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DETECTION OF FRACTURING IN ROCKS USING
ACOUSTIC EMISSIONS
by
Aniket Arun Surdi
A thesis submitted to the faculty of
The University of Utah
in partial fulfillment of the requirements for the degree of
Master of Science
Department of Mechanical Engineering
The University of Utah
December 2010
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Copyright Aniket Arun Surdi 2010
All Rights Reserved
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T h e U n i v e r s i t y o f U t a h G r a d u a t e S c h o o l
STATEMENT OF THESIS APPROVAL
The thesis of Aniket Arun Surdi
has been approved by the following supervisory committee members:
Sidney Green , Chair 03/12/2010
Date Approved
Rebecca Brannon , Member 03/12/2010
Date Approved
John McLennan , Member 03/12/2010
ate pprove
and by Timothy A. Ameel , Chair of
the Department of Mechanical Engineering
and by Charles A. Wight, Dean of The Graduate School.
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ABSTRACT
Acoustic Emission (AE) signals are elastic body waves produced by a sudden release
of acoustic energy, as a result of a localized or a distributed failure, and of redistribution
of stresses (e.g. grain crushing, grain sliding, microscopic fracturing and macroscopic
fracturing). Acoustic emission technology (AET) uses AE events to locate fractures in
real time. This technology is of particular importance for mapping the propagation of
hydraulic fractures in the subsurface and particularly important on tight reservoirs.
Results give the operator an opportunity to visualize the fracture development, during
hydraulic treatment, and potentially take corrective actions to control fracture growth, if
necessary. For these applications, understanding the sources of AE during fracturing in
rocks is of critical importance for characterizing the final fracture geometry.
In this work, controlled fracturing tests were conducted on relatively homogeneous
and isotropic sandstone rock slabs to map fracture propagation, using AET. Fracturing
was done by pressurizing a drilled borehole in the sample using an inflated cylindrical
bladder. The experimental configuration permitted some control of the final fracture.
Finite element analysis (FEA) was used to understand the stress distributions at specific
times, during the fracturing process, and based on these results; the distribution of AE
events was anticipated in time.
A strong correlation between the stress concentrations from FEA and localized AE
was observed. Acoustic emissions were detected before, during and after the visible
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failure of the rock. AE localizations show that, before and after the failure, the highest
density of AE events exist in the vicinity of the region where the fracture eventually
develops. This indicates that an incipient fracture develops slowly, before the rapid
unstable fracturing, generating a large amount of AE events during the process. The
rapid fracturing process generates a considerably smaller number of AE events. Results
also show a low density of localized AE events away from the fracture.
The petrographic analysis verifies the development of incipient fracturing as a
precursor to fracturing and fracture detachment. Grain level damage in the form of grain
crushing and sliding and submillimeter fracture branching are observed. The sub-
millimeter fracture branching events are outside the resolution of AE localization.
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To my parents,
Arun and Sunita Surdi
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TABLE OF CONTENTS
ABSTRACT...................................................................................................................... iii
ACKNOWLEDGEMENTS.......................................................................................... viii
1 INTRODUCTION......................................................................................................... 1
1.1 Motivation ................................................................................................................. 1
1.2 Background ............................................................................................................... 3
1.2.1 Introduction to Acoustic Emission Technology ................................................. 3
1.2.2 Literature Review ............................................................................................... 3
1.3 Localization of Acoustic Events ............................................................................... 6
1.4 Thesis Structure ....................................................................................................... 15
2 ERRORS IN LOCATING ACOUSTIC EVENTS................................................... 16
2.1 Introduction ............................................................................................................. 16
2.2 Coupling .................................................................................................................. 16
2.3 Wave Velocity Model ............................................................................................. 19
2.4 Wave Onset Detection ............................................................................................. 21
2.4.1 Amplitude Threshold-Crossing Method ........................................................... 22
2.4.2 Akaike Information Criterion Picker ................................................................ 22
2.4.3 Comparison of Arrival Picking Methods.......................................................... 28
2.5 Conclusions ............................................................................................................. 28
3 INSTRUMENTATION AND TEST SETUP........................................................... 31
3.1 Introduction ............................................................................................................. 31
3.2 Sample Materials ..................................................................................................... 31
3.3 Instrumentation........................................................................................................ 32
3.3.1 Acoustic Emissions Monitoring Equipment ..................................................... 32
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3.3.2 Borehole Pressurizing System .......................................................................... 35
3.3.3 Fracture Mapping and Data Visualization ........................................................ 37
3.4 Experimental Setup ................................................................................................. 39
4 EXPERIMENTAL PROCEDURE AND RESULTS............................................... 40
