Hydraulic fracturing stimulation designs are moving towards tighter spaced clusters, longer stage length, and more proppant volumes. However, effectively evaluating the hydraulic fracturing stimulation efficiency remains a challenge. Distributed fiber optic sensing, which includes DAS and DTS, can continuously monitor the hydraulic fracturing stimulation downhole and be compared with other monitoring technology such as microseismic. The DAS and DTS data, when integrated with the microseismic, highlight processes relevant to the completion design and allow for a better understanding and interpretation of each dataset. This paper outlines a workflow to improve processing and interpretation of DAS and DTS data. In addition, an estimate of the slurry distribution can be made. These methods will be demonstrated for a horizontal Wolfcamp well in the Permian Basin. Here we compare key aspects of the microseismic, DAS, and DTS results in several fracture stages to understand the downhole geomechanical processes. In order to interpret the DTS data a thermal model is developed (using DTS data) to simulate the temperature behavior after pumping has ceased. A slurry distribution is obtained by matching the simulated temperature with the measured temperature from DTS. In addition, the DAS data signal is studied in the frequency domain and the dominant frequencies are identified that are mostly related to fluid flow and to reduce the background noise. This time frequency analysis enhances the ability to monitor and optimize well treatments. After reducing the background noise, the acoustic intensity is correlated to the slurry distribution. The fluid distribution data from DAS and DTS are compared with the microseismic and near field strain to better understand the completion processes. We utilized fiber optic microseismic to better understand and compare it to conventional microseismic. Finally, we highlight the dynamics of strain and microseismic signature as fluid moves from an offset well completion into the prior stimulated fiber well to better understand the reservoir and far field effects of the completion.

1. URTeC: 291
Hydraulic Fracturing Stimulation Monitoring with Distributed Fiber
Optic Sensing and Microseismic in the Permian Wolfcamp Shale Play
Vikram Jayaram, Robert Hull, Jed Wagner and Shuang Zhang
Pioneer Natural Resources Company
Copyright 2019, Unconventional Resources Technology Conference (URTeC) DOI 10.15530/urtec-2019-291
This paper was prepared for presentation at the Unconventional Resources Technology Conference held in Denver, Colorado, USA,
22 to 24 July 2019.
The URTeC Technical Program Committee accepted this presentation on the basis of information contained in an abstract submitted
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author without the written consent of URTeC is prohibited.
Abstract
Hydraulic fracturing stimulation designs are moving towards tighter spaced clusters, longer stage length,
and more proppant volumes. However, effectively evaluating the hydraulic fracturing stimulation
efficiency remains a challenge. Distributed fiber optic sensing, which includes Distributed Acoustic
Sensing (DAS) and Distributed Temperature Sensing (DTS), can continuously monitor the hydraulic
fracturing stimulation downhole and be compared with other monitoring technology such as microseismic.
The DAS and DTS data, when integrated with the microseismic, highlight processes relevant to the
completion design and allow for a better understanding and interpretation of each dataset.
This paper outlines a workflow to improve processing and interpretation of DAS and DTS data. In addition,
an estimate of the slurry distribution can be made. These methods will be demonstrated for a horizontal
Wolfcamp well in the Permian Basin. Here we compare key aspects of the microseismic, DAS, and DTS
results in several fracture stages to understand the downhole geomechanical processes. In order to interpret
the DTS data a thermal model is developed (using DTS data) to simulate the temperature behavior after
pumping has ceased. A slurry distribution is obtained by matching the simulated temperature with the
measured temperature from DTS. In addition, the DAS data signal is studied in the frequency domain and
the dominant frequencies are identified that are mostly related to fluid flow and to reduce the background
noise. This time frequency analysis enhances the ability to monitor and optimize well treatments.
After reducing the background noise, the acoustic intensity is correlated to the slurry distribution. The fluid
distribution data from DAS and DTS are compared with the microseismic and near field strain to better
understand the completion processes. We utilized fiber optic microseismic to better understand and
compare it to conventional microseismic.
Finally, we highlight the dynamics of strain and microseismic signature as fluid moves from an offset well
completion into the prior stimulated fiber well to better understand the reservoir and far field effects of the
completion.

