Geology topics

Ashton M. Wiens

Publications and source records attributed to Ashton M. Wiens.

14 recordsLinked to original sources

USGS2026

Neutron scattering reveals fractionation of natural gas mixtures in unconventional petroleum reservoir pores: Perspectives on energy resource recovery and storage

In unconventional petroleum reservoirs hydrocarbon fluids are hosted by both mineral and organic matter pores. These pores can have diameters that range from microns to less than a single nanometer and, for unconventional reservoirs, there is evidence that small pores ( <20 nm diameter) may constitute a large proportion of the available space. Understanding subsurface volumes and how fluids behave in them can be helpful for predicting hydrocarbon production and storage in the subsurface. One area with knowledge gaps regarding hydrocarbon behavior in small pores is the possibility for mixtures to fractionate (i.e., unmix) based on pore size or pore type. Mixture fractionation as a function of pore size could impact recovery of hydrocarbons, drive compositional shifts during production, and limit fluid storage within candidate reservoirs. To investigate natural gas fractionation in small geologic pores, we applied total neutron scattering to probe methane-ethane mixtures at reservoir pressures (up to ≈30 MPa) and temperature (60°C) within a sample from the Upper Cretaceous Niobrara Formation. Neutron scattering data reveal only minor fractionation occurs between methane and ethane in 20-nm diameter sample mesopores. Increased fractionation is observed for sample micropores, with up to 72% (±1% at 1-sigma) methane found in 2 nm diameter pores following injection of a 50%-50% methane-ethane mixture. These data provide rarely available direct experimental observations of hydrocarbon mixture behavior under nanoconfinement in a sample from an important unconventional petroleum reservoir. Our results are discussed in the context of evaluating hydrocarbon resources in unconventional reservoir meso- and micropores, reconciling observed gas composition changes during production, and more broadly, understanding subsurface pore volumes within an energy storage framework.

Fuel↗

USGS2026

Uncertainty quantification of geologic energy storage in depleted gas reservoirs using material balance equations embedded in a hierarchical errors-in-variables model

The storage potential for gas in geologic settings, such as depleted hydrocarbon reservoirs and solution-mined salt caverns, is becoming salient to future energy infrastructure planning. Technologies such as carbon capture, utilization, and storage, carbon dioxide-enhanced oil recovery, and natural gas and hydrogen storage help meet growing energy demands, reduce carbon emissions to meet climate goals, and provide energy security amid geopolitical uncertainties. Therefore, estimates of underground gas storage capacity could be useful for efficiently navigating the energy transitions. Material balance is a fundamental method in reservoir engineering for estimating original gas in place and potential storage capacity at the scale necessary for national assessments of subsurface pore space resources. However, the deterministic method cannot accommodate multiple data sources or quantify uncertainty in predictions. In this study, a method that embeds material balance equations within a hierarchical errors-in-variables model is proposed which allows the estimation of the distributions of reservoir properties needed for assessments. Uncertainties associated with these reservoir properties have traditionally been expert-elicited, whereas the uncertainty estimates from the proposed models are data-driven. Capacity and uncertainty estimates can be used in a probabilistic resource assessment, supplementing information traditionally used by assessors or even replacing this expert elicitation step when data are unavailable. Various regression models are compared in a case study of the Michigan Basin, a large contributor to the United States’ current natural gas storage capacity. In particular, errors-in-variables models help ameliorate regression dilution and can quantify uncertainty in predictions of pressure in addition to storage capacity. Overfitting is addressed by quantifying generalization error and model averaging in simple and stratified cross-validation against reported working gas capacity, representing the varying quality and quantity of available data. Incorporating a statistical framework into existing numerical methods in reservoir engineering can improve the quality of estimation, and in particular, this method brings rigor to uncertainty quantification as part of a larger effort by the U.S. Geological Survey to assess domestic energy gas storage resources in depleted hydrocarbon reservoirs.

