Truck Traffic and Truck Loads
Associated with Unconventional Oil
and Gas Development in Texas
Implementation Report RR-16-01

Prepared for Texas Department of Transportation
Maintenance Division
Prepared by
Texas A&M Transportation Institute

Cesar Quiroga
Senior Research Fellow

Ioannis Tsakapis
Assistant Research Scientist

William Holik
Assistant Research Scientist

Edgar Kraus
Research Engineer

TxDOT Contract No. 47-4PV1A007
TTI Contract No. 409186

August, 2016

Jing Li
Assistant Research Scientist

 TABLE OF CONTENTS
Page
LIST OF FIGURES ........................................................................................................................ ii 
LIST OF TABLES ......................................................................................................................... iv 
INTRODUCTION .......................................................................................................................... 1 
DESCRIPTIVE STATISTICS AND COUNTY MAPS................................................................. 2 
INTRODUCTION ...................................................................................................................... 2 
DESCRIPTIVE STATISTICS .................................................................................................... 4 
Oil and Gas Well Locations .................................................................................................... 4 
Historical Evolution of Oil and Gas Wells ........................................................................... 17 
High-Level Forecasting ........................................................................................................ 20 
COUNTY MAPS ...................................................................................................................... 30 
TRAFFIC LOADS FOR DEVELOPING AND OPERATING INDIVIDUAL WELLS ............ 35 
INTRODUCTION .................................................................................................................... 35 
PROCESS TO DETERMINE TRUCKLOADS ....................................................................... 35 
ASSUMPTIONS ....................................................................................................................... 38 
RESULTS ................................................................................................................................. 40 
EXCEL TEMPLATE TO CALCULATE TRUCKLOADS ..................................................... 43 
TRAFFIC LOADS FOR SEGMENT AND CORRIDOR-LEVEL ANALYSES ........................ 46 
INTRODUCTION .................................................................................................................... 46 
TRAVEL DEMAND MODELING APPROACH AND ASSUMPTIONS ............................. 46 
CASE STUDY .......................................................................................................................... 48 
Trip Generation ..................................................................................................................... 50 
Trip Distribution ................................................................................................................... 52 
Route Assignment ................................................................................................................. 53 
ESAL Calculations................................................................................................................ 54 
Results ................................................................................................................................... 54 
APPLICABILITY OF THE METHODOLOGY ...................................................................... 62 
REFERENCES ............................................................................................................................. 68 
APPENDIX. OIL AND GAS WELL INFORMATION BY COUNTY ..................................... 70 

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Page i

 LIST OF FIGURES
Page
Figure 1. Counties Analyzed in the Eagle Ford Shale, Permian Basin, and Barnett Shale
Regions. .................................................................................................................................... 3 
Figure 2. Completed Oil and Gas Wells in Texas (1977-2015). ................................................... 5 
Figure 3. Uncompleted Oil and Gas Wells. ................................................................................... 6 
Figure 4. Wells Injecting Liquids, Air, or Gas (1983-2015). ........................................................ 7 
Figure 5. Completed Oil and Gas Wells (2005-2008). .................................................................. 8 
Figure 6. Completed Oil and Gas Wells (2009-2012). .................................................................. 9 
Figure 7. Completed Oil and Gas Wells (2013-2015). ................................................................ 10 
Figure 8. Cumulative Number of Oil and Gas Wells (2009-2011).............................................. 11 
Figure 9. Cumulative Number of Oil and Gas Wells (2009-2013).............................................. 12 
Figure 10. Cumulative Number of Oil and Gas Wells (2009-2015)............................................ 13 
Figure 11. Cumulative Number of Horizontal Oil and Gas Wells (2009 2011). ......................... 14 
Figure 12. Cumulative Number of Horizontal Oil and Gas Wells (2009-2013). ........................ 15 
Figure 13. Cumulative Number of Horizontal Oil and Gas Wells (2009-2015). ........................ 16 
Figure 14. Price of Average Monthly Imported Crude Oil Price (adapted from [1]). ................. 17 
Figure 15. Permitted Oil and Gas Wells. ..................................................................................... 18 
Figure 16. Completed Oil and Gas Wells. ................................................................................... 19 
Figure 17. Duration between Permit Approval and Well Completion. ....................................... 19 
Figure 18. Number of Completed Wells as a Function of Crude Oil Prices. .............................. 20 
Figure 19. Crude Oil Price vs. Number of New (Vertical and Horizontal) Wells
Statewide. ................................................................................................................................ 21 
Figure 20. Price of Crude Oil vs. Number of New Wells Statewide. .......................................... 23 
Figure 21. Price of Crude Oil vs. Number of New Wells in the Top 10 Producing
Counties in the Eagle Ford Shale Region. .............................................................................. 24 
Figure 22. Annual Average Price of Crude Oil vs. Total (Annual) Number of New Wells
in the Top 10 Producing Counties in the Eagle Ford Shale Region. ...................................... 25 
Figure 23. Price of Natural Gas vs. Number of New Wells in the Top 5 Producing
Counties in the Barnett Shale Region. .................................................................................... 26 
Figure 24. Annual Average Price of Natural Gas vs. Total (Annual) Number of New
Wells in the Top 5 Producing Counties in the Barnett Shale Region. .................................... 27 
Figure 25. Price of Crude Oil vs. Number of New Wells in the Top 2 Producing Counties
in the Permian Basin Region................................................................................................... 28 
Figure 26. Price of Crude Oil vs. Number of New Wells in the Top 2 Producing Counties
in the Permian Basin Region................................................................................................... 29 
Figure 27. Map of Completed Wells in the Eagle Ford Shale Region. ....................................... 31 
Figure 28. Wells Completed in Karnes County – Color Coded by Top Producers. .................... 31 
Figure 29. PDF Map Document for Karnes County with Layers. ............................................... 32 
Figure 30. PDF Map Layer Menu. ............................................................................................... 33 
Figure 31. Comparison between Completed Wells in 2014, Completed Wells in 2015,
and Permitted (non-Completed) Wells. .................................................................................. 34 
Figure 32. Wells Completed in Karnes County. .......................................................................... 49 
Figure 33. Location of Potential Suppliers Used for the Analysis. ............................................. 50 
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Page ii

 Figure 34. Total Number of ESALs (Trips to the Well) – One Well. ......................................... 56 
Figure 35. Total Number of ESALs (Trips from the Well) – One Well. ..................................... 56 
Figure 36. Total Number of ESALs (Higher Directional ESALs) – One Well. .......................... 57 
Figure 37. Total Number of ESALs (Higher Directional ESALs) – 10 Wells. ........................... 58 
Figure 38. Total Number of ESALs (Higher Directional ESALs) – 100 Wells. ......................... 58 
Figure 39. Total Number of ESALs (Higher Directional ESALs) – 200 Wells. ......................... 59 
Figure 40. Total Number of ESALs (Higher Directional ESALs) – 493 Wells. ......................... 59 
Figure 41. ESALs According to the TxDOT RHiNo Database. .................................................. 61 
Figure 42. Total Number of ESALs (Higher Directional ESALs) – One Well (b=0.02). ........... 63 
Figure 43. Total Number of ESALs (Higher Directional ESALs) – One Well (b=1). ................ 63 
Figure 44. Total Number of ESALs (Higher Directional ESALs) – One Well (b=1.5). ............. 64 
Figure 45. ESAL-Miles vs. b Parameter – One Well. ................................................................. 65 
Figure 46. ESAL-Miles vs. b Parameter – 493 Wells.................................................................. 65 
Figure 47. ESAL Distributions in Karnes City Due to 493 Wells in Karnes County.................. 66 
Figure 48. ESAL Distributions in Karnes City Due to 493 Wells in Karnes County
(Alternative Scenario). ............................................................................................................ 67 

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 LIST OF TABLES
Page
Table 1. Overview of Datasets Received from the Railroad Commission. ................................... 2 
Table 2. Distribution of Loaded Single Axles of Equipment Trucks. ......................................... 39 
Table 3. Number of Trucks Needed to Develop, Operate, and Re-Frack a Well. ....................... 41 
Table 4. Number of Trucks and ESALs per Well (Barnett Shale Region). ................................. 42 
Table 5. Number of Trucks and ESALs per Well (Eagle Ford Shale Region). ........................... 42 
Table 6. Number of Trucks and ESALs per Well (Permian Basin Region). ............................... 42 
Table 7. Input Parameters to Determine Number of Trucks and ESALs. ................................... 43 
Table 8. Trucks Needed to Develop, Operate, and Maintain an Oil Well (Note: Users
populate cells in red; other cells are calculated automatically). ............................................. 44 
Table 9. Volume of Trucks and Number of ESALs per Well. .................................................... 45 
Table 10. Trucks Needed to Operate a Gas Well in the Barnett Shale Region (Note:
Users populate cells in red; other cells are calculated automatically). ................................... 45 
Table 11. Data Used in Case Study. ............................................................................................ 48 
Table 12. Truckloads Needed for Individual Wells in the Eagle Ford Shale Region. ................. 51 
Table 13. Example Trip Distribution Based on Different b Values. ........................................... 53 
Table 14. ESALs per Truck Type in the Eagle Ford Shale Region. ............................................ 55 
Table 15. Miles of On-System and Off-System Roads Used to Develop and Operate
Wells. ...................................................................................................................................... 60 
Table 16. Total Number of New Wellheads Completed per County. .......................................... 70 
Table 17. Total Number of New Wellends Completed per County. ............................................ 75 
Table 18. Total Number of New Directional Wells Completed per County. .............................. 79 
Table 19. Total Number of New Vertical Wellheads Completed per County. ............................ 84 
Table 20. Total Number of New Horizontal Wellends Completed per County. ......................... 88 

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Page iv

 INTRODUCTION
Energy developments that rely on horizontal drilling and hydraulic fracturing (also called
fracking) technologies generate enormous amounts of truck traffic on state, county, and local
roads. Secondary roads, in particular, were never designed to carry such high truck traffic
volumes and heavy loads. The result has been accelerated degradation of pavements and
roadside infrastructure, as well as increases in congestion and crash and fatality rates.
Quantifying the number of truck trips and resulting 18-kip equivalent single axle loads (ESALs)
associated with the development and operation of oil and gas wells is a critical requirement for
designing and maintaining pavement structures on energy sector roads. However, this is not
enough. In order to implement roadway design, construction, and maintenance plans in energy
sector areas, it is necessary to document the location, number, and characteristics of existing and
planned well developments. It is also important to map out the routes that trucks are likely to use
during the development and operation of those wells.
Research Report RR-15-01, submitted to the Texas Department of Transportation (TxDOT) in
August 2015, described the work completed by the Texas A&M Transportation Institute (TTI) to
characterize truck traffic and truck loads associated with unconventional oil and gas
developments in Texas. Activities that TTI completed included, but were not limited to, the
following:











Obtain and process data from the Railroad Commission of Texas (RRC).
Obtain and process data from the Department of Public Safety (DPS).
Obtain and process data from the Texas Department of Motor Vehicles (TxDMV).
Obtain and process weigh-in-motion (WIM) data from the Transportation Planning and
Programming (TPP) Division at TxDOT.
Reach out to the oil and gas industry to obtain information about typical numbers of
trucks needed to develop oil and gas wells.
Collect video data at selected WIM stations and correlate these data with corresponding
WIM station data.
Prepare county-level maps to document oil and gas well developments.
Understand and document how the RDTEST68 computer program works.
Estimate truck axle weights and equivalent single axle loads (ESALs).
Prepare average ten heaviest wheel load daily (ATHWLD) estimates.

This report provides an update to several analyses completed in 2015, particularly in relation to
data received from the Railroad Commission, descriptive statistics and county maps, and
estimation of ESALs for individual wells. The report also describes a geographic information
system (GIS)-based methodology to estimate truck volumes and ESALs at the individual
roadway segment level for any number of oil or gas wells that are developed and operated in a
geographic area.

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Page 1

 DESCRIPTIVE STATISTICS AND COUNTY MAPS
INTRODUCTION
TTI gathered and processed data from the Railroad Commission to document locations and
trends of oil and gas energy developments in the state. The outcome of this task was an updated
geodatabase of oil and gas developments, which included GIS files of oil and gas permit
locations as well as drilling permit attribute data. Table 1 provides an overview of the various
datasets received from the Railroad Commission.
Table 1. Overview of Datasets Received from the Railroad Commission.
RRC Data
Collection

Digital Map
Data

Drilling Permit
Data

RRC Dataset

Dataset Description

Date Range

Size

File
Format

API Data

Oil and gas well attribute
data

1900-01/2016

0.28 GB

.dbf and
.txt

Wells

Surface/bottom/directional
oil and gas well locations

1977-02/2016

1.8 GB

.shp (and
related)

Location of inter- and
intrastate pipelines

1990-01/2016

1.97 GB

.shp (and
related)

Spatial Pipeline
Data
Permit Master &
Trailer &
Lat/Long

Data about drilling permits
including location

03/1922-02/2016

1.14 GB .dat

Oil and Gas
Production Data
Production Data Query

Oracle dump of the
production data

01/1993-04/2016

.dmp
2.82 GB (Oracle
dump)

Oil and Gas
Regulatory
Data

Information about
underground injection
wells: inventory, permit,
10/1970-01/2016
monitoring pressure testing,
and enforcement action data

Underground
Injection Control

0.21 GB .txt

With the data gathered from the Railroad Commission, TTI prepared a series of updated tables,
figures, and maps to document locations and trends of oil and gas energy developments, with a
focus on the Barnett Shale, Eagle Ford Shale, and Permian Basin regions (Figure 1).

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Page 2

 Figure 1. Counties Analyzed in the Eagle Ford Shale, Permian Basin, and Barnett Shale
Regions.
Some of the reported RRC data from 2015 may be incomplete and therefore are not as reliable as
data from previous years. The reason is the lag between when certain events occur and when
RRC updates the corresponding database records. For example, there is lag between the date
that an operator completes a well, the date the operator submits the completion report to the
Railroad Commission, and the date the RRC database officially registers a well as completed and
ready for production. Although the Railroad Commission has allowed operators to submit
completion reports online since February 2011, the completion date lag causes the inventory of
completed wells to lag behind the actual number of completed wells in the field.

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Page 3

 DESCRIPTIVE STATISTICS
Oil and Gas Well Locations
This section includes a small sample of maps that illustrate major trends in recent years. The
appendix provides a more extensive sample of county-level tables that document oil and gas
developments in the state. The sample in this section includes the following maps:


Figure 2 shows the location of 431,309 completed oil and gas wellheads in the state from
1977-2015. The figure also shows the location of 79,093 completed oil and gas wells
from 2010-2015.



Figure 3 shows the location of 32,123 uncompleted oil and gas wells with expired drilling
permits from 2010-2015. The figure also shows the location of 21,420 uncompleted oil
and gas wells with active drilling permits as of December 31, 2015.



Figure 4 shows the location of wells that are used to inject liquids, air, or gas into nonproductive zones. Wells that inject liquids into non-productive zones (also called
disposal wells) are of particular interest because they are used to dispose unwanted fluids
that result from the development or operation of active production wells.
Figure 4 also shows the location of wells that are used to inject liquids, air, or gas into
productive zones. In most cases, the purpose of injecting fluids into a field is to increase
pressure that causes oil and gas to migrate toward adjacent active production wells.



Figure 5 shows the number of completed oil and gas wells by county from 2005-2008.



Figure 6 shows the number of completed oil and gas wells by county from 2009-2012.



Figure 7 shows the number of completed oil and gas wells by county from 2013-2015.



Figure 8 shows the cumulative number of oil and gas wells by county from 2009-2011.



Figure 9 shows the cumulative number of oil and gas wells by county from 2009-2013.



Figure 10 shows the cumulative number of oil and gas wells by county from 2009-2015.



Figure 11 shows the cumulative number of horizontal oil and gas wells by county from
2009-2011.



Figure 12 shows the cumulative number of horizontal oil and gas wells by county from
2009-2013.



Figure 13 shows the cumulative number of horizontal oil and gas wells by county from
2009-2015.

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Page 4

 1977-2015

2010-2015

Figure 2. Completed Oil and Gas Wells in Texas (1977-2015).
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Page 5

 Expired
Permits
(2010-2015)

Active
Permits as of
December
2015

Figure 3. Uncompleted Oil and Gas Wells.
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Page 6

 Injection
wells into
nonproductive
zones

Injection
wells into
productive
zones

Figure 4. Wells Injecting Liquids, Air, or Gas (1983-2015).
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Page 7

  

2005?2006

Number of New Wells

E0
E140
.41-120
.121-300
.301-700
->700

Interstate Highways

 
 
 
 
 
 
  

 

 

2007?2008

 

Number of New Wells

E0
E140
.41-120
.121-300
.301-700
->7oo

Interstate Highways

 
 
 
 
 
 
  

 

Figure 5. Completed Oil and Gas Wells (2005-2008).

Page 8

 

 

Number of New Wells

E0
E140
.41-120
.121-300
.301-700
->700

 

 

 

 

 

 

 

  
 
  

 

 

 

2009?2010

 

 

Number of New Wells

E0
E140
.41-120
.121-300
.301-700
->7oo

Interstate Highways

 
 
 
 
 
 
  

2011?2012

 

 

Figure 6. Completed Oil and Gas Wells (2009-2012).

Page 9

 

 

2013?2014

Number of New Wells

E0
E140
E41420
.121-300
.301-700
->700

Interstate Highways

 
 
 
 
 
 
  

 

 

2015

 

Number of New Wells

E0
E140
E41420
.121-300
.301-700
->700

Interstate Highways

 
 
 
 
 
 
  

 

Figure 7. Completed Oil and Gas Wells (2013-2015).
Page 10

 

2009-2010

2009-2011

Figure 8. Cumulative Number of Oil and Gas Wells (2009-2011).
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Page 11

 2009-2012

2009-2013

Figure 9. Cumulative Number of Oil and Gas Wells (2009-2013).
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 2009-2014

2009-2015

Figure 10. Cumulative Number of Oil and Gas Wells (2009-2015).
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 2009-2010

2009-2011

Figure 11. Cumulative Number of Horizontal Oil and Gas Wells (2009 2011).
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 2009-2012

2009-2013

Figure 12. Cumulative Number of Horizontal Oil and Gas Wells (2009-2013).
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 2009-2014

2009-2015

Figure 13. Cumulative Number of Horizontal Oil and Gas Wells (2009-2015).
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Page 16

 Historical Evolution of Oil and Gas Wells
Figure 14 provides a historical view of the price of imported oil since 1974. In the late 1970s, oil
prices were high due in part to instability in the oil supply that resulted from the Iranian
Revolution of 1979 and the beginning of the Iraq-Iran War in 1980. High oil prices encouraged
energy conservation, which, in turn, resulted in lower consumption. The resulting oversupply in
oil caused a significant reduction in oil prices in the mid-1980s. The price of oil remained low
until the mid-2000s. Oil prices accelerated quickly from around 2004 until July 2008, when the
economic recession hit. In response, oil prices collapsed. In five months, the price of imported
oil decreased from $127.77/barrel (or $139.23/barrel in May 2016 dollars) to $35.59/barrel (or
$40.18/barrel in May 2016 dollars). After that, prices recovered quickly until reaching another
peak in April 2011 ($113.02/barrel or $120.37/barrel in May 2016 dollars). Prices remained
above $100/barrel until June 2014. However, by January 2015, the price of oil had decreased to
$44.74/barrel (or $45.45 in May 2016 dollars). Since the beginning of 2015, the price of oil has
fluctuated around $50/barrel. The current short-term Energy Information Administration (EIA)
forecast is that the price of oil will remain between $40-$50/barrel through the end of 2017.