2. URTeC 291 2
Introduction
The Permian basin is the largest productive basin in the United States. It is currently responsible for most
of the recent increase in U.S. oil production.
In Figure 1 we show several key deep shale reservoirs within the Permian basin, which is divided into the
Delaware basin to the west and the Midland basin to the east. Multiple operators are currently targeting
horizontal wells in this basin.
In 2017, Pioneer Natural Resources installed a fiber optic system on the outside of a horizontal well for the
purposes of (1) recording the development of physical changes resulting from the completion near the
injection site; and (2) recording the far field interaction with offsetting horizontal stimulations. Pioneer has
implemented similar efforts elsewhere to better understand the horizontal and vertical extents of various
completion designs in unconventional resources (Hull et al. 2019).
A horizontal well in the Midland basin was equipped with several downhole pressure and temperature
gauges. The well also had a fiber optic cable for DAS and DTS measurements. The monitor well and several
adjacent horizontal wells were stimulated in a zipper sequence to develop our understanding of pressure,
temperature, and strain changes related to the stimulations. Data were processed in-house and integrated
with the addition data that was collected in an effort to develop our understanding of the physical mechanics
at play.
Figure 1. Shows some of the key landing zones in the Midland side of the Permian basin, regional geology of key uplifts
surrounding the study area, and a geographic map of the study area highlighted with the box.

3. URTeC 291 3
In our workflow, we first convert the measured fiber-based temperature collected during the stimulation
into a temperature difference by subtracting it from the pre-frac formation temperature. We then build a
2D thermal model for the well and surrounding rock matrix. From this we can create a simulated
temperature for the DTS during and after the stimulation. Our results show a very good match with the
measured temperatures. Using the DTS model and the temporal DTS data, we can construct a slurry
distribution for the stage and the individual clusters.
In our example, we observe changes in the DTS signal to detect fluid entering the formation across clusters
and what may be the effective transmission of the completion through time. The DAS signal is also
traditionally used to determine the fluid flow through the perforations into the reservoir.
The background acoustic signal is first studied and then removed from the main signal. The dominant
frequency bands related to the injection process are retained. To better quantify the performance of the
stage, the parameter “uniformity” is defined as an indicator of how the slurry is distributed into each cluster.
We can compare the uniformity of the DAS slurry measured data to the DTS slurry data.
In our data sets, we have found the DTS slurry allocations can be utilized to better understand the
distribution of fluid and, in general, track the more conventionally presented DAS allocation. By having
both calculations we can interpret some of the physical processes taking place at the perforations, recognize
and understand variations between data sets, and provide insight into the stimulation that we may have
otherwise missed.
Beyond modeling and comparing the near wellbore DTS and DAS, the microseismic data can also be
integrated into these data. This combination of data sets further define key relationships between fluid,
pressure, and acoustic activity within a stimulation stage. In addition the development of the hydraulic
stimulation through time in the far field, away from the stimulated well, is also highlighted.
Acquisition Setup for the Project
Pioneer Natural Resources ran a permanently installed fiber optic line in a 10,000-ft horizontal Wolfcamp
Shale well in Midland County. This well was also equipped with downhole pressure gauges and was
observed by downhole geophones. During the stimulation of the instrumented well and its offsets, DAS,
DTS, and microseismic were recorded. The DAS and DTS data were used for both near field, instrumented
well stimulation, and far field offset well stimulation observations. Microseismic was recorded on both
wells. In Figure 2 we show the collection of instrumentation utilized for the acquisition of DAS and DTS.
The downhole pressure and temperature sensors were installed outside the wellbore at various locations
across the horizontal section of the wellbore.
Figure 2. Represents the collection of instrumentation utilized for recording the hydraulic stimulation. We used VSI
(Versatile Seismic Imager) geophone arrays (left), as well as external pressure gauges (bottom left), and fiber optics (upper,
middle and right) for the acquisition.

