Economic viability of shale gas production in the Marcellus Shale; indicated by production rates, costs and current natural gas prices

The U.S. natural gas industry has changed because of the recent ability to produce natural gas from unconventional shale deposits. One of the largest and most important deposits is the Marcellus Shale. Hydraulic fracturing and horizontal drilling have allowed for the technical feasibility of production, but concerns exist regarding the economics of shale gas production. These concerns are related to limited production and economic data for shale gas wells, declines in the rates of production, falling natural gas prices, oversupply issues coupled with slow growth in U.S. natural gas demand, and rising production costs. An attempt to determine profitability was done through the economic analysis of an average shale gas well using data that is representative of natural gas production from 2009 to 2011 in the Marcellus Shale. Despite the adverse conditions facing the shale gas industry it is concluded from the results of this analysis that a shale gas well in the Marcellus Shale is profitable based on NPV, IRR and breakeven price calculations.

Applications of LiDAR remote sensing of forest structure in the Upper Great Lakes region, USA

We used active remote sensing technology to characterize forest structure in a northern temperate forest on a landscape- and local-level in the Upper Peninsula of Michigan. Specifically, we used a form of active remote sensing called light detection and ranging (e.g., LiDAR) to aid in the depiction of current forest structural stages and total canopy gap area estimation. On a landscape-level, LiDAR data are shown not only to be a useful tool in characterizing forest structure, in both coniferous and deciduous forest cover types, but also as an effective basis for data-driven surrogates for classification of forest structure. On a local-level, LiDAR data are shown to be a benchmark reference point to evaluate field-based canopy gap area estimations, due to the highly accurate nature of such remotely sensed data. The application of LiDAR remote sensed data can help facilitate current and future sustainable forest management.