1) A geospatial model of the Barnett Shale region was created using GIS software to analyze groundwater quality data and determine if variations are associated with hydraulically fractured gas wells. 2) The study found that elevated concentrations of certain groundwater constituents, including beryllium, are likely related to natural gas production and beryllium could be used as an indicator of fracturing impacts. 3) Results also indicated that gas well density and formation pressures correlate to changes in regional water quality, whereas proximity to gas wells alone does not, providing indirect evidence that micro annular fissures may transport fluids from fractured wells to groundwater.

1. Elucidating hydraulic fracturing impacts on groundwater quality using a
regional geospatial statistical modeling approach
Taylour G. Burton a
, Hanadi S. Rifai b,
⁎, Zacariah L. Hildenbrand c,d
, Doug D. Carlton Jr d,e
,
Brian E. Fontenot d
, Kevin A. Schug d,e
a
Civil and Environmental Engineering, University of Houston, W455 Engineering Bldg. 2, Houston, TX 77204-4003, United States
b
Civil and Environmental Engineering, University of Houston, N138 Engineering Bldg. 1, Houston, TX 77204-4003, United States
c
Inform Environmental, LLC, Dallas, TX 75206, United States
d
Collaborative Laboratories for Environmental Analysis and Remediation, University of Texas at Arlington, Arlington, TX 76019, United States
e
Department of Chemistry & Biochemistry, The University of Texas at Arlington, Arlington, TX, United States
H I G H L I G H T S
• Migration pathways from fractured
wells to groundwater are poorly under-
stood
• Geospatial modeling correlated ground-
water chemicals to Barnett fractured
wells
• Increased Beryllium strongly associated
with hydraulically fractured gas wells
• Indirect evidence of pollutant migration
via microannular fissures in well casing
• Large-scale and spatial approach needed
to detect groundwater quality changes
G R A P H I C A L A B S T R A C T
A relative increase in beryllium concentrations in groundwater for the Barnett Shale region from 2001 to 2011
was visually correlated with the locations of gas wells in the region that have been hydraulically fractured over
the same time period.
a b s t r a c ta r t i c l e i n f o
Article history:
Received 25 October 2015
Received in revised form 17 December 2015
Accepted 18 December 2015
Available online xxxx
Editor: D. Barcelo
Hydraulic fracturing operations have been viewed as the cause of certain environmental issues including ground-
water contamination. The potential for hydraulic fracturing to induce contaminant pathways in groundwater is
not well understood since gas wells are completed while isolating the water table and the gas-bearing reservoirs
lay thousands of feet below the water table. Recent studies have attributed ground water contamination to poor
well construction and leaks in the wellbore annulus due to ruptured wellbore casings. In this paper, a geospatial
model of the Barnett Shale region was created using ArcGIS. The model was used for spatial analysis of ground-
water quality data in order to determine if regional variations in groundwater quality, as indicated by various
groundwater constituent concentrations, may be associated with the presence of hydraulically fractured gas
wells in the region. The Barnett Shale reservoir pressure, completions data, and fracture treatment data were
evaluated as predictors of groundwater quality change. Results indicated that elevated concentrations of certain
Keywords:
Barnett Shale
Natural gas
Science of the Total Environment 545–546 (2016) 114–126
⁎ Corresponding author.
E-mail addresses: tgburton@uh.edu (T.G. Burton), rifai@uh.edu (H.S. Rifai), zac@informenv.com (Z.L. Hildenbrand), doug.carlton@mavs.uta.edu (D.D. Carlton), brian.fonteno@mavs.
uta.edu (B.E. Fontenot), kschug@uta.edu (K.A. Schug).
http://dx.doi.org/10.1016/j.scitotenv.2015.12.084
0048-9697/© 2015 Elsevier B.V. All rights reserved.
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

2. groundwater constituents are likely related to natural gas production in the study area and that beryllium, in this
formation, could be used as an indicator variable for evaluating fracturing impacts on regional groundwater qual-
ity. Results also indicated that gas well density and formation pressures correlate to change in regional water
quality whereas proximity to gas wells, by itself, does not. The results also provided indirect evidence supporting
the possibility that micro annular fissures serve as a pathway transporting fluids and chemicals from the frac-
tured wellbore to the overlying groundwater aquifers.
© 2015 Elsevier B.V. All rights reserved.
Bottom-hole pressure
Geographic Information Systems (GIS)
Cluster analysis
Micro annular defects
1. Introduction
Hydraulic fracturing is a technology used in oil and gas production to
increase hydrocarbon recovery from low-permeability formations. Dur-
ing a hydraulic fracturing operation, fluid is injected into an oil and gas
well at high pressures—a process that fractures the rock of the hydrocar-
bon bearing formation thereby increasing its hydraulic conductivity and
the rate of flow of oil and gas from the formation to the wellbore. Hy-
draulic fracturing techniques, developed as early as 1949, have signifi-
cantly improved since the 1980s such that they currently allow
production from low-permeability shale formations that have histori-
cally been considered a non-producible resource (Murray, 2013). The
first hydraulic fracturing treatment in a horizontal wellbore was per-
formed in 1992 in the Barnett Shale. However, the combined advances
in horizontal drilling and hydraulic fracturing (in addition to other
novel technologies such as use of high-volume of fracturing fluids,
clustered-multi-well pads, and long laterals) have propelled fracturing
of horizontal wells to become an industry standard practice in the de-
velopment of low-permeability shale formations (Smith and Hannah,
1996).
Hydraulic fracturing use has increased significantly since the mid-
2000s and has become a subject of controversy concerning potential
risks to human health and the environment (Finkel and Hays, 2013;
Rahm, 2011; Walton and Woocay, 2013; McKenzie et al., 2012;
Preston et al., 2014; Ziemkiewicz et al., 2014; Eaton, 2013; Meng,
2015; Révész et al., 2012; and Werner et al., 2015). Research is needed
to address health and safety issues in the development of oil and gas re-
sources including the cumulative impacts of tightly spaced wells that
are more difficult to quantify (Vidic et al., 2013).
A heightened interest in the impact of hydraulic fracturing on
groundwater exists since this subject is not as well understood despite
the fact that several studies have been undertaken. Well casing failures,
contaminant migration through fractures, surface spills, and/or waste-
water disposal are all potential pathways that could lead to groundwa-
ter contamination. A risk-model by Rozell and Reaven (2012) proposed
that disposal of wastewater had the highest risk for contaminating
ground water while other studies demonstrated that contamination
may be from the subsurface. Methane concentrations in groundwater
(primarily in the Marcellus Shale), for example, were evaluated in
some studies as an indicator of potential communication between
water aquifers and gas wells; the distance to gas well operations was
shown to be a statistically significant variable for methane concentra-
tions in ground water samples by Osborn et al. (2011) and Jackson
et al. (2013).
The study of methane concentrations alone, however, may not be a
straightforward indication that groundwater contamination has oc-
curred, particularly since other research studies have demonstrated
that methane concentrations and chemical properties were correlated
to the geophysical environment and topography (Molofsky et al.,
2011; Warner et al., 2012; Molofsky et al., 2013), and to the distance
to natural faults (Moritz et al., 2015). A study by Fontenot et al. (2013)
evaluated heavy metal concentrations in groundwater as indicators of
groundwater contamination and presented statistically significant
higher median concentrations of heavy metals in water quality
samples taken in proximity to natural gas extraction activities in the
Barnett Shale region in Texas (the region studied in this work). The
aforementioned studies suggested that some impact to groundwater
from hydraulic fracturing operations could be observed; however, it is
unclear whether the migration of methane gas coincided with the mi-
gration of other groundwater contaminants.