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Distributed Acoustic Sensing (DAS)
DAS is a newly adapted technology that can measure the acoustic signature in the near wellbore region.
These data can be used to visualize and understand important downhole parameters such as active
perforations, flow rate, etc. As noted previously, the permanent fiber provides an ability to monitor for the
life of the well the entire length of the wellbore. However, DAS is still not completely understood due to
the complexity of the acoustic phenomenon it records and our lack of understanding around what that
represents physically.
During a hydraulic stimulation, real-time fluid distribution was recorded for each cluster using DAS and
DTS. The DTS real-time temperature recorded during and after the stimulation provides a window into
understanding the treatment effects, as shown in Figure 3. In addition, cross-well interaction during the
offset well treatment was also observed from DAS and DTS.
It is important to note that the raw acoustic information from the DAS was processed using signal
processing workflows, and various metrics were computed, including sound pressure level and other signal
metrics. Fluid distribution results were provided during stimulation from the DAS data. Project data were
recorded, processed, and delivered to stakeholders, allowing for observations to be made prior to subsequent
stages. In addition, analytics were compiled during the project to allow for the comparison of stage-to-stage
performance.
The processing framework also involved a cloud-based solution where terabytes worth of DAS data were
processed and stored on the Data Lake. Processing and computations were performed using a high-end
Linux data science virtual machine from the Data Lake.
Figure 3. Shows the responses from DAS and DTS correlated with pump schedule during a single stage. The red triangles
on the left edge of the DAS/DTS plot represent the location of the perf clusters and the green triangle block indicates the
location of the plug for this stage.

5. URTeC 291 5
Distributed Temperature Sensing (DTS) and Numerical Modeling
While temperature logs have been a part of standard production logging packages for years, downhole fiber
optic DTS technology has introduced a continuous measurement of both temporal and spatial temperature,
allowing an entire well’s response to flow to be recorded. Downhole temperature can be recorded during
fracturing, shut-in, and production to provide continuous and integrated information. Integrated DTS
interpretation provides information on fracture/flow distribution, providing key insights into what occurred
during fracture treatments. It also identifies variations in treatment design and execution, should they exist,
and makes it potentially possible to improve the efficiency of multistage fracture stimulation. To
demonstrate the sensitivity of the DTS measurement, Figure 4 shows the change in the DTS at the casing
collars, which is then used for depth calibration of the fiber. As shown here, the sensitivity of the DTS
imaging allows the geoscientist to even pick up casing collar locations with a high degree of accuracy.
In this paper, a thermal model (Figure 5) is developed to simulate the temperature behavior after pumping
stops. A slurry/proppant distribution is obtained by matching the simulated temperature with the measured
temperature from DTS.
Figure 4. This figure shows the change in the DTS at the casing collars which is then used for depth calibration of the fiber.
From DTS, we first convert the temperature into a temperature difference by subtracting the geothermal
temperature. With the calibration of the thermal properties, the simulated temperature matches the measured
temperature very well, as indicated in Figure 6. A slurry/proppant distribution is generated from this
information. To better quantify the performance of the stage, the parameter “uniformity” is defined as an
indicator of how evenly the slurry/proppant is distributed into each cluster. In addition, the fluid interaction
with previous stages are detected automatically based on the rate of temperature change.

6. URTeC 291 6
The temperature behavior during the fracturing process has previously been studied, taking in to account
both a fracture propagation model and a temperature model (Huckabee 2009).
Below we briefly discuss the numerical model to simulate downhole temperature. We start the discussions
of the pre-requisites of the model and governing equations. The problem is simplified by assuming that a
single transverse fracture is created instantaneously at the beginning of injection, thus fixing the geometry
for the entire injection period. Seth et al. (2010) presented a simple analytical solution for fluid temperature
along the fracture during the hydraulic stimulation process. The fluid leak-off to the formation is ignored
for purpose of simplicity. We can now use an analytical solution to generate the initial temperature profile.
2
where –
Y = coordinate in the y-direction which origin is shifted to the
reservoir center
w = fracture width
h = heat transfer coefficient on fracture face
r and fr = liquid phase, rock matrix, and fracture, respectively
Initial Condition: for warmback temperature simulation, the initial condition is the temperature
profile after injection.
 Thermal properties include which is the material conductivity (from general heat conduction
equation), is the density and is the specific heat capacity
Boundary Condition: Neumann
 Meshing: Tartan grid in x-direction, uniform grid in r-direction
51 grids in x-direction, 61 grids in r-direction
 Accordingly, in Seth et al. 2010 two dimensionless quantities are defined:
2
Here and denote initial temperature and injection fluid temperature respectively. The temperature
profile itself does not change with time while the temperature front advanced with time. Then, the analytical
solution is highly dependent on the value of the heat transfer coefficient .