4.1 Centre Borehole Fracturing Test ............................................................................. 40
4.1.1 Stress Distributions during Pressurization ........................................................ 41
4.1.2 AE Results ........................................................................................................ 46
4.2 Offset Borehole Fracturing Test .............................................................................. 55
4.3 Conclusions ............................................................................................................. 62
5 ROCK MICROSTRUCTURE ANALYSIS............................................................. 64
5.1 Introduction ............................................................................................................. 64
5.2 Rock Classification ................................................................................................. 64
5.3 Petrographic Analysis ............................................................................................. 66
5.4 Thin Sections ........................................................................................................... 69
5.4.1 Thin Section Regions........................................................................................ 69
5.4.2 Vertical Thin Sections ...................................................................................... 69
5.4.3 Horizontal Thin Sections .................................................................................. 71
5.5 Relation of Rock Damage to AE ............................................................................. 75
5.6 Conclusions ............................................................................................................. 76
6 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS............................ 77
6.1 Summary ................................................................................................................. 77
6.2 Conclusions ............................................................................................................. 78
6.3 Recommendations ................................................................................................... 80
REFERENCES................................................................................................................ 82
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ACKNOWLEDGEMENTS
I would sincerely like to thank Dr. Roberto Suarez-Rivera and Dr. Sidney Green for
their mentoring, and providing valuable guidance and motivation throughout this study. I
appreciate the assistance of Pablo Duran for test setup. The petro graphic analysis of rock
done by John Petriello proved valuable for this study. This work would not have been
possible without the funds provided by Dr. Roberto Suarez-Rivera and TerraTek Inc. I
would also like to thank Dr. Doug Ekart for his assistance and contributions to the work
in this study. I am really grateful to my parents, Arun and Sunita Surdi, and my sister,
Archana, who motivated me to pursue my Masters education and have been true
inspirations throughout my life. Finally, this work would not have been possible without
the love, care and support of my soon to be wife, Sharanya.
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CHAPTER 1
INTRODUCTION
1.1Motivation
Investigation to use acoustic emission technology (AET) to locate defects in rocks has
gained importance in the last decade, as all the unconventional oil and gas wells are now
hydraulically fractured to stimulate production. Fracture simulation engineers spend a
considerable amount of time designing and simulating the hydraulic fractures and
forecast the surface area that will be generated by the fracturing job. Field engineers
execute hydraulic fracturing jobs as designed by the fracture simulation engineers. The
surface area generated during hydraulic fracturing and the associated increase in the wells
productivity measures the success of the fracturing job. Therefore, it is essential for the
fracture simulation engineer and the field engineer to know the amount of surface area
generated by the hydraulic fracture. Thus, the need to visualize the surface area
generated by the hydraulic fractures is increasing. Acoustic energy is released during the
fracturing process and is detected using transducers on the surface. Advanced data
acquisition and data processing techniques make it possible to locate the sources of the
acoustic events almost instantaneously. Thus, acoustic emissions have the ability to
locate fractures in real time and give the operator a potential opportunity to control the
fracture size. With this in mind, it is necessary to understand the sources of acoustic
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emissions during hydraulic fracturing. In addition, establishing hydraulic connectivity
between localized acoustic emission (AE) events is necessary to characterize the
fracturing process.
The velocity model used for localization of acoustic events introduces uncertainties in
localizations. This is an important limitation in the application of this technology to
unconventional gas reservoirs because of their strongly heterogeneity and high
anisotropy. In addition, as the fractures are created, the velocity is anticipated to change.
However, the most important limitation in the use of this method is that the real sources
of acoustic emissions and the hydraulic connectivity between the localized AE events is
still not understood completely.
In this thesis, controlled fracturing experiments were conducted and the fracturing
process was monitored using AE. The experimental configuration provided strong
control on the final fracture geometry, which facilitated the understanding of stress
distributions as the fracture propagated and anticipating the distribution of AE events at
different stages of wellbore pressurization. Results show that a large number of AE
events are localized near the fracture and fewer events are localized away from the
fracture where there is no visible damage. In addition, a considerable amount of AE
activity is detected before, during and after the visible failure. The prefracture, fracture
and postfracture events can be discriminated in time, but are not easily discriminated
otherwise. Further, the acoustic events located away from the actual fracture, although
believed to be real, are difficult to identify. The events occurring away from the fracture
with no connectivity with the visible fracture can be termed as rock matrix effects or rock
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complaining. If acoustic events of rock complaining and rock fracturing are not
discriminated properly, the geometry of the fractures is grossly underestimated.
