Environmental Impacts of Replacing Slickwater with Low/No-Water Fracturing Fluids for Shale Gas Recovery

The environmental impacts of a typical hydraulic fracturing operation for shale gas recovery were evaluated using life cycle assessment, with energy demands for well drilling and fracturing determined from GHGfrack model. Dominant environmental impacts stem from well construction, which are >63% in all categories (e.g., global warming and eutrophication), and mainly due to diesel fuel combustion and steel production. The relative impacts related to water use (i.e., fracturing fluid components, water/wastewater transportation, and wastewater disposal) are relatively small, ranging from 5 to 22% of total impacts in all categories; freshwater consumption for fracturing is also a small fraction of available water resources for the shale play considered. The impacts of replacing slickwater with CO₂ or CH₄-foam fracturing fluid (≤10 vol % water) were evaluated; total impacts decrease <12%, and relative impacts related to water use decrease to 2–9% of total impacts. Hence, switching to a foam-based fracturing fluid can substantially decrease water-related impacts (>60%) but has only marginal effects on total environmental impacts. Changes in lateral well length, produced to fresh-water ratios, fracturing fluid composition, and LCA control volume do not change these findings. More benefits could potentially be realized by considering water versus foam-related impacts of ecological health and energy production

Network Efficiency.

<p>Local and global efficiency of pediatric brain networks of (a) 2-weeks-olds, (b) 1-year-olds and (c) 2-year-olds. All networks exhibit small-world nature, which is characterized by local efficiency greater than comparable random networks, and global efficiency greater than regular lattices <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Latora1" target="_blank">[5]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Latora2" target="_blank">[6]</a>. There is a general trend of efficiency increase with age. The neonatal brain network shows significantly lower efficiency compared to the other two age groups.</p

Average Lengths of Connection Fibers (mm).

<p>Average Lengths of Connection Fibers (mm).</p

Node Degree Distributions.

<p>Single-scale, scale-free and broad-scale <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-Amaral1" target="_blank">[39]</a> are characterized by Gaussian/exponential decay, power law decay, and truncated power law decay, respectively. The node degree distributions give good indication that the pediatric brain networks are broad-scale in nature. In the double logarithmic plots, the degree distribution decays linearly before a sharp cutoff. The gradient magnitudes of the fitted lines are 3.921, 2.784 and 2.764 for (a), (b) and (c), respectively.</p

Regions of Interest Based on the Automated Anatomical Labeling (AAL) Template.

<p>Regions of Interest Based on the Automated Anatomical Labeling (AAL) Template.</p

Betweenness Centrality and Vulnerability.

<p>Removal of a node with high betweenness generally results in a significant disruption of information flow in the brain network as indicated by a higher vulnerability value. The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by the total number of possible connections .</p

Inter-Hemispheric Correlation of Node Betweenness.

<p>Each circle gives the left and right betweenness value for each node. Each age group shows a rightward assymetry - indicated by the slope values 0.2843, 0.6202, and 0.4738, respectively (1 indicates perfect symmetry). The dashed lines indicate 95% confidence interval. The betweenness centrality value is normalized by division by .</p

The proposed framework for segmentation of serial infant brain MR images.

<p>The proposed framework for segmentation of serial infant brain MR images.</p

The average Dice ratios of different methods on 28 subjects.

<p>The proposed method achieves significant (p0.01) higher Dice ratio than other methods.</p

Obtaining the Connectivity Matrix.

<p>A schematic digram illustrating the major processes involved in generating the final connectivity maps. Streamline fiber tractography was performed on each diffusion tensor image and a connectivity matrix was computed based on the AAL <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0024678#pone.0024678-TzourioMazoyer1" target="_blank">[23]</a> ROIs. The fiber count matrices were constructed by enumerating the number of fibers connecting each region pair. The connectivity matrix, indicating consistent connections, was generated by thresholding the fiber count statistics.</p