Figure 14. Price of Average Monthly Imported Crude Oil Price (adapted from [1]).
Figure 15 shows the number of permitted oil and gas wells from 1977-2015. Figure 16 shows
the number of oil and gas wells completed during the same period. The Railroad Commission
differentiates between “surface” wells (which correspond to the X,Y locations of the wellheads)
and “bottom” wells (which correspond to the X,Y locations of the bottom end of the wells).
Generally speaking, a “surface” well corresponds to the location of a wellhead (in the case of
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Page 17

 vertical wells) or a placeholder for all the wellheads that are connected to horizontal wells at the
same pad location, all of which share the same American Petroleum Institute (API) number. For
vertical wells, the relationship between “surface” well and “bottom” well locations is one-to-one.
For horizontal wells, the relationship can be one-to-one (if there is only one lateral) or,
increasingly, one-to-many. In 2015, for the first time, the number of new horizontal wells was
higher than the number of new vertical wells. Industry insiders anticipate the number of new
horizontal wells to continue to grow at a higher rate than the number of new vertical wells.
The amount of time needed to develop wells is increasing. As Figure 17 shows, the median
duration between permit approval and well completion increased from about one month in 1977
to almost three months in 2014. The mean duration increased from a month and a half in 1977 to
more than four months in 2014. There was also a significant increase in the median and mean
durations from 2014 to 2015, which is consistent with a recent trend reported in the mass media
about energy developers drilling wells but not immediately fracking those wells until oil prices
become more favorable. Figure 17 also shows the 10th and 90th percentile durations. In
particular, the 90th percentile duration increased from approximately three months in 1977 to
more than ten months in 2015. The volatility of this duration is probably associated with
uncertainties that some individual operators experience, e.g., delays in drilling equipment
deliveries or truck shortages.

Figure 15. Permitted Oil and Gas Wells.

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Page 18

 Figure 16. Completed Oil and Gas Wells.

Figure 17. Duration between Permit Approval and Well Completion.

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 High-Level Forecasting
By combining EIA monthly crude oil price data with RRC well completion data, it is possible to
estimate drilling activity as a function of the price of crude oil. Figure 18 shows the number of
completed vertical wellheads, horizontal wellends, and total wells per month compared to the
average monthly price of crude oil (adjusted to May 2016 dollars). At the state level, there is a
strong correlation between the price of crude oil and the number of completed wells. The
correlation is stronger for horizontal wellends than for vertical wellheads, and it is the highest
after introducing a “lag” effect of three months (to account for the time it might take for energy
developers to react to changes in oil prices). All correlations are positive, meaning that if the
price of crude oil increases the number of wells completed increases.

3.2 ∗
 5.5 ∗
8.7 ∗

514
0.40 
76
0.85 
438
0.83 

Note: The regression analysis period covered years 1990 through 2014.

Figure 18. Number of Completed Wells as a Function of Crude Oil Prices.
Based on this information, TTI used several regression model to estimate the number of
completed wells as a function of the price of crude oil. As an illustration, Figure 18 shows linear
regression equations for vertical and horizontal wells. In Figure 19, linear, logarithmic, and
power function trend lines are fit to the statewide dataset of monthly crude oil price versus total
wells completed per month. Although the data exhibit a slightly curved pattern, the linear
regression fit appears to be the most suitable based on the R2 values. Notice in Figure 19 that
different symbols and colors are used to highlight variations in the price of crude oil and the
corresponding number of wells completed over time. The figure clearly shows data clusters that
indicate specific trends in the industry.
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 Figure 19. Crude Oil Price vs. Number of New (Vertical and Horizontal) Wells Statewide.
Figure 20 shows the number of new vertical and horizontal wells completed statewide. R2 values
were lower for vertical wells than for horizontal wells. When comparing the number of vertical
and horizontal wells in Figure 20 (more specifically 2010-2014 data), the number of vertical
wells is decreasing while the number of horizontal wells is increasing. In addition, when
comparing the slopes of the linear regression trend lines, the slope for horizontal wells is greater
than that of vertical wells, suggesting a faster response to price fluctuations for horizontal wells.
Figure 21 is similar to Figure 20, except the data points in Figure 21 are limited to the Eagle Ford
Shale Region. In this region, the number of vertical wells completed remained roughly the same
regardless of the price of crude oil. However, there was a noticeable trend beginning in 2010.
From 2010-2014, the number of vertical wells decreased, while the number of horizontal wells
increased rapidly. The price of crude oil had no effect on the number of horizontal wells
completed prior to 2010. As shown, the number of horizontal wells completed was roughly
constant even though prices ranged from $20-$140/barrel. Starting in 2010, the number of
horizontal wells increased dramatically from less than 50 per month to over 300 per month,
while the price of oil rose from $80/barrel to $120/barrel.
Figure 22 shows the annual average price of crude oil versus the number of wells completed per
year in the Eagle Ford Region. The arrows are used to connect data from one year to the next.
As such, the figure shows the evolution in the number of wells completed as the price of crude
oil changes. For horizontal wells, the figure shows that, beginning in 2010, as the price of crude
oil began to increase, the industry responded by drilling and completing an increasingly higher
number of wells. Very quickly, the number of wells completed no longer was a function of the
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Page 21

 price of crude oil, i.e., the number of wells completed continued to increase, regardless of the
price of crude oil. This trend should logically suggest a maximum point beyond which the
number of wells would begin to decrease. More than likely, once data points for 2015 are
included in the chart, this downward trend will become evident.
Figure 23 and Figure 24 show similar figures for the Barnett Shale Region. In this region, from
1990-1999, only a small number of vertical wells and nearly no horizontal wells were completed.
Starting in 2000, the number of vertical wells and horizontal wells increased, although vertical
wells increased at a greater rate than horizontal wells. This trend reversed from 2005 to 2009 as
the number of horizontal wells outpaced the number of vertical wells completed. Starting in
2010, the number of both vertical and horizontal wells completed begins to decline each year, as
the price of natural gas decreased substantially around that time.
The cyclical variations in the price of natural gas made it critical to use annualized data for the
Barnett Shale Region, as shown in Figure 24. From 1990-1999, the price of natural gas was
roughly constant and the number of vertical wells completed was very similar each year.
Starting in 2000, a period of volatility started, with the number of vertical wells completed not
well correlated with the price of natural gas. For example, from 2003-2008, the number of
vertical wells decreased even though the price of natural gas increased. In recent years, the
number of vertical wells has been nearly zero even though the price of natural gas has been
higher than in other periods when the number of wells completed was substantially higher.
For horizontal wells, the annualized chart (Figure 24) reveals an interesting trend that illustrates
how the industry has responded to changes in the price of natural gas. Beginning in 2003, the
number of horizontal wells began to increase rapidly as the price of natural gas increased. At
some point, the price of natural gas stopped increasing, but the number of wells continued to
increase. This trend continued until about 2009 when the number of wells began to decrease
rapidly. For forecasting purposes, this trend highlights the need to take into consideration both
sides of the curve: when prices are moving from lower to higher and when prices are moving
from higher to lower. When prices are moving from lower to higher, the number of horizontal
wells completed per year is less than when prices are moving from higher to lower, even if the
average annual price is the same.
Figure 25 shows the price of crude oil versus the number of vertical and horizontal wells per
month in the Permian Basin Region. This analysis included the two highest producing counties
because that is where most horizontal drilling took place in the region. Other counties in the
Permian Region have many more wells, of which the majority are low-producing vertically
drilled wells. The number of vertical and horizontal wells completed remained constant until
2011. Starting in 2012, the number of horizontal wells began to increase, but this number was
still lower than the number of vertical wells. In 2013, the number of vertical wells and
horizontal wells completed were similar, and in 2014, the number of horizontal wells completed
was considerably greater than the number of vertical wells.
Figure 26 shows the annual average price of crude oil versus the number of vertical and
horizontal wells completed per year. Although the numbers are specific to the Permian Basin
Region, the overall trends are similar to those observed in the Eagle Ford Region.
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Page 22

 (a) Vertical Wells

(b) Horizontal Wells

Figure 20. Price of Crude Oil vs. Number of New Wells Statewide.

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Page 23

 (a) Vertical Wells

(b) Horizontal Wells

Figure 21. Price of Crude Oil vs. Number of New Wells in the Top 10 Producing Counties in the Eagle Ford Shale Region.

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Page 24

 (a) Vertical Wells

(b) Horizontal Wells

Figure 22. Annual Average Price of Crude Oil vs. Total (Annual) Number of New Wells in the Top 10 Producing Counties in
the Eagle Ford Shale Region.
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Page 25

 (a) Vertical Wells

(b) Horizontal Wells

Figure 23. Price of Natural Gas vs. Number of New Wells in the Top 5 Producing Counties in the Barnett Shale Region.

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Page 26

 (a) Vertical Wells

(b) Horizontal Wells

Figure 24. Annual Average Price of Natural Gas vs. Total (Annual) Number of New Wells in the Top 5 Producing Counties in
the Barnett Shale Region.
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Page 27

 (a) Vertical Wells

(b) Horizontal Wells

Figure 25. Price of Crude Oil vs. Number of New Wells in the Top 2 Producing Counties in the Permian Basin Region.

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Page 28

 (a) Vertical Wells

(b) Horizontal Wells

Figure 26. Price of Crude Oil vs. Number of New Wells in the Top 2 Producing Counties in the Permian Basin Region.

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Page 29

 COUNTY MAPS
The geodatabase of oil and gas developments, which included GIS files of oil and gas permit
locations as well as drilling permit attribute data, enabled the production of a wide range of maps
to document locations and trends of oil and gas energy developments in the state. With the
exception of the GIS files, processing the data involved activities, such as, but not limited to,
processing the oil/gas production oracle data dump file, processing oil and gas master files,
processing drilling permit master files, and processing the underground injection control file. As
an illustration of the types of maps that are possible with the geodatabase, Figure 27 shows a
map of completed wells in the Eagle Ford Shale region. Figure 28 shows a map of completed
wells in Karnes County, with color codes representing the top oil well operators in the county.
The figure shows both wellhead and horizontal drilling locations. Other types of maps, e.g., the
county shaded maps shown in Figure 13, are also possible using the geodatabase.
TTI also prepared maps for each county located in the Barnett Shale region, Eagle Ford Shale
region, and Permian Basin region. In total, 120 county maps in portable document format (PDF)
were prepared. As an illustration, Figure 29 depicts the locations of the wellheads, wellends, and
directional wells in Karnes County. The maps include completed wells, expired wells, and wells
that are not expired and not completed as of December 31, 2015. The maps are accessible online
at https://txdot.sharepoint.com/sites/division-MNT/SitePages/Home.aspx. Implementation
Report IR-16-01 and Energy Sector Brief ESB-16-06 include instructions on how to use the
maps.
To facilitate user understanding of the data, each PDF file contains layers that can be turned on
or off, each layer representing a specific type of information, as shown in Figure 30. The layers
panel is visible on the left side of the document. By clicking the eye next to each layer, the
layers may be turned off or on. Completed wells are grouped by completion year as classified by
the Railroad Commission. Not completed and not expired wells are shown individually. Several
background layers are included in the maps, such as the roadways, railroads, pipelines, county
lines, city limits, and geological formations. The labels for county, city, and major roadway
names may be turned off and on from the layer panel. Energy Sector Brief ESB-16-16 provides
additional instructions and information on how to use the maps.

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Page 30

 Figure 27. Map of Completed Wells in the Eagle Ford Shale Region.

Figure 28. Wells Completed in Karnes County – Color Coded by Top Producers.
RR-16-01

Page 31

 

Page 32

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Page 33

 Wells
completed in
2014

Wells
completed in
2015

Permitted
(noncompleted)
wells as of
December
2015

Figure 31. Comparison between Completed Wells in 2014, Completed Wells in 2015, and
Permitted (non-Completed) Wells.
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Page 34

 TRAFFIC LOADS FOR DEVELOPING AND OPERATING INDIVIDUAL
WELLS
INTRODUCTION
This chapter describes a methodology to determine truckloads (more specifically ESALs) in
connection with the development and operation of a typical horizontal, hydraulically-fracked oil
or gas well in the Eagle Ford Shale, Permian Basin, and Barnett Shale regions. Along with the
results of an analysis of the anticipated number of wells that will use specific corridors in each of
these regions, maintenance engineers and supervisors can use the ESAL estimates to design
flexible pavements along those corridors.
PROCESS TO DETERMINE TRUCKLOADS
The general process to determine truckloads (more specifically ESALs) in connection with the
development and operation of typical horizontal, hydraulically-fractured oil and gas wells in the
Eagle Ford Shale, Permian Basin, and Barnett Shale regions of the state involved the following
sets of activities:


Determine well development and operation phases and activities. Well development
involves pad preparation, drilling, and hydraulic fracturing. Well operation involves the
continuous extraction of hydrocarbon products (oil, condensate, and/or gas) and water.
Well operation also includes various maintenance activities. One such activity is refracking, which, if it happens, might conceivably occur at various times during the
lifetime of a well. For the analysis, the researchers assumed that all oil and condensate
production is moved by truck from a well to a designated truck off-load facility and all
gas production is transported by pipeline.



Determine the number of trucks per well phase activity, from pad construction to drilling,
fracking, and operation of a typical well over a 20-year period. Because of the difficulty
in obtaining direct, meaningful information from the industry, it became necessary to
estimate truck volumes by relying on a literature review from around the country,
information gathered by TxDOT officials, and well counts and other statistics from the
Texas Railroad Commission (3). The researchers also used data from the FracFocus
database (for the amount of water, sand, and additives used for fracking operations) (4).
Specific analyses and assumptions included the following:
o Number of trucks per well activity during well operation in the Eagle Ford
Shale and the Permian Basin region. For oil production, the researchers
analyzed February 2014 oil well counts by county from data provided by the
Railroad Commission.
 In the case of the Eagle Ford Shale region, the researchers analyzed data
from 29 counties and calculated an average oil production per well based
on the ten counties with the highest production of oil and condensate and
the number of regular producing wells in that month. The researchers then
calculated the number of trucks needed for oil production per well and
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Page 35

 year assuming 306 lb/barrel as the unit weight of oil, a load capacity of
41,000 lb for steel tank trucks, a load capacity of 52,500 lb for aluminum
tank trucks, and a fleet ratio of steel to aluminum tank trucks of 7/3.
 For the Permian Basin region, the researchers analyzed 37 counties.
However, the focus was on Reeves and Loving because these two counties
experienced most of the unconventional energy development in the region
and had a higher average oil production per well and month than all other
counties. The researchers calculated an average oil production per well
based on these two counties, assuming the same calculation parameters
given above for the Eagle Ford Shale. This methodology filtered out
counties that had higher total production levels, but relied primarily on a
myriad of vertical wells that have been developed and operated using
traditional techniques.
 To estimate water during production, the researchers calculated an
oil/condensate ratio for each county based on February 2014 oil
production data from the Railroad Commission. The researchers then
applied the county-based oil/condensate factor to estimate oil plus
condensate production per county. Using county-based liquid injection
data from the Railroad Commission, the researchers then established a
ratio of liquids injected per oil plus condensate produced for the top 10 oil
per well producing counties. This factor was then applied to the average
amount of oil produced in the Eagle Ford Shale per well to estimate
liquids that needed to be transported from each well and injected into the
ground. The procedure for the Permian Basin region was similar, except
that only the two highest producing counties in February of 2014 were
used for the calculation of the disposal liquid ratio.
o Number of trucks per well activity during well operation in the Barnett Shale
region. As mentioned, the researchers assumed that all gas would be transported
via pipeline. To estimate the number of trucks needed to transport the small
amount of oil and condensate production at each gas well, the researchers used the
top five producing counties in the Barnett Shale region (under the assumption that
all produced water from a well is transported by truck to a disposal facility within
the same county). However, the researchers did not calculate the amount of
produced water based on the liquid injected per oil produced ratio. Instead, the
researches reviewed the number of active wells and liquids injected for the five
highest producing counties in February of 2014 to calculate an average volume of
liquids injected per well. With this information, the researchers then calculated
the number of trucks needed to transport produced water per well and year.
o Average number of water trucks per well during hydraulic fracturing
operations. For the three regions, the researchers used 2013, 2014, and 2015 data
from the FracFocus database and converted the average water volume obtained
from the database to trucks needed to move the water. To estimate the number of
trucks needed, the researchers assumed that a steel tank truck could carry 130
barrels of water, aluminum tank trucks could carry 150 barrels of water, and that
the fleet ratio of steel to aluminum tank trucks was 7/3.

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 o Average number of sand and additive trucks per well. For the three regions,
the researchers used the FracFocus database to estimate the average mass of sand
and additives used per well. The researchers converted the average mass of sand
and additives into the number of trucks needed, assuming a capacity of 41,000
pounds for steel tank trucks, 52,500 pounds for aluminum tank trucks, and a fleet
ratio of steel to aluminum tank trucks of 7/3.
o Average number of flowback water trucks. No reliable estimates were
available to calculate this number. Based on anecdotal information, the research
team assumed that 25 percent of water needed for hydraulic fracturing would need
to be removed and transported away from each well.
o Average number of trucks needed for re-fracking. For all three regions, the
number of trucks needed to re-frack a well was assumed to be the same as the
number of wells needed for the fracking operation during the initial development
of the well.


Estimate the axle weight distribution for the truck types used for each phase of well
development. Deploying portable weigh-in-motion (WIM) systems in the immediate
vicinity of a well under development was not technically or financially feasible. For this
reason, an indirect approach was implemented, which relied on WIM readings from the
network of permanent WIM stations along major TxDOT corridors and concurrent video
data collection at the WIM station locations. The researchers used more than 50,000
sample trucks that were captured via video screenshots and their corresponding WIM
readings to develop aggregated axle weight distributions at 1,000-lb intervals. This
approach involved some level of error because the truck traffic composition on major
corridors was probably somewhat different from the truck traffic composition on
secondary corridors in energy sector areas. However, the resulting characterization of the
truck fleet provided at least a first-order approximation of the typical axle weight
distributions that might occur on these corridors. For additional information, refer to
Research Report RR-15-01, Implementation Report IR-16-02, and Energy Sector Brief
ESB-16-08 (3, 5, 6).



Determine a “typical” truck type and truck axle configuration for each well development,
production, and re-fracking activity. The video screenshots from the WIM stations
enabled the identification of multiple truck types. The researchers assigned one truck
type to each well activity, e.g., truck type “rig truck” to the “drilling pad and construction
equipment” activity and truck type “water truck” to the “hydraulic fracturing water”
activity. Based on the related WIM data, the researchers analyzed the axle configurations
of all truck types to determine the most prevalent axle configuration for each truck type.
For example, of the 19 different axle configurations for rig trucks in the WIM station
video screenshot sample, the most prevalent truck axle configuration was single-singletridem (with 28 percent of all rig trucks). The researchers then assigned the most
prevalent truck axle configuration to each truck type used for a well activity.



Calculate empty truck weights. The researchers calculated the empty weights of each
truck type based on the selected truck axle configuration and the following assumed axle
weights of unloaded (empty) axle types:
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 o Single axle: 5,000 lbs.
o Tandem axle: 14,000 lbs.
o Tridem axle: 21,000 lbs.


Calculate load equivalency factors (LEFs) for loaded and unloaded axles using industrystandard AASHTO road test equations (7). For unloaded axles, the researchers
calculated the LEF for a particular axle type and weight given the unloaded axle weights
above and the AASHTO road test equations. For loaded axle weights of a particular axle
type and truck type, the researchers calculated LEFs in 1,000-pound intervals by
multiplying the LEF for each weight group with the relative frequency of that weight
group and then added all interval LEFs to arrive at an axle type LEF. This calculation
only included counts of axle weights larger than the unloaded axle weights given above.
Table 2 provides an example of the calculation for loaded single axles of equipment
trucks, which resulted in an axle type LEF of 0.5288. The result of this process was an
axle type LEF for each axle type of each truck type, both for loaded and unloaded trucks.



Select trip load condition (loaded or empty) for each well activity in each well
development phase. For example, during well development, the assumption was that
drilling rig trucks would arrive loaded and leave loaded, while fracking water trucks
would arrive loaded and leave empty.



