Alteration of Fractured Foamed Cement Exposed to CO2-Saturated Water: Implications for Well Integrity.

Geologic CO2 storage (GCS) is a method to mitigate the adverse impact of global climate change. Potential leakage of CO2 from fractured cement at the wellbore poses a risk to the feasibility of GCS. Foamed cement is widely applied in deepwater wells where fragile geologic formations cannot support the weight of conventional cement. Thus, it is critical to know whether fractures in foamed cement self-seal in a similar manner as conventional cement systems. This study is the first to investigate the changes in physical and chemical attributes of foamed cement under dynamic flow conditions using CO2-saturated water. Self-sealing of fractures in the cement was observed at a solution flow rate of 0.1 mL/min and a pressure of 6.9 MPa. The formation of CaCO3 precipitates in pore spaces and fractures led to a decrease in permeability by 1 order of magnitude. The extents of self-sealing in foamed cement samples, specifically the 20 and 30% air volume formulations, were similar to that of conventional cements. We attribute this to the greater alteration depth in the foamed cement, which compensated for the reduced availability of Portlandite and higher initial porosity. The results can be used to evaluate the risk of leakage associated with foamed cement.

A database and framework for carbon ore resources and associated supply chain data.

The Carbon Ore Resources Database (CORD) is a working collection of 399 data files associated with carbon ore resources in the United States. The collection includes spatial/non-spatial, filtered, processed, and secondary data files with original data acquisition efforts focused on domestic coal resources. All data were acquired via open-source, online sources from a combination of 18 national, state, and university entities. Datasets are categorized to represent aspects of carbon ore resources, to include: Geochemistry, Geology, Infrastructure, and Samples. Geospatial datasets are summarized and analyzed by record and dataset density or the number of records or datasets per 400 square kilometer grid cells. Additionally, the "CORD Platform," an ArcGIS Online geospatial dashboard web application, enables users to interact and query with CORD datasets. The CORD provides a single database and location for data-driven analytical needs associated with the utilization of carbon ore resources.

Geographic, seasonal, and precipitation chemistry influence on the abundance and activity of biological ice nucleators in rain and snow.

Biological ice nucleators (IN) function as catalysts for freezing at relatively warm temperatures (warmer than -10 degrees C). We examined the concentration (per volume of liquid) and nature of IN in precipitation collected from Montana and Louisiana, the Alps and Pyrenees (France), Ross Island (Antarctica), and Yukon (Canada). The temperature of detectable ice-nucleating activity for more than half of the samples was > or = -5 degrees C based on immersion freezing testing. Digestion of the samples with lysozyme (i.e., to hydrolyze bacterial cell walls) led to reductions in the frequency of freezing (0-100%); heat treatment greatly reduced (95% average) or completely eliminated ice nucleation at the measured conditions in every sample. These behaviors were consistent with the activity being bacterial and/or proteinaceous in origin. Statistical analysis revealed seasonal similarities between warm-temperature ice-nucleating activities in snow samples collected over 7 months in Montana. Multiple regression was used to construct models with biogeochemical data [major ions, total organic carbon (TOC), particle, and cell concentration] that were accurate in predicting the concentration of microbial cells and biological IN in precipitation based on the concentration of TOC, Ca(2+), and NH(4)(+), or TOC, cells, Ca(2+), NH(4)(+), K(+), PO(4)(3-), SO(4)(2-), Cl(-), and HCO(3)(-). Our results indicate that biological IN are ubiquitous in precipitation and that for some geographic locations the activity and concentration of these particles is related to the season and precipitation chemistry. Thus, our research suggests that biological IN are widespread in the atmosphere and may affect meteorological processes that lead to precipitation.

Characterizing microbial diversity and the potential for metabolic function at -15 °c in the Basal ice of taylor glacier, antarctica.

Measurement of gases entrapped in clean ice from basal portions of the Taylor Glacier, Antarctica, revealed that CO2 ranged from 229 to 328 ppmv and O2 was near 20% of the gas volume. In contrast, vertically adjacent sections of the sediment laden basal ice contained much higher concentrations of CO2 (60,000 to 325,000 ppmv), whereas O2 represented 4 to 18% of the total gas volume. The deviation in gas composition from atmospheric values occurred concurrently with increased microbial cell concentrations in the basal ice profile, suggesting that in situ microbial processes (i.e., aerobic respiration) may have altered the entrapped gas composition. Molecular characterization of 16S rRNA genes amplified from samples of the basal ice indicated a low diversity of bacteria, and most of the sequences characterized (87%) were affiliated with the phylum, Firmicutes. The most abundant phylotypes in libraries from ice horizons with elevated CO2 and depleted O2 concentrations were related to the genus Paenisporosarcina, and 28 isolates from this genus were obtained by enrichment culturing. Metabolic experiments with Paenisporosarcina sp. TG14 revealed its capacity to conduct macromolecular synthesis when frozen in water derived from melted basal ice samples and incubated at -15 °C. The results support the hypothesis that the basal ice of glaciers and ice sheets are cryospheric habitats harboring bacteria with the physiological capacity to remain metabolically active and biogeochemically cycle elements within the subglacial environment.

In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study.

BACKGROUND: Three methods were developed for the application of stoichiometry-based network analysis approaches including elementary mode analysis to the study of mass and energy flows in microbial communities. Each has distinct advantages and disadvantages suitable for analyzing systems with different degrees of complexity and a priori knowledge. These approaches were tested and compared using data from the thermophilic, phototrophic mat communities from Octopus and Mushroom Springs in Yellowstone National Park (USA). The models were based on three distinct microbial guilds: oxygenic phototrophs, filamentous anoxygenic phototrophs, and sulfate-reducing bacteria. Two phases, day and night, were modeled to account for differences in the sources of mass and energy and the routes available for their exchange. RESULTS: The in silico models were used to explore fundamental questions in ecology including the prediction of and explanation for measured relative abundances of primary producers in the mat, theoretical tradeoffs between overall productivity and the generation of toxic by-products, and the relative robustness of various guild interactions. CONCLUSION: The three modeling approaches represent a flexible toolbox for creating cellular metabolic networks to study microbial communities on scales ranging from cells to ecosystems. A comparison of the three methods highlights considerations for selecting the one most appropriate for a given microbial system. For instance, communities represented only by metagenomic data can be modeled using the pooled method which analyzes a community's total metabolic potential without attempting to partition enzymes to different organisms. Systems with extensive a priori information on microbial guilds can be represented using the compartmentalized technique, employing distinct control volumes to separate guild-appropriate enzymes and metabolites. If the complexity of a compartmentalized network creates an unacceptable computational burden, the nested analysis approach permits greater scalability at the cost of more user intervention through multiple rounds of pathway analysis.