7. URTeC 291 7
Figure 5 shows a tabulation of commonly used thermal properties for Water and Shale. The figure also shows the simulation
result depicting the injecting fluid volume influence on long term warmback.
Given the boundary conditions are , 0 0 and 0, 1, the analytical solution is
provided as:
0 0
0
 The solution is then given as a similarity solution provided below (Han 2012):
, erfc
√4
0
Where, erfc (·) denotes the complementary error function. In Figure 6 we can see a high degree of
correlation between warmback time and difference in temperature when we compare simulated versus
measured in each of the clusters (utilizing the modeled approach).

8. URTeC 291 8
Figure 6. Shows the actual vs modeled thermal changes in the wellbore when warmback begins. We have adjusted the
warmback time to be zero once a cluster shuts down. From the different slopes in the warmback signatures we can estimate
the contribution of fluid across individual clusters.
Integration of the Microseismic with the Near Feld Strain
By integrating the microseismic with the fiber optic data, we can observe changes near and far field within
the rock during stimulation. Figure 7 shows some of the key aspects of the microseismic event timing
including where it occurs in the near field along the wellbore. The actual microseismic positional data
beyond their relationship along the wellbore is not shown here, but in general for this stage, the
microseismic cloud develops from the perforations extending in distance in the SHmax direction in time
throughout the stimulation.
Microseismic events can initiate adjacent to the completion perforation interval as the guns are being
pumped as well as when the sealing ball is pumped prior to a stage. Figure 7 (A), DAS indicates good
transmission of energy across the perforation starts at point B as the stage comes up to rate with cooling of
the fiber shortly afterward at point C. One key aspect is that the microseismic occurs early across most of
the perforation interval, and develops outward here in a slightly toe-ward direction through time as indicated
at time D. Minor variations in surface pressure, DAS changes, and MS are noted at time line highlighted
at point E. At the end of the stage during shutdown, pressure is abruptly lowered and along the main
perforation interval we observe an increase in the microseismic events F. Thermal warming begins shortly
afterward as noted by the point G. For this stage and other stages, the noted increase in microseismic
activity as pumps shut down suggests that we may be imaging the closing down of the fracture aperture.

9. URTeC 291 9
Figure 7. Shows the temporal relationship of the DAS, DTS, and completion, as well as the occurrence of the microseismic
along the wellbore. Microseismic events are colored and sized for magnitude. The horizontal blue lines highlight the
treatment interval while the vertical lines mark key points in the stage.
Offset Stimulation Monitoring with Fiber Optics to Understand the Far Field
Pioneer Natural Resources recorded offset completions into the permanent fiber well to better understand
far field deformational changes in the reservoir. The goal in this study is to record the low frequency strain
and thermal variations on the fiber for stimulations at various well spacings, and then relate this information
to the progression of the zipper frac through time and space. The method involves both leading and lagging
the offset completion with the fiber well in the zipper frac sequence to better understand the relationship
between the stimulation of the virgin rock and that of prior stimulated zones.
Beyond recording the offset stimulation strain on the fiber, Pioneer utilized the fiber to recorded
microseismic events. We also utilized a conventional microseismic geophone array to compare the two
technologies and calibrate the geophone based microseismic data to the fiber data. These data allow us to
establish a temporal understanding of the hydraulic fracture geometry and conductivity in three-dimensions.
The fiber based microseismic can complement our understanding of the progression of the stimulation
adjacent to the offsetting fiber well.
The complexity of the hydraulic stimulation and well-to-well interaction can be better described and
understood using the microseismic, strain and thermal data. Establishing strain and thermal relationships
through the virgin reservoir can highlight the degree of natural fractures, which potentially contribute to the
complexity of the stimulation. For example, does the hydraulic stimulation show up on the offset well along
SHmax at one or two localized points, or is it more diffuse along the lateral position of the fiber well beyond
what we would consider the stimulation deformation width? These data can be utilized to help answer
questions like these while also reveal the existence of natural fractures that may provide communication
pathways in unconventional plays.