Estimate the number of ESALs for each phase. This process involved calculating loaded
truck ESALs and empty truck ESALs and adding the ESALS for each well activity to
arrive at a total number of ESALs for trips to the well and a total number of ESALs for
trips leaving the well.

ASSUMPTIONS
Assumptions behind the calculations to estimate the number of ESALs after the completion of a
well included the following:


All fluids extracted from an oil or gas well are transported by truck from the well to a
designated terminal facility. Oil and condensate are transported to a truck off-load
terminal facility for transportation via pipeline to a midstream or downstream facility.
Overtime, the industry might build a pipeline network to connect each well directly to the
existing pipeline infrastructure, therefore bypassing the roadway infrastructure.
However, it is not clear whether this would ever happen and over how many years. For
the analysis, the researchers assumed that all oil and condensate are transported by truck
to a designated truck off-load terminal facility over the 20-year period of analysis. For
gas extraction, the assumption is that all gas is transported by pipeline from the well
location.

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Page 38

 Table 2. Distribution of Loaded Single Axles of Equipment Trucks.
Bin
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
11,000
12,000
13,000
14,000
15,000
16,000
17,000
18,000
19,000
20,000
21,000
22,000
23,000
24,000
25,000
26,000
27,000
28,000
29,000
30,000
31,000
32,000
33,000
34,000
35,000
36,000
37,000
38,000
39,000
40,000
Total



Frequency Relative Frequency
0.00%
0.00%
0.00%
0.00%
0.00%
32
0.95%
35
1.04%
70
2.07%
104
3.08%
286
8.46%
653
19.31%
766
22.66%
344
10.17%
137
4.05%
121
3.58%
125
3.70%
129
3.82%
114
3.37%
128
3.79%
87
2.57%
87
2.57%
53
1.57%
46
1.36%
28
0.83%
19
0.56%
7
0.21%
5
0.15%
0.00%
1
0.03%
1
0.03%
2
0.06%
0.00%
1
0.03%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
3,381

LEF
4.50487E-05
0.000312777
0.001229419
0.003524391
0.008243056
0.016695976
0.030356254
0.050741157
0.079328454
0.117546161
0.16684122
0.228799497
0.305276017
0.398503072
0.51116283
0.646427202
0.807975392
1
1.227209588
1.49483216
1.808621405
2.174866035
2.600401822
3.09262573
3.659511559
4.309626595
5.052148904
5.896885017
6.85428781
7.935474471
9.15224447
10.51709749
12.04325125
13.74465933
15.63602873
17.73283753
20.05135223
22.60864517
25.4226117
28.51198738

0.0002
0.0003
0.0011
0.0024
0.0099
0.0322
0.0518
0.0311
0.0161
0.0183
0.0239
0.0308
0.0337
0.0465
0.0385
0.0465
0.0341
0.0354
0.0256
0.0206
0.0089
0.0075
0.0020
0.0023
0.0054
0.0036
0.5288

All water extracted from a well is transported by truck to a disposal facility where the
water is injected into the ground. It is not clear whether the industry would ever build a
pipeline network to connect each well to a disposal facility and, if so, at what point. The
assumption is that the pipeline network is not built, forcing all the produced water to be
transported by truck from the well location. Because of the cost to transport the water,
RR-16-01

Page 39

 the assumption is that water extracted from a well is hauled a relatively short distance,
typically within the same county.


As wells age, hydrocarbon production decreases. At the same time, the proportion of
produced water increases. At this point, it is not clear how the total volume of fluids
extracted from the ground (including oil, condensate, and water) would evolve over time.
For simplicity, the researchers assumed the total volume to remain approximately
constant over time. To develop an estimate, the researchers queried the total oil and
condensate production per county from the Railroad Commission database, queried the
total amount of water injected into non-productive wells (i.e., disposal wells) from the
Railroad Commission database, and then divided the total volume by the number of
horizontal wells in the county.

Input variables assumed in the calculations included the following:


Pavement Structural Number (SN) = 3.0.



Pavement Terminal Serviceability Index (Pt) = 2.5.



Analysis period = 20 years.



Number of re-fracking events per analysis period = 4.



Disposal liquid ratio (or ratio of the volume of disposed water to the volume of oil and
condensate):
o Eagle Ford Shale: 0.26.
o Barnett Shale: Not applicable because most of the hydrocarbon production is gas.
o Permian Basin: 2.29.



Ratio of steel to aluminum tank trucks = 7:3 (or 2.33).



Flowback water volume during fracking = 25% of the water used for fracking.

RESULTS
Table 3 summarizes the number of trucks needed to develop, operate, and re-frack a well in the
Eagle Ford Shale, Barnett Shale, and Permian Basin regions. Table 4 through Table 6
summarize the results of the ESAL calculation analysis for each region.

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 Table 3. Number of Trucks Needed to Develop, Operate, and Re-Frack a Well.
Well Development
Drilling pad and construction equipment
Drilling rig
Drilling fluid and materials
Drilling equipment: casing, drilling pipe
Fracking equipment: pump trucks, tanks
Fracking water:
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand:
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal
Total

Well Production Activity
Produced water (steel tank)
Produced water (aluminum tank)
Oil and condensate production (steel tank)
Oil and condensate production (aluminum tank)
Total

Well Re-Fracking Activity
Fracking equipment: pump trucks, tanks
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal
Total

RR-16-01

Number of Trucks
Barnett
Eagle Ford
Permian
Shale
Shale
Basin
70
70
70
4
4
4
59
59
59
54
54
54
74
74
74
533
1,021
527
373
715
369
160
306
158
57
147
66
40
103
46
17
44
20
4
24
11
133
255
132
988
1,708
997
Number of Trucks per Year
Barnett
Eagle Ford
Permian
Shale
Shale
Basin
41
65
181
14
22
62
8
249
79
3
83
27
66
418
349
Number of Trucks per Event
Barnett
Eagle Ford
Permian
Shale
Shale
Basin
74
74
74
373
715
369
160
306
158
40
103
46
17
44
20
4
24
11
133
255
132
801
1,521
810

Page 41

 Table 4. Number of Trucks and ESALs per Well (Barnett Shale Region).
Item
Number of trucks
ESALs (trip to well)
ESALs (trip from well)

Development
988
1,363
474

Production
Per Year
Total
66
1,320
5
98
93
1,864

Re-Fracking
Per Event
Total
801
3,205
1,070
4,281
423
1,694

Total
5,513
5,742
4,031

Table 5. Number of Trucks and ESALs per Well (Eagle Ford Shale Region).
Item
Number of trucks
ESALs (trip to well)
ESALs (trip from well)

Development
1,708
2,261
689

Production
Per Year
Total
418
8,366
31
625
591
11,815

Re-Fracking
Per Event
Total
1,521
6,085
1,968
7,871
639
2,555

Total
16,160
10,757
15,059

Table 6. Number of Trucks and ESALs per Well (Permian Basin Region).
Item
Number of trucks
ESALs (trip to well)
ESALs (trip from well)

Development
997
1,381
472

Production
Per Year
Total
349
6,975
26
519
492
9,850

Re-Fracking
Per Event
Total
810
3,239
1,089
4,354
422
1,689

Total
11,211
6,254
12,011

Overall, the results indicate the following:


The total number of trucks per well over a 20-year period in the Eagle Ford Shale region
is almost three times as high as in the Barnett Shale region and almost 50% higher than in
the Permian Basin region. One of the reasons is that wells in the Eagle Ford Shale region
are using considerably more water and sand than in the other two regions. In addition,
hydrocarbon production in the Eagle Ford Shale region is primarily oil and condensate
(requiring truck transportation to an off-loading facility), whereas in the Barnett Shale,
hydrocarbon production is mainly dry gas (and the assumption is that all this gas is
transported by pipeline).



Although the number of re-fracking events over 20 years is unknown, the anticipated
impact is that, all other things being equal, the number of trucks needed to re-frack a well
in the Eagle Ford Shale region would be higher than in the Barnett Shale or Permian
Basin regions. The reason, as mentioned above, is that the amount of water and sand
needed to frack a well in the Eagle Ford Shale region is higher than in the other two
regions.



For both the Eagle Ford Shale and Permian Basin regions, the number of ESALs for trips
leaving the well over 20 years is considerable higher than the corresponding number of
ESALs for trips going to the well. The reason is the cumulative effect of operating the
well over 20 years (more specifically by transportation oil and condensate by truck). By
comparison, in the Barnett Shale region, the cumulative effect of operating the well is
relatively minor, by re-fracking would have the highest impact on the number of ESALs.

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 EXCEL TEMPLATE TO CALCULATE TRUCKLOADS
An Excel spreadsheet template enables users to calculate the following for each well:




Total number of trucks needed by phase activity and analysis period.
Total amount of ESALs for trips to the well by phase activity and analysis period.
Total amount of ESALs for trips leaving the well by phase activity and analysis period.

The spreadsheet calculates these values based on inputs the user provides in various places of the
spreadsheet, as shown in the red cells in Table 7 and Table 8. Once all the cells shaded in red are
populated, the spreadsheet calculates the number of trucks and ESALs per well for the selected
analysis period, both for trips to the well and trips leaving the well, as show in Table 9. If
needed for further analysis, the Excel file also includes all the data and details used for the
calculations.
Table 7. Input Parameters to Determine Number of Trucks and ESALs.

Input 
Pavement Structural Number (SN) =
Pavement Terminal Serviceability Index (Pt) =
Analysis Period (Years) =
Number of Re‐Fracking Events per Analysis Period =
Disposal Liquid Ratio =
Ratio of Steel to Aluminum Tank Trucks =
Flowback water ratio =

3.0 
2.5 
20 
4 
0.26 
2.33 
0.25 

The pavement structural number and terminal serviceability index are flexible
pavement design parameters that affect the calculation of load equivalency factors.
The analysis period covers the development, operation, and maintenance phases of
an oil or gas well for pavement design purposes. It assumes continuous operation of
the well. A well could operate past the analysis period.
The number of re-fracking events per analysis period represents the number of times
a well is re-fracked during the analysis period.
The disposal liquid ratio represents the ratio of produced water to oil and condensate
(by volume), which must be transported by truck to a disposal facility.
The ratio of steel to aluminum tank trucks is the ratio of the number of steel tank
trucks to the number of aluminum tank trucks.
The flowback water ratio is the ratio of the volume of water recovered to the volume
of water injected during fracking.

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 Table 8. Trucks Needed to Develop, Operate, and Maintain an Oil Well (Note: Users
populate cells in red; other cells are calculated automatically).

Trucks Needed to Develop and Complete a Well 
Truck Volume 
Well Development Activity 
(per Well) 
Drilling pad and construction equipment 
Drilling rig 
Drilling fluid and materials 
Drilling equipment: casing, drilling pipe 
Fracking equipment: pump trucks, tanks 
Fracking water 
Fracking water (steel tank) 
Fracking water (aluminum tank) 
Fracking sand 
Fracking sand (steel tank) 
Fracking sand (aluminum tank) 
Other additives and fluids 
Flowback water removal 
Total 

                            70  
                              4  
                            59  
                            54  
                            74  
                      1,021  
                         715  
                         306  
                         147  
                         103  
                           44  
                           24  
                         255  
                     1,708  

Trucks Needed for Oil Production 
Truck Volume 
Well Production Activity 
(per Well and Year) 
Produced water (steel tank) 
Produced water (aluminum tank) 
Oil production (steel tank) 
Oil production (aluminum tank) 
Total 

                                65  
                                22  
                              249  
                                83  
                              418  

Trucks Needed for Re‐Fracking 
Truck Volume 
Well Re‐Fracking Activity 
(per Well and Event) 
Fracking equipment: pump trucks, tanks 
Fracking water (steel tank) 
Fracking water (aluminum tank) 
Fracking sand (steel tank) 
Fracking sand (aluminum tank) 
Other additives and fluids 
Flowback water removal 
Total 

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                                74  
                              715  
                              306  
                              103  
                                44  
                                24  
                              255  
                          1,521  

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 Table 9. Volume of Trucks and Number of ESALs per Well.
Output 
Development
Per Analysis 
Period 

  
Total volume of trucks 
per well 
Total ESALs per well, 
trip to well 
Total ESALs per well, 
trip from well 

Production 

Re‐Fracking 

Total 

Per  Per Analysis  Per  Per Analysis  Per Analysis 
Year 
Period 
Event
Period 
Period 

1,708 

418 

8,366  1,521 

6,085  

16,160 

2,261 

31 

625  1,968 

7,871  

10,757 

689 

591 

2,555  

15,059 

11,815 

639 

As mentioned previously, the liquid disposal ratio is not applicable in the Barnett Shale region
because most of the corresponding hydrocarbon production is gas. For this region, the volume of
produced water is based on the amount of water injected into the ground at disposal facilities at
the top five gas producing counties. Table 10 shows the corresponding number of trucks needed
to carry the water. The table also shows a small number of trucks needed to haul oil and
condensate, based on average production levels as reported by the Texas Railroad Commission.
Table 10. Trucks Needed to Operate a Gas Well in the Barnett Shale Region (Note: Users
populate cells in red; other cells are calculated automatically).

Trucks Needed for Gas Production 
Truck Volume 
Well Production Activity 
(per Well and Year) 
Produced water (steel tank) 
                                41  
Produced water (aluminum tank) 
                                14  
Oil and condensate production (steel tank) 
                                  8  
Oil and condensate production (aluminum tank)                                   3  
Total 
                                66  

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 TRAFFIC LOADS FOR SEGMENT AND CORRIDOR-LEVEL ANALYSES
INTRODUCTION
This chapter describes a GIS-based methodology to estimate truck volumes and ESALs at the
individual roadway segment level for any number of oil or gas wells that are developed and
operated in a geographic area. The methodology uses inputs such as, but not limited to, locations
of anticipated wells; identification and location of equipment, materials, and other supplies
needed to develop and operate the wells; number and type of trucks needed for each
development and operation activity; evaluation of loaded and unloaded number of ESALs for
each truck type, and length of the analysis period.
The methodology described in this report uses four-step travel demand modeling principles that
were adapted to take into account specific trip generation, trip distribution, and route assignment
characteristics of typical unconventional energy developments in the state. Anticipated
applications of the methodology include, but are not limited to, estimation of truck volumes and
ESALs at the roadway segment and corridor levels, determination of roadway and roadside
maintenance needs, prioritization of pavement maintenance and rehabilitation projects,
evaluation of truck route plans, analysis of traffic operations and safety impacts, and analysis of
congestion and access management requirements. The report documents the results of a case
study in Karnes County using wells that were completed in 2013.
TRAVEL DEMAND MODELING APPROACH AND ASSUMPTIONS
Four-step travel demand modeling is a commonly used procedure for urban transportation
planning, which typically includes trip generation, trip distribution, mode choice, and route
assignment components (8). Examples of application of these components for energy-sector
truck traffic analysis are available in the literature (8, 9, 10). The analysis presented in this paper
also adopted the four-step travel demand modeling process to determine truck volumes and
ESALs for pavement maintenance and design purposes. However, the modeling approach was
specifically adapted to take into account unique trip generation, trip distribution, and route
assignment characteristics of typical unconventional energy developments.
Because the main focus was on truck traffic, trips requiring non-truck vehicles (e.g., pickup
trucks, utility trucks, and personal vehicles) were not included in the analysis. Although the
number of non-truck trips needed to develop and operate oil and gas wells could be quite
significant (some estimates place the number of these trips as being of the same of magnitude as
the number of truck trips), the corresponding impact on the total number of ESALs would be
very small. Further refinements of the modeling approach could include non-truck trips for
applications such as emergency evacuations and traffic and congestion management.
Specific assumptions and modeling approach for the trip generation, trip distribution, and trip
assignment components follow.


Trip Generation. Wells are trip producers, making well locations the origin of all trips
(whether trucks arrive at or leave well locations). Locations that provide or receive
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 equipment, materials, and other supplies are trip attractors. For trip productions, the
number of trips corresponds to the number of trucks needed for each well development or
operation activity, making trip productions constrained. The number of trucks needed for
each activity was obtained through parallel efforts based on a comprehensive literature
review from around the county, information gathered by TxDOT officials, well counts
and other statistics from the Railroad Commission of Texas (RRC), and data from the
FracFocus database (11). For trip attractions, individual supplier capacity can limit the
number of attracted trips. For simplicity, suppliers were not assumed to be capacityconstrained, i.e., suppliers have sufficient materials and supplies to address the needs of
the well developments they serve.


Trip Distribution. A number of methods are available to estimate trips between trip
productions and attractions, including growth factor methods, gravity models, and
destination choice models (12). Growth factor methods require an existing trip
distribution matrix to be available. Gravity models rely on short path calculations and
impedance measures between trip productions and attractions, such as travel time or
travel cost. Commonly used impedance functions include exponential, inverse power,
and gamma. Destination choice models are a generalization of gravity models, which use
a wider range of explanatory variables and are, therefore, more data intensive. Because
posted speed limit data and other roadway characteristics were available for all roadway
segments, the researchers selected a gravity model to determine the number of trips
between trip productions and trip attractions. The researchers also used an inverse power
impedance function because this kind of function only required the estimation of one
parameter (13).



Route Assignment. Several methods are available to assign routes, e.g., all-or-nothing,
user equilibrium, and system optimum (14). All-or-nothing assignment allocates trips to
single, minimum-cost paths without considering roadway capacity or impact of traffic on
travel cost. This method is frequently unrealistic in urban areas where congestion is
common, but it may be more suitable for rural and relatively uncongested areas. Both
user equilibrium assignment and system optimum assignment consider roadway capacity
and impact of traffic on travel cost. User equilibrium assignment assumes that all
travelers strive to find a path with minimum travel time. System optimum assignment
assumes that travelers cooperate with each other to minimize total system travel time.
For the analysis, the researchers used an all-or-nothing assignment because most energy
developments occur in rural areas with sparse transportation networks and occasional
congestion. With truck trips assigned to routes, the last step was to convert the assigned
number of trips on each roadway segment to ESALs using the ESAL calculations for
individual truck types. It is worth mentioning that this study used ESAL factors based on
weigh-in-motion (WIM) data collected along energy-sector corridors (11). Specifically,
the researchers captured over 50,000 snapshots of energy-related trucks through video
data collected at four selected WIM stations and matched each snapshot to its
corresponding WIM record. Nine truck types of interest were identified and the axle load
distributions for each truck type were developed, including single axle, tandem, tridem,
and quadrem axles for each truck type. Based on these axle load distributions, truck
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 type-specific ESAL factors were then prepared using existing AASHTO formulations as
described in Chapter 3.
CASE STUDY
The researchers conducted a case study using wells completed in Karnes County in 2013 to
evaluate the feasibility of the modeling approach. The researchers conducted the analysis using
TransCAD 7.0. To obtain input data such as well locations or material supplier locations, the
researchers reached out to TxDOT officials, explored various databases, and conducted extensive
Google searches to collect comprehensive information. Table 11 provides a listing of the various
datasets used for the analysis. Figure 32 shows the location of wells completed in Karnes
County. Figure 33 shows the locations of the suppliers listed in Table 11. As Figure 33 shows,
although all the wells used for modeling were located in Karnes County, the researchers included
a large number of surrounding counties to account for a wide range of material or service
supplier locations.
Table 11. Data Used in Case Study.
Dataset
Completed wells (2013)
Aggregate suppliers
Drilling rig and equipment suppliers
Water suppliers
Pipe and casing suppliers
Fracking sand suppliers
Chemical suppliers
Injection disposal wells (as of 2014)
Crude oil terminals

Number of
Records
493
14
3
9
4
2
9
615
2

Source of Information
Railroad Commission of Texas
TxDOT Corpus Christi District Office
TxPROS oversize/overweight permit database
TxDOT Karnes City Area Office
Google search
TxDOT Karnes City Area Office
Google search
Railroad Commission of Texas
TxDOT Karnes City Area Office

For the modeling effort, the researchers used wellhead locations. As Figure 32 shows, most
wells completed in Karnes County are horizontal wells in which the lateral component is
approximately one mile long. Although Figure 32 suggests one wellhead location for several
laterals, in reality each wellhead has its own lateral (with its own API number). This
characteristic facilitated modeling of the number of trucks and ESALs at the individual lateral
level.

