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Denitrifying bioreactors, consisting of water flow control structures and a woodchip-filled trench, are a promising approach for removing nitrate from agricultural subsurface or tile drainage systems. To better understand the seasonal dynamics and the ecological drivers of the microbial communities responsible for denitrification in these bioreactors, we employed microbial community "fingerprinting" techniques in a time-series examination of three denitrifying bioreactors over 2 years, looking at bacteria, fungi, and the denitrifier functional group responsible for the final step of complete denitrification. Our analysis revealed that microbial community composition responds to depth and seasonal variation in moisture content and inundation of the bioreactor media, as well as temperature. Using a geostatistical analysis approach, we observed recurring temporal patterns in bacterial and denitrifying bacterial community composition in these bioreactors, consistent with annual cycling. The fungal communities were more stable, having longer temporal autocorrelations, and did not show significant annual cycling. These results suggest a recurring seasonal cycle in the denitrifying bioreactor microbial community, likely due to seasonal variation in moisture content.

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Shale reservoirs have become one of the most promising natural gas resources in the energy industry. However the complex nature of shale, combined with limited production history, make predicting recovery very difficult in these reservoirs, especially early in the life of the well. The hyperbolic form of the Arps equation has been modified for use in estimating reserves and forecasting production in shale reservoirs. Although the ease and accessibility of this method make it convenient for most applications, the Arps equation has limitations (assumes boundary-dominated flow, and assumes that flowing bottomhole pressure, drainage area, permeability and skin factor are all constant). By analyzing production data using Rate Transient Analysis (RTA) theory, analytic models can be developed and used to forecast production and recovery in shale gas reservoirs. Because RTA-based models are not subject to many of the assumptions imposed in the development of the Arps equation, these models are much more versatile than decline curves derived using the Arps modified hyperbolic equation. In this paper, we present a practical workflow for performing RTA to determine the key performance parameters of horizontal shale wells with multiple fractures. This methodology provides a deterministic approach to estimate long term well performance in a shale reservoir. One important advantage over using the Arps equation is the ability to forecast production under different operating strategies. Using this workflow, the production and economic impacts resulting from different completion designs and operational scenarios (e.g., delayed installation of compression) can be studied. This methodology was implemented on data sets from over 150 Marcellus Shale wells. Forecasts determined using this workflow compare favorably to the forecasts estimated by decline curve analysis and reservoir simulation. Field examples from Marcellus wells are included in this paper to demonstrate the workflow and results.

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