10. URTeC 291 10
Figure 8. Shows the development of the microseismic onto the offset fiber well for one stage. The microseismic events are
colored in time and sized by magnitude. Here the fiber well has been completed prior. We see no effect on the thermal DTS
for this stage but do see the interaction across the perforations in the DAS as 4 or 5 lineation through time. The injection
depths of the offset well are noted in the red arrows. The DAS interaction occurs over 400ft of interval over midrange to low
frequencies and starts up after about 1/2 of the stage has been pumped and continues after the pumps have stopped.
In our far field study, we can track the evolution of the stimulation from the offset well stimulation with the
conventional and fiber based microseismic, tying together the DAS strain and DTS in the instrumented
well. In Figure 8 we observed strain hits and thermal interactions with the fiber from the offset well and
tied these observations to the microseismic development. By integrating these data, we have developed a
more complete understanding of the physical processes occurring within these stimulations and their
interactions with the reservoir.
Some of the key observations we have made utilizing these various datasets, as they relate to the far field,
are noted below.
From the microseismic data:
 In Figure 8 we establish a direct correlation of the progression of the stimulation from the offsetting
well on the fiber well with the progression of the microseismic through time.
 Microseismic extents correlate in general to the strain deformation envelopes noted on the fiber.
 Fiber-based microseismic, not shown here, gives a qualitative estimation of the stimulation building
out from the offset well towards the fiber well.
From the strain and thermal interactions we note:
 There appears to be more complex strain hits on the fiber when we stimulate the offset well through
previously stimulated rock.
 Strain effects appear to be overall broad and occur earlier in time than the thermal warming related
to the pressure front. We observe cooling only on some stages.
 We see more thermal pressure related warming heel-ward than toe-ward from the offset
stimulation. This may be related to a stress shadow of the current offset stage.
 Strain and microseismic data indicate that fluid moving within the hydraulic fracture continued
through time even after pumping stopped on the offset well as shown in Figure 8.
 Strain through the virgin rock often focuses over short intervals on the fiber, and at times broadens
out to a length exceeding two stages during the pumping of the offset stimulation.
 When stimulating the offset adjacent well after the stimulation of the fiber well (as shown in Figure
8), we see broad strain signatures exceeding 2 to 3 stage lengths. This highlights the perforation
clusters of the original fiber well as pressure and likely fluid communication is established.

11. URTeC 291 11
 A low frequency strain signature is observed more than two stages beyond the current stage
 Extension of the stimulation over 750ft typically took over 30 minutes to travel through the prior
completed stimulation of the fiber well.
 Extension for the most part followed the max horizontal stress direction out of the perforations in
the offset well, with limited interpreted natural fracture interaction carrying fluid substantially heel-
ward beyond the offset deformation zone.
 Thermal variations are noted on the offset fiber for some stages that correlate in depth to the change
in strain on the fiber.
Fiber-Based Microseismic
As outlined prior, fiber optics can be used to record microseismic data. There are numerous advantages, but
also some disadvantages (Hull et. al 2017). While fiber optics can image a microseismic event, it is not
omnidirectional. A fiber optic line is sensitive to energy propagating along the length of the fiber more than
it is across it. Further, the fiber acts as a single component geophone. To locate an event in 3D space requires
the fiber to be located across multiple azimuths to correctly position the event. Here, fiber-based
microseismic was typically recorded only from the horizontal part of the instrumented well, resulting in
some limitations in defining the exact placement of the microseismic events for most of the well.
There is no information provided on depth for fiber oriented only horizontally, given that the fiber records
are a single component. Further, when events late in a stage span both sides of the fiber well, it is unknown
from which direction the events originate.
Fortunately, in this project additional depth work was possible in heel stages where both vertical and
horizontal fiber could record large events. This allowed us to establish some depth control for the fiber-
based microseismic events that in general compared well to the depths recorded by the geophone-based
microseismic. Correlation of both microseismic datasets gave confidence that in the fiber based
microseismic depiction of the cloud extents for a given stage were good.
Distributed Acoustic Processing
The frequency of the energy occurring along the fiber during the completion was analyzed. Low frequency
strain build up on the fiber was observed and correlated with the current and prior stage. This is commonly
referred to as a stress shadow. Stress shadow effects are noted by Ugueto et al. (2019), and for some parts
of the analysis we removed these trends to better normalize variability at the cluster level. In addition,
localized changes occurring at the cluster level at higher frequencies within the stage are noted, as shown
in Figure 9.
In Figure 9 waterfall plots are utilized to assess common frequencies and noted changes occurring across
the clusters that could be related to changes in fluid depicted in Figure 10. Once DTS and DAS fluid
allocations at the cluster level had been obtained, the two techniques were compared to better understand
stage to stage uniformity.