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 Wells completed through 2015.  
Figure shows wellheads, 
laterals, and wellends.  

Wells completed in 2013.  
Figure shows wellheads, 
laterals, and wellends  

Figure 32. Wells Completed in Karnes County.

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 Figure 33. Location of Potential Suppliers Used for the Analysis.

Trip Generation
The researchers prepared a trip generation table showing the number of trips generated by trip
productions and attractions. Trip generation calculations involved the following assumptions:


Trips are loaded or unloaded depending on the trip purpose and direction. For example,
for fracking water, the trip from a supplier to a well is loaded while the return trip is
empty. For flowback or produced water disposal, the trip to the well is empty while the
trip to the disposal facility is loaded.



All wells need the same amount of resources and number of trucks for each activity
regardless of operator or number of wellheads developed at the same pad location. The
activities considered in this study include well development activities (e.g., pad
preparation, drilling), well operation activities (e.g., production), and well re-fracking.
Table 12 shows the number of trucks needed for each development, operation, and refracking activity based on results described in Chapter 3. The use of temporary water
lines was not considered in this analysis. However, a sensitivity analysis is possible in
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 order to examine the impact of variations in resources needed, e.g., in relation to the use
of temporary water lines to decrease the number of trucks needed to carry fracking
water, or in relation to the impact due to multiple wells developed at the same pad
location within a short duration. The researchers are currently conducting a separate
research project (TxDOT Research Project 0-6886) to evaluate the use of temporary
water lines and their impact on the transportation network, including both pavement
structures and roadside infrastructure.
Table 12. Truckloads Needed for Individual Wells in the Eagle Ford Shale Region.
Well Development Activity
Drilling pad and construction equipment
Drilling rig
Drilling fluid and materials
Drilling equipment: casing, drilling pipe
Fracking equipment: pump trucks, tanks
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal
Total
Well Production Activity
Produced water (steel tank)
Produced water (aluminum tank)
Oil production (steel tank)
Oil production (aluminum tank)
Total
Well Re-fracking Activity
Fracking equipment: pump trucks, tanks
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal
Total



Truck Volume
(per well)
70
4
59
54
74
715
306
103
44
24
255
1,708
Truck Volume
(per well and year)
65
22
249
83
418
Truck Volume
(per well and event)
74
715
306
103
44
24
255
1,515

Supplier
Aggregate suppliers
Drilling rig and equipment suppliers
Water suppliers
Pipe and casing suppliers
Drilling rig and equipment suppliers
Water suppliers
Water suppliers
Fracking sand suppliers
Fracking sand suppliers
Chemical suppliers
Injection disposal wells
Supplier
Injection disposal wells
Injection disposal wells
Crude oil terminals
Crude oil terminals
Supplier
Drilling rig and equipment suppliers
Water suppliers
Water suppliers
Fracking sand suppliers
Fracking sand suppliers
Chemical suppliers
Injection disposal wells

All suppliers have sufficient capacity to address the needs of individual wells. Because
suppliers are not capacity-constraint and information about pricing of materials and
services from various suppliers was not available, the choice of suppliers for each well is
only based on travel time. In some cases, there was information that suggested specific
trip characteristics or trends. For example, many water trucks use steel tanks. However,
WIM data records indicated a substantial number of aluminum tank trucks (15). Using
WIM data records as a guidance, the researchers assumed a 7/3 split between the number
of steel tank trucks and aluminum tank trucks.
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 

Pavement impact is assumed to be linearly dependent on the amount of traffic, e.g.,
doubling the number of trucks of the same type and loaded similarly would result in
twice the number of ESALs. This characteristic made it possible to simplify the
modeling effort considerably by only having to run TransCAD models once for each trip
purpose assuming a normalized number of trips per development or operation activity:
100. During the route assignment step, the researchers multiplied the resulting number
of assigned trips per roadway segment by the corresponding number of trucks in Table
12 and then divided by 100 to obtain the correct number of assigned trucks per roadway
segment for each development or operation activity.

Trip Distribution
In the absence of any additional information about factors that contribute to the selection of
material or service suppliers, this selection was only based on travel time considerations. More
specifically, the researchers assumed that the impedance between a well and a supplier location
was only a function of the shortest travel time between them. Posted speed limit data from the
TxDOT Road–Highway Inventory Network (RHiNo) database was used to provide a measure of
travel time between origins and destinations. A literature review on the specific connection
between pavement conditions and traveling speeds, which would have provided additional
insight about travel times on energy sector corridors, particularly secondary roads such as farmto-market (FM) roads, was inconclusive. Nevertheless, the modeling environment enables users
to modify speed data either for entire groups of roadway segments or for individual roadway
segments to conduct sensitivity analyses. With this approach, it is possible to evaluate, for
instance, the conditions under which trucks would begin to drive more often on unpaved county
roads instead of on-system state roads.
The production-constrained gravity model used to determine the number of trips between each
well and each supplier location was as follows:
 

∑
where:

Tij = Truck flow produced by trip production i and attracted to trip attraction j.
Pi = Number of trips produced by trip production i.
Aj = Number of trips attracted to trip attraction j.
f(dij) = Friction factor between trip production i and trip attraction j.
z = Number of trip attractions.
The output was an origin-destination matrix showing the choice of suppliers for each well and
the associated number of trips for each choice of supplier.
The friction factor is calculated by using the following inverse power impedance function:

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 where:
dij = Impedance between trip production i and trip attraction j.
b = Model parameter.
In the absence of real-world data to calibrate the b parameter, the researchers tried several values
ranging from 0.5 to 1.5 in addition to the default value of 0.02 in TransCAD. A previous study
in Canada suggested the value of b could range from 0.48 to 1.39 for different census areas in
Canada (13). Although the study is dated and based on work-related trips in urban areas, it could
be used as a reference to select b values for sensitivity analysis purposes.
In an effort to replicate the decreasing likelihood of a trip as a function of travel time in rural
areas, the default value of 0.02 in TransCAD was considered unrealistic because the likelihood
of a trip would depend very little on the travel time between an origin and a destination. A b
value of 0.5 would more likely represent the relationship between travel times and the
corresponding likelihood of trips. For example, for one well and nine water suppliers located in
the study area, a b value of 0.02 would result in an approximately even distribution of trips
among the nine water sources regardless of travel time (Table 13). By increasing the value of b
to 0.5, the distribution of trips between the well and the various water sources would change
significantly, resulting in more trips to water sources that have a shorter travel time. This effect
would be even more noticeable by using a b value of 1 or 1.5 (Table 13). Calibration based on
field data would further reduce the level of uncertainty associated with b.
Table 13. Example Trip Distribution Based on Different b Values.
Water
Supplier
1
2
3
4
5
6
7
8
9
Total

Travel Time between Well
and Water Supplier
(minute)
4.7
8.0
10.8
13.1
13.4
14.2
16.2
23.3
27.2

Percentage of Total Water Trips
b = 0.02

b = 0.5

b=1

b = 1.5

11.3%
11.2%
11.2%
11.1%
11.1%
11.1%
11.1%
11.0%
10.9%
100%

17.9%
13.7%
11.8%
10.7%
10.6%
10.3%
9.6%
8.0%
7.4%
100%

26.9%
15.8%
11.7%
9.6%
9.4%
8.8%
7.8%
5.4%
4.6%
100%

37.7%
16.9%
10.7%
8.0%
7.8%
7.1%
5.8%
3.4%
2.7%
100%

Route Assignment
As mentioned, the researchers used an all-or-nothing assignment method to complete the route
assignment step. In essence, this method assigned all the trips between each origin-destination
pair to the shortest path between that pair, regardless of roadway capacity or congestion. The
output was segment-based truck flow for each direction of travel.

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 ESAL Calculations
For the conversion of truck volumes to ESALs, the researchers used the results of an analysis
that estimated ESALs for each truck type based on axle weight distributions from WIM station
readings. Details of this analysis are available in Implementation Report IR-16-03. Table 14
shows the number of ESALs for each truck type.
The researchers then calculated the total number of ESALs for each segment (in each direction)
by multiplying the number of assigned trucks for each activity by the corresponding number of
ESALs per truck, by adding the number of ESALs for each phase, and by aggregating the three
phases of development, operation, and re-fracking. Assuming an analysis period of 20 years and
four re-fracking events during this period, the cumulative number of ESALs for each direction of
segment i was as follows:
_

_

20

_

4

_

where:
Total_ESALi = Total cumulative ESALs for one direction of segment i
Dev_ESALi = Cumulative ESALs from development activities for one direction of segment i
Prod_ESALi = Cumulative ESALs from production activities for one direction of segment i
Refrc_ESALi = Cumulative ESALs from re-fracking activities for one direction of segment i.
Results
The first set of runs involved determining ESAL values due to the development and operation of
one well over 20 years. Because the number of ESALs depends on the direction of travel (to the
well or away from the well), the researchers prepared three sets of results for each roadway
segment: ESALs for trips to the well, ESALs for trips from the well, and higher directional
ESALs (i.e., the higher ESAL value between the two directions of travel). Figure 34 shows the
total number of ESALs for trips to the well, Figure 35 shows the total number of ESALs for trips
from the well, and Figure 36 shows higher directional ESALs. As expected, roadway segments
near the well had a much higher number of ESALs than segments farther away from the well. In
the immediate vicinity of the well, the total number of ESALs was 10,757 for trips to the well
and 15,059 for trips from the well. The total number of ESALs decreased as the roadway
segments were farther away from the well.
Notice in Figure 35 that FM 81 southeast of the well would be expected to have over 10,000
ESALs for trips from the well during the 20-year analysis period. Part of the reason is the
location of two crude oil truck off-load terminals on FM 81 approximately four miles southeast
of the well.
The second set of runs involved increasing the numbers of wells. The following scenarios were
completed: 10 wells, 100 wells, 200 wells, and 493 wells (i.e., the same number of wells
completed in 2013). The 10-well, 100-well, and 200-well scenarios involved a random selection
of wells from the total population of 493 wells completed in 2013.
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 Table 14. ESALs per Truck Type in the Eagle Ford Shale Region.
Well Development Activity

Truck Type

Drilling pad and construction equipment
Drilling rig
Drilling fluid and materials
Drilling equipment: casing, drilling pipe
Fracking equipment: pump trucks, tanks
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal

5-axle dump
5-axle rig
5-axle water
5-axle flatbed
5-axle equipment
5-axle water
5-axle water
5-axle water
5-axle sand
5-axle sand
5-axle sand

Well Production Activity
(per year)

Truck Type

Produced water (steel tank)
Produced water (aluminum tank)
Oil and condensate production (steel tank)
Oil and condensate production (aluminum tank)
Well Re-Fracking Activity
(per event)
Fracking equipment: pump trucks, tanks
Fracking water (steel tank)
Fracking water (aluminum tank)
Fracking sand (steel tank)
Fracking sand (aluminum tank)
Other additives and fluids
Flowback water removal

5-axle water
5-axle water
5-axle water
5-axle water

Truck Type
5-axle equipment
5-axle water
5-axle water
5-axle sand
5-axle sand
5-axle water
5-axle water

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ESALs per
Truck
(Trip to Well)

ESALs per
Truck
(Trip from Well)

1.177
8.676
1.412
1.709
2.606
1.412
1.412
1.876
1.876
1.412
0.092

0.092
8.676
0.092
0.066
2.606
0.092
0.023
0.092
0.023
0.092
1.412

ESALs per
Truck
(Trip to Well)

ESALs per
Truck
(Trip from Well)

0.092
0.023
0.092
0.023

1.412
1.412
1.412
1.412

ESALs per
Truck
(Trip to Well)

ESALs per
Truck
(Trip from Well)

2.606
1.412
1.412
1.876
1.876
1.412
0.092

2.606
0.092
0.023
0.092
0.023
0.092
1.412

 Figure 34. Total Number of ESALs (Trips to the Well) – One Well.

Figure 35. Total Number of ESALs (Trips from the Well) – One Well.
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 Figure 36. Total Number of ESALs (Higher Directional ESALs) – One Well.
Figure 37 through Figure 40 show the spatial distribution of higher directional ESALs for the
four scenarios analyzed. The figures clearly show that the number and extent of roadway
segments affected increase as the number of wells increases and their location is more widely
spread out throughout Karnes County. FM 81 southeast of SH 80 had the highest number of
ESALs. In the 493-well scenario (Figure 40), the number of directional ESALs on FM 81
southeast of SH 80 was higher than 4.4 million.
Table 15 provides a summary of the number of miles involved for each scenario and ESAL
interval, as well as the corresponding percentage with respect to the total number of network
miles. For example, for the 100-well scenario, approximately 37 miles (or 11% of the on-system
network) would have 250,000-500,000 ESALs over 20 years. By comparison, for the 493-well
scenario, approximately 67 miles (or 20% of the on-system network) would have
250,000-500,000 ESALs over 20 years.

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 Figure 37. Total Number of ESALs (Higher Directional ESALs) – 10 Wells.

Figure 38. Total Number of ESALs (Higher Directional ESALs) – 100 Wells.
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 Figure 39. Total Number of ESALs (Higher Directional ESALs) – 200 Wells.

Figure 40. Total Number of ESALs (Higher Directional ESALs) – 493 Wells.
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 Table 15. Miles of On-System and Off-System Roads Used to Develop and Operate Wells.
Higher Directional
ESALs
(20 Years)
>0 – 25,000
>25,000 – 50,000
>50,000 – 100,000
>100,000 – 200,000
>200,000 – 500,000
>500,000 – 1,500,000
>1,500,000 – 3,000,000
>3,000,000 – 4,412,000
Total
Higher Directional
ESALs
(20 Years)
>0 – 25,000
>25,000 – 50,000
>50,000 – 100,000
>100,000 – 200,000
>200,000 – 500,000
>500,000 – 1,500,000
>1,500,000 – 3,000,000
>3,000,000 – 4,412,000
Total
Higher Directional
ESALs
(20 Years)
>0 – 25,000
>25,000 – 50,000
>50,000 – 100,000
>100,000 – 200,000
>200,000 – 500,000
>500,000 – 1,500,000
>1,500,000 – 3,000,000
>3,000,000 – 4,412,000
Total
*

1 Well
On-System
Off-System Roads
Roads Used
Used
Miles Percent* Miles Percent**
198
59%
31
2%
198
59%
31
2%

10 Wells
On-System
Off-System
Roads Used
Roads Used
Miles Percent
Miles Percent
274
81%
88
7%
18
5%
3
0.2%
8
2%
300
88%
91
7%

100 Wells
On-System
Off-System Roads
Roads Used
Used
Miles Percent* Miles Percent**
104
31%
215
17%
71
21%
26
2%
54
16%
11
0.8%
51
15%
3
0.2%
37
11%
1
0.1%
3
0.9%
320
95%
255
21%

200 Wells
On-System
Off-System
Roads Used
Roads Used
Miles Percent
Miles Percent
61
18%
260
21%
38
11%
45
4%
75
22%
12
1%
63
19%
14
1%
67
20%
2
0.2%
16
5%
1
0.1%
2
0.6%
321
95%
333
27%

493 Wells
On-System
Off-System Roads
Roads Used
Used
Miles Percent
Miles
Percent
61
18%
260
21%
38
11%
45
4%
75
22%
12
1%
63
19%
14
1%
67
20%
2
0.2%
16
5%
1
0.1%
2
0.6%
321
95%
333
27%

Percentage of the total of 337 miles of on-system (i.e., state-maintained) roads in Karnes County.
Percentage of the total of 1,243 miles of off-system (i.e., county or local) roads in Karnes County.

**

For comparison purposes, Figure 41 shows the spatial distribution of ESALs listed in the TxDOT
Road-Highway Inventory Network (RHiNo) database. Of particular interest are corridors where
the cumulative ESALs in Figure 40 are higher than the corresponding ESALs listed in the
RHiNo database. Examples include FM 81, SH 80 south of FM 627, FM 1144, FM 1353, and
FM 2102.

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 Figure 41. ESALs According to the TxDOT RHiNo Database.
TTI researchers also estimated the number of ESAL-miles for all five scenarios. ESAL-miles
are frequently used to measure the combined effect of ESALs at any given location and distance
driven to provide an overall measure of the amount of pavement “consumed.” The results
indicate that for roads in Karnes County, developing and operating one well over 20 years could
result in 0.308 million ESAL-miles. As the number of wells increases, the total number of
ESAL-miles also increases: 4.35 million ESAL-miles for 10 wells, 46.3 million ESAL-miles for
100 wells, 90.7 million ESAL-miles for 200 wells, and 223 million ESAL-miles for 493 wells.
The number of ESAL-miles could be used for a variety of applications, including the estimation
of a marginal cost that reflects the additional use of the pavement structure due to the
development and operation of any number of oil and gas wells in a geographic area. Depending
on the specific application, the marginal cost estimation could vary drastically, which highlights
the need for a careful assessment of the assumptions behind the calculation. For example, a
cursory review of data from several states could suggest a unit cost of approximately 3 cents per
ESAL-mile, which, for one well in Karnes County, would translate into a total marginal cost of
approximately $9,000. For 493 wells, the cost would be $6.7 million (16). However, the
information gathered did not shed light as to what was included in the unit cost assessment. By
comparison, assuming 74 cents per ESAL-mile (16), the cost for one well in Karnes County
would be approximately $228,000. For 493 wells, the cost would be $165 million.

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Page 61

 APPLICABILITY OF THE METHODOLOGY
The methodology described in this report enables users to map truck traffic in connection with
energy developments to the surface transportation network in the state. The methodology uses
the four-step travel demand modeling approach. A case study was conducted to evaluate the
feasibility of the methodology using data for well development and operation in Karnes County
located in the Eagle Ford Shale area in South Texas. The spatial distribution of ESALs due to
development and operation of a number of wells in Karnes County over an analysis period was
obtained as the output of methodology. Examples of potential applications of the methodology
include, but are not limited to, the following:


Forecast the spatial distribution of ESALs due to the development and operation of any
number of wells. For wells that are in the development stage, analysis can be conducted
to evaluate the future impact due to well development, operation, and re-fracking on the
transportation network. For wells that are in production, the analysis can focus on future
impact due to well production and re-fracking activities. The resulting ESAL distribution
can also be used to estimate the total cost of pavement damage by multiplying the total
number of ESAL-miles by a marginal pavement cost.



Forecast the spatial-temporal distribution of ESALs due to the development and
operation of any number of wells. For example, it may be of interest to determine how
the spatial distribution of ESALs evolves over time during the development, production,
and re-fracking phases of multiple wells. The methodology enables users to forecast
spatial-temporal distributions of ESALs by aggregating ESALs associated with each well
during the analysis period.



Evaluate alternative scenarios by conducting sensitivity analyses. One potential
application could be to evaluate the reduction in truck traffic impact on the transportation
network resulting from various temporary water pipe implementations. Another
application could be to calibrate various assumptions to improve the simulation
procedure. For example, in the step of trip distribution, the researchers assumed the
impedance was travel time and the b parameter of inverse power impedance function was
0.5. A previous study in Canada suggested the value of b ranged from 0.48 to 1.39 for
different census areas in Canada based on the impedance of trip length (13). Although
the study is dated and based on work-related trips in urban area, it may be used as a
reference to select values for b to conduct sensitivity analyses.
Keeping all the other assumptions the same as in the scenario shown in Figure 36, the
researchers used b values of 0.02, 1.0, and 1.5 to simulate different alternative scenarios.
Figure 42, Figure 43, and Figure 44 show the distribution ESALs due to one well in
Karnes County based on different values of b. In relation to Figure 36, the number of
affected segments is the same across all four scenarios because the change in b only
affects the amount of trips assigned between an origin and a destination, but not the
choice of routes. According to the figures, the b parameter was found to be sensitive to
the ESAL distribution on segments that are in vicinity of the well.