The toxic elements found in hydraulic fracturing wastewater
streams should be considered in the study of hydraulic fracturing im-
pacts on groundwater. These elements have the potential to contact
the groundwater system and may serve as indicator variables for exam-
ining changes in groundwater quality related to hydraulic fracturing op-
erations. Wastewater from hydraulic fracturing operations contains
toxic elements originating in the shale and from chemicals used in the
fracturing treatment including total dissolved solids, volatile sub-
stances, bromide, naturally occurring radioactive materials, and heavy
metals such as arsenic, barium, beryllium, uranium, and zinc (Gordalla
et al., 2013; Ternes, 2012; Rahm et al., 2013; Lester et al., 2015; and
Chermak and Schreiber, 2014). Harkness et al. (2015) attributed the
high bromide and chloride content in the wastewater to the brine
from the shale reservoir; Rowan et al. (2011) demonstrated that high
salinity mobilizes radionuclides, increasing exposure to radioactive
waste such as radium 226.
Even less well understood than the impacts of fracturing on ground
water quality are the potential pathways for pollutant migration to
groundwater from the shale formations. The study presented in this
paper addresses this knowledge gap and investigates gas migration as
a transportation mechanism of contaminants into groundwater. Recent
studies have concluded that ground water contamination is due to poor
well construction (Jackson et al., 2013) and that leaks in the wellbore
annulus are due to ruptured wellbore casings (Darrah et al., 2014). A
study presented by Ingraffea et al. (2014) developed a risk assessment
model for casing and cement impairment for oil and gas wells in Penn-
sylvania concluding that unconventional wellbores are at a greater risk
for impairment than conventional wellbores, and periods of intense
drilling have resulted in lowered wellbore integrity. Another study indi-
cated that wellbore integrity failure rates vary significantly based on
geographical region and noted that more wellbore monitoring would
be required to better understand failure rates (Davies et al., 2014).
In this paper, the research presented differs from the aforemen-
tioned studies where groundwater contamination was attributed to a
noticeable failure in the wellbore systems. The research presented in
this work investigates how minor defects in the wellbore system,
which are far more common than a major defect, may still be significant
to cause widespread impacts of fracturing operations on groundwater.
Gas can permeate through small cracks in the annular cement sheath
(see Section 4). The working hypothesis is that the expansion of natural
gas, released from the producing formation during the hydraulic frac-
turing process, is the mobilizing mechanism that allows chemicals in a
gas–fluid mixture to make contact with the water table above the
shale formation (in the case of wellbores with a defect in the annular ce-
ment sheath). Because the formation pressure in a well will drive the
gas velocity and volume of gas generated, the reservoir pressure is ex-
amined as a predictor variable for elevated concentrations of indicator
constituents in groundwater. It is presumed that a larger volume of
gas flow will contribute to a greater accumulation of contaminants in
the aquifer system.
Additionally, the expansion of gas hypothesis presented above dic-
tates that groundwater quality changes, when present, will only be
115T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

3. evidenced via a regional analysis that takes into account the relatively
significant number of hydraulically fractured wells in a given produc-
ing region, and the natural variability in ground water constituents
(complicated by the paucity of groundwater quality data). The re-
gional analysis has to also take into account the differing geologic
strata, the separation in depth between the groundwater formations,
and the varying formation pressure over the region. This approach
is novel and has not been applied to a shale producing formation/
groundwater aquifer system as of yet to the best of the knowledge
of the authors.
The present study focuses on the Barnett Shale region in Texas from
2001 (pre-fracturing) to 2011 (post- peak fracturing period) mainly due
to the availability of historical ground water quality data and more re-
cent, albeit relatively limited, detailed ground water quality sampling
studies (Fontenot et al., 2013; Thacker et al., 2015 and Hildenbrand
et al., 2015). The study relies on geospatial modeling using Geographic
Information Systems (GIS) tools to correlate the changes in groundwa-
ter quality to fractured well characteristics such as bottom hole pressure
and distance from the fractured well. The study illustrates regional
trends and correlations and demonstrates the possibility of using con-
stituents uniquely associated with fracturing activities as indicator var-
iables of regional ground water quality changes. The results from the
study provide indirect evidence supporting the hypothesis that
wellbore construction is a key potential pathway for contaminant trans-
port to groundwater.
2. Study area — the Barnett Shale
The Barnett Shale, located in the Fort Worth Basin of northeast
Texas, is predominately a gas-bearing hydrocarbon formation with an
above-normal pressure gradient of 0.52 psi/ft (Bowker, 2007) that re-
quires fracturing to extract the gas. The Barnett Shale was chosen for
four reasons: (1) there were relatively extensive water quality data
sets for the region available from the Texas Water Development Board
(TWDB) (TWDB, 2014), and the University of Texas Arlington (UTA)
(Fontenot et al., 2013; Hildenbrand et al., 2015); (2) the production his-
tory in the Barnett presented an opportunity for comparing water qual-
ity samples taken in the year 2000 and prior, to samples taken between
2011 and 2014; (3) the Barnett is predominately a gas bearing forma-
tion; and (4) wells in the Barnett Shale require hydraulic fracturing for
production; in this sense gas migration could be attributed to the hy-
draulic fracturing process where gas flow would not occur without the
well having been fractured. The study presented here evaluated the
Barnett Shale wells, both vertical and horizontal since nearly all have
been fractured.
Horizontal drilling has been very active in the Barnett Shale since
2002 with lateral lengths varying between 500 and 3500 ft
(Montgomery et al., 2006). Gas well data for the region were obtained
from the Texas Railroad Commission (RRC, 2014), fracfocus.org, and
drillinginfo.com. Water and gas well data were collected for the Bosque,
Clay, Collin, Cooke, Dallas, Denton, Ellis, Erath, Grayson, Hamilton, Hill,
Hood, Hunt, Jack, Johnson, Kaufman, Montague, Palo Pinto, Parker,
Rockwall, Somervell, Tarrant, and Wise counties in north Texas (see
Fig. S1 in Supporting information). There were over 30,000 gas wells
in the study area with more than 18,000 of them concentrated in Den-
ton, Tarrant, Parker, and Wise counties as can be seen in Fig. S1. Fig. S1
also shows that the deepest part of the Barnett is to the northeast in
Denton County.
3. The Trinity aquifer
The major water aquifer for the region is the Trinity, a group of four
sandstone layers with varying lateral extents (Harden, 2004). The Trin-
ity was modeled as a single continuous sandstone layer that mainly em-
bodies the Paluxy sandstone formation, the uppermost layer in the
region. Vertical water well depth references from the TWDB were
used to create a contour of this sandstone layer in ArcGIS (see Fig. 1,
black dots indicate water well sample locations). As shown in Fig. 1,
the depth of the aquifer ranges from approximately 30 ft (~10 m) to
more than 4000 ft (~1219 m) below the surface, thus placing the Paluxy
sand between 2500 and 7500 ft (~762 to 2286 m) above the Barnett
Shale, with a greater thickness between the formations in the northeast.
The hydraulic conductivity for the Paluxy is 5.8 ft/day (1.77 m/day)
(Harden, 2004) with an average gradient of 0.009 ft/ft (m/m) over the
contoured layer shown in Fig. 1. The travel distance over the 10-year pe-
riod of the study was estimated to be on the order of 1000 ft (~305 m)
using Darcy's Law and the aforementioned estimates of gradient and
hydraulic conductivity for the Paluxy. This distance was taken into con-
sideration throughout the study as will be seen subsequently in the
paper.