1.2Background
1.2.1Introduction to Acoustic Emission Technology
Acoustic emissions are elastic body waves produced by fractures, which cause a
redistribution of stresses and release of acoustic energy. The possibility of detecting
microsiesmic activity in controlled laboratory experiments with rocks was demonstrated
in [1]. This initiated the research in the field of acoustic emissions (AE), commonly
known as acoustic emission technology (AET), or acoustic technology (AT). In recent
years, AET has emerged as one of the most important nondestructive testing techniques.
Traditional ultrasonic testing involves active ultrasonic transmission and analysis of
waves collected after they travel through the material, including defects in the material.
In contrast, acoustic emission monitoring is a passive seismic technology that analyzes
ultrasonic emissions produced by localized failure. AET does not require an active
source as the defect itself acts as a source. Hence, acoustic emissions have the ability to
detect the formation and propagation of a fracture in a structure, in real time.
1.2.2Literature Review
Rocks are complex due to their in-homogeneity, high attenuations, complex velocity
and anisotropy, and hence monitoring AE on rocks is relatively difficult. Experimental
AE measurements on rock specimens in laboratory have been extensive. The focus of AE
research in rocks can be classified into three main categories, namely, parametric, signal-
based analysis, source localization and characterization of source mechanism.
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Initial AE recording systems lacked the capability to record and store a large number
of signals over a short period. This limited AE analysis based on parametric evaluations
and signal-based interpretations. The conventional AE analysis included measuring the
number of hits, emission counts, peak amplitude, duration, rise time and energy of the
signal. Parametric analysis has been used to detect changes in cement [2] [3], and to
estimate the damage of civil structures [4] [5].
The advances in the fields of microelectronics and microcomputers triggered the
development of recording systems, and currently, multichannel high-frequency transient
recorders with high data processing and storing capability are available. With these
developments in microelectronics, the initial focus of counting the number of events
changed to evaluation of signal parameters [6].
The Kaiser effect states that acoustic events during a restressing cycle will occur only
after the previous maximum stress is exceeded [7]. S. Yoshikawa [8] studied the Kaiser
effect and demonstrated a new method to estimate the previous maximum stress state to
which a rock was subjected even if Kaiser Effect is not observed in first loading. Their
results show two types of AE; Type I AE exists above the previous maximum stress and
Type II exists below the previous maximum stress. D. Lockner proved that Kaiser effect
is not observed in all types of rocks [9].
D. Lockner and J. D. Byerlee [10] conducted controlled triaxial hydrofracture
experiments in laboratory on Weber sandstone and proved that shear fractures can be
induced in hydrofracturing, depending on the stress conditions and rock permeability, by
controlling the rate of injection. They used AE to monitor the hydrofracturing process
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and found that the initial activity occurred near the borehole and then moved towards the
edge of the sample, along the fracture zone.
The use of AET to detect formation of compaction bands in rock during axial testing
has been shown in [11]. The authors show that the nucleation of compaction bands is
indicated by the clustering of AE events near the notches followed by an increase in AE
activity. They monitored the P-wave velocity across the compaction bands and identified
the completion of compaction band by the significant decrease in velocity of P-wave
propagation across the compaction band. Through microstructural analysis, it was
demonstrated that outside the process zone of the compaction band, the rock was mostly
undeformed. They also estimated that the highest amplitude events had a location
uncertainty less than 1 mm.
Triaxial compression experiments were conducted by [12] to monitor the velocity
changes and the AE activity associated to deformation. Results indicate that different
types of rocks show different changes in velocity under axial load. Polarity analysis was
used to determine the AE source. They also demonstrated that during initial stress
differential, a significant amount of AE activity was associated with tensile events;
however, closer to the failure, an increase in shear events was observed. It was suggested
that the tensile cracks formed initially were connected by shear cracks formed closer to
failure.
Several researchers have demonstrated that AE events indicate formation of
microcracks, during initial stressing and eventual fault nucleation closer to the failure
[13] [14] [15] [16] . In a three point bending test [17] [18] performed on a prestressed
bridge girder, the AE locations adjacent to the crack were estimated to have an
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uncertainty of 15 mm; however, the sources beyond the crack were localized poorly. S.