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Page 62

 Figure 42. Total Number of ESALs (Higher Directional ESALs) – One Well (b=0.02).

Figure 43. Total Number of ESALs (Higher Directional ESALs) – One Well (b=1).
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Page 63

 Figure 44. Total Number of ESALs (Higher Directional ESALs) – One Well (b=1.5).
In addition to the ESAL maps, TTI calculated the total number of ESAL-miles for one-well
scenario and 493-well scenario by summing up the multiplication of ESALs for both directions
of a segment and segment length. The results provide a high-level aggregate measure to quantify
pavement damage due to truck traffic for either study area or Karnes County.
Figure 45 shows the total ESAL-miles on roads in the study area and in Karnes County
due to one well based on different b values, while Figure 46 shows total ESAL-miles due
to 493 wells. The results indicate that b became less sensitive to total ESAL-miles in the
smaller area (i.e., Karnes County) compared to the larger area (i.e., the study area). In
addition, b was less sensitive to the total number ESAL-miles in the 493-well scenario
compared to the one-well scenario.

RR-16-01

Page 64

  900,000
 800,000
 700,000

Total ESAL‐Miles

 600,000
 500,000
 400,000
 300,000
 200,000

Study Area

 100,000

Karnes County
 ‐
0.00

0.50

1.00

1.50

b Value

Figure 45. ESAL-Miles vs. b Parameter – One Well.

Figure 46. ESAL-Miles vs. b Parameter – 493 Wells.


Forecast the spatial distribution of ESALs in urban areas due to well developments that
take place in rural areas. One potential application could be to determine the need and
feasibility of alternative truck routes. As an illustration, Figure 47 provides a zoomed-in
view of Figure 40 around Karnes City. With all 493 wells developed throughout the
county, there would be a substantial amount of truck traffic within city limits. The results
RR-16-01

Page 65

 of the simulation could shed some light as to expected truck volumes and traffic patterns,
which could be used to determine whether (and when) alternative truck routes may be
warranted.

Figure 47. ESAL Distributions in Karnes City Due to 493 Wells in Karnes County.
In reality, traffic that travels on state roads tends to stay on those roads even within city
limits. The modeling software enables users to manage this characteristic by modifying
impedance functions or altering certain parameters, e.g., posted speed limits along certain
corridors. As an illustration, Figure 48 shows the spatial distribution of ESALs in Karnes
City with modified speed limit for local roads to ensure that most traffic traveling within
city limits stays within state highways.

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Page 66

 Figure 48. ESAL Distributions in Karnes City Due to 493 Wells in Karnes County
(Alternative Scenario).
Anticipated enhancements of the methodology include the following:


The methodology did not take into account economies of scale that operators implement
when developing multiple wells within a short period, which would result in a lower
number of trucks per well. Future enhancements would enable a variety of logistical
assumptions, e.g., by combining trips involving multiple wells for a number of
development or production activities. These wells could be located either on the same
pad or on different pads.



The methodology assumed that the water and sand needed for fracking operations were
uniform throughout the region. Through a separate initiative, TTI researchers are
currently conducting an analysis to determine spatial and temporal variations in the
amount of water and sand used. These results could be used to fine tune the estimates,
which could have an impact on trip modeling results.



The methodology did not consider non-truck trips needed to develop and operate oil and
gas wells because these trips would likely result in a very small number of ESALs.
Future refinements could include non-truck trips for applications such as emergency
response and congestion management.

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Page 67

 REFERENCES
1. Real Prices Viewer. U.S. Energy Information Administration, Washington, D.C., August
2015. http://www.eia.gov/forecasts/steo/realprices/. Accessed August 01, 2016.
2. Texas Administrative Code Title 16, Part 1, Chapter 3, Rule §3.29. Hydraulic Fracturing
Chemical Disclosure Requirements.
3. C.A. Quiroga, E. Kraus, I. Tsapakis, J. Li, W. Holik. Truck Traffic and Truck Loads
Associated with Unconventional Oil and Gas Developments in Texas. Report RR-15-01.
Texas A&M Transportation Institute, Texas Department of Transportation, College
Station, Texas, August 2015.
4. Ground Water Protection Council (GWPC) and Interstate Oil and Gas Compact
Commission (IOGCC). 2014. FracFocus Chemical Disclosure Registry. FracFocus
Data Download. https://fracfocus.org/data-download. Accessed July 21, 2016.
5. Truck Axle Weight Distributions. Implementation Report IR-16-02. Texas A&M
Transportation Institute, Texas Department of Transportation, College Station, Texas,
July 2016.
6. Traffic Loads for Developing and Operating Individual Wells. Energy Sector Brief ESB16-08. Texas A&M Transportation Institute, Texas Department of Transportation,
College Station, Texas, July 2016.
7. AASHTO Guide for Design of Pavement Structures, Fourth Edition. American
Association of State Highway and Transportation Officials, Washington, D.C., 1998.
8. Gutekunst, J., Motamed, M., Alrashidan, A., & Machemehl, R. B. (2016). Pavement
Maintenance Prioritization Tool for Effects of Hydraulic Fracturing. Transportation
Research Record: Journal of the Transportation Research Board, (2580), 65-70.
9. Dybing, A., Lee, E., DeHaan, C., & Dharmadhikari, N. (2013). Impacts to Montana State
Highways Due to Bakken Oil Development (No. FHWA/MT-13-002/8219).
10. Bratlien, A., Dybing, Alan., Holt, L., Horner, T., Kazemi, Y., Lee, E., Lu, P., Mielke, J.,
Tolliver, D., Wentz, B., Dang, V., DeHaan, C., Dharmadhikari, N., Ifepe, C., Park, Y.,
Yuan, F., and Zheng, Z. (2014). Infrastructure Needs: North Dakota's County, Township
and Tribal Roads and Bridges: 2015-2034.
11. Quiroga, C., Tsapakis, I., Li, J., Holik, W., Kraus, E. Truck Traffic and Truck Loads
Associated with Unconventional Oil and Gas Developments in Texas. 2016 Update.
Report RR-16-01. Texas A&M Transportation Institute, Texas Department of
Transportation, College Station, Texas, August 2016.
12. Meyer, M. D., & Miller, E. J. (2000). Urban transportation planning Second Edition.
13. Hutchinson, B. G., & Smith, D. P. (1979). Empirical studies of the journey to work in
urban Canada. Canadian Journal of Civil Engineering, 6(2), 308-318.
14. Patriksson, M. (2015). The traffic assignment problem: models and methods. Courier
Dover Publications.

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Page 68

 15. Quiroga, C., Kraus, E., Tsapakis, I., Li, J., Holik, W. (2015) Truck Traffic and Truck
Loads Associated with Unconventional Oil and Gas Developments in Texas. Report RR15-01. Texas A&M Transportation Institute, Texas Department of Transportation,
College Station, Texas.
16. Murillo, J., Ahmed, A., Labi, S. (2014). Using Regional Freight Traffic Assignment
Modeling to Quantify the Variability of Pavement Damage for Highway Cost Allocation
and Revenue Analysis. Final Report, USDOT Region V Regional University
Transportation Center.

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Page 69

 APPENDIX. OIL AND GAS WELL INFORMATION BY COUNTY
This appendix includes supplemental information pertaining to the completion of oil and gas
wells in Texas. The tables included in this appendix are as follows:
Table 16 shows the total number of new wellheads completed per county.
Table 17 shows the total number of new wellends completed per county.
Table 18 shows the total number of new directional well completed per county.
Table 19 shows the total number of new vertical wellheads completed per county.
Table 20 shows the total number of new horizontal wellends completed per county.
Note that the number of wells in Table 16, Table 17, and Table 18 corresponds to actual wells,
i.e., multiple permits may be issued for a single well location and have subsequently been
removed from this well count. The number of wells in Table 19 and Table 20 corresponds to the
number of completed permits associated with the wells since wells may be active for multiple
time periods resulting in additional truck traffic.
Table 16. Total Number of New Wellheads Completed per County.
County
Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell
Bexar
Blanco
Borden
Bosque
Bowie
Brazoria
Brazos
Brewster
Briscoe
Brooks
Brown
Burleson
Burnet
Caldwell

2006
23
278
18
9
60
0
11
8
0
0
0
3
59
0
0
0
19
13
1
20
18
1
0
29
7
9
0
15

2007
25
286
32
11
77
0
3
6
0
2
4
3
52
0
2
0
42
3
1
20
17
0
1
24
15
8
0
42

2008
38
497
29
11
104
0
6
28
0
1
15
6
61
0
1
0
20
2
1
30
24
0
0
50
11
36
0
15

2009
21
399
12
2
91
0
16
14
0
0
1
1
30
0
2
0
8
0
1
8
23
0
0
14
5
3
0
14

RR-16-01

2010
5
834
2
2
80
0
36
9
0
0
5
1
36
0
4
0
28
0
1
32
42
0
0
23
43
33
0
30

2011
7
1153
2
2
93
0
52
9
0
0
3
5
21
0
0
0
41
0
0
38
26
0
0
22
45
14
0
39

Page 70

2012
10
1100
4
1
101
0
147
8
0
0
2
12
40
0
5
0
43
0
0
53
27
0
0
16
10
5
0
35

2013
10
705
0
3
95
0
175
9
0
0
3
12
29
0
47
0
55
1
0
31
71
0
0
15
5
14
0
15

2014
9
821
0
1
110
0
293
5
0
0
1
16
15
0
14
0
45
0
1
30
76
0
0
14
7
78
0
30

2015
2
191
0
2
49
0
72
2
0
0
0
4
1
0
0
0
9
0
0
21
12
0
0
3
2
12
0
9

 County
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell
Cottle
Crane
Crockett
Crosby
Culberson
Dallam
Dallas
Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher
Floyd
Foard
Fort Bend
Franklin
Freestone
Frio
Gaines
Galveston
Garza

2006
10
17
0
0
3
1
0
14
44
0
24
14
31
4
0
0
29
0
3
19
71
0
9
159
290
1
9
0
0
27
0
0
225
40
25
59
0
58
23
229
31
3
0
56
0
0
14
12
1
0
42
0
215
11
215
14
46

2007
11
16
0
1
5
0
0
2
54
0
18
9
14
12
0
0
33
0
4
23
70
3
7
175
287
37
7
0
7
37
0
0
229
46
23
36
0
80
52
151
26
15
0
64
0
0
6
18
1
28
35
2
241
55
164
13
42

2008
9
22
0
0
7
0
0
13
76
3
40
41
20
13
0
0
28
0
2
27
71
3
12
148
311
24
9
0
10
36
0
0
291
63
24
55
0
74
12
225
20
21
0
77
1
0
12
35
0
9
34
9
213
42
162
8
50

2009
7
15
1
1
5
4
0
8
22
0
6
21
13
8
0
0
11
0
2
7
38
0
2
58
64
25
8
1
8
17
0
0
128
28
11
29
0
26
7
235
19
10
0
12
1
0
8
22
0
1
23
1
165
18
64
1
39

RR-16-01

2010
3
14
0
1
0
3
0
30
10
0
14
19
29
45
0
0
24
0
2
4
105
0
5
83
168
41
18
0
9
36
0
0
167
60
11
137
0
32
4
453
49
9
0
4
1
0
14
30
1
2
38
2
124
20
208
7
29

2011
2
7
3
0
15
5
0
33
12
0
18
44
32
42
0
0
18
0
4
11
130
0
4
116
94
95
47
0
1
61
0
0
99
174
10
332
0
24
6
488
7
4
0
2
2
0
15
36
0
0
61
4
96
73
211
7
27

Page 71

2012
3
10
0
1
18
4
0
41
5
0
25
13
35
40
0
4
7
0
1
5
69
0
1
144
148
121
42
1
2
57
0
0
67
226
6
583
0
35
4
689
9
0
0
0
4
2
27
40
1
4
43
10
45
82
160
7
29

2013
2
8
0
4
10
5
0
32
8
0
17
29
25
44
0
0
4
0
4
5
38
0
2
213
210
127
65
0
4
48
0
0
88
359
6
524
0
56
4
668
4
0
0
0
4
0
14
41
0
3
78
11
16
70
180
5
39

2014
1
17
0
2
19
6
0
42
15
0
11
12
17
23
0
0
7
0
8
4
33
0
5
160
214
187
85
0
0
53
0
0
53
317
9
612
0
40
4
494
6
0
0
0
4
0
39
38
0
1
87
29
16
99
231
2
45

2015
0
7
0
0
0
1
0
7
9
0
3
2
3
11
0
0
1
0
2
2
10
0
1
76
54
34
48
0
0
8
0
0
17
97
0
244
0
16
4
102
3
0
0
0
2
0
9
18
0
0
7
4
5
18
94
1
11

 County
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin
Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson
Hidalgo
Hill
Hockley
Hood
Hopkins
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr
Kimble
King
Kinney
Kleberg
Knox
Lamar
Lamb
Lampasas
La Salle

2006
0
70
88
2
54
10
66
6
4
22
0
3
26
11
41
12
266
4
9
0
272
14
135
44
115
144
0
16
42
1
0
41
27
67
37
5
1
26
29
6
531
44
9
0
0
25
12
0
3
21
0
18
3
0
3
1
58

2007
0
78
53
3
8
9
62
8
2
12
0
3
16
8
35
12
302
3
6
0
255
15
152
49
90
269
1
19
72
3
0
145
22
129
31
9
1
36
15
9
839
50
13
0
0
22
21
0
1
24
0
21
4
0
2
6
45

2008
0
79
58
6
7
5
59
11
7
13
0
0
31
10
47
15
293
4
10
0
295
19
135
114
79
200
0
11
96
4
0
91
69
55
53
16
1
30
8
18
898
53
21
0
0
35
32
0
0
27
0
14
8
0
8
5
55

2009
0
67
12
7
3
6
19
9
3
2
0
0
16
3
39
7
128
2
15
0
112
11
51
28
40
38
1
6
60
0
0
10
41
32
23
12
0
23
2
10
423
38
18
0
0
20
24
0
1
10
0
5
2
0
4
1
32

RR-16-01

2010
0
217
26
41
1
13
11
10
7
7
0
0
4
5
43
14
93
7
26
0
121
5
45
10
57
25
0
11
129
0
0
15
50
33
21
9
0
35
3
17
360
40
105
0
0
17
28
0
1
14
0
13
1
0
4
1
102

2011
0
588
16
171
10
16
18
9
13
0
0
0
7
6
55
8
64
8
39
0
132
3
47
0
50
42
1
19
219
0
0
13
168
70
15
9
0
26
2
8
241
41
276
0
0
13
21
0
0
13
0
26
5
0
3
0
239

Page 72

2012
0
732
7
287
14
20
15
19
17
1
0
0
8
11
51
2
45
10
34
0
115
4
39
0
32
47
3
21
312
0
0
22
237
126
20
8
0
35
3
6
102
37
490
1
0
0
22
0
0
12
0
21
8
0
3
1
513

2013
0
579
1
381
10
23
26
14
20
0
0
0
8
9
56
7
35
4
19
0
119
5
37
0
41
5
1
32
429
0
0
12
263
122
16
9
0
26
6
5
18
52
492
2
0
6
35
0
0
29
0
18
7
0
0
1
605

2014
0
526
4
252
1
23
25
20
4
1
0
0
7
6
25
22
72
6
37
0
117
13
54
0
57
7
2
34
397
0
0
17
262
254
23
3
0
17
4
5
5
38
611
0
0
5
20
0
0
36
0
13
6
0
0
0
637

2015
0
114
1
57
0
9
5
1
2
0
0
0
2
8
13
5
8
1
4
0
37
6
12
0
3
0
1
3
133
0
0
3
66
19
7
0
0
5
0
2
1
10
112
1
0
2
2
0
0
9
0
7
0
0
1
0
238

 County
Lavaca
Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard
Midland
Milam
Mills
Mitchell
Montague
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos
Polk
Potter
Presidio
Rains
Randall
Reagan
Real
Red River
Reeves
Refugio
Roberts
Robertson
Rockwall

2006
99
11
74
36
87
121
45
0
41
8
2
1
0
50
12
12
118
0
37
65
1
9
227
22
0
122
97
12
49
0
0
209
37
21
21
39
43
3
13
77
366
352
0
193
11
34
0
0
0
207
0
0
46
78
116
100
0

2007
58
8
73
35
121
98
35
0
47
7
5
0
2
57
5
6
176
0
36
46
4
8
153
8
1
199
67
1
42
0
0
227
14
6
35
44
36
4
11
61
372
291
0
270
22
3
0
1
0
169
0
0
48
77
99
116
0

2008
57
17
60
32
156
145
49
0
44
12
3
0
3
84
9
12
191
0
59
184
18
9
225
3
0
228
124
7
36
0
1
229
28
7
49
40
72
6
6
105
336
240
0
374
28
5
0
0
0
252
1
1
58
70
120
165
0

2009
26
8
36
13
57
56
25
0
27
21
3
3
0
40
7
1
119
0
24
104
16
6
166
54
0
160
70
4
27
0
0
87
10
11
18
29
47
2
4
64
178
83
0
163
13
2
0
1
0
141
1
1
30
79
31
98
0

RR-16-01

2010
35
14
42
15
50
69
52
0
52
19
3
0
0
81
18
3
402
0
25
26
45
6
347
81
0
154
190
5
20
0
0
71
10
15
40
23
84
7
11
30
146
63
0
225
16
11
0
0
0
440
0
0
56
84
41
53
0

2011
26
16
67
17
26
116
74
0
56
8
11
0
0
148
24
3
635
0
15
34
54
22
565
222
0
164
207
7
27
0
0
70
6
13
70
33
85
10
5
13
117
105
0
117
16
16
0
0
0
385
0
2
220
71
58
43
0

Page 73

2012
36
14
41
14
19
118
110
0
171
8
9
1
0
305
42
9
786
0
14
14
100
16
545
331
0
162
214
5
46
0
0
36
11
4
72
14
123
12
11
35
127
135
0
102
17
3
0
0
0
381
1
1
330
62
72
38
0

2013
61
12
39
14
14
113
181
0
156
4
7
1
0
411
61
7
612
0
10
12
114
6
484
319
0
107
167
10
20
0
0
2
11
5
66
18
142
24
7
51
154
50
0
114
12
4
0
0
0
320
0
1
346
46
61
32
0

2014
128
26
25
9
10
130
162
0
150
5
3
6
0
476
69
8
672
0
8
5
47
3
593
39
0
46
105
3
47
0
1
3
7
7
47
10
157
9
5
76
103
30
0
112
5
15
0
0
0
392
0
1
369
60
85
14
0

2015
19
0
4
0
4
16
35
0
99
4
2
4
0
135
2
4
145
0
1
0
11
3
217
0
0
9
1
0
26
0
1
1
2
2
26
7
30
3
0
18
38
6
0
77
0
8
0
0
0
155
0
0
156
42
20
3
0

 County
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor
Terrell
Terry
Throckmorton
Titus
Tom Green
Travis
Trinity
Tyler
Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson
Winkler
Wise
Wood
Yoakum
Young
Zapata
Zavala

2006
79
332
1
0
4
30
0
52
105
42
53
18
104
19
127
32
53
17
167
0
314
15
66
54
21
8
26
0
3
26
40
230
1
16
4
48
7
32
339
7
282
57
173
108
55
12
0
3
98
219
15
231
41
274
16

2007
40
349
1
8
19
21
0
19
58
48
80
15
51
34
127
36
71
24
60
0
615
17
45
51
22
0
26
0
2
29
48
298
0
10
18
34
0
34
203
4
239
29
196
125
32
17
1
2
161
189
7
125
55
235
14

2008
46
308
1
31
10
19
0
31
109
85
121
79
20
55
165
55
53
40
124
0
785
15
38
41
26
2
18
0
2
25
24
476
0
1
10
51
3
26
232
3
352
65
238
160
28
16
0
2
127
283
4
177
76
246
7

2009
10
90
1
52
16
8
0
18
85
48
66
10
13
6
51
13
52
40
40
0
540
18
2
30
10
0
46
0
1
18
7
194
0
9
6
36
0
16
101
2
196
30
75
135
21
15
1
4
16
186
12
35
37
91
7