4. Contamination via micro-annular defects in a wellbore
In order to demonstrate the potential for the wellbore to serve as a
contaminant migration pathway, an analysis was undertaken to quan-
tify the flow velocity in a micro-annular crack or fissure, based on a
width of defect between 10 and 100 μm. The velocity was calculated
using an equation previously presented in a study of CO2 storage wells
(Deremble et al., 2010):
vf ¼ −
w2
12cf
=
dP
dS
þ ρg cos αð Þ
ð1Þ
where vf is the mean fluid velocity, w is the width of the defect, dP
dS
is the
change in reservoir pressure over a vertical well distance, µf is the fluid
density, g is the gravitational constant, ρ is the density of the groundwa-
ter, and α is the angle of the wellbore.
Using a dP
dS
value of −0.54 psi/ft, (based on the reservoir pressure of
the study area), and a µf value of 0.0113 cP for a gas mixture of 85%
methane and 15% CO2, the mean fluid velocity was determined to be be-
tween 81.4 and 8137 ft/day (~24.8 and 2480 m/day) for the 10 and
100 μm defect widths, respectively. Clearly, this flow velocity is signifi-
cant, indicating that even in a cemented wellbore, natural gas could
make contact with the water table on a scale of 1–10 days in an
8000 ft (~2438 m) deep vertical well.
In a hydraulically fractured well, a fluid mixture of water and natural
gas would flow through such micro-annular pathways. The fluid mix-
ture has the potential to entrain contaminants existing naturally in the
methane gas and formation brine, as well as chemicals from the fracture
treatment. While the extent to which these contaminants are soluble in
the fluid mixture is not taken into consideration in the study, it is rea-
sonable to assume that some change in groundwater quality over the
region may occur given the significantly large number of gas wells
that would be present in the region. It also follows that such a change
in groundwater constituent concentrations may be possible to observe
using reservoir pressure gradients and the locations of gas wells in the
study region as predictor variables. It should be noted that the flow ve-
locity calculated above would be even greater in a scenario of greater
change in reservoir pressure or when more wells are drilled in a given
region.
5. Research approach and methodology
The research approach used qualitative and quantitative
methods to study ground water quality changes over the relatively
large Barnett Shale region. The research methodology relied on sta-
tistical testing and GIS geospatial and correlation analyses as will be
seen in the remainder of the section. Two types of analyses were
conducted: in the first set of analyses, visual and statistical analyses
were undertaken to determine if there were correlations between
the ground water quality data, distance from hydraulic fracturing
116 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

4. wells, fracturing well density, and reservoir pressure gradients. In
the second set of analyses, the gas wells were clustered based on
10 specific properties related to gas well construction. The clusters
were then correlated to constituent concentrations in order to de-
termine if there were correlations between specific well construc-
tion variables and observed constituent concentrations post
hydraulic fracturing.
5.1. Reservoir pressure gradient in the Barnett
A model of the reservoir pressure gradient (RPG) in the Barnett was
developed using flowing bottom hole pressure (BHP) data for over 2046
gas wells from G-10 completion records stored at the RRC in Austin, TX
(only pressure values taken prior to production were used in this anal-
ysis since the purpose of the analysis was to evaluate the magnitude of
the RPG prior to production). The BHP values estimate the excess pres-
sure in the rock (in excess of normal hydrostatic) that is due to the con-
version of oil to gas over time (gas is compressed when trapped in the
reservoir rock; as gas is generated, it will expand, Barker, 1990). Corre-
sponding True Vertical Depths (TVD) of well locations with a recorded
BHP were used to calculate the reservoir pressure gradient at each
well location using the equation:
Reservoir Pressure Gradient
psi
ft
¼
Flowing BHP psið Þ þ 0:433
psi
ft
 Vertical Shale Depth ftð Þ
Vertical Shale Depth ftð Þ
: ð2Þ
The RPG values at their corresponding locations were contoured in
ArcGIS resulting in the plot shown in Fig. 2. As can be seen in Fig. 2, res-
ervoir pressure gradients ranged from 0.45 to 0.90 psi/ft (trending up-
wards in a northeasterly direction towards Denton County). The
average gradient in the dataset was 0.53 psi/ft, which corresponded
well with literature values (see for example, Bowker, 2007).
5.2. Water quality data
The water quality data used in the study, combining samples from
the TWDB and UTA and containing data for 31 groundwater constitu-
ents, are listed in Table 1. The samples used in the study had a depth
Fig. 1. Location of water wells in the Paluxy overlain with Contoured Aquifer Depth.
117T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

5. (of well sample) reference that corresponded to the aquifer depth from
the contour plot shown in Fig. 1 at that water sample location.
Scatter plots of concentration data for all sampled ground water
wells as a function of time and spatial plots of the concentration data
were prepared and visually inspected for each constituent. Based on a
visual inspection of the resulting graphs/plots (cannot all be shown
due to space limitations), it became evident that: (i) the data were rel-
atively very sparse for many of the constituents (e.g., dissolved radium
226 concentrations shown in Fig. S2); (ii) the data for all constituents
exhibited significant variability over time and spatially (e.g., dissolved
iron concentrations shown in Fig. S3 (top shows over time and bottom
shows spatial distribution); and (iii) patterns or trends were not dis-
cernible for many constituents from the data (e.g., iron data shown in
Fig. S3 show no discernible trends whereas those in Fig. S4 for barium
and beryllium show a marked increase in the range of observed concen-
tration ranges in recent years). These findings confirmed that it would
be difficult to determine the associations, if any, between change in
ground water quality over time and hydraulic fracturing activities with-
out incorporating a spatial analysis of the water samples and their prox-
imity to hydraulic fracturing operations. Changes in the context of
specific variables from hydraulic fracturing activities, became the guid-
ing principle for the methodology and approaches used in the study as
described below.
5.3. Visual analysis of regional groundwater quality constituent change
Qualitative visual analyses were undertaken to evaluate trends or
changes, if any, in the ground water quality data over time and space.
Contour plots were created in ArcGIS for 20 of the constituents that
had both historical (pre-2001) and current (2011–2014) water quality
data. For each constituent, two contour plots were created, one using
water samples taken before the year 2001, and one using samples
taken during 2011–2014 (non-detect values were replaced with a
zero for contouring purposes). In ArcGIS, the Radial Basis Function
(RBF), generally used in groundwater modeling (Kresic, 2006, p. 78)
when water quality data sets are small, was used. Using this contour
function, some of the plots created a negative value contour. The Inverse
Distance Weighting (IDW) method was used in such cases to avoid the
negative values generated by the RBF.
Fig. 2. Contour plot of Barnett Shale pressure gradient (RPG) in psi/ft.
118 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

6. A spatial extraction and visualization methodology was developed
to evaluate the spatial changes in groundwater quality between the
two contour plots and correlate the observed changes to fracturing ac-
tivities. A grid of 19,594 data points, covering an area of 15,048 mi2
was draped over the contour plots described above. The contour values
at the grid data points were extracted from the pre-2001 and post-2011
plots using the Extract Multi-Values to Points function in ArcGIS. In the
instances where both a historical and current value was extracted, the
difference was taken between the two values and then reimported
into the ArcGIS model. The IDW contour function was applied to the dif-
ference values to create a continuous contour plot of constituent con-
centration change. While this method involves a certain amount of
smoothing and spatial interpolation, it is a reasonably valid approach
when comparing spatially variable datasets at two points in time, as is
the case here. It should be noted that the spatial extent of the contour
plots that were generated does not cover the entire study area of inter-
est. Additionally, the contour plots for each constituent and year were
not the same spatial extent (since this depended on sample availability).