Koppel and T. Vogel [19] conducted a pull out experiment in concrete cubes. Their
results show that although the failure was apparent only near the region of pullout, the
AE hypocenters were distributed through the cube where failure was least expected.
In spite of all these contributions, there remain gaps in our understanding of the real
sources of the acoustic emissions. The acoustic emissions events localized away from the
actual failure are not well understood, and are usually assumed to be localization
artifacts, which may not be true. Acoustic emissions localized away from the actual
fracture may be associated to grain level failure, due to the stress redistribution in the
rock during the fracturing process. This grain level failure associated to the redistribution
of stresses causing the release of acoustic energy can be referred to as rock matrix effect.
The understanding of acoustic emissions associated to the rock matrix is still unclear.
1.3Localization of Acoustic Events
The mathematical problem of localization was solved long before the invention of
acoustic emissions by L. Geiger [20]. Locating the source of acoustic events accurately
is critical in understanding the damage. Existing AE data acquisition systems have the
capability to simultaneously acquire data from several transducers. Elastic waves
emerging from an acoustic source will reach these transducers at different times
depending on the distance between the source and the transducer. The time difference in
wave arrival at each transducer and the wave velocity in the sample can be used to locate
the source of the event. Several other techniques have been developed, but the main
concept of difference in the time of arrival remains common. Localization can be
classified in mainly two types: zonal localization and point localization. There are three
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types of point localization techniques based on the number of coordinates that need to be
estimated for an acoustic source, namely, 1D, 2D and 3D localization.
1D localization assumes that the source is located on a line connecting two points.
Two sensors are sufficient for 1D localization. The 1D localization method is described
in [21].
In the 2D method of localization, the x and y coordinates of the source are calculated.
This method does not provide any information about the depth of the source, and is used
when the thickness of a sample is relatively small compared to the length and width of
the sample. A minimum of three sensors, or in other words, three arrival times are
required for 2D localization. The hyperbolic triangulation method will be used for
triangulating AE in this thesis,and hence is described in depth. Other methods that have
been tested for 2D triangulation can be found in [22] [23].
The hyperbolic triangulation method of localization is also based on difference in the
time of arrivals. It works on the principle that the sensors at different distances from the
source of the acoustic event will detect the signal at different times and assumes the
material to be homogeneous and isotropic. Using the time of wave arrival at the three
transducers and a homogeneous velocity of wave propagation, the epicenter can be
calculated using hyperbola method as described in [22] [24].
A hyperbola can be drawn between each pair of sensors and the intersection of all the
hyperbolas is the location of the event.
Consider the sensor layout shown in Figure 1.1.
Let, t1, t2, and t3, be the time of wave arrival at sensor 1, sensor 2, and sensor 3,
respectively.
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Figure 1.1: Layout for 2 D triangulation
C0(x0, y0) = Center coordinates between sensor 1 and sensor 2
C1(x1, y1) = Center coordinates between sensor 2 and sensor 3
C2(x2, y2) = Center coordinates between sensor 1 and sensor 3
(t)1-2 = t2t1= difference in time of wave arrival between sensor 1 and sensor 2
(t)2-3 = t3t2 = difference in time of wave arrival between sensor 2 and sensor 3
(t)1-3 = t3t1= difference in time of wave arrival between sensor 1 and sensor 3
V = Velocity of wave propagation
Now, consider sensors 1 and 2, shown in Figure 1.2
Let, transducer 1 and 2, be the focal points F1 and F2, respectively.
Hyperbola is a locus of points such that the difference of the distance to the two foci is
a constant equal to 2a, the distance between two vertices.
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Figure: 1.2: Hyperbola between two transducers
r2 - r1 = 2a = v (t) 1-2= v (t2t1) 1.1
Now, equation of Hyperbola between transducers 1 and 2 is given by,
(x-x0) /a (y-y0) /b = 1 1.2
Substituting b = (c-a) we get,
(x-x0) /a (y-y0) /(c-a) = 1 1.3
Substituting a = (v (t) 1-2 / 2) we get,
(x-x0)/ (v (t) 1-2 / 2) (y-y0)
/ (c- (v (t) 1-2 / 2)) = 1 1.4
Here v, c, x0, y0, (t)1-2 are the known entities, and x, y are the unknown terms.
Similarly, consider sensors 2 and 3.