RR-16-01

2010
28
50
6
73
12
13
0
29
78
59
91
5
11
5
41
24
41
80
59
0
540
8
6
24
15
0
14
0
0
12
4
489
0
0
5
28
0
17
234
3
313
50
132
118
35
14
0
10
27
234
8
121
54
48
11

2011
26
39
6
65
16
24
0
45
100
48
44
8
10
4
35
50
22
81
29
0
516
20
2
30
16
1
16
0
3
16
10
581
0
0
5
20
0
7
281
1
385
48
200
162
39
9
0
46
31
161
9
147
45
33
46

Page 74

2012
18
55
4
25
9
17
0
45
79
41
21
10
11
3
40
41
50
75
26
0
243
19
2
31
32
5
25
0
0
12
1
620
0
0
8
20
1
7
306
2
445
45
204
136
67
7
0
39
49
181
18
163
68
5
75

2013
24
54
1
11
3
19
0
32
141
56
10
14
12
0
25
47
50
59
13
0
117
34
0
19
58
5
17
0
0
9
3
524
0
2
4
22
7
11
298
11
369
36
209
135
34
6
1
53
68
156
6
220
64
14
66

2014
18
57
1
12
2
9
0
25
133
36
14
1
18
0
43
79
43
68
0
0
92
32
1
11
60
21
13
0
3
2
14
552
0
0
6
15
5
4
283
12
457
33
92
147
39
8
2
27
61
91
12
222
65
14
78

2015
12
22
0
1
0
2
0
5
55
12
0
6
5
0
6
15
3
11
0
0
30
10
1
9
18
2
3
0
0
1
6
191
0
0
0
1
0
2
61
0
50
7
5
35
20
0
0
1
23
8
13
88
18
2
44

 Table 17. Total Number of New Wellends Completed per County.
County
Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell
Bexar
Blanco
Borden
Bosque
Bowie
Brazoria
Brazos
Brewster
Briscoe
Brooks
Brown
Burleson
Burnet
Caldwell
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell
Cottle
Crane
Crockett
Crosby
Culberson

2006
26
291
19
9
60
0
11
8
0
0
0
3
62
0
0
0
21
13
1
24
41
1
0
29
7
19
0
15
10
17
0
0
4
1
0
14
45
0
24
18
31
4
0
0
29
0
3
19
71
0
9
160
293
1
10

2007
27
294
32
11
77
0
3
7
0
2
4
3
56
0
2
0
49
3
2
22
36
0
1
25
15
19
0
43
11
16
0
1
9
0
0
2
55
0
18
10
14
12
0
0
34
0
4
23
70
3
7
184
297
37
7

2008
39
506
29
13
104
0
6
28
0
1
20
6
63
0
1
0
21
2
1
30
51
0
0
52
11
70
0
15
10
22
0
0
15
0
0
16
78
3
41
43
20
13
0
0
32
0
2
27
71
3
13
151
314
24
9

2009
22
411
12
3
91
0
17
15
0
0
1
1
30
0
2
0
8
0
1
12
41
0
0
15
5
6
0
15
7
15
3
2
15
4
0
10
22
0
6
22
13
8
0
0
11
0
2
7
38
0
2
59
64
26
9

RR-16-01

2010
5
868
2
2
80
0
42
9
0
0
6
1
36
0
4
0
33
0
1
35
106
0
0
24
43
102
0
30
3
14
0
1
0
3
0
37
10
0
14
22
29
46
0
0
24
0
2
4
106
0
5
85
169
41
18

2011
7
1169
2
2
95
0
52
9
0
0
3
5
21
0
0
0
50
0
0
44
41
0
0
24
45
38
0
43
2
7
3
0
22
6
0
38
12
0
18
45
33
42
0
0
18
0
4
11
131
0
4
118
98
96
47

Page 75

2012
10
1113
5
1
102
0
147
8
0
0
2
12
42
0
5
0
44
0
0
55
34
0
0
19
10
8
0
49
3
10
0
1
28
4
0
48
6
0
25
15
35
41
0
4
7
0
1
5
69
0
1
147
150
122
42

2013
10
710
0
3
95
0
176
9
0
0
3
12
29
0
47
0
56
1
0
34
75
0
0
16
5
21
0
19
2
8
0
4
16
5
0
36
8
0
17
31
25
44
0
0
4
0
4
5
38
0
2
213
210
127
65

2014
12
827
0
1
110
0
293
6
0
0
1
16
15
0
14
0
46
0
1
31
82
0
0
14
7
78
0
34
1
17
0
2
19
6
0
45
16
0
11
12
17
23
0
0
7
0
8
4
34
0
5
160
215
187
85

2015
2
191
0
2
49
0
72
2
0
0
0
4
1
0
0
0
9
0
0
21
12
0
0
3
2
12
0
9
0
7
0
0
0
1
0
8
10
0
3
2
3
11
0
0
1
0
2
2
10
0
1
76
54
34
48

 County
Dallam
Dallas
Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher
Floyd
Foard
Fort Bend
Franklin
Freestone
Frio
Gaines
Galveston
Garza
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin
Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson
Hidalgo
Hill
Hockley
Hood
Hopkins

2006
0
0
32
0
0
228
40
25
92
0
59
23
239
31
3
0
56
0
0
38
12
1
0
44
0
217
20
216
14
46
0
71
91
2
54
11
66
11
4
24
0
3
31
13
42
12
268
5
9
0
273
14
139
45
136
146
0

2007
0
8
42
0
0
231
47
23
62
0
80
54
157
30
16
0
64
0
0
13
20
2
28
35
3
243
62
171
15
42
0
79
53
7
9
9
62
15
3
15
0
3
16
11
35
12
302
3
6
0
256
15
159
51
117
270
1

2008
0
10
38
0
0
292
72
24
84
0
74
12
237
21
21
0
77
1
0
21
35
0
9
34
9
214
52
172
10
50
0
79
58
8
7
6
60
17
8
17
0
0
31
12
48
16
297
4
10
0
298
19
138
117
112
200
0

2009
1
8
18
0
0
129
31
11
38
0
28
7
236
19
10
0
13
1
0
16
22
0
1
23
1
166
21
70
1
39
0
69
12
10
3
6
19
16
3
7
0
0
16
3
39
9
133
2
15
0
112
11
51
28
45
38
1

RR-16-01

2010
0
9
36
0
0
168
61
11
166
0
33
4
458
50
9
0
5
1
0
29
30
1
2
40
2
128
29
229
7
29
0
220
26
49
1
14
11
17
7
8
0
0
4
5
46
16
93
7
26
0
121
5
47
10
65
25
0

2011
0
1
62
0
0
101
175
10
344
0
25
6
496
7
4
0
2
2
0
20
38
0
0
61
4
97
80
218
7
29
0
589
17
187
10
16
18
10
18
0
0
0
7
6
56
9
64
8
39
0
134
3
50
0
54
42
1

Page 76

2012
1
2
57
0
0
70
226
6
596
0
35
4
698
9
0
0
0
4
2
34
41
1
4
44
10
46
88
163
7
29
0
733
7
297
14
22
16
23
20
1
0
0
8
11
51
3
46
10
34
0
116
4
40
0
36
47
4

2013
0
4
48
0
0
104
359
6
540
0
57
4
672
4
0
0
0
4
0
20
42
0
3
78
11
16
81
182
7
39
0
581
1
386
10
24
26
14
27
0
0
0
8
11
56
10
35
4
19
0
120
5
37
0
44
5
1

2014
0
0
53
0
0
54
322
9
637
0
41
4
498
6
0
0
0
4
0
46
38
0
1
88
29
16
128
235
2
46
0
526
4
254
1
24
26
21
6
1
0
0
7
6
25
23
72
6
37
0
119
13
56
0
57
7
2

2015
0
0
8
0
0
17
97
0
256
0
16
4
106
3
0
0
0
2
0
9
18
0
0
7
4
5
18
95
1
11
0
114
1
58
0
9
5
2
3
0
0
0
2
10
13
5
8
1
4
0
37
6
12
0
3
0
1

 County
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr
Kimble
King
Kinney
Kleberg
Knox
Lamar
Lamb
Lampasas
La Salle
Lavaca
Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard
Midland
Milam
Mills
Mitchell
Montague

2006
17
43
1
0
45
27
67
38
5
2
29
29
6
535
44
9
0
0
26
13
0
3
22
0
20
3
0
3
1
64
102
35
75
36
87
128
47
0
42
8
3
1
0
50
19
12
120
0
40
79
1
9
227
22
0
122
97

2007
24
73
3
0
156
22
131
31
21
1
41
15
9
841
50
13
0
0
24
21
0
1
24
0
24
4
0
3
6
50
61
19
74
39
121
101
39
0
50
7
6
0
2
57
6
6
178
0
39
68
4
8
161
9
1
199
69

2008
13
96
4
0
103
69
55
54
37
1
31
8
18
898
53
23
0
0
38
33
0
0
27
0
15
8
0
9
5
55
57
33
60
34
156
170
54
0
44
15
3
0
3
85
12
12
191
0
59
210
18
9
233
3
0
228
127

2009
6
61
0
0
22
41
32
23
26
0
24
2
10
426
38
18
0
0
20
24
0
1
10
0
5
2
0
4
1
33
26
18
38
14
57
57
25
0
29
25
3
3
0
40
8
1
119
0
25
109
16
6
170
54
0
160
70

RR-16-01

2010
11
130
0
0
18
50
33
21
12
0
38
3
17
360
40
109
0
0
18
28
0
1
14
0
15
1
0
6
1
105
38
21
43
16
51
70
52
0
54
23
3
0
0
86
20
3
402
0
25
27
45
6
348
84
0
154
190

2011
19
220
0
0
13
170
71
15
21
0
34
2
8
241
41
276
0
0
14
21
0
0
13
0
26
5
0
3
0
239
28
37
69
18
26
116
74
0
56
9
11
0
0
148
26
3
635
0
17
34
54
22
565
224
0
164
208

Page 77

2012
21
314
0
0
22
237
128
21
16
0
39
3
6
102
37
492
1
0
0
22
0
0
12
0
21
8
0
3
1
515
36
39
43
14
20
118
111
0
177
13
11
1
0
305
46
9
788
0
14
14
100
16
548
333
0
162
217

2013
34
429
0
0
12
264
123
16
12
0
28
9
5
19
52
494
3
0
6
35
0
0
29
0
19
7
0
0
1
611
63
21
39
15
14
114
185
0
161
4
7
1
0
413
62
7
612
0
11
12
114
6
489
319
0
108
167

2014
35
397
0
0
17
267
254
23
3
0
18
4
5
6
38
613
0
0
5
20
0
0
36
0
13
6
0
0
0
638
129
27
25
13
10
132
162
0
152
5
3
6
0
477
71
8
673
0
9
7
47
3
593
39
0
46
105

2015
4
133
0
0
3
66
19
7
0
0
5
0
2
2
10
112
1
0
3
2
0
0
9
0
7
0
0
1
0
238
19
0
4
0
4
16
35
0
100
4
2
4
0
135
2
4
145
0
1
0
11
3
218
0
0
9
1

 County
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos
Polk
Potter
Presidio
Rains
Randall
Reagan
Real
Red River
Reeves
Refugio
Roberts
Robertson
Rockwall
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor
Terrell
Terry
Throckmorton
Titus
Tom Green

2006
12
56
0
0
228
37
24
21
40
43
3
13
77
366
357
0
274
13
62
0
0
0
208
0
0
48
78
117
120
0
79
332
3
0
4
30
0
52
107
42
71
18
104
19
129
32
56
17
167
0
316
15
71
66
21
8
26

2007
1
44
0
0
233
14
6
35
47
36
4
12
61
374
295
0
360
44
6
0
1
0
169
0
0
52
77
99
148
0
40
349
4
12
19
22
0
19
58
48
124
15
51
34
128
39
71
24
61
0
616
17
48
55
23
0
26

2008
7
39
0
1
241
28
7
49
40
75
6
6
109
337
242
0
445
52
5
0
0
0
252
2
1
71
70
122
203
0
46
311
1
32
10
19
0
31
115
85
179
79
20
55
167
55
53
40
124
0
787
15
39
51
26
2
18

2009
7
31
0
0
87
11
15
18
30
48
2
5
64
179
84
0
253
22
4
0
2
0
141
1
1
31
80
32
117
0
10
90
1
53
16
10
0
18
94
48
79
19
13
6
51
13
52
40
40
0
540
19
2
32
10
0
46

RR-16-01

2010
11
21
0
0
71
10
26
42
26
84
7
13
31
149
63
0
308
22
19
0
0
0
440
0
0
60
84
42
67
0
28
50
6
74
13
13
0
29
78
60
100
5
11
5
43
25
41
81
59
0
540
8
6
27
15
0
14

2011
9
35
0
0
71
6
14
70
35
86
10
5
13
120
105
0
176
25
17
0
0
0
385
0
2
224
71
58
71
0
26
39
6
65
16
25
0
45
101
48
44
8
11
4
35
50
22
81
29
0
516
20
2
31
16
1
16

Page 78

2012
5
73
0
0
36
11
4
72
14
124
12
11
36
128
137
0
148
22
3
0
0
0
381
1
1
332
62
73
44
0
18
55
4
26
9
18
0
45
80
41
22
10
11
3
41
41
50
75
27
0
243
19
2
34
32
5
25

2013
10
20
0
0
2
11
6
67
19
142
24
7
52
155
50
0
178
12
4
0
0
0
321
0
1
354
48
63
34
0
24
54
1
11
3
19
0
32
143
56
17
14
12
0
27
50
51
59
14
0
118
34
0
24
58
5
17

2014
3
47
0
1
3
7
7
47
10
158
9
6
76
103
30
0
185
5
15
0
0
0
394
0
1
374
60
86
15
0
18
57
1
12
2
9
0
25
134
37
15
1
21
0
45
80
44
68
0
0
92
32
1
13
60
21
13

2015
0
26
0
1
1
2
2
26
7
30
3
0
19
38
8
0
154
0
8
0
0
0
156
0
0
163
42
20
3
0
12
23
0
1
0
3
0
5
56
12
0
6
5
0
6
16
3
11
0
0
30
11
1
12
18
2
3

 County
Travis
Trinity
Tyler
Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson
Winkler
Wise
Wood
Yoakum
Young
Zapata
Zavala

2006
0
3
59
40
233
1
16
4
48
7
32
347
26
287
57
175
108
55
12
0
9
106
220
21
238
41
277
32

2007
0
2
69
48
303
0
11
18
34
0
34
222
8
241
29
197
125
32
18
1
7
164
190
7
132
55
239
34

2008
0
2
62
24
477
0
1
10
51
3
26
250
10
361
68
241
160
28
16
0
2
132
287
4
183
76
250
18

2009
0
1
50
7
194
0
9
6
38
0
17
105
3
198
30
75
135
21
15
1
6
19
187
14
36
37
92
17

2010
0
0
32
4
493
0
0
5
28
0
17
240
10
321
51
134
118
35
16
0
10
27
238
9
122
54
48
11

2011
0
3
30
10
589
0
0
5
20
0
7
288
1
389
50
202
162
39
9
0
52
34
164
11
156
45
37
60

2012
0
0
17
1
622
0
0
8
20
1
7
308
4
447
46
206
136
67
7
0
40
50
184
20
163
68
5
98

2013
0
0
11
3
530
0
2
4
22
7
11
300
26
369
37
212
135
35
6
1
63
70
161
6
221
64
14
83

2014
0
3
2
14
557
0
0
6
15
5
4
284
12
457
33
92
147
39
9
2
28
62
100
12
223
65
14
104

2015
0
0
1
6
192
0
0
0
1
0
2
62
0
50
7
5
35
20
0
0
2
24
8
13
89
18
2
49

Table 18. Total Number of New Directional Wells Completed per County.
County
Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell
Bexar
Blanco
Borden
Bosque
Bowie
Brazoria
Brazos
Brewster
Briscoe
Brooks

2006
7
15
4
7
0
0
0
2
0
0
0
0
12
0
0
0
3
13
0
15
37
0
0
7

2007
5
10
12
10
0
0
0
2
0
0
0
0
11
0
0
0
14
3
1
8
31
0
0
4

2008
3
11
6
6
0
0
0
2
0
0
10
0
20
0
0
0
5
2
1
5
43
0
0
12

2009
1
16
2
2
0
0
2
4
0
0
0
0
1
0
0
0
0
0
1
7
40
0
0
2

RR-16-01

2010
0
47
1
2
0
0
27
4
0
0
1
0
5
0
0
0
6
0
1
10
99
0
0
8

2011
0
156
0
0
4
0
47
1
0
0
0
3
1
0
0
0
11
0
0
13
33
0
0
7

Page 79

2012
0
315
3
0
2
0
138
2
0
0
2
0
15
0
0
0
9
0
0
30
33
0
0
4

2013
2
140
0
1
0
0
167
2
0
0
2
0
5
0
0
0
7
1
0
23
73
0
0
4

2014
3
158
0
1
1
0
290
2
0
0
1
2
4
0
0
0
16
0
0
18
80
0
0
2

2015
0
42
0
1
0
0
70
0
0
0
0
0
0
0
0
0
0
0
0
3
12
0
0
1

 County
Brown
Burleson
Burnet
Caldwell
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell
Cottle
Crane
Crockett
Crosby
Culberson
Dallam
Dallas
Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher
Floyd
Foard
Fort Bend
Franklin
Freestone

2006
0
12
0
2
3
0
0
0
2
0
0
9
6
0
1
4
1
0
0
0
5
0
0
0
1
0
0
3
13
0
3
0
0
6
0
0
167
6
0
47
0
3
2
27
2
3
0
40
0
0
29
0
0
0
18
0
36

2007
0
13
0
3
3
0
0
0
5
0
0
0
23
0
1
1
0
0
0
0
3
0
1
0
4
0
0
15
20
0
2
0
7
5
0
0
211
16
0
35
0
7
16
14
12
15
0
56
0
0
10
2
1
0
7
1
34

2008
0
49
0
5
5
0
0
0
10
0
0
10
20
0
6
2
0
0
0
0
7
0
0
0
4
0
1
10
18
0
4
0
10
2
0
0
278
38
0
44
0
11
0
22
7
21
0
73
0
0
14
0
0
0
13
0
33

2009
0
4
0
9
0
0
3
1
11
0
0
5
2
0
0
1
1
0
0
0
0
0
0
0
15
0
0
3
2
2
3
0
8
1
0
0
126
16
0
29
0
6
0
5
1
10
0
9
0
0
13
2
0
0
1
0
46

RR-16-01

2010
0
92
0
19
0
0
0
0
0
0
0
15
3
0
0
5
0
1
0
0
1
0
0
0
65
0
0
16
4
2
9
0
9
0
0
0
165
52
1
156
0
7
0
20
1
9
0
1
0
0
24
1
0
0
8
1
46

2011
0
37
0
25
0
0
3
0
17
1
0
14
2
0
4
7
2
0
0
0
2
0
0
0
106
0
0
46
36
1
16
0
1
1
0
0
99
169
1
336
0
2
0
55
0
4
0
0
0
0
16
2
0
0
19
3
40

Page 80

2012
0
7
0
41
2
0
0
0
18
1
0
28
1
0
2
3
3
1
0
0
3
0
0
0
52
0
0
70
95
3
19
0
2
0
0
0
63
218
0
589
0
2
0
176
0
0
0
0
0
0
30
4
0
0
9
7
13

2013
0
19
0
16
1
0
0
0
10
1
0
30
2
0
5
3
2
0
0
0
1
0
0
0
15
0
0
70
157
1
52
0
4
2
0
0
89
351
0
531
0
2
0
147
0
0
0
0
0
0
16
4
0
0
5
5
5

2014
0
77
0
24
0
0
0
1
12
1
0
18
7
0
3
6
1
0
0
0
0
0
0
0
9
0
0
22
183
0
80
0
0
13
0
0
53
316
0
620
0
1
0
76
0
0
0
0
0
0
41
1
0
0
7
7
5