The resulting plots of groundwater change varied in extent and shape
and while they were used to qualitatively model large scale trends in
groundwater quality changes over the region, it must be kept in mind
that inaccuracies may exist within the contour due to data availability
limitations.
The locations of the gas wells in the region were plotted on the con-
stituent contour plots to determine if there was a visual correlation that
can be observed between the change in concentration plot and the loca-
tion of the gas wells. In a second visual analysis, the RPG contour (Fig. 2)
was plotted over the constituent concentration change to evaluate the
hypothesis that a higher RPG would correlate to a more observable
change in groundwater constituent concentrations.
5.4. Proximity to gas wells nonparametric statistical Mann–Whitney U-Test
The Mann–Whitney U-Test, a nonparametric comparison test, was
used to determine if there was a significant statistical difference in
groundwater quality measurements between water samples taken
near gas wells, and those that were not. The Mann–Whitney U-Test is
typically used to compare the distribution of two data sets that are ran-
domly sampled, independent, and that could be ranked, where sample
sets are not necessarily equal in size/and or not normally distributed
(Paulson, 2003). In this study, the P-value of significance was for a
two-tailed test with a 0.95 confidence interval. Results yielding a P-
value less than 0.05 were considered statistically significant.
The water samples were divided into two groups: (1) those with no
gas wells existing within 1 mile (control group), and (2) water samples
having at least one gas well within a 1000 ft distance (test group, recall
that the average travel distance within the Paluxy was estimated to be
1000 ft in the 10 years of fracturing activities) (see Fig. 3). The Matlab
software was used to calculate distances between water and gas wells,
where the locations of water wells were calculated relative to the loca-
tions of gas wells using the haversine function (the haversine function
gives the shortest distance over the earth's surface between two points
on a sphere based on their longitude and latitude).
Many of the samples in the ground water data were non-detect (ND)
values. The nonparametric Mann–Whitney test was thus performed for
each constituent three times: (1) in the first test, the ND values were set
to zero, (2) in the second test, the ND values were set to 1/2 the detec-
tion limit (as defined by the measuring instrument), and (3) in the third
test, the ND values were excluded. Non-detect values were not differen-
tiated within the TWDB samples, yielding similar results for dissolved
alpha (also known as gross alpha particle activity — a measure of the
total amount of radioactivity in a water sample attributable to the radio-
active decay of alpha-emitting elements), dissolved aluminum, dis-
solved boron, dissolved molybdenum, dissolved radium 226, dissolved
radium 228, and dissolved vanadium for the three methods dealing
with the ND values described above.
5.5. Gas well density as a predictor variable of regional GW constituent
change
The purpose of this analysis was to analyze the impact of the density
of gas wells in a given area on groundwater quality. Contour plots were
created for the groundwater samples taken between 2011 and 2014 for
31 constituents. The data extracted from the contour plots were used in
the analysis as were the data extracted from plots of reservoir pressure
gradients (RPG) shown in Fig. 2. The first step in the analysis was to cre-
ate a raster file denoting the relative gas well density in the study region.
This was done by converting the shapefile of gas well locations to a ras-
ter dataset using the Point to Raster tool in the Arc Toolbox (see Fig. 4).
The raster displays a grid of rectangular cells, each cell being 0.01
squared degrees representing an area of 0.04 mile2
(note that the cell
boundaries 0.2 miles are greater in length than the maximum 1000 ft
distance of groundwater transport calculated in Section 3 above). The
count of gas wells within each pixel was color coded in the raster
(shown in Fig. 4), thus red indicates a higher count of gas wells within
the pixel than the yellow does, for example.
The second step was to generate a grid of evenly spaced data points
for further analysis. The grid of data points was created such that the
data point locations were in the centroid of each raster cell created in
step 1 above. The values of the local reservoir pressure gradient, well
count, and the water constituent concentrations were extracted at
each data point using the Extract Multi-Value Points function. The
data points were categorized by well density using two categories: a
high well density category and a zero density category. Cells considered
to have a high density had a well count of 18–54 per cell, and zero den-
sity had no wells. The high well density category was further separated
by pressure gradient into categories of 0.4–0.49 psi/ft, 0.5–0.59 psi/ft,
Table 1
Constituents evaluated in the Barnett region.
Groundwater constituent Source Sample years Number of samples
Dissolved alpha (pc/L) TWDB 1976–2011 380
Dissolved aluminum (ppb) TWDB 1939–2011 786
Total arsenic (ppb) TWDB/UTA 1949–2014 570
Total barium (ppb) TWDB/UTA 1985–2014 542
Total benzene (mg/L) UTA 2012–2014 379
Total beryllium (ppb) TWDB/UTA 1994–2014 466
Total boron (ppb) TWDB 1948–2011 756
Total bromide (mg/L) UTA 2012–2014 379
Dissolved bromide (mg/L) TWDB 1988–2011 738
Total chloride (mg/L) UTA 2012–2014 379
Total copper (ppb) TWDB/UTA 1980–2014 503
Dissolved oxygen (mg/L) TWDB/UTA 1983–2014 464
Total ethanol (mg/L) UTA 2011–2014 454
Total ethyl benzene (mg/L) UTA 2012–2014 379
Total iron (ppb) TWDB/UTA 1923–2014 1899
Total methanol (mg/L) UTA 2011–2014 454
Total molybdenum (ppb) UTA 2012–2014 379
Dissolved molybdenum (ppb) TWDB 1989–2011 756
Total nickel (ppb) TWDB/UTA 1994–2014 461
Total nitrate (mg/L) TWDB/UTA 1975–2014 540
pH UTA 2011–2014 452
Dissolved phosphorus (mg/L) TWDB 1952–2011 316
Dissolved radium 226 (pc/L) TWDB 1977–2011 150
Dissolved radium 228 (pc/L) TWDB 1988–2011 150
Redox potential (mV) TWDB/UTA 1990–2014 721
Total selenium (ppb) TWDB/UTA 1977–2014 559
Total sulfate (mg/L) UTA 2012–2014 379
Water temperature ( C) TWDB/UTA 1963–2014 1167
Total dissolved solids (mg/L) UTA 2011–2014 452
Dissolved vanadium (ppb) TWDB 1989–2011 734
Total zinc (ppb) TWDB/UTA 1980–2014 514
Notes:
1. The constituents were based upon data availability and relatedness to hydraulic
fracturing.
2. A total of 20 of the 31 constituents had historical (pre-2001) and current (2011–2014)
sample data.
119T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

7. 0.6–0.69 psi/ft, 0.7–0.79 psi/ft, and 0.80–0.89 psi/ft. The aforementioned
categorization procedures resulted in a total of six subgroups of data as
shown in Table 2. As can be seen in Table 2, subgroup zero with 4161
cells had no wells in any of those cells; whereas subgroups 1 through
6 had 18–54 wells per cell with an increasing RPG as the subgroup
number increased from 1 to 6. As might be expected, the number
of cells exhibiting a large number of wells was much smaller than
4161 and ranged from a minimum of 9 cells for subgroup 5 (RPG be-
tween 0.7–0.79 psi/ft) to 68 cells for Subgroup 3 (RPG between 0.5–
0.59 psi/ft). Subgroup 6 with the largest RPG range of 0.8–0.89 psi/ft
had 11 cells.
5.5.1. Comparison of control group (Subgroup 1) to test group (Subgroup 6)
In this test, a difference in data distribution between Subgroup 1 and
Subgroup 6 was evaluated using the Mann–Whitney U test. Subgroup 6
was considered to be the group with the highest risk areas in the model
due to a high density of gas wells and a high reservoir pressure gradient,
whereas Subgroup 1 represents the lowest risk areas where no gas wells
were present. The data in Subgroup 1 and Subgroup 6 for each of the 31
constituents were plotted as Boxplots in Minitab for distribution com-
parison (plots not shown).