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r3 - r2 = 2a = v (t) 2-3= v (t3t2) 1.5
Equation of hyperbola between 2 and 3 is given by,
(x-x1) /a (y-y1) /b = 1 1.6
Substituting b = (c - a) we get,
(x-x1)/ a (y-y1)
/ (c-a) = 1 1.7
Substituting a = (v (t) 2-3 / 2) we get,
(x-x1)/ (v (t) 2-3 / 2) (y-y1)
/ (c- (v (t) 2-3 / 2)) = 1 1.8
Here v, c, x1, y1,(t)2-3 are the known entities, and x, yare the unknown terms.
And, similarly consider sensors 1 and 3,
r3 - r1 = 2a = v (t) 1-3= v (t3t1) 1.9
Now, equation of Hyperbola between transducers 1 and 3 is given by,
(x-x2) /a (y-y2) /b = 1 1.10
Substituting b = (c-a) we get,
(x-x2) / a (y-y2)/ (c-a) = 1 1.11
Substituting a = (v (t) 1-3 / 2) we get,
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(x-x2)/ (v (t) 1-3 / 2) (y-y2)
/ (c- (v (t) 1-3 / 2)) = 1 1.12
Here v, c, x2, y2, (t)1-3 are the known entities, and x, y are the unknown terms.
The three equations of hyperbola are:
(x-x0)/ (v (t) 1-2 / 2) (y-y0)
/ (c- (v (t) 1-2 / 2)) = 1 1.13
(x-x1)/ (v (t) 2-3 / 2) (y-y1)
/ (c- (v (t) 2-3 / 2)) = 1 1.14
(x-x2) / (v (t) 1-3 / 2) (y-y2) / (c- (v (t) 1-3 / 2)) = 1 1.15
The source of acoustic emission is the intersection point of the three hyperbolas.
These three hyperbolas may not intersect at a point due to an error in the measurements.
In such cases, the localization accuracy can be improved by using more sensors and
performing statistical analysis. An example to improve location accuracy in an over-
determined case is given in [25]. From the data collected for this work, a localized event
using the hyperbolic triangulation method in Vallen Visual AE software is shown in
Figure 1.3.
The 3D method of localization is used to calculate the x, y, and z coordinates of the
source. This method provides information about the depth of the source. A minimum of
four arrival times is required to compute a result. Consider source P and four sensors
located spatially at distances R1, R2, R3 and R4, respectively, as shown in Figure 1.4.
The time of arrival difference-based triangulation is based on the following equations.
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Figure 1.3: 2D hyperbolic localization of a fracturing acoustic event
Let the coordinates of the source and the sensors be:
Source P = (x0, y0, z0)
Sensor 1 = (x1, y1, z1)
Sensor 2 = (x2, y2, z2)
Sensor 3 = (x3, y3, z3)
Sensor 4 = (x4, y4, z4)
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Figure 1.4: 3D localization of an acoustic event
Let,
V = velocity of wave propagation
t0= time of wave arrival at sensor 1
t12 = difference in the time of arrival between sensor 1 and 2
t13= difference in the time of arrival between sensor 1 and 3
t14= difference in the time of arrival between sensor 1 and 4
The radius of the spheres in this case are given by,
R1= vt0)
1.16
R2= (v (t0+t12))
1.17
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R3= (v (t0+t13))
1.18
R4= (v (t0+t14))
1.19
Now, using the basic equation of sphere, the equation of wave reaching sensor 1 is,
(x1x0) + ( y1- y0) + ( z1- z0) = (vt0)
1.20
the equation of wave reaching sensor 2 is,
(x2x
0)+ ( y
2- y
0) + ( z
2- z
0) = (v(t
0+t
12)) 1.21
the equation of wave reaching sensor 3 is,
(x3x0) + ( y3- y0) + ( z3- z0) = (v(t0+t13)) 1.22
and the equation of wave reaching sensor 4 is,
(x4x0) + ( y4- y0) + ( z4- z0) = (v(t0+t14)) 1.23
Here, x0, y0, z0 and t0 are the unknown entities. Thus, we have four equations and four
unknowns. Hence, a result is computable.
Localization accuracy of acoustic emissions is affected by several factors, such as
coupling of the transducer to the rock surface, accurate estimation of arrival time,
velocity model and geometric effects, etc. The application and testing conditions
determine the coupling material used to couple the transducer to the test sample.
