandstone is a thick sandstone that underlies many significant coal fired power plants (emitting at least 1 million tonnes per year) in the midwestern United States including the states of Illinois, Indiana, Kentucky, Michigan, and Ohio. The initial commercial sequestration sites are expected to inject 1 to 2 million tonnes of CO 2 per year. Depending on the geologic structure and permeability anisotropy, the CO 2 injected into the Mt. Simon are expected to migrate less than 3 km. After 30 years of continuous injection followed by 100 years of shut-in, the plume from a 1 million tonnes a year injection rate is expected to migrate 1.6 km for a 0 degree dip reservoir and over 3 km for a 5 degree dip reservoir. The region where reservoir pressure increases in response to CO 2 injection is typically much larger than the CO 2 plume. It can thus be anticipated that there will be basin wide interactions between different CO 2 injection sources if multiple, large volume sites are developed. This interaction will result in asymmetric plume migration that may be contrary to reservoir dip. A basin- scale simulation model is being developed to predict CO 2 plume migration, brine displacement, and pressure buildup for a possible future sequestration scenario featuring multiple CO 2 storage sites within the Illinois Basin Mt. Simon Sandstone. Interactions between different sites will be evaluated with respect to impacts on pressure and CO 2 plume migration patterns. ?? 2009 Elsevier Ltd. All rights reserved.

Conference Paper↗

USGS2009

Geochemical Modeling of Carbon Sequestration, MMV, and EOR in the Illinois Basin

The Illinois State Geologic Survey is conducting several ongoing CO 2 sequestration projects that require geochemical models to gain an understanding of the processes occurring in the subsurface. The ISGS has collected brine and freshwater samples associated with an enhanced oil recovery project in the Loudon oil field. Geochemical modeling allows us to understand reactions with carbonate and silicate minerals in the reservoir, and the effects they have had on brine composition. For the Illinois Basin Decatur project, geochemical models should allow predictions of the reactions that will take place before CO 2 injection begins. ?? 2009 Elsevier Ltd. All rights reserved.

Conference Paper↗

USGS2007

Denitrification in the shallow ground water of a tile-drained, agricultural watershed

Nonpoint-source pollution of surface water by N is considered a major cause of hypoxia. Because Corn Belt watersheds have been identified as major sources of N in the Mississippi River basin, the fate and transport of N from midwestern agricultural watersheds have received considerable interest. The fate and transport of N in the shallow ground water of these watersheds still needs additional research. Our purpose was to estimate denitrification in the shallow ground water of a tile-drained, Corn Belt watershed with fine-grained soils. Over a 3-yr period, N was monitored in the surface and ground water of an agricultural watershed in central Illinois. A significant amount of N was transported past the tile drains and into shallow ground water. The ground water nitrate was isotopically heavier than tile drain nitrate, which can be explained by denitrification in the subsurface. Denitrifying bacteria were found at depths to 10 m throughout the watershed. Laboratory and push-pull tests showed that a significant fraction of nitrate could be denitrified rapidly. We estimated that the N denitrified in shallow ground water was equivalent to 0.3 to 6.4% of the applied N or 9 to 27% of N exported via surface water. These estimates varied by water year and peaked in a year of normal precipitation after 2 yr of below average precipitation. Three years of monitoring data indicate that shallow ground water in watersheds with fine-grained soils may be a significant N sink compared with N exported via surface water. ?? ASA, CSSA, SSSA.

Journal of Environmental Quality↗

USGS2006

Application of artificial neural networks to assess pesticide contamination in shallow groundwater

In this study, a feed-forward back-propagation neural network (BPNN) was developed and applied to predict pesticide concentrations in groundwater monitoring wells. Pesticide concentration data are challenging to analyze because they tend to be highly censored. Input data to the neural network included the categorical indices of depth to aquifer material, pesticide leaching class, aquifer sensitivity to pesticide contamination, time (month) of sample collection, well depth, depth to water from land surface, and additional travel distance in the saturated zone (i.e., distance from land surface to midpoint of well screen). The output of the neural network was the total pesticide concentration detected in the well. The model prediction results produced good agreements with observed data in terms of correlation coefficient (R = 0.87) and pesticide detection efficiency (E = 89%), as well as good match between the observed and predicted "class" groups. The relative importance of input parameters to pesticide occurrence in groundwater was examined in terms of R, E, mean error (ME), root mean square error (RMSE), and pesticide occurrence "class" groups by eliminating some key input parameters to the model. Well depth and time of sample collection were the most sensitive input parameters for predicting the pesticide contamination potential of a well. This infers that wells tapping shallow aquifers are more vulnerable to pesticide contamination than those wells tapping deeper aquifers. Pesticide occurrences during post-application months (June through October) were found to be 2.5 to 3 times higher than pesticide occurrences during other months (November through April). The BPNN was used to rank the input parameters with highest potential to contaminate groundwater, including two original and five ancillary parameters. The two original parameters are depth to aquifer material and pesticide leaching class. When these two parameters were the only input parameters for the BPNN, they were not able to predict contamination potential. However, when they were used with other parameters, the predictive performance efficiency of the BPNN in terms of R, E, ME, RMSE, and pesticide occurrence "class" groups increased. Ancillary data include data collected during the study such as well depth and time of sample collection. The BPNN indicated that the ancillary data had more predictive power than the original data. The BPNN results will help researchers identify parameters to improve maps of aquifer sensitivity to pesticide contamination. ?? 2006 Elsevier B.V. All rights reserved.

Science of the Total Environment↗

USGS2005

Estimating Transmissivity from the Water Level Fluctuations of a Sinusoidally Forced Well

The water levels in wells are known to fluctuate in response to earth tides and changes in atmospheric pressure. These water level fluctuations can be analyzed to estimate transmissivity (T). A new method to estimate transmissivity, which assumes that the atmospheric pressure varies in a sinusoidal fashion, is presented. Data analysis for this simplified method involves using a set of type curves and estimating the ratio of the amplitudes of the well response over the atmospheric pressure. Type curves for this new method were generated based on a model for ground water flow between the well and aquifer developed by Cooper et al. (1965). Data analysis with this method confirmed these published results: (1) the amplitude ratio is a function of transmissivity, the well radius, and the frequency of the sinusoidal oscillation; and (2) the amplitude ratio is a weak function of storativity. Compared to other methods, the developed method involves simpler, more intuitive data analysis and allows shorter data sets to be analyzed. The effect of noise on estimating the amplitude ratio was evaluated and found to be more significant at lower T. For aquifers with low T, noise was shown to mask the water level fluctuations induced by atmospheric pressure changes. In addition, reducing the length of the data series did not affect the estimate of T, but the variance of the estimate was higher for the shorter series of noisy data.

Groundwater↗