5.5.2. Inter-subgroup correlations
In this analysis, correlations between constituent concentrations
within the data points in each subgroup were found considering the hy-
pothesis that strong correlations between constituent concentrations
would be related to elevated constituent levels and would indicate a re-
lationship between gas well fracturing and contaminant migration.
Matlab was used to find correlations between the constituent levels in
Subgroups 1–6. The R-squared value was obtained from a linear regres-
sion model for each constituent pair within each data Subgroup. The
Matlab code was run 6 times; for each run, a 31 × 31 output matrix
was created, a row and column for each constituent — the cell
intersected by each row and column contains the R-squared value for
the two constituent variables. For each output matrix, the results were
divided into R-squared values less than 0.5 (weaker correlations) and
R-squared values greater than 0.5 (stronger correlations) since R-
squared varies between 0 and 1.
5.6. Cluster analysis
A spatial clustering analysis was undertaken in ArcGIS for 2049 gas
wells in order to explore the relationships between variables associated
Fig. 3. Locations of control group and test group groundwater samples (the black dots indicate the location of gas wells).
120 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

8. with well completions/hydraulic fracturing and change in groundwater
constituent concentrations. The cluster analysis methodology presented
here developed spatial clusters of wellbore completions/fracturing data
with values that were similar in magnitude. The analysis used the Local
Moran's I method built-in within the ArcGIS Cluster Analysis tool that
returns the Local Moran's I index, z-score, p-value, and cluster/outlier
type. I is the spatial statistic of spatial association (the function identifies
where high or low values cluster spatially, and features with values that
are very different from surrounding feature values). The z-scores and p-
values are measures of statistical significance that indicate whether the
apparent similarity or dissimilarity is more pronounced than one would
expect in a random distribution. The analysis returns an output of clus-
ter locations denoted by HH, LL, and Not Significant rankings of the clus-
tered variable (where HH denotes statistically significant (0.05 level)
cluster of high values and LL for a statistically significant (0.05 level)
cluster of low values).
A total of ten well completions/hydraulic fracturing properties were
analyzed as shown below. The studied variables were all related to the
potential for a well to rupture during hydraulic fracturing:
1–2) Surface casing/bottom hole casing: The size of the casing is impor-
tant to the wellbore system, where pressure ratings decrease with
smaller pipe diameter, increasing the risk for wellbore failure;
3) Injected fluid volume: A greater volume of injected fluid would
expose the wellbore system to high pressures for a longer dura-
tion, potentially weakening the integrity of the well. Additionally,
larger volumes of pumped fluid indicate that the hydraulic frac-
tures may be larger with a potential to release a greater amount
of gas;
4) Injected weight of sand: Injecting more sand could cause erosion
of the perforations and form a microannular pathway. A larger
sand volume indicates a larger-scale fracture treatment and
greater potential for erosion;
5) Total vertical depth: A deeper gas well has increased distance be-
tween the shale and water aquifer thereby potentially decreasing
the risk of groundwater contamination;
6) Volume of N2: Nitrogen gas is usually injected into shallower
wells and could act as a mobilizer of contaminants as it flows
freely back to the surface;
7) Length of lateral: A longer lateral section in the wellbore could in-
crease the risk of an insufficient cement barrier due to the poten-
tial settling of cement in the horizontal wellbore. The lateral
length was found by subtracting the Total Vertical Depth of the
Wellbore from the Measured Depth of the entire wellbore. Note
that some wells were vertical, resulting in a lateral length of
zero. This cluster analysis effectively made a comparison between
vertical and horizontal wells, where LL clusters were vertical wells
with a lateral length of zero, and HH clusters designated the lon-
gest horizontal section;
8) Aquifer-perforation thickness: Perforated wellbore sections above
the Total Vertical Well Depth increase the risk of wellbore rupture.
Fig. 4. Gas well density raster map. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
Table 2
Data subgroup descriptions.
Subgroup
number
Wells per 0.4
mi2
Reservoir pressure
gradient
(psi/ft)
Number of data
points
1 0 – 4161
2 18–54 0.43–0.49 35
3 18–54 0.50–0.59 68
4 18–54 0.60–0.69 22
5 18–54 0.70–0.79 9
121T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

9. Thus, a lower value of the thickness between the perforations and
the water aquifer indicates an increased risk of groundwater
contamination;
9) Wells existing in 1 mile: The number of gas wells within a 1 mile
distance from the gas well of interest was evaluated to assess the
cumulative effects of tightly spaced hydraulic fracturing wells; the
hypothesis being that a greater number of gas wells may increase
the possibility of a contaminant pathway to groundwater from a
gas well even if the pathway has not been specified; and
10) Bottom hole pressure/reservoir pressure gradient: A high reser-
voir pressure would increase the flow rate and volume of natural
gas from the reservoir, possibly increasing the amount of contam-
inants mobilized.
The aforementioned wellbore completion parameters were pri-
marily sourced from the Texas Railroad Commission G-1 comple-
tion forms which were stored on the Texas Railroad Commission
online servers; forms filed prior to 2010 were stored on a separate
server (http://www.rrc.state.tx.us/about-us/resource-center/
research/online-research-queries/imaged-records-menu/) than
those filed after 2010 (http://webapps.rrc.state.tx.us/CMPL/
publicHomeAction.do). Some wells were missing data related to
fluid and sand volumes; the data were searched for in Fracfocus.
org. At each well location, the depth of the Paluxy and the Bottom
Hole Pressure (BHP) were extracted from the ArcGIS model. In the
analysis, BHP was analyzed instead of the RPG since there is not
enough variability in the data values of RPG for a meaningful clus-
ter analysis in ArcGIS.
Beryllium concentrations were extracted from ArcGIS at the
resulting cluster locations and were evaluated to determine if el-
evated concentrations were associated with specific clusters of
the wellbore variables 1 through 10 discussed above. Beryllium
was used as an indicator variable in this analysis based upon the
results from the gas well density correlations (as will be seen in
the Results and discussion section of the paper), where this con-
stituent was found to exhibit a relationship to hydraulic fracturing
in all analyses. The HH and LL clusters for each of the wellbore var-
iables 1 through 10 were compared using the Mann–Whitney U
Test to the corresponding beryllium concentrations at the cluster
locations. Thus, if a statistically significant difference in beryllium
concentration was detected between locations of the HH and LL
clusters, then this would indicate that the specific wellbore design
parameter is important and indicative of a potential contaminant
pathway in the wellbore system.
6. Results and discussion
6.1. Visual analyses of groundwater quality constituent change
A total of 40 plots for the 20 groundwater constituents considered in
the analysis were evaluated and the change in each constituent was
compared to the spatial distribution of gas wells and variations in the
reservoir pressure gradient. A qualitative assessment of the plots dem-
onstrated that a correlation between the changes in groundwater con-
stituent concentrations, gas well locations, and the reservoir pressure
gradient could not be clearly deduced for most of the plots, with the ex-
ception of total beryllium. Fig. 5 illustrates a visual correlation between
increased total beryllium concentrations and the location of gas wells.
As can be seen in Fig. 5, the areas with the greatest positive change in
total beryllium, denoted by red, are associated with the presence of
gas wells while the area of the plot showing a decrease in total beryl-
lium, denoted in green, is not. While qualitative in nature, and
representing trends in groundwater quality over a region with inherent
inaccuracies within the plot due to data availability limitations, the
trends in Fig. 5 were considered significant particularly since similar
trends were not apparent in the majority of the other plots.