2015
0
12
0
4
0
0
0
0
0
0
0
5
4
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
40
0
46
0
0
0
0
0
17
96
0
249
0
0
0
32
0
0
0
0
0
0
9
0
0
0
0
0
1

 County
Frio
Gaines
Galveston
Garza
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin
Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson
Hidalgo
Hill
Hockley
Hood
Hopkins
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr
Kimble
King
Kinney
Kleberg
Knox

2006
14
7
12
2
0
1
15
0
0
2
26
11
2
8
0
1
16
3
8
3
29
1
0
0
13
2
34
44
25
133
0
6
2
0
0
7
0
28
3
4
1
13
1
0
522
0
2
0
0
9
1
0
0
1
0
9
0

2007
11
11
13
0
0
1
12
4
1
3
16
14
3
3
0
1
0
5
7
8
39
0
0
0
10
1
35
49
31
267
0
12
6
0
0
15
0
50
1
16
0
24
1
0
835
0
5
0
0
8
1
0
0
0
0
14
0

2008
17
12
6
1
0
1
11
3
0
3
21
15
4
4
0
0
4
4
9
7
110
0
0
0
53
4
34
116
39
197
0
8
0
0
0
13
0
21
3
35
0
13
3
0
890
1
15
0
0
11
6
0
0
0
0
11
0

2009
6
10
0
1
0
2
1
4
0
0
10
15
2
5
0
0
0
0
3
3
80
0
0
0
14
2
11
28
10
38
0
3
1
0
0
15
2
3
3
24
0
10
1
0
425
0
11
0
0
3
1
0
0
0
0
2
0

RR-16-01

2010
26
26
6
3
0
6
2
47
0
4
7
17
4
2
0
0
2
3
7
10
86
0
0
0
62
2
21
10
14
25
0
0
2
0
0
5
3
4
4
11
0
25
0
0
359
0
96
0
0
4
7
0
0
1
0
7
0

2011
70
29
6
2
0
30
2
169
0
7
5
8
12
0
0
0
4
4
8
2
46
0
0
0
115
0
14
0
14
42
0
0
2
0
0
0
36
9
2
19
0
23
1
0
241
0
260
0
0
4
0
0
0
2
0
5
0

Page 81

2012
78
15
4
1
0
29
0
278
0
17
4
19
11
0
0
0
4
6
1
2
33
1
0
0
107
0
17
0
10
47
1
5
6
0
0
0
109
16
2
11
0
22
0
0
102
0
476
1
0
0
1
0
0
2
0
4
0

2013
76
32
7
5
0
72
0
381
0
14
4
11
14
0
0
0
4
6
3
6
22
0
1
0
112
0
10
0
13
5
1
6
11
0
0
0
166
2
5
6
0
11
4
0
19
1
489
1
0
1
15
0
0
0
0
2
0

2014
125
65
2
3
0
156
0
251
0
16
5
19
4
0
0
0
2
2
0
13
41
1
0
0
113
4
11
0
13
7
0
21
33
0
0
0
165
1
4
2
0
7
0
0
5
0
610
0
0
1
3
0
0
4
0
2
0

2015
17
16
1
0
0
48
0
55
0
6
0
1
3
0
0
0
1
2
1
3
2
0
0
0
35
6
0
0
0
0
0
1
18
0
0
0
49
0
1
0
0
0
0
0
2
0
108
1
0
2
0
0
0
1
0
0
0

 County
Lamar
Lamb
Lampasas
La Salle
Lavaca
Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard
Midland
Milam
Mills
Mitchell
Montague
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos
Polk
Potter
Presidio
Rains
Randall
Reagan
Real
Red River
Reeves

2006
0
0
0
8
16
24
9
17
12
101
10
0
1
0
1
0
0
5
10
1
2
0
13
33
0
0
3
0
0
0
4
4
7
0
0
53
0
17
0
4
11
0
8
4
51
328
0
90
5
33
0
0
0
1
0
0
7

2007
0
2
0
8
12
12
11
13
17
76
16
0
6
0
1
0
0
0
4
0
2
0
8
61
0
0
14
2
0
0
18
1
2
0
0
57
0
3
0
13
7
0
9
23
94
278
0
106
35
3
0
0
0
0
0
0
15

2008
0
2
0
2
19
22
18
9
33
136
16
0
3
3
0
0
0
5
7
5
1
0
10
65
0
0
13
0
0
0
42
2
5
0
0
72
1
4
0
6
43
0
5
62
122
237
0
157
41
0
0
0
0
0
1
0
35

2009
0
0
0
26
2
16
13
5
13
50
10
0
4
4
0
0
0
12
6
0
1
0
5
18
0
0
8
0
0
0
56
4
4
0
0
23
1
9
0
7
32
0
3
31
104
79
0
129
15
2
0
2
0
1
0
1
5

RR-16-01

2010
0
2
0
100
6
13
18
7
21
64
26
0
16
4
0
0
0
58
14
1
1
0
6
22
0
0
3
4
0
0
171
10
1
0
0
41
1
19
9
6
67
0
11
4
92
63
0
178
13
9
0
0
0
1
0
0
10

2011
0
0
0
231
15
29
36
8
17
110
60
0
26
1
0
0
0
131
18
0
3
0
6
23
1
0
7
6
0
4
195
4
14
0
0
66
0
9
20
10
66
1
3
4
80
103
0
69
17
2
0
0
0
14
0
1
43

Page 82

2012
0
0
0
505
25
34
29
3
5
114
92
0
115
6
3
0
0
284
31
2
9
0
0
14
0
0
18
7
0
9
196
0
35
0
0
23
2
3
10
2
111
1
8
11
97
137
0
51
9
0
0
0
0
63
1
0
50

2013
0
0
0
599
46
19
21
6
0
108
166
0
132
0
2
0
0
391
36
2
24
0
6
11
0
0
62
4
0
10
147
5
0
0
0
0
4
3
8
5
129
5
4
6
104
45
0
70
4
0
0
0
0
166
0
0
131

2014
0
0
0
625
107
26
13
8
1
127
142
0
130
0
0
0
0
456
51
2
136
0
1
6
0
0
189
0
0
3
90
3
0
0
0
2
0
2
5
1
131
5
5
1
81
29
0
93
3
10
0
0
0
301
0
0
301

2015
0
0
0
237
16
0
4
0
0
15
32
0
89
4
0
0
0
128
1
0
63
0
1
0
0
0
111
0
0
0
0
0
0
0
0
0
0
1
0
1
29
2
0
2
29
6
0
93
0
8
0
0
0
134
0
0
141

 County
Refugio
Roberts
Robertson
Rockwall
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor
Terrell
Terry
Throckmorton
Titus
Tom Green
Travis
Trinity
Tyler
Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson
Winkler
Wise
Wood
Yoakum
Young

2006
1
16
44
0
0
22
2
0
0
1
0
1
15
0
32
1
65
16
19
0
3
0
3
0
311
0
17
15
0
0
0
0
1
56
1
17
0
2
0
1
0
7
12
22
35
3
28
0
0
4
0
6
14
116
11
31
0

2007
0
22
53
0
0
39
3
12
6
8
0
0
7
1
92
0
32
33
15
3
0
0
2
0
605
0
9
4
1
0
0
0
1
63
2
20
0
4
1
0
0
6
32
6
33
3
38
0
0
5
0
5
9
158
0
22
1

2008
0
25
70
0
1
37
0
30
2
5
0
0
24
2
129
0
14
54
15
5
0
0
1
0
779
2
7
16
0
0
0
0
0
56
4
22
0
0
0
3
1
1
46
9
61
9
47
0
0
2
0
1
10
256
4
35
0

2009
1
11
46
0
0
23
1
50
2
8
0
0
33
0
55
9
4
6
11
3
0
0
0
0
530
1
1
4
0
0
0
0
0
47
0
11
0
0
1
3
0
3
12
2
56
3
32
0
0
8
0
3
7
175
7
3
0

RR-16-01

2010
4
27
34
0
0
21
5
71
6
4
0
0
10
3
82
0
2
5
2
1
1
6
1
0
539
0
0
4
0
0
0
0
0
29
2
27
0
0
1
0
0
4
42
10
209
4
114
0
1
6
0
5
4
218
3
21
0

2011
1
41
47
0
1
14
5
64
5
6
0
3
8
0
36
0
2
4
1
1
1
1
1
0
516
0
0
2
0
1
0
0
2
21
0
18
0
0
0
0
0
3
95
1
345
5
186
0
0
3
0
40
9
153
3
46
1

Page 83

2012
1
64
36
0
0
27
4
24
2
4
0
3
7
0
19
0
6
3
3
6
11
1
1
0
243
0
0
7
2
3
0
0
0
8
1
21
0
0
0
0
1
3
118
4
425
3
192
0
3
0
0
36
13
176
8
64
2

2013
3
47
30
0
0
25
0
11
1
4
0
13
29
1
11
1
3
0
6
6
17
0
3
0
117
1
0
7
12
4
0
0
0
5
0
93
0
0
0
3
3
2
119
20
355
2
201
0
4
0
0
59
20
154
2
89
2

2014
0
74
15
0
0
36
0
12
1
1
0
10
42
1
13
0
8
0
15
6
12
2
0
0
92
0
0
5
23
8
0
0
1
2
3
235
0
0
1
1
2
1
122
4
444
1
83
0
4
2
0
26
28
84
4
141
2

2015
1
18
2
0
0
16
0
1
0
2
0
1
19
0
0
0
2
0
2
1
0
0
0
0
30
1
0
3
2
1
0
0
0
1
2
109
0
0
0
0
0
1
28
0
45
0
3
1
2
0
0
1
8
8
9
50
0

 County
Zapata
Zavala

2006
87
22

2007
82
30

2008
82
17

2009
29
15

2010
11
8

2011
10
52

2012
1
87

2013
5
74

2014
0
97

2015
0
48

Table 19. Total Number of New Vertical Wellheads Completed per County.
County
Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell
Bexar
Blanco
Borden
Bosque
Bowie
Brazoria
Brazos
Brewster
Briscoe
Brooks
Brown
Burleson
Burnet
Caldwell
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell

2006
23
289
19
12
60
0
14
8
0
0
0
3
56
0
0
0
23
0
1
22
2
1
0
33
7
4
0
13
11
17
0
0
2
1
0
20
46
0
24
12
33
4
0
0
38
0
3
20
72
0

2007
23
293
34
12
78
0
3
7
0
2
8
3
56
0
2
0
41
0
1
26
3
0
1
27
17
4
0
42
12
16
0
1
2
0
0
4
55
0
18
8
16
12
0
0
40
0
3
24
72
3

2008
37
504
28
12
105
0
6
34
0
1
8
6
54
0
1
0
19
0
1
35
7
0
0
51
12
16
0
11
10
24
0
0
2
1
0
19
72
3
37
39
22
15
0
0
29
0
2
27
78
3

2009
21
413
11
3
92
0
15
18
0
0
1
1
36
0
2
0
9
0
1
10
0
0
0
14
5
1
0
6
7
15
1
0
0
4
0
13
20
0
6
20
14
8
0
0
11
0
2
7
31
0

RR-16-01

2010
6
837
1
5
83
0
16
9
0
0
4
1
41
0
4
0
28
0
0
35
1
0
0
29
45
0
0
11
4
14
1
1
0
3
0
34
11
0
14
16
30
45
0
0
25
0
2
4
61
0

2011
7
1154
2
4
94
0
5
10
0
0
3
4
26
0
0
0
33
0
0
38
4
0
0
24
45
0
0
18
2
7
3
0
2
5
0
37
11
0
14
38
33
44
0
0
20
0
4
12
30
0

Page 84

2012
10
1063
1
1
99
0
10
11
0
0
0
12
29
0
5
0
39
0
0
51
0
0
0
17
10
2
0
8
3
10
0
1
3
3
0
41
5
0
24
10
35
40
0
4
6
0
1
5
18
0

2013
11
659
0
4
95
0
9
9
0
0
1
12
26
0
47
0
51
0
0
32
0
0
0
16
5
1
0
3
3
8
0
4
0
4
0
34
6
0
12
27
23
46
0
0
4
0
4
5
23
0

2014
8
718
0
1
109
0
3
5
0
0
0
14
12
0
14
0
40
0
1
30
1
0
0
15
7
1
0
10
1
17
0
2
12
6
0
44
8
0
8
6
17
24
0
0
7
0
9
4
24
0

2015
2
152
0
2
49
0
2
2
0
0
0
4
1
0
0
0
9
0
0
21
0
0
0
3
2
0
0
5
0
8
0
0
0
1
0
7
7
0
3
2
3
11
0
0
1
0
2
2
9
0

 County
Cottle
Crane
Crockett
Crosby
Culberson
Dallam
Dallas
Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher
Floyd
Foard
Fort Bend
Franklin
Freestone
Frio
Gaines
Galveston
Garza
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin
Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson

2006
9
170
297
1
6
0
0
28
0
0
92
40
26
44
0
60
22
228
29
0
0
17
0
0
6
12
2
0
44
0
235
10
222
14
46
0
76
107
2
54
12
68
1
2
21
0
2
16
10
45
14
279
3
9
0
276
15

2007
7
170
277
37
9
0
0
37
0
0
45
40
26
32
0
82
40
157
15
0
0
9
0
0
1
17
0
28
33
2
248
52
165
13
43
0
85
57
2
7
9
68
1
0
10
0
2
17
6
35
12
303
4
7
0
257
15

2008
12
155
312
24
7
0
0
35
0
0
17
42
25
39
0
75
12
225
17
0
0
4
1
0
4
38
0
9
32
10
212
33
162
9
50
0
81
77
4
7
5
62
3
4
10
0
0
28
8
53
15
259
4
10
0
261
20

2009
2
63
66
24
5
1
0
17
0
0
5
20
11
8
0
27
7
254
20
0
0
6
1
0
2
20
0
1
25
1
156
15
60
1
38
0
72
16
5
3
7
18
1
1
1
0
0
18
3
41
7
67
3
16
0
106
11

RR-16-01

2010
6
76
165
41
9
0
0
39
0
0
3
11
11
8
0
26
4
490
49
0
0
4
1
0
5
31
1
2
38
3
111
4
195
7
26
0
231
30
2
1
13
9
2
3
6
0
0
2
3
48
15
12
7
26
0
66
5

2011
4
84
62
94
31
0
0
63
0
0
2
8
10
8
0
27
6
523
7
0
0
2
2
0
5
38
0
0
61
1
89
10
209
8
27
0
593
17
7
10
12
15
3
6
0
0
0
3
2
55
8
21
8
41
0
19
3

Page 85

2012
1
80
55
121
23
1
0
59
0
0
4
9
7
8
0
35
4
666
9
0
0
0
4
2
4
36
1
4
43
3
46
10
162
9
29
0
717
7
12
14
11
12
3
10
1
0
0
4
7
51
3
16
10
36
0
11
4

2013
2
160
53
127
13
0
0
47
0
0
0
11
6
9
0
57
4
648
4
0
0
0
4
0
4
41
0
3
77
6
13
5
179
6
34
0
516
1
4
10
13
24
3
13
0
0
0
4
4
56
7
14
4
19
0
8
5

2014
5
145
37
188
5
0
0
46
0
0
0
7
9
16
0
41
4
463
6
0
0
0
4
0
4
39
0
1
87
29
12
2
226
2
43
0
376
4
3
1
10
26
2
2
1
0
0
5
6
25
22
31
5
38
0
5
10

2015
1
75
20
34
2
0
0
8
0
0
0
1
0
7
0
16
4
96
3
0
0
0
2
0
0
18
0
0
7
4
4
1
93
1
11
0
67
1
3
0
6
5
1
1
0
0
0
1
7
13
5
6
1
4
0
2
2

 County
Hidalgo
Hill
Hockley
Hood
Hopkins
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr
Kimble
King
Kinney
Kleberg
Knox
Lamar
Lamb
Lampasas
La Salle
Lavaca
Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard

2006
152
1
99
12
0
12
43
1
0
44
27
44
40
6
0
34
32
7
14
47
8
0
0
27
12
0
4
21
0
17
3
0
3
1
59
112
4
78
42
98
27
46
0
42
9
1
1
0
54
7
12
124
0
48
48
1
9

2007
171
3
69
4
1
17
75
3
0
138
24
83
36
5
1
44
16
11
7
54
10
0
0
31
22
0
1
24
0
22
4
0
1
6
45
72
5
72
37
134
24
29
0
45
7
4
0
2
61
3
6
179
0
44
4
4
8

2008
153
0
55
2
0
9
99
4
0
84
78
38
65
4
1
40
8
27
8
53
13
0
0
39
33
0
0
28
0
16
8
0
6
5
54
60
9
61
34
153
13
43
0
47
9
4
0
3
85
7
8
199
0
62
149
19
9

2009
57
0
37
0
1
4
71
0
0
3
44
32
31
3
0
25
1
12
1
39
7
0
0
23
26
0
1
10
0
5
2
0
4
1
8
28
1
36
13
55
6
19
0
24
17
3
3
0
28
1
1
137
0
25
92
16
7

RR-16-01

2010
54
1
53
0
0
12
147
0
0
12
53
32
23
7
0
38
3
19
1
43
14
0
0
18
26
0
1
14
0
13
1
0
3
1
4
37
3
34
19
42
5
30
0
38
16
3
0
0
30
8
3
453
0
28
4
45
6

2011
48
0
48
0
1
21
229
0
0
14
133
65
19
5
0
26
2
8
0
45
16
0
0
13
21
0
0
13
0
29
5
0
3
0
8
14
2
44
17
18
6
15
0
31
7
11
0
0
19
9
3
652
0
18
11
53
22

Page 86

2012
39
0
29
0
3
18
315
0
0
23
128
121
23
3
0
34
3
6
0
37
17
1
0
0
21
0
0
12
0
21
8
0
3
1
10
13
2
18
15
19
4
21
0
61
2
8
1
0
23
16
8
797
0
14
1
100
16

2013
37
0
36
0
0
30
423
0
0
12
98
130
17
9
0
26
5
5
0
52
4
2
0
6
35
0
0
29
0
18
7
0
0
1
9
19
1
18
15
14
6
20
0
29
4
5
1
0
23
27
7
599
0
9
2
114
6

2014
55
0
54
0
2
26
368
0
0
17
100
256
24
2
0
17
4
6
1
38
5
0
0
5
21
0
0
35
0
13
6
0
0
0
13
26
1
12
9
10
5
23
0
20
5
3
6
0
21
37
6
542
0
9
1
47
3

2015
12
0
3
0
1
3
115
0
0
3
17
19
7
0
0
5
0
2
1
10
4
1
0
2
2
0
0
9
0
7
0
0
1
0
3
5
0
1
0
4
1
3
0
11
0
2
4
0
7
1
4
82
0
1
0
11
3

 County
Midland
Milam
Mills
Mitchell
Montague
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos
Polk
Potter
Presidio
Rains
Randall
Reagan
Real
Red River
Reeves
Refugio
Roberts
Robertson
Rockwall
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor

2006
228
22
0
146
96
12
44
0
0
202
37
25
23
49
40
3
14
80
396
25
0
138
10
20
0
0
0
209
0
0
44
137
110
102
0
83
417
1
0
4
35
0
52
106
42
45
17
101
3
134
34
54
17
168
0
12
15

2007
150
8
1
216
54
2
40
0
0
223
14
6
35
62
35
4
11
41
374
13
0
193
10
1
0
1
0
170
0
0
41
137
93
116
0
41
445
1
0
19
28
0
23
54
49
39
15
52
1
131
38
75
24
61
0
17
18

2008
226
3
0
230
89
7
33
0
1
216
28
8
55
48
33
6
6
51
322
5
0
315
17
6
0
0
0
253
1
1
40
114
105
157
0
47
327
1
2
10
23
0
31
100
83
61
79
17
1
171
55
60
40
123
0
13
13

2009
161
55
0
161
17
4
25
0
0
81
9
9
19
37
17
2
4
36
137
6
0
100
10
1
0
1
0
140
1
0
26
112
25
92
0
12
87
0
3
16
9
0
18
72
50
32
8
14
0
53
10
56
40
40
0
12
18

RR-16-01

2010
368
80
0
156
20
3
19
0
0
37
10
9
38
28
19
8
12
27
118
0
0
169
13
7
0
0
0
441
0
0
49
111
17
48
0
29
39
2
2
15
13
0
31
77
58
26
5
11
0
44
25
43
82
59
0
3
8

2011
583
218
0
166
17
7
20
0
0
6
6
6
72
34
20
10
6
10
65
3
0
72
12
15
0
0
0
374
0
1
183
95
17
30
0
26
26
1
1
16
25
0
42
96
50
13
8
11
0
36
52
22
83
29
0
1
23

Page 87

2012
555
326
0
160
23
5
23
0
0
14
10
4
71
14
13
12
12
27
36
1
0
69
17
3
0
0
0
319
0
1
299
73
9
13
0
23
30
0
2
10
17
0
43
79
41
2
11
11
0
40
35
40
78
26
0
0
20

2013
443
315
0
100
21
10
20
0
0
2
7
5
64
18
14
23
7
64
55
5
0
80
13
4
0
0
0
155
0
1
238
62
19
4
0
24
30
1
0
2
20
0
20
134
55
1
13
11
0
26
47
33
59
12
0
0
36

2014
425
39
0
43
16
3
47
0
1
1
7
7
45
11
29
6
5
77
27
2
0
57
5
6
0
0
0
92
0
1
84
64
12
0
0
20
21
1
0
1
12
0
16
127
37
4
1
14
0
45
79
31
67
0
0
0
34

2015
108
0
0
9
1
0
26
0
1
1
2
2
30
7
1
2
0
17
9
2
0
22
0
0
0
0
0
22
0
0
21
42
2
1
0
12
8
0
0
0
2
0
4
51
12
0
6
3
0
6
15
3
11
0
0
0
10

 County
Terrell
Terry
Throckmorton
Titus
Tom Green
Travis
Trinity
Tyler
Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson
Winkler
Wise
Wood
Yoakum
Young
Zapata
Zavala

2006
62
46
21
8
30
0
2
6
43
219
1
15
4
69
7
32
340
2
274
74
195
111
56
16
0
1
92
135
8
232
41
284
9

2007
39
48
22
0
28
0
1
10
49
299
0
10
19
50
0
36
186
2
233
45
219
127
32
19
1
0
155
43
8
124
59
239
6

2008
32
28
26
2
22
0
2
9
23
472
0
1
10
67
2
27
202
1
327
83
250
160
32
21
0
2
125
34
1
177
84
251
1

2009
1
30
10
0
47
0
1
3
7
195
0
9
5
45
0
16
94
2
164
34
47
138
22
18
1
3
12
12
7
37
37
89
1

2010
7
23
16
0
16
0
0
6
2
489
0
0
5
39
0
18
200
0
121
65
27
119
35
17
0
6
27
17
11
122
55
50
5

2011
2
32
17
0
16
0
3
14
10
578
0
0
5
21
0
7
204
0
52
67
15
164
40
9
0
11
24
8
8
146
47
34
8

2012
2
24
32
2
25
0
0
12
0
614
0
0
8
22
0
7
201
0
26
47
13
136
64
8
0
3
37
5
15
161
68
4
12

2013
0
14
47
1
17
0
0
7
3
464
0
2
4
21
6
11
196
4
21
47
13
138
30
6
1
3
49
3
5
215
67
11
8

2014
1
10
39
21
13
0
2
2
11
330
0
0
6
14
3
4
193
8
15
33
9
147
35
10
2
2
35
7
12
205
63
14
6

2015
1
9
16
2
3
0
0
1
4
83
0
0
0
1
0
2
41
0
6
7
2
34
18
0
0
0
16
0
8
77
18
2
1

Table 20. Total Number of New Horizontal Wellends Completed per County.
County
Anderson
Andrews
Angelina
Aransas
Archer
Armstrong
Atascosa
Austin
Bailey
Bandera
Bastrop
Baylor
Bee
Bell
Bexar
Blanco
Borden
Bosque
Bowie

2006
5
36
4
0
0
0
0
0
0
0
0
0
10
0
0
0
3
13
0

2007
5
23
1
0
0
0
0
0
0
0
0
0
11
0
0
0
12
3
0

2008
2
18
4
0
0
0
0
0
0
0
12
0
17
0
0
0
5
2
0

2009
0
29
1
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
0

RR-16-01

2010
0
51
1
0
0
0
29
0
0
0
2
0
5
0
0
0
11
0
1

2011
0
23
0
0
2
0
47
0
0
0
0
1
1
0
0
0
17
0
0

Page 88

2012
0
49
4
0
3
0
138
0
0
0
2
0
14
0
0
0
9
0
0

2013
0
57
0
0
0
0
166
0
0
0
2
0
4
0
0
0
7
1
0

2014
2
118
0
0
1
0
290
0
0
0
1
2
3
0
0
0
6
0
0

2015
0
39
0
0
0
0
70
0
0
0
0
0
0
0
0
0
0
0
0

 County
Brazoria
Brazos
Brewster
Briscoe
Brooks
Brown
Burleson
Burnet
Caldwell
Calhoun
Callahan
Cameron
Camp
Carson
Cass
Castro
Chambers
Cherokee
Childress
Clay
Cochran
Coke
Coleman
Collin
Collingsworth
Colorado
Comal
Comanche
Concho
Cooke
Coryell
Cottle
Crane
Crockett
Crosby
Culberson
Dallam
Dallas
Dawson
Deaf Smith
Delta
Denton
DeWitt
Dickens
Dimmit
Donley
Duval
Eastland
Ector
Edwards
Ellis
El Paso
Erath
Falls
Fannin
Fayette
Fisher

2006
0
45
0
0
0
0
15
0
2
0
0
0
0
2
0
0
0
2
0
0
7
0
0
0
0
0
0
0
0
1
0
0
2
2
0
4
0
0
9
0
0
137
2
0
54
0
0
2
16
2
3
0
44
0
0
32
0

2007
0
37
0
0
0
0
15
0
1
0
0
0
0
7
0
0
0
5
0
1
2
0
0
0
0
0
0
1
0
2
0
0
17
22
0
0
0
8
10
0
0
184
12
0
40
0
0
14
6
19
16
0
55
0
0
15
3

2008
0
48
0
0
0
0
55
0
8
0
0
0
0
13
0
0
0
12
0
6
4
0
0
0
0
0
0
0
0
2
0
0
7
6
0
3
0
10
4
0
0
274
36
0
47
0
3
0
13
5
21
0
73
0
0
17
0

2009
0
41
0
0
0
0
5
0
11
0
0
0
2
15
0
0
0
2
0
0
2
0
0
0
0
0
0
0
0
7
0
0
0
0
2
4
0
8
0
0
0
123
13
0
33
0
2
0
0
0
10
0
8
0
0
16
2

RR-16-01

2010
0
102
0
0
0
0
104
0
21
0
0
0
0
0
0
0
0
3
0
0
8
0
2
0
0
0
0
0
0
47
0
0
11
4
0
9
0
9
0
0
0
166
64
0
161
0
6
0
3
0
9
0
0
0
0
21
0

2011
0
37
0
0
0
0
38
0
29
0
0
0
0
20
0
0
0
1
0
4
7
0
0
0
0
0
0
0
0
101
0
0
36
34
2
16
0
1
0
0
0
99
187
0
343
0
0
0
12
0
4
0
0
0
0
16
0

Page 89

2012
4
36
0
0
0
0
7
0
43
0
0
0
0
24
1
0
0
0
0
2
5
1
0
0
0
1
0
0
0
51
0
0
71
97
0
19
0
2
0
0
0
66
226
0
608
0
0
0
36
0
0
0
0
0
0
30
5

2013
0
75
0
0
0
0
19
0
14
0
0
0
0
16
1
0
2
4
0
5
4
2
0
0
0
0
0
0
0
15
0
0
57
157
0
53
0
4
1
0
0
104
352
0
535
0
0
0
24
0
0
0
0
0
0
16
4

2014
0
79
0
0
0
0
77
0
24
0
0
0
0
7
0
0
0
8
0
3
6
0
0
0
0
0
0
0
0
10
0
0
19
177
0
80
0
0
7
0
0
54
314
0
618
0
0
0
35
0
0
0
0
0
0
42
1

2015
0
12
0
0
0
0
12
0
4
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
34
0
46
0
0
0
0
0
17
96
0
249
0
0
0
7
0
0
0
0
0
0
9
0

 County
Floyd
Foard
Fort Bend
Franklin
Freestone
Frio
Gaines
Galveston
Garza
Gillespie
Glasscock
Goliad
Gonzales
Gray
Grayson
Gregg
Grimes
Guadalupe
Hale
Hall
Hamilton
Hansford
Hardeman
Hardin
Harris
Harrison
Hartley
Haskell
Hays
Hemphill
Henderson
Hidalgo
Hill
Hockley
Hood
Hopkins
Houston
Howard
Hudspeth
Hunt
Hutchinson
Irion
Jack
Jackson
Jasper
Jeff Davis
Jefferson
Jim Hogg
Jim Wells
Johnson
Jones
Karnes
Kaufman
Kendall
Kenedy
Kent
Kerr

2006
0
0
0
0
6
18
17
0
0
0
0
0
0
0
0
0
11
2
2
0
1
14
2
0
0
3
2
0
0
0
0
0
44
38
134
0
7
1
0
0
9
0
30
0
0
2
0
0
0
524
0
2
0
0
0
0
0

2007
2
0
2
0
7
7
30
0
0
0
2
0
6
2
0
1
14
5
5
0
1
0
4
0
0
8
0
0
0
2
0
0
49
61
265
0
12
3
0
0
22
0
55
0
16
0
0
0
0
838
0
5
0
0
0
0
0

2008
0
0
2
0
15
20
22
0
1
0
0
0
6
0
0
1
15
4
14
0
0
5
2
0
0
44
0
0
0
38
0
0
117
72
197
0
4
0
0
0
20
0
21
0
33
0
0
1
0
890
1
11
0
0
0
0
0

2009
0
0
0
0
12
10
11
0
1
0
2
0
6
0
0
2
15
2
7
0
0
0
0
0
0
67
0
0
0
9
0
0
28
8
38
0
2
0
0
0
19
3
3
1
23
0
0
1
0
425
0
11
0
0
0
0
0

RR-16-01

2010
0
0
0
0
21
27
37
0
3
0
7
0
47
0
3
3
14
6
2
0
0
2
2
0
0
87
0
0
0
58
0
0
9
13
25
0
0
0
0
0
6
6
5
0
8
0
0
0
0
360
0
104
0
0
0
2
0

2011
0
0
0
3
8
77
11
0
2
0
26
0
182
0
4
4
8
14
0
0
0
4
6
0
0
44
0
0
0
118
0
0
0
6
42
0
0
1
0
0
0
38
8
1
18
0
0
0
0
241
0
263
0
0
0
0
0

Page 90

2012
0
0
0
7
3
81
6
0
1
0
26
0
290
0
11
4
21
10
0
0
0
4
4
0
0
33
1
0
0
107
0
1
0
7
47
0
5
0
0
0
0
109
11
0
13
0
1
0
0
102
0
479
0
0
0
1
0

2013
0
0
1
5
3
77
2
0
5
0
66
0
388
0
14
2
11
14
0
0
0
4
6
0
0
21
0
1
0
112
0
0
0
8
5
1
3
7
0
0
0
165
0
2
0
0
0
5
0
19
0
496
0
0
0
0
0

2014
0
0
0
0
4
129
8
0
2
0
153
0
251
0
14
0
18
6
0
0
0
2
1
0
0
41
1
0
0
114
3
0
0
3
7
0
9
29
0
0
0
164
1
0
1
0
0
0
0
4
0
612
0
0
0
0
0

2015
0
0
0
0
1
17
1
0
0
0
48
0
54
0
3
0
0
2
0
0
0
1
2
0
0
2
0
0
0
35
4
0
0
0
0
0
0
18
0
0
0
49
0
0
0
0
0
0
0
0
0
108
0
0
0
0
0

 County
Kimble
King
Kinney
Kleberg
Knox
Lamar
Lamb
Lampasas
La Salle
Lavaca
Lee
Leon
Liberty
Limestone
Lipscomb
Live Oak
Llano
Loving
Lubbock
Lynn
McCulloch
McLennan
McMullen
Madison
Marion
Martin
Mason
Matagorda
Maverick
Medina
Menard
Midland
Milam
Mills
Mitchell
Montague
Montgomery
Moore
Morris
Motley
Nacogdoches
Navarro
Newton
Nolan
Nueces
Ochiltree
Oldham
Orange
Palo Pinto
Panola
Parker
Parmer
Pecos
Polk
Potter
Presidio
Rains

2006
0
0
0
4
0
0
0
0
4
3
29
3
0
1
102
6
0
0
0
2
0
0
0
14
1
4
0
0
55
0
0
3
0
0
0
4
0
12
0
0
32
0
4
0
0
9
0
0
5
4
333
0
242
5
47
0
0

2007
0
0
0
2
0
0
2
0
8
5
14
6
2
4
77
9
0
7
0
2
0
0
4
3
0
2
0
0
93
0
0
17
2
0
0
18
1
7
0
0
17
0
0
0
0
7
0
0
25
15
284
0
210
35
5
0
0

2008
0
0
0
0
0
0
3
0
2
10
23
4
0
7
160
12
0
2
6
0
0
0
3
8
4
0
0
0
75
0
0
13
0
0
0
36
0
6
0
0
31
0
6
0
0
47
0
0
66
33
237
0
169
38
0
0
0

2009
0
0
0
0
0
0
0
0
33
1
17
4
0
2
51
9
0
4
8
0
0
0
12
7
0
0
0
0
19
0
0
6
0
0
0
55
4
6
0
0
7
2
7
0
0
33
0
0
31
47
78
0
168
12
3
0
0

RR-16-01

2010
0
0
0
4
0
0
2
0
108
1
18
11
0
9
64
26
0
17
6
0
0
0
61
12
1
0
0
0
23
0
0
1
4
0
0
170
7
2
0
0
36
0
12
3
4
68
0
0
3
44
63
0
152
11
18
0
0

2011
0
0
0
2
0
0
0
0
237
13
37
25
0
8
110
61
0
29
2
0
0
0
133
16
0
3
0
0
23
1
0
1
6
0
0
192
0
15
0
0
66
0
9
2
2
65
0
0
4
58
102
0
125
13
2
0
0

Page 91

2012
0
0
0
0
0
0
0
0
507
25
37
23
0
1
116
92
0
118
11
1
0
0
292
30
1
4
0
0
13
0
0
3
7
0
2
194
0
50
0
0
22
3
2
2
0
111
0
0
9
93
135
0
86
7
0
0
0

2013
0
0
0
0
0
0
0
0
605
45
20
21
0
0
107
164
0
134
0
2
0
0
398
35
1
24
0
1
10
0
0
50
4
0
10
147
0
0
0
0
0
4
0
4
0
130
1
0
5
103
45
0
110
0
0
0
0

2014
0
3
0
0
0
0
0
0
626
102
26
13
0
1
128
142
0
132
0
0
0
0
459
32
2
139
0
0
5
0
0
172
0
0
3
89
0
0
0
0
2
0
0
4
0
129
3
0
1
78
28
0
132
0
9
0
0

2015
0
0
0
0
0
0
0
0
235
15
0
3
0
0
15
32
0
89
4
0
0
0
128
1
0
63
0
0
0
0
0
113
0
0
0
0
0
0
0
0
0
0
0
0
0
29
1
0
2
29
4
0
132
0
8
0
0

 County
Randall
Reagan
Real
Red River
Reeves
Refugio
Roberts
Robertson
Rockwall
Runnels
Rusk
Sabine
San Augustine
San Jacinto
San Patricio
San Saba
Schleicher
Scurry
Shackelford
Shelby
Sherman
Smith
Somervell
Starr
Stephens
Sterling
Stonewall
Sutton
Swisher
Tarrant
Taylor
Terrell
Terry
Throckmorton
Titus
Tom Green
Travis
Trinity
Tyler
Upshur
Upton
Uvalde
Val Verde
Van Zandt
Victoria
Walker
Waller
Ward
Washington
Webb
Wharton
Wheeler
Wichita
Wilbarger
Willacy
Williamson
Wilson

2006
0
0
0
0
10
0
15
46
0
0
0
0
0
0
0
0
1
1
0
31
1
6
16
0
0
5
0
0
0
305
0
7
22
0
0
0
0
1
54
0
20
0
1
0
0
0
0
18
24
20
0
1
0
0
0
0
16

2007
0
0
0
0
21
0
11
36
0
0
2
0
12
0
0
0
0
5
1
89
0
1
33
0
2
0
0
0
0
601
0
7
6
0
0
0
0
1
60
0
16
0
0
1
0
0
0
47
6
16
0
4
0
0
0
0
7

2008
0
0
0
0
37
0
19
50
0
1
7
0
31
0
0
0
0
16
2
123
0
3
54
4
5
0
0
1
0
776
2
7
24
0
0
0
0
0
57
1
22
0
0
0
0
1
0
72
9
32
0
12
0
0
0
0
0

2009
0
1
0
1
6
0
9
25
0
0
11
1
51
0
0
0
0
20
0
52
10
0
6
2
3
0
0
0
0
530
0
1
3
0
0
0
0
0
48
0
6
0
0
1
0
0
0
23
0
46
0
30
0
0
0
0
3

RR-16-01

2010
0
1
0
0
14
0
28
19
0
0
14
5
73
0
1
0
0
1
3
80
0
0
5
0
0
0
0
0
0
539
0
0
4
0
0
0
0
0
28
2
2
0
0
1
0
0
0
43
10
223
0
109
0
0
0
0
5

2011
0
15
0
1
46
0
41
45
0
1
15
5
64
0
1
0
3
4
0
34
0
0
4
0
0
1
0
1
0
515
0
0
1
0
1
0
0
0
17
0
7
0
0
0
0
0
0
87
1
344
0
188
0
0
0
0
37

Page 92

2012
0
63
2
0
48
0
66
32
0
0
26
4
24
0
3
0
3
1
0
20
0
0
3
0
6
11
0
0
0
244
0
0
10
2
3
0
0
0
3
1
17
0
0
0
0
3
0
106
4
425
0
195
0
3
0
0
37

2013
0
168
0
0
114
0
44
30
0
0
26
0
11
1
2
0
13
8
1
15
1
1
0
0
2
20
0
2
0
118
0
0
8
11
4
0
0
0
7
0
75
0
0
0
2
1
0
103
22
349
0
197
0
5
0
0
62

2014
0
303
0
0
289
0
74
15
0
0
36
0
12
1
0
0
10
6
0
10
0
4
0
0
2
13
1
0
0
92
0
0
1
23
0
0
0
1
0
3
235
0
0
0
1
2
0
92
4
442
0
83
0
4
0
0
26

2015
0
133
0
0
151
0
18
2
0
0
14
0
1
0
0
0
1
4
0
0
0
2
0
0
0
0
0
0
0
30
0
0
0
2
0
0
0
0
0
2
109
0
0
0
0
0
0
22
0
44
0
3
1
2
0
0
2

 County
Winkler
Wise
Wood
Yoakum
Young
Zapata
Zavala

2006
19
89
14
0
0
1
29

2007
9
150
0
7
0
2
32

2008
9
252
4
7
0
5
19

2009
6
174
5
0
0
4
19

RR-16-01

2010
2
220
0
0
0
1
7

2011
9
158
2
8
1
0
55

Page 93

2012
15
179
5
2
2
1
90

2013
23
157
1
5
1
3
83

2014
26
93
0
18
2
0
98

2015
8
8
5
11
0
0
48