12. URTeC 291 12
Figure 9. Shows the frequency of energy in hertz across five clusters and their temporal changes in energy through time.
From here we can compare key changes in the energy for each cluster and its frequency content in an attempt to relate this
to the downhole dynamics and fluid distribution over each cluster. Some of the noted variations are highlighted in the red
boxes. As shown above for Cluster 4, the DAS data terminated early.
Figure 10. On the left, the DAS shows computed energy thru each cluster for a given stage for fluid and an allocated
percentage based on energy when proppant was being placed downhole. On the right, the DTS assessment is shown for the
same stage using the modeled approach.
Geomechanics and Microseismic
The datasets acquired here support the basic concept that as the pressure increases in the hydraulic fractures,
as well as in the surrounding rock adjacent to the fractures, stress increases. The fiber, because it is coupled
to the rock through the cement, translates these stress changes as strain changes within the fiber optic line.
The temporal strain changes on the DAS can be recorded and displayed over multiple frequencies. As the
hydraulic fracture tip propagates, it introduces deformation and slippage along the fracture face as well as
introduces increases in pressure and strain to the surrounding rock (van der Baan et al. 2013). The

13. URTeC 291 13
deformation process may result in mechanical slippage over a broad range of frequencies that are recorded
as stress drop. Microseismic recording of these stress drops tracks the development of this stress envelope
through time.
The microseismic data presented in this study was processed using commercial vendors. This microseismic
dataset has some uncertainty in the locations of the microseismic events as all such datasets do. Despite
these uncertainties, this dataset has demonstrated clear associations with the strain and thermal effects noted
on the fiber data. That is, in general, for most stages examined herein we see direct correlation of the fiber-
recorded strain changes with the presence of the microseismic near or adjacent to the fiber shown in Figure
8.
It is important to note the microseismic is not typically tracking individual fractures in these very tight
reservoir rock stimulations, but the broader strain-induced deformational zone and the mechanical processes
occurring within these zones.
Geomechanics and Integrating the Near Field and Far Field Data
Two key observations can be made on the fiber-based data that have not been sufficiently discussed in the
literature.
One observation is that fast warmback was experienced on the thermal data. Here the data suggest that the
stimulation fluid is moving away from the well and down the fracture after the completion has stopped
pumping. If the fluid were to remain around the perforations and fiber, the rock should have retained the
cooler fluid temperature with a generally observed slow warmback occurring over days. Note in the near
field data are observations for clusters that return to reservoir temperature within a short period of time
(minutes), shown in Figure 8.
This phenomenon is interpreted to be the in-situ fluid in the stimulated rock next to the fracture and fiber
may be quickly replacing the cold fluid introduced into the fracture as the fracture extends post shutdown.
In fact, in the data displayed in Figure 7, evidence of aperture closing is observed. This was also noted by
van der Baan et al. (2013) who demonstrated that microseismic event activity nearest to the wellbore falls
off when the pumps shut down.
A second observation we have noted on the microseismic data and offsetting fiber well, is that the recorded
deformation continues for a substantial amount of time, often tens of minutes, after pumping has stopped
on the adjacent well, shown in Figure 8. Our data suggests, as noted by Meyer and Bazan (2001), that the
fluid is moving, introducing stress and strain changes in and around the fractured reservoir for upwards of
an hour or more.
Conclusions
A better understanding of the physical processes taking place in the near and far field wellbore environment
during a hydraulic stimulation can be obtained by integrating microseismic, fiber optics, and downhole
pressure data. This understanding can be further improved by visualizing these data, both three
dimensionally as well as temporally,
Developing multidisciplinary teams that can integrate these substantial data sets and reduce the information
into transferable learnings is key to a successful outcome.
Based on this work, we can further develop our hydraulic stimulation models to optimize completions,
increase recovery factors, and reduce communication with offset wells in unconventional plays.
Acknowledgements
We would like to thank Pioneer Natural Resources for allowing us to publish this material; Silixa for
recording the fiber data; and Schlumberger for the microseismic data.
The data acquired and assessed here could not have been accomplished without the help of a large team
effort across multiple groups in multiple cities. We would like to thank Pioneer for allowing us to pursue
such a robust project.

14. URTeC 291 14
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