Extensive research has been conducted over the years to improve the accuracy of
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CHAPTER 2
ERRORS IN LOCATING ACOUSTIC EVENTS
2.1Introduction
Locating the source of acoustic events is one of the most significant aspects in acoustic
emission studies. Locating acoustic events has gained importance due to its increased
application in real time fracture monitoring in oil and gas fields. Earthquake seismology
and acoustic emissions are strongly related as the localization of acoustic sources is a
crucial factor in both fields [26]. In acoustic emission monitoring, several factors
introduce uncertainty in localizations, such as coupling of the transducers, wave velocity
used for triangulation and time of arrival detection on waveforms. Factors introducing
errors in localizations and the methods that can help reduce these errors are discussed in
this chapter.
2.2Coupling
The coupling of the transducer to the surface of the test sample is one of the most
critical components of acoustic emissions monitoring. The difference in the acoustic
impedance of PZT transducers and air is typically in the order of 105N.s.m
-3. This
significant acoustic impedance mismatch between the two media results in huge
transmission losses. For this reason, the transducers have to be in complete contact with
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the test sample to avoid any air gaps. A coupling medium is usually used to reduce the
impedance mismatch and disperse the air between the transducer and test sample.
Depending on the applications and test conditions, different types of coupling media,
such as liquid, gel, etc. are available. Rocks are composed of different minerals and are
granular and discontinuous in nature. Rock surfaces, however finely smoothened, have
irregularities. These irregularities distort the frequency and amplitude of the waveforms
collected by the AE transducers, if not coupled properly using appropriate coupling
media. The waveform recorded by a poorly coupled transducer is shown in Figure 2.1.
Rocks are porous; liquid coupling material will penetrate the rock and introduce air
between contacting surfaces, resulting in poor coupling of the transducer to the rock
specimen. The most successful method of coupling in rocks is attaching the transducer
on the rock surface using glue or epoxy. However, there is a high chance of damaging
the transducer while attempting to decouple it from the test specimen. A method to
couple the transducers to the rock surface was tested. Aluminum disks of one-inch
diameter and -inch thickness were machined. These disks were smoothened and
polished to achieve mirror finish. Five-minute epoxy was used to glue the aluminum
disks to the surface of the rock. The aluminum plates coupled to the rock are shown in
Figure 2.2. Transducers were attached to these mirror finished aluminum disks using
putty. Putty, being visco-elastic in nature, maintains contact between transducer and
aluminum plates. The waveform recorded by a well-coupled transducer using this
method of coupling is shown in Figure 2.3.
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Figure 2.3: Waveform collected by a well-coupled transducer
2.3Wave Velocity Model
A well-defined velocity model is required for accurate localization of AE events. The
fracturing tests done for this thesis work were performed under no confining pressure.
Hence, wave propagation measurements to define the velocity model were obtained
under unstressed conditions. An auto calibration process performed using the Vallen AE
system was used to determine the velocity of wave propagation from each acoustic sensor
to all others. This process consists of sequential firing of the transducers, one at a time,
until all the transducers are considered. The autocalibration process as illustrated in [27]
is shown in Figure 2.4. The following results are also described in [28]. The following
assumptions were made for analysis of velocity measurements: (i) The material is
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Figure 2.5: Relationship of onset time versus measured distance
2.4Wave Onset Detection
The accurate detection of the first arrival of the P-wave is of great importance in
locating the source of acoustic emission and characterization of the velocity model. The
onset of acoustic wave can be chosen visually or can be determined using an automatic
picker. The method to identify and pick the onset of a phase has been described in [29].
The classification of onset detection mechanisms can be found in [30].
Depending upon the testing conditions and the size and properties of the material,
there can be few to thousands of events. Manual arrival picking on all the waveforms is
time consuming and therefore not practical. Therefore, an automatic, arrival picking
method is preferable for analysis. Amplitude threshold crossing is a commonly used
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method in commercially available software, as it is a simple method to pick the arrival
time, in real time, and has acceptable accuracy.
Over the decades, several algorithms have been developed to perform automatic onset
picking of P-waves. Methods published for P-wave onset picking include [31],
Polarization analysis [32], Autoregressive techniques [33], [34], [35], [29] , Maximum
Kurtosis and K-Statistics Criteria [36] and Hinckley Criterion [37]. The accuracy of
arrival time picking within 10 % of manual picking using AIC picker has been reported
in [38].