The visual correlation between the reservoir pressure gradient and
the change in total beryllium shown in Fig. 6 is not strong, however, it
should be noted that the reddish pressure contours overlay areas of
red shading only indicating that the highest changes in total beryllium
concentration correlate well with the areas of highest RPGs. While
some of the green RPG contours overlap areas of red total beryllium
shading, the green contours tend to emanate from the yellow-green
areas of the plot.
The aforementioned finding when taken in conjunction with the fact
that the majority of the other constituents were not well correlated to
well density and/or RPGs led to the conclusion that beryllium deserves
consideration as an indicator in gas well production, and the potential
impact from fracturing on ground water quality. Beryllium also deserves
consideration as a potential indicator variable for wellbore integrity is-
sues in hydraulic fracturing operations. Since beryllium is almost
never found at detectable concentrations in ground water aquifers, its
presence at relatively elevated levels can be construed to indicate mi-
gration through microannular defects in the wellbore. The average
transport distance of 1000 ft (~305 m) in 10 years does not provide
an alternate explanation in this case because of the level of observed be-
ryllium concentrations as will be seen later in the paper.
6.2. Proximity to gas well nonparametric statistical Mann–Whitney U-Test
The results of Mann–Whitney U-Tests do not strongly indicate that
proximity to gas wells was associated with degraded water quality
(see Table S1 in the Supporting information). The results from Test 1
(non-detects as zeros) and 2 (non-detects as 1/2 the detection limit) in-
dicated a statistically significant difference between the control group
and test group for arsenic (P = 0.04), chloride (P = 0.01), dissolved ox-
ygen (P = 0.03), selenium (P = 0.0), water temperature (P = 0.0), and
total dissolved solids (P = 0.0). The median of the test group samples
(the ones expected to be affected by proximity to gas wells), however,
was lower than the control group, with the exception of selenium
(P = 0.0) and dissolved oxygen (P = 0.03) that were higher. These re-
sults emphasize the need to address groundwater quality change at a
regional scale taking into account the density of gas wells, their depth
and the pressure gradient.
In Test 3, where the non-detect values were omitted, the results
demonstrated a statistically significant difference in median concentra-
tion between arsenic (P = 0.04), beryllium (P = 0.03), chloride (P =
0.01), dissolved oxygen (P = 0.02), water temperature (P = 0.0), and
total dissolved solids (P = 0.0). Outside of dissolved oxygen, the median
of the test group was less for all constituents except for beryllium,
where the median concentration was higher. This provided further evi-
dence that beryllium concentrations are related to gas well production
in the Barnett.
6.3. Gas well density nonparametric Mann–Whitney statistical test
The maximum, minimum, median, and mean values for each con-
stituent for the six data subgroups described in Table 2 are shown in
Table S2 in Supporting information (recall that Subgroup 1 represents
a zero-well density and Subgroup 6 represents a high well density
with high pressure gradient). The values in the table exceeding the
EPA Primary Water Quality drinking standard are highlighted in yellow
(note that the Primary drinking water quality standards are not avail-
able for all constituents studied).
As can be seen in Table S2, the mean, median, and maximum values
of beryllium exceed the EPA threshold in Subgroup 6, which is not the
case for any of the other constituents or Subgroups. The maximum con-
centration values detected for arsenic, benzene, and beryllium exceed
the enforceable standard and are present in Subgroup 1. A boxplot of
the beryllium concentrations for Subgroup 1 and Subgroup 6 was cre-
ated in Minitab and is shown in Fig. 7. As can be seen in Fig. 7, the me-
dian beryllium concentration is elevated in Subgroup 6. This finding
122 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

10. supports the use of beryllium as an indicator variable for evaluating hy-
draulic fracturing impacts on ground water quality within a region.
6.3.1. Comparison of control group (Subgroup 1) and test group (Subgroup
6)
The Mann–Whitney U-Test demonstrated a statistically significant
difference between the median concentrations for most of the constitu-
ents. Total arsenic (P = 0.04), total beryllium (P = 0.0), dissolved bro-
mide (P = 0.02), total copper (P = 0.0), total ethanol (P = 0.04), total
methanol (P = 0.0), dissolved radium 228 (P = 0.0), and water temper-
ature (P = 0.0) have a significant greater median concentration in Sub-
group 6 than in Subgroup 1. Dissolved aluminum (P = 0.04), total
bromide (P = 0.01), total chloride (P = 0.0), dissolved oxygen (P =
0.02), total iron (P = 0.0), total selenium (P = 0.01), total sulfate
(P = 0.0), total dissolved solids (P = 0.0), and dissolved vanadium
(P = 0.0) had a significant lower median concentration in Subgroup 1.
The median concentration of nitrate (P = 0.03) in both groups was 0.
The results of the Mann–Whitney test for all constituents are shown
in Table S3 in the Supporting information.
As can be seen in Table S3, nine of the constituents tested
(highlighted in gray) have a relationship to hydraulic fracturing activity
where the mean of Subgroup 6 is greater than Subgroup 1, except for
dissolved oxygen which showed a lower concentration as would be ex-
pected (due to higher groundwater temperatures). The increased arse-
nic in the data may be expected since arsenic is present in natural gas as
trimethylarsine and processing plants are equipped to remove it
(Kidnay et al., 2006). Bromide from the shale reservoir may be dissolved
in water particles produced with the natural gas where it is contacting
the water table as it travels through the micro-annulus, explaining the
increase in the presence of dissolved bromide. Interestingly, there is
no statistically significant difference in total bromide concentrations be-
tween the Subgroups. The increased copper may be associated with the
hydraulic fracturing chemicals or shale rock properties and increased
ethanol is likely related to the hydraulic fracturing chemicals. The in-
crease in radium 228, and beryllium (a radionuclide) may be attributed
to the produced gas as shale formations have naturally occurring radio-
active materials.
6.3.2. Inter-subgroup correlations
The analysis demonstrated a strong correlation between constituent
concentrations in the data subgroups associated with high reservoir
pressure gradient and high density of gas wells. The number of constit-
uent correlations with a value greater than 0.5 was significantly higher
in the Subgroups associated with a high reservoir pressure and high
Fig. 5. Change in total beryllium and location of gas wells in the Barnett. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)
123T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

11. well density than in other subgroups. Subgroup 5 and Subgroup 6 had
the greatest correlation between constituents, whereas the control
group, Subgroup 1, had almost no correlations. A summary of the results
is shown in Table 3.
A general trend was seen whereby an increase in reservoir pressure
was accompanied by an increase in the correlation between constituent
variables. Despite the low number of data points in Subgroups 5–6,
there were high correlations between various constituents yielding a
strong indication that high well density and reservoir pressure are pre-
dictor variables for groundwater quality changes in the Barnett. In Sub-
groups 5–6, the concentration of Beryllium was strongly correlated to
the concentration of the other constituents. Since beryllium concentra-
tion was demonstrated to be related to gas well operations, a correlation
between beryllium and another constituent would indicate that ele-
vated levels of other constituents in the groundwater may have some
relationship to gas well operations as well.
6.4. Cluster analysis
The Cluster analysis performed in ArcGIS showed significant cluster-
ing for 8 of the 10 clustering variables—Injected Fluid (Cluster 3) and ni-
trogen (Cluster 5) did not have significant clustering and were omitted
from the evaluation. Thus, HH and LL cluster locations were found for
the remaining 8 clustering variables. The concentration of beryllium
(from 2011 to 2014 sample data) extracted at the HH and LL cluster lo-
cations for 2 of the variables: Surface Casing and Bottom Hole Casing
found no statistical significance for the two variables. The results of
the Mann–Whitney U-Test for the remaining clusters, however, were
significant and of interest.
As expected, a higher density of wells (variable 9) and higher bottom
hole pressure (variable 10) were associated with a higher median beryl-
lium concentration. Clusters with a greater vertical depth (variable
5) had a lower median concentration of beryllium. Likewise, clusters
with a decreased thickness between the Trinity and the uppermost per-
foration (variable 8) were associated with a higher median concentra-
tion. The results of the Lateral Length clusters (variable 7) showed
that the LL clusters (vertical wells having a lateral length of zero)
were associated with a greater median beryllium concentration than
the HH cluster values. Additionally, clusters of high injected weight of
sand (variable 4) were not associated with a higher median beryllium
concentration, further indicating that the contamination pathway is
not related to the horizontal wellbore. These results are logical and
should be expected based upon an understanding of fracture extension,
where fractures tend to extend upwards. In vertical wellbores, this
means that the fractures are parallel to the annulus, possibly creating
a breach in the wellbore system.
Fig. 6. Change in total beryllium and contoured RPG in the Barnett.
124 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

12. 7. Conclusions
The results from this research emphasize the need to study ground-
water quality and hydraulic fracturing relationships in a spatial context
at the regional scale, and with respect to the geophysical characteristics
of the wellbore environment. This is particularly noted in comparing the
results in Sections 6.2 and 6.3, where it was demonstrated that the den-
sity of wells per area establishes a relationship between groundwater
quality changes and hydraulic fracturing. Modeling water quality with
respect to specific characterization of the wellbore environment
(Section 5.5) resulted in statistically significant differences in median
constituent concentrations that indicated that degraded groundwater
quality has some relationship to hydraulic fracturing operations. Addi-
tionally, this research demonstrated that while a number of constitu-
ents can serve as indicators of groundwater quality, total beryllium
was found to be associated with gas well production and to be the stron-
gest indicator variable for detecting a pathway between gas wells and
groundwater.
By identifying an appropriate indicator variable such as beryllium in
this case, the results of a cluster analysis of well design and hydraulic
fracturing parameters allowed identification of a possible origin of the
contaminant pathways in the wellbore environment. The results from
the study indicated that contaminant pathways are formed in the verti-
cal section of a wellbore, where the fracture extends parallel to the
wellbore potentially creating a microannular pathway in the cement
sheath. Thus, improving hydraulic fracturing treatment and wellbore
designs may reduce the potential impact of natural gas production on
fresh water resources.
While most of the constituents tested did not have sample concen-
trations exceeding the EPA MCL threshold, strong correlations between
various constituents with beryllium (which appears to be highly related
to hydraulic fracturing and exceeds the MCL) may indicate that the
other constituents do indeed have elevated concentration levels that
may also be associated with the presence of hydraulic fracturing
operations.
This study demonstrated that while the quality of groundwater may
not be directly associated with proximity to gas wells; it is impacted by
the high density of gas wells in an area. In the Barnett Shale region, the
highest density of gas wells is located in the highest pressure gradient
region. A high density of gas wells treated in a small area may cause
an intersection of pressure cones in the subsurface, possibly increasing
the reservoir pressure and/or fracture treatment pressure and affecting
the integrity of the vertical wellbore. To what extent that may be occur-
ring is unknown within the context of data available for this study.
However, future work on this subject should further investigate this
issue by incorporating more specific knowledge of the hydraulic fractur-
ing treatment pressures (particularly the cumulative effects of multiple
fracturing events within a mile of a groundwater water well), wellbore
pressure limitations, and reservoir rock properties into the model.
Acknowledgments
The Texas Water Commission on Environmental Quality (TCEQ) pro-
vided support for this research; their support is gratefully acknowl-
edged. However, it is noted that the work presented in the paper is
the sole product of the authors. Mr. Tom Holley, Interim Chair of the Pe-
troleum Engineering Department at the University of Houston is ac-
knowledged for providing access to DrillingInfo.com and for his
constructive comments and support during the development of the
research.
References
Barker, C., 1990. Calculated volume and pressure changes during the thermal cracking of
oil to gas in reservoirs (1). AAPG Bull. 74 (8), 1254–1261.
Fig. 7. Beryllium concentration boxplot for Subgroup 1 (right) and Subgroup 6 (left).
Table 3
Correlated constituent levels per data subgroup.
Subgroup
number
Number of Pearson
coefficients above
0.5
Number of beryllium
Pearson
coefficients above 0.5
1 18 0
2 73 6
3 23 2
4 228 11
125T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126

13. Bowker, K.A., 2007. Barnett shale gas production, Fort Worth Basin: issues and discussion.
AAPG Bull. 91 (4), 523–533. http://dx.doi.org/10.1306/06190606018.
Chermak, J.A., Schreiber, M.E., 2014. Mineralogy and trace element geochemistry of gas
shales in the United States: environmental implications. Int. J. Coal Geol. 126, 32–44.
Darrah, T.H., Vengosh, A., Jackson, R.B., Warner, N.R., Poreda, R.J., 2014. Noble gases iden-
tify the mechanisms of fugitive gas contamination in drinking-water wells overlying
the Marcellus and Barnett Shales. Proc. Natl. Acad. Sci. U. S. A. 111 (39), 14076–14081.
http://dx.doi.org/10.1073/pnas.1322107111.
Davies, R.J., Almond, S., Ward, R.S., Jackson, R.B., Adams, C., Worrall, F., Herringshaw, L.G.,
Gluyaa, J.G., Whitehead, M.A., 2014. Oil and gas wells and their integrity: implications
for shale and unconventional resource exploitation. Mar. Pet. Geol. 56, 239–254.
Deremble, L., Loizzo, M., Huet, B., Lecampion, B., Quesada, D., 2010. “Assessment of leak-
age pathways along a cemented annulus”. Paper presented at the Society of Petro-
leum Engineers. SPE International Conference on CO2 Capture, Storage, and
Utilization 2010.
Eaton, T.T., 2013. Science-based decision-making on complex issues: Marcellus shale gas
hydrofracking and New York City water supply. Sci. Total Environ. 461–462, 158–169.
http://dx.doi.org/10.1016/j.scitotenv.2013.04.093.
Finkel, M.L., Hays, J., 2013. The implications of unconventional drilling for natural gas: a
global public health concern. Public Health 127 (10), 889–893.
Fontenot, B.E., Hunt, L.R., Hildenbrand, Z.L., Carlton, D.D., Oka, H., Walton, J.L., ... Schug,
K.A., 2013. An evaluation of water quality in private drinking water wells near natural
gas extraction sites in the Barnett shale formation. Environ. Sci. Technol. 47 (17),
10032–10040.
Gordalla, B.C., Ewers, U., Frimmel, F.H., 2013. Hydraulic fracturing: a toxicological threat
for groundwater and drinking-water? Environmental Earth Sciences 70 (8),
3875–3893.
Harden, R.W., 2004. Northern Trinity, Woodbine Aquifer Groundwater Availability Model.
RW Harden Assoc.
Harkness, J.S., Dwyer, G.S., Warner, N.R., Parker, K.M., Mitch, W.A., Vengosh, A., 2015. Io-
dide, bromide, and ammonium in hydraulic fracturing and oil and gas wastewaters:
environmental implications. Environ. Sci. Technol. 49 (3), 1955–1963.