Mathematical Geosciences↗

USGS2026

Quantitative mineral resource assessment of lithium pegmatite deposits in the southern Appalachian orogen

The first quantitative mineral resource assessment for undiscovered lithium pegmatite deposits in the southern Appalachian region of the United States was conducted. Permissive tracts for lithium pegmatite deposits were delineated by integrating lithological, tectonic, geochemical, geophysical and mineral occurrence data. Lithium pegmatite prospectivity of the tracts was ranked with simplified mappable criteria, including proximity to Paleozoic felsic intrusions and major lithotectonic structures, stream sediment geochemical anomalies, and pegmatite occurrence data. The geospatial data and permissive tracts were used to estimate the number of undiscovered lithium pegmatite deposits. These estimates were integrated into probabilistic simulations along with a new global lithium pegmatite grade and tonnage dataset to quantify potential contained undiscovered lithium resources. An economic filter was applied to convert the probabilistic estimates of contained lithium into recoverable material. The identified lithium pegmatite resources for the Carolina Lithium and Kings Mountain deposits, North Carolina, contain 1589 thousand tons (kt) of Li 2 O. The median contained undiscovered resource for the southern Appalachian orogen was estimated to be 2240 kt Li 2 O. At 90% confidence, the region contains at least 130 kt Li 2 O, and 10,700 kt at 10% confidence. After applying economic filters, the median recoverable contained resource was 1430 kt Li 2 O, corresponding to approximately 201 years of current lithium imports for consumption in the United States. North and South Carolina are likely to contain most of these resources. Coarse data resolution and intra-state variations in the geological data contribute to uncertainty of undiscovered lithium pegmatite resources. Continued efforts to harmonize disparate geospatial datasets with updated or new information can improve the accuracy and precision of estimated undiscovered lithium pegmatite resources in the study area and at broader scales.

Alabama, Georgia, Maryland, North Carolina, South ↗

USGS2026

Nonstationary demographic state-space models using unreplicated counts for species undergoing environmental stressors

A fundamental task in ecological statistics is to estimate abundance and growth rate distributions from wildlife monitoring data to inform conservation management. Modeling time series of wildlife populations presents a number of challenges from both statistical and ecological perspectives, including discreteness; lack of replication; nonstationarity; and observation, demographic, and other phenomenological processes. Nonstationary dynamics are often exhibited by populations undergoing environmental stressors. Models must account for these characteristics to produce reliable estimates of abundance and trends, yet estimation can be challenging with unreplicated data. We propose nonstationary demographic state-space models using unreplicated counts for populations undergoing environmental stressors. A reduced growth rate model matches the complexity of the unreplicated count data, and a fecundity bound on growth rate distributions allows the separation of processes affecting growth rates like environmental stressors from those affecting abundance external to growth rates like migration. NDSSMs allow for the embedding of nonstationary model components, and we explore the use of changepoints, volatility clustering, and migration processes. We apply the proposed nonstationary models in case studies of herons affected by predator/competitor reestablishment and three bat species affected by a fungal pathogen causing white-nose syndrome. Nonstationary models outperform stationary models and generalized linear mixed effects models according to model scoring and visual inspection of predictions, and provide estimates more consistent with published values. Incorporating migration improves model fit universally, even with approximate one-way immigration, most likely because populations are extirpated, recolonized, and increase multiple-fold over the upper bound set by species fecundity. In addition, estimates of the timing and severity of the environmental stressor differed for models with migration. Including nonstationary and demographic components in a fecundity-bounded growth rate model improves inference and benefits interpretability of hyperparameters. In turn, this adjusts uncertainties in predictions of abundance and growth rates over time, providing the ingredients needed for informed conservation analysis and for directing future monitoring of at-risk species.

Journal of Agricultural, Biological and Environmen↗

USGS2026

Hierarchical mixture models and high-resolution monitoring data can inform siting and operational strategies to mitigate bat fatalities at wind turbines

Bats provide critical ecosystem services, but bat fatalities due to wind energy development may imperil some bat populations. Statistical models are used to estimate the total fatalities that occur based on carcasses observed during monitoring surveys. Current models often estimate fatalities aggregated across species, time, and/or turbines, but fall short of reliably informing siting and operational collision mitigation strategies that account for species-specific fatality patterns on a fine spatiotemporal scale. We developed a hierarchical mixture model for estimating species-specific covariate effects and total fatalities per species at each turbine on weekly intervals. We applied the model to a high-resolution dataset of bat carcasses found during turbine searches across nineteen wind facilities in Iowa over two years. Our model explains species-specific variation in bat fatalities at individual wind turbines according to turbine proximity to bat habitat, turbine design specifications, seasonal trends, and weather conditions such as nightly air temperature, air pressure, and wind speed. Turbines located on the edge of wind facilities had higher fatalities, and proximity to roosting and foraging habitat accounted for variation in species-specific fatality estimates. These insights into turbine placement effects can inform siting strategies. We also discovered species-specific relationships with average nightly wind speed and air temperature, among other weather conditions, that could inform operational mitigation strategies such as smart curtailment. Our model can transform observations of carcasses found during turbine searches across multiple facilities, years, and variable search efforts into estimates of total fatalities per species associated with species-specific spatial, temporal, and environmental covariate effects.

Ecological Informatics↗

USGS2025

A probabilistic assessment methodology for the evaluation of geologic energy storage capacity—Natural gas storage in depleted hydrocarbon reservoirs

The need for energy storage, particularly underground, where capacity and duration may far exceed battery storage technologies, is especially relevant given the increasing demands for reliable power alongside the development of intermittent renewable electricity sources. Geologic energy storage facilities already exist, and expanded use would enable storing gases such as methane and hydrogen. In 2018, a National Academies of Sciences, Engineering, and Medicine report, “Future Directions for the U.S. Geological Survey's Energy Resources Program,” recommended that the U.S. Geological Survey (USGS) prioritize assessing underground energy storage in geologic formations in the United States. The U.S. Geological Survey has since developed a methodology for assessing natural gas storage capacities in depleted hydrocarbon reservoirs on a national scale. The methodology introduced in this report prescribes three approaches for calculating gas storage capacity. This methodology relies on the availability of input data, including cumulative hydrocarbon production records, reservoir petrophysical properties, and reservoir pressure data. Assessment inputs can be obtained from public, State-level databases and propriety national-scale databases, although the use of analogs could be warranted for estimating input parameters. Probabilistic assessment results are aggregated to play, petroleum province, regional, and national scales. The steps defined in this report are demonstrated on the Michigan Basin Province, which includes the Mississippian Sandstone Gas Play and the Clinton Structural Play. This methodology could be used to systematically and consistently assess hydrocarbon plays and provinces for natural gas storage capacity across the United States.

Michigan↗

USGS2025

Estimation of dynamic geologic CO2 storage resources in the Illinois Basin, including effects of brine extraction, anisotropy, and hydrogeologic heterogeneity

Since the vast majority of carbon dioxide (CO 2 ) storage resources in the United States are in deep saline aquifers, optimizing the use of these saline storage resources could be crucial for efficient development of geologic CO 2 storage (GCS) resources and basin- or larger-scale deployment of GCS in the country. Maximum CO 2 injection rates can be enhanced by extracting brine from the CO 2 storage unit. However, disposal of the extracted brine is both a technological and economic challenge. The lowest-cost option would likely be reinjection of the extracted brine into another formation above or below the CO 2 storage unit. Therefore, it is important to estimate brine injectivity as it will constrain the potential to increase CO 2 injectivity at an injection site that has access to multiple geologic storage units where either CO 2 or brine can be injected. Using a simulation-optimization framework, coupled with a non-isothermal, multiphase CO 2 -water-salt equation-of-state module, we developed a computationally efficient method for evaluating optimization of simultaneous CO 2 injection, brine extraction, and brine (re)injection at hypothetical injection sites deployed across a geologic basin. The Illinois basin is ideal for testing our methodology because it contains multiple geologic storage units with seals in between them to isolate injection of CO 2 in one unit from interfering with the injection of either brine or CO 2 in another unit above or below it. In addition, we investigated the relative effects of variation in key geologic parameters as well as two reservoir structures (hydrogeologic heterogeneity/anisotropy and homogeneity/isotropy) on CO 2 injectivities and enhancement of CO 2 injectivity through extracting brine. Results suggest that permeability, depth, and especially thickness of the storage unit could be the most influential parameters determining CO 2 injectivity. They also suggest that only injecting CO 2 into the storage unit with the greatest injectivity, enhancing that unit’s injectivity by extracting brine, and disposing of the produced brine in other suitable units could maximize total CO 2 injectivity in limited regions of the basin. At the majority of simulated injection sites, however, we found that injecting CO 2 into all of the accessible and suitable storage units was more likely to maximize the CO 2 storage resource.

Illinois, Kentucky, Indiana↗

USGS2025

Multi-scale predictors of Northern Long-eared Bat (Myotis septentrionalis) occupancy in the United States

Historically, Myotis septentrionalis (Northern Long eared Bat) was among the most common forest-interior species in North America. Largely due to high mortality from white-nose syndrome, this species has experienced severe population declines across its range. To create an updated species distribution map representing summer occupancy probabilities from 2017 to 2022, we integrated stationary acoustic data with live-capture data from the database of the North American Bat Monitoring Program into a multi-scale, multi-method occupancy modeling framework. Our results provide data-driven predictions with quantified uncertainty for summer occupancy probabilities for Northern Long-eared Bats at 2 spatial scales across the range of the species, while also accounting for inherent observation biases (e.g., imperfect detection).

Journal of North American Bat Research↗

USGS2025

Geothermal potential of orphan oil and gas wells

The United States is estimated to have hundreds of thousands of orphan oil and gas wells. Orphan wells are abandoned wells that are both unremediated and have no responsible operator. While traditionally considered environmental and economic liabilities, orphan oil and gas wells may offer new opportunities in sustainable geothermal energy development. This study evaluates the potential of repurposing orphan wells for geothermal energy production. We analyzed more than 1.4 million bottom-hole temperature (BHT) records from oil and gas well logs to create corrected temperature-depth profiles in a grid across the United States. Total depth values, where available, in documented orphan wells from a U.S. Geological Survey (USGS) database were then correlated to these temperature-depth profiles to estimate a corrected BHT for each orphan well. The orphan wells were then categorized as having low (<90°C), moderate (90–150°C), and high (>150°C) geothermal potential, identifying them as wells in the U.S. that could be used to access geothermal resources. In addition, repurposing these wells could contribute to broader environmental and economic goals, including well remediation, rogue methane emissions reduction, and energy production. This study provides a framework for integrating inactive well inventories with geothermal resource assessments and further highlights the potential for orphan wells to play a transformative role in expanding geothermal energy capacity in the United States.

contiguous United States↗

USGS2025

Waning greenhouse gas emissions from U.S. Federal lease coal production by the mid-21st century

This study presents estimates of future years (2024–2051) United States Federal lease coal production and the resulting greenhouse gas (GHG) emissions from the combustion, transport, and mining of that fuel. Results from the coal production estimate indicate a decline in production from Federal leases; with known production of 240 million short tons (mtn) in 2023 and a projected decline to 34.0 mtn by 2051, which represents a reduction to 14.2% of the 2023 value. In parallel with this projection, total GHG emissions are estimated to decrease from 402.2 million metric tons of carbon dioxide equivalent (MMT CO 2 eq.) in 2024 to 55.0 MMT CO 2 eq. in 2051, a decline to 13.7% of 2024 emissions estimates. The reductions in coal production and emissions are mainly the result of planned coal combustion power plant closures, with major projected closures in 2037 and 2048. However, GHG emissions estimates for future years can be uncertain as they rely heavily on coal production estimates from operators' public business plans and other publicly available resources. Forward looking plans of this type are subject to significant changes if economic and political factors deviate from current information. Results suggest that average GHG emissions over the time series breakout to 95% end point combustion, 3.7% transportation combustion emissions, and 1.3% fugitive emissions, although there is uncertainty associated with these figures. Uncertainty stemming from production projections, sector distributions, and emissions factors on the future emissions estimates increases with time, ranging from −28% to +48% within the 2024–2051 timeframe.

Alabama, Colorado, Montana, North Dakota, Utah, Wy↗

USGS2024

A methodology to estimate CO2 and energy gas storage resources in depleted conventional gas reservoirs

Depleted hydrocarbon reservoirs are subsurface geological structures capable of sequestering vast quantities of carbon dioxide (CO 2 ) as well as storing other energy gases for later usage, such as natural gas, and potentially hydrogen (H 2 ). Here we outline a methodology to quantify multi-gas storage resources in depleted conventional gas reservoirs for usage in assessments by the United States Geological Survey (USGS) at the scale of sedimentary basins. The methodology consists first of quantifying accessible pore volume in a depleted reservoir for natural gas storage using up to three equations. Input data are derived from commonly reported or estimated reservoir parameters and natural gas production volumes, and equations may be combined in linear models to improve pore volume estimates. Storage estimates from these equations are tested and validated for 31 reservoirs in the Michigan Basin Province, USA that were previously converted to underground gas storage facilities and have known (federally reported) natural gas storage capacities. Secondly, natural gas storage capacities can be transformed via fluid substitution calculations to estimate the storage resources for non-native fluids, applied here for, CO 2 , H 2 , and methane-H 2 blends, accounting for molecule-specific deviations from ideal gas behavior at reservoir pressures and temperatures as well as differing storage efficiencies. Importantly, the storage of non-native fluids may not be appropriate in all depleted gas reservoir settings due to potential risks like leakage, in particular in the case of H 2 storage, requiring additional knowledge of caprock sealing capacity. Given this caveat, we demonstrate the fluid substitution method for natural gas reservoirs of the Northern Niagaran Reef and Southern Niagaran Reef USGS plays in the Michigan Basin Province, as these trends of Silurian pinnacle reefs are capped with tight-sealing evaporite facies. The deterministic equations outlined from this methodology can be incorporated into future probabilistic USGS gas storage assessments for CO 2 , H 2 , and natural gas resources in the United States.

Michigan↗

USGS2024

New methodology for assessing underground natural gas storage resources – Example from Michigan Basin, United States

Energy consumption in the United States (U.S.) and across the world is shifting away from traditional fossil fuels like coal and oil, and towards natural gas and renewable sources, including hydrogen. Because gas demand is typically greatest during cold seasons and renewable sources sometimes produce variable supplies, it is important to store energy for use when demand exceeds supply. Whereas batteries and tanks typically store energy above ground, geologic (underground) storage may be able to retain much greater quantities of energy over much longer durations (e.g., Matos et al. 2019; Buursink et al. 2023). Consequently, the U.S. Geological Survey (USGS) is developing a methodology to locate new geologic energy storage sites with an initial focus on depleted hydrocarbon reservoirs followed by estimates of associated pore space or gas storage capacity. Depleted hydrocarbon fields in Michigan Basin in the U.S. already host large quantities of injected natural gas (about 681 billion cubic feet of seasonal gas capacity in 42 gas storage facilities; U.S. Energy Information Administration 2024). Therefore, we used this basin as an example to assess potential new storage resources alongside existing storage capacity and carbon dioxide sequestration resources (e.g., Katz and Coats 1968; Haagsma et al. 2020). In Michigan Basin gas storage is taking place in reservoirs within several key hydrocarbon producing formations with different lithologies, including the informal Stray sandstone of the Michigan Formation and the pinnacle reefs of the Niagara Group (Gautier et al. 1995; Swezey et al. 2015). The assessment methodology introduced here consists of two steps relying on both well information and reservoir data.

Michigan↗

USGS2023

BatTool: Projecting bat populations facing multiple stressors using a demographic model

Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface ( https://rconnect.usgs.gov/battool ) facilitating user-defined scenario analyses, and its intended uses and limitations (Wiens et al., US Geol Surv Data Release 2022; Wiens et al., US Geol Surv Softw Release 2022). We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.

Methods in Ecology and Evolution↗

USGS2022

Gaussian process forecasts Pseudogymnoascus destructans will cover coterminous United States by 2030

White-nose syndrome has been decimating populations of several bat species since its first occurrence in the Northeastern United States in the winter 2006–2007. The spread of the disease has been monitored across the continent through the collaboration of many organizations. Inferring the rate of spread of the disease and predicting its arrival at new locations is critical when assessing the current and predicting the future status and trends of bat species. We developed a model of disease spread that simultaneously achieves high-predictive performance, computational efficiency, and interpretability. We modeled white-nose syndrome spread using Gaussian process variations to infer the spread rate of the disease front, identify areas of anomalous time of arrival, and provide future forecasts of the expected time of arrival throughout North America. Cross-validation of model predictive performance identified a stationary Gaussian process without an additional residual error process as the best-supported model. Results indicated that white-nose syndrome is likely to spread throughout the entire continental United States by 2030. These annually updatable model predictions will be useful in determining the horizon over which disease management actions must take place as well as in status and trend assessments of disease-affected bats.

Ecology and Evolution↗