2.4.1Amplitude Threshold-Crossing Method
The amplitude threshold-crossing picker is a simple method for picking the P-wave
arrival on waveforms. This method consists of applying a threshold level just above the
noise level to pick the arrival of the P-wave. This method is illustrated in Figure 2.6 using
Vallen Visual AE software. The zero on the time scale shows the arrival picked by the
threshold-crossing method on the waveform. The amplitude threshold-crossing approach
is not suitable on signals with small amplitudes, high noise levels or low signal-to-noise
ratio [39]. For these conditions, the use of a dynamic threshold method called the
STA/LTA picker has been demonstrated in [40]. Similar approaches based on the
STA/LTA method used to detect arrivals on waveforms can be found in [31] [41].
2.4.2Akaike Information Criterion Picker
The detection mechanism should be able to find the arrival of the P-wave against the
background noise. Due to the low magnitude of energy in the acoustic emissions, the
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the AIC is applied only to the portion of signal containing the onset of P-wave [42]. The
procedure for selecting the time window for onset picking is as follows:-
Consider a waveform associated to an AE event, as shown in Figure 2.7. Hilbert
transform leads to an envelope of the signal. The Hilbert transform R(t) of a real-time-
dependent function R(t) is defined as [43]:
(2.1)
where, t denotes the time. Hilbert transformation generates a phase shift of by
transforming the time series. For a time-dependent function E(t), the envelope can be
calculated by [43]:
(2.2)
The Hilbert envelope is squared and normalized, and a constant threshold value is
applied to all the signals. A time window is selected before and after the point where it
crosses the threshold. The Hilbert transform of a waveform with the applied threshold
and the selected window of interest containing the arrival of P-wave is shown in Figure
2.8. The AIC picker is applied to this time window and the lowest value of AIC gives the
arrival of the P-wave.
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Figure 2.7: A typical acoustic emission waveform
Figure 2.8: Squared and normalized Hilbert envelope of the waveform
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Autoregressive modeling of a seismogram by dividing it into two stationary segments
as forward prediction model and backward prediction model is shown in [34]. It is also
shown that the change in the order of the autoregressive (AR) coefficient represents the
change in the characteristic of a seismogram. Typically, seismic noise has lower order
AR process and seismic signal has higher order AR [35]. This method has been
successfully used in single as well as multicomponent traces of broadband or short period
seismogram to detect the onset of P-waves [35].
For signal x of length N, the AIC value is calculated as [34]:
AIC (k) = (k - M) log ( F2
) + (N - M - k) log( B2
) + 2M (2.3)
where,
( F2
)Variance of prediction errors of forward model
( B2
)Variance of prediction errors of backward model
M - Order of an AR process fitting the data
AIC function can be calculated without using the AR co-efficient [33]. AIC is
calculated directly from the waveform, and the minimum value of AIC indicates the onset
of the P-wave.
For signal x, the AIC value is defined as [33]:
AIC (k) = k*log (variance(x [1, k])) + (n-k-1)*log (variance(x [k+1, n])) (2.4)
where, kGoes through the entire waveform.
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The AIC picker algorithm finds the arrival as the least AIC value [42]. Therefore, it is
essential to identify a time period that includes the region of interest [42]. The AIC picker
can find the arrival accurately in that time period.
Figure 2.9 shows the steps involved in picking the P-wave arrival using the AIC
method. It shows a waveform associated with an acoustic emission at the top, its squared
and normalized Hilbert envelope, with applied threshold and time interval selected for
arrival picking, in the middle and P-wave arrival in the chosen time window at the
bottom.
Figure 2.9: AIC arrival picking on an acoustic signal with high signal-to-noise ratio.
Acoustic Signal (top), corresponding squared and normalized amplitude (middle)calculated with Hilbert transform. Applied threshold level is drawn on the envelope and
time window is chosen for arrival picking. AIC is used for arrival picking (bottom).
Square shows the threshold crossing arrival and circle shows the AIC arrival.
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2.4.3Comparison of Arrival Picking Methods
The software used for this work limited the use of the amplitude threshold-crossing
method for arrival picking. Therefore, it was necessary to estimate the errors in
localization using this method. From the acoustic emission data collected for this thesis,
15 events with different magnitudes of amplitude were selected. The arrival times on
these events were picked automatically using the amplitude threshold picker, the AIC
picker and manually. The manual arrival picking method was considered the most
accurate. The comparison of localizations using the amplitude threshold and AIC picking
with manual picking of arrival times are shown inFigure 2.10.