Hildenbrand, Z.L., Carlton, D.D., Fontenot, B.E., Meik, J.M., Walton, J.L., Taylor, J.T., Thacker,
J.B., Korlie, S., Shelor, C.P., Henderson, D.J., Kadjo, A.F., Roelke, C., Dasgupta, P.K.,
Hudak, P., Burton, T., Rifai, H., Schug, K.A., 2015. A comprehensive analysis of ground-
water quality in the Barnett Shale Region. Environ. Sci. Technol. http://dx.doi.org/10.
1021/acs.est.5b01526.
Ingraffea, A.R., Wells, M.T., Santoro, R.L., Shonkoff, S.B., 2014. Assessment and risk analysis
of casing and cement impairment in oil and gas wells in Pennsylvania, 2000–2012.
PNAS 111 (30), 10955–10960.
Jackson, R.B., Vengosh, A., Darrah, T.H., Warner, N.R., Down, A., Poreda, R.J., Osborn, S.,
Zhao, K., Karr, J.D., 2013. Increased stray gas abundance in a subset of drinking
water wells near Marcellus shale gas extraction. Proc. Natl. Acad. Sci. U. S. A. 110
(28), 11250–11255. http://dx.doi.org/10.1073/pnas.1221635110.
Kidnay, A.J., Parrish, W.R., McCartney, D.G., 2006. Fundamentals of Natural Gas Processing.
218. CRC Press.
Kresic, N., 2006. Hydrogeology and Groundwater Modeling. CRC press, Boca Raton, FL,
p. 78.
Lester, Y., Ferrer, I., Thurman, E.M., Sitterley, K.A., Korak, J.A., Aiken, G., Linden, K.G., 2015.
Characterization of hydraulic fracturing flowback water in Colorado: implications for
water treatment. Sci. Total Environ. 512–513, 637–644.
McKenzie, L.M., Witter, R.Z., Newman, L.S., Adgate, J.L., 2012. Human health risk assess-
ment of air emissions from development of unconventional natural gas resources.
Sci. Total Environ. 424, 79–87.
Meng, Q., 2015. Spatial analysis of environment and population at risk of natural gas
fracking in the State of Pennsylvania, USA. Sci. Total Environ. 515–516, 198–206.
Molofsky, L.J., Connor, J.A., Farhat, S.K., Wylie Jr., A.S., Wagner, T., 2011. Methane in Penn-
sylvania water wells unrelated to Marcellus shale fracturing. Oil Gas J. 109 (19),
54–67+93.
Molofsky, L.J., Connor, J.A., Wylie, A.S., Wagner, T., Farhat, S.K., 2013. Evaluation of meth-
ane sources in groundwater in northeastern Pennsylvania. Ground Water 51 (3),
333–349. http://dx.doi.org/10.1111/gwat.12056.
Montgomery, S.L., Jarvie, D.M., Bowker, K.A., Pollastro, R.M., 2006. Mississippian Barnett
Shale, Fort Worth basin, north-central Texas: gas-shale play with multi-trillion
cubic foot potential: reply. AAPG Bull. 90 (6), 967–969. http://dx.doi.org/10.1306/
02090605186.
Moritz, A., Hélie, J.-F., Pinti, D.L., Laroque, M., Barnetche, D., Retaileau, S., Lefebvre, R.,
Gélinas, Y., 2015. Methane baseline concentrations and sources in shallow aquifers
from the shale gas-prone region of the St. Lawrence Lowlands (Quebec, Canada). En-
viron. Sci. Technol. 49 (7), 4765–4771.
Murray, K.E., 2013. State-scale perspective on water use and production associated with
oil and gas operations, Oklahoma, U.S. Environ. Sci. Technol. 47 (9), 4918–4925.
http://dx.doi.org/10.1021/es4000593.
Osborn, S.G., Vengosh, A., Warner, N.R., Jackson, R.B., 2011. Methane contamination of
drinking water accompanying gas-well drilling and hydraulic fracturing. Proc. Natl.
Acad. Sci. U. S. A. 108 (20), 8172–8176.
Paulson, D.S., 2003. Applied Statistical Designs for the Researcher. CRC Press, NY, New
York, p. 525.
Preston, T.M., Chesley-Preston, T.L., Thamke, J.N., 2014. A GIS-based vulnerability assess-
ment of brine contamination to aquatic resources from oil and gas development in
eastern Sheridan County, Montana. Sci. Total Environ. 472, 1152–1162.
Rahm, D., 2011. Regulating hydraulic fracturing in shale gas plays: the case of Texas. Energ
Policy 39 (5), 2974–2981. http://dx.doi.org/10.1016/j.enpol.2011.03.009.
Rahm, B.G., Bates, J.T., Bertoia, L.R., Galford, A.E., Yoxtheimer, D.A., Riha, S.J., 2013. Waste-
water management and Marcellus Shale gas development: trends, drivers, and plan-
ning implications. J. Environ. Manag. 120, 105–113.
Railroad Commission (RRC), 2014. http://www.rrc.state.tx.us/aboutus/resource-center/
research/online-research-queries/#OilandGas.
Révész, K.M., Breen, K.J., Baldassare, A.J., Burruss, R.C., 2012. Carbon and hydrogen isotopic
evidence for the origin of combustible gases in water-supply wells in north-central
Pennsylvania. Appl. Geochem. 27 (1), 361–375.
Rowan, F., Engle, M., Kirby, C., Kraemer, T., 2011. Radium content of oil and gas field pro-
duced waters in the northern Appalachian Basin (USA)—Summary and discussion of
data. US Geological Survey Scientific Investigation Report, pp. 2011–5135 31 pp.
Rozell, D.J., Reaven, S.J., 2012. water pollution risk associated with natural gas extraction
from the Marcellus Shale. Risk Anal. 32 (8), 1382–1393.
Smith, M.B., Hannah, R.R., 1996. High-permeability fracturing: the evolution of a technol-
ogy. J. Pet. Technol. 48, 628–633.
Ternes, M.E., 2012. Regulatory programs governing shale gas development. Chem. Eng.
Prog. 108 (8), 60–64.
Thacker, J.B., Carlton Jr., D.D., Hildenbrand, Z.L., Kadjo, A.F., Schug, K.A., 2015. Chemical
analysis of flowback and produced water from unconventional drilling operations.
Water 7, 1568–1579.
Texas Water Development Board (TWDB), 2014. https://www.twdb.texas.gov/
groundwater/data/gwdbrt.sp.
Vidic, R.D., Brantley, S.L., Vandenbossche, J.M., Yoxtheimer, D., Abad, J.D., 2013. Impact of
shale gas development on regional water quality. Science 340. http://dx.doi.org/10.
1126/science.1235009.
Walton, J., Woocay, A., 2013. Environmental issues related to enhanced production of nat-
ural gas by hydraulic fracturing. Journal of Green Building 8 (1), 62–71.
Warner, N.R., Jackson, R.B., Darrah, T.H., Osborn, S.G., Down, A., Zhao, K., White, A.,
Vengosh, A., 2012. Geochemical evidence for possible natural migration of Marcellus
formation brine to shallow aquifers in Pennsylvania. Proc. Natl. Acad. Sci. U. S. A. 109
(30), 11961–11966.
Werner, A., Vink, S., Watt, K., Jagals, P., 2015. Environmental health impacts of unconven-
tional natural gas development: a review of the current strength of evidence. Sci.
Total Environ. 505, 1127–1141.
Ziemkiewicz, P.F., Quaranta, J.D., Darnell, A., Wise, R., 2014. Exposure pathways related to
shale gas development and procedures for reducing environmental and public risk.
Journal of Natural Gas Science and Engineering 16, 77–84.
126 T.G. Burton et al. / Science of the Total Environment 545–546 (2016) 114–126