It can be seen that for the highest amplitude events, both the methods produce accurate
results within 0.5 cm accuracy of the manual picking. The accuracy of localization using
amplitude threshold picking is less for the medium and low amplitude events. The
uncertainties in localization for the lowest amplitude AE events can be up to 3cm.
2.5Conclusions
Good coupling of transducers to the surface of the test sample is crucial because the
amplitude and energy of the acoustic emissions is low and poor coupling will result in
signal and frequency losses. The method used for coupling the transducers was efficient
and provided good contact. The velocity model used in the localization algorithm plays a
critical role in the accuracy of AE location. Most of the localization algorithms use
homogeneous velocity models. The velocities of wave propagation were measured
along several paths, using the auto calibration process in Vallen AMSY-5 system, and
homogenized for modeling purposes.
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.
Figu
re2.1
0:Comparisonoflocalizationresultsusingdiffe
rentarrivalpickingmethod
s
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The reliable onset of ultrasonic transmissions and acoustic waves is important for the
analysis of AE data and the interpretation of corresponding results. The amplitude
threshold-crossing method and AIC method of automatic onset picking were compared to
manually picked onset times (considered as most accurate). For high amplitude events,
both the methods produce as good results as the manual picking. The AIC method of
arrival picking produces better results for lower amplitude events; however, the software
used for arrival picking for this study uses amplitude threshold picking. Developing a
method/program to apply the AIC algorithm to all the waveforms is beyond the scope of
this thesis. Therefore, for this work, there will be small errors in localization associated
to arrival picking, and vary as a function of amplitude from 0.5 cm to 3cm.
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CHAPTER 3
INSTRUMENTATION AND TEST SETUP
3.1Introduction
The aim for the current thesis work is to conduct fracturing tests on rock samples and
detect the fracturing using acoustic emissions. The test required a pressurizing system to
inflate the borehole, drilled in the rock, without wetting the rock. In case of a leak, the
fluid can permeate into the region surrounding the borehole to a significant extent,
depending upon the porosity of the rock. This complicates the process of AE localization
because the velocity of acoustic wave propagation in dry rocks is lower than the velocity
of acoustic wave propagation in saturated rocks. In this case, a heterogeneous velocity
model is required to locate the source of the AE. However, most of the localization
algorithms are limited to use homogeneous velocity for triangulation. This was the
primary reason for which a dry fracturing test was chosen.
3.2Sample Materials
Most rocks are inherently heterogeneous and anisotropic in nature. In heterogeneous
rocks, the velocity of acoustic wave propagation varies in different directions, making
triangulation of AE location difficult. For this study, two types of rocks, namely
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CarbonTan and TerraTek sandstone, were used for the fracturing tests, due to their fairly
homogeneous and isotropic properties. Laboratory tests were done to determine the
properties of these rocks. Mechanical properties of the rocks are listed inTable 3.1.
3.3Instrumentation
3.3.1Acoustic Emissions Monitoring Equipment
Acoustic Emission data were collected using a portable Vallen AMSY-5 data
acquisition system. The equipment is shown inFigure 3.1. PZT transducers are widely
used in AE monitoring. The basic setup of a PZT transducer is shown in Figure 3.2.
DECI (VS-150 M) transducers with 150 kHz resonant frequency were used to detect and
record AE waveforms. These transducers have maximum sensitivity between 100 kHz to
300 kHz, but have the capability to detect the signals with a frequency between 100 kHz
to 450 kHz. Mirror finished aluminum plates were glued to the rock using epoxy to
ensure a flat surface for coupling the transducers. Putty was used to couple the
transducers on to the aluminum plates.
Table 3.1: Rock properties
Rock Name Bulk
Desnsity
(g/cm3)
Porosity
(%)
Unconfined
compressive
strength (psi)
Youngs
Modulus
(106psi)
Poissons
Ratio
Carbon Tan 2.25 12.2 7200
TerraTek
Sandstone
2.46 6.80 23,000 5.5 0.21
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Figure 3.1: AE data acquisition system
The AE transducers have microdot connectors to connect the cables transmitting the
acquired signals to the preamplifiers. The cables connecting the transducer to the
preamplifier are recommended to be less than 1.2m due to the capacitive load on the
transducers [27]. The acquired transducer signals were amplified by 34 dB using Vallen
AEP3 preamplifiers with high pass filter of 95 kHz and low pass of 1000 kHz. These
preamplifiers have low input noise, which allows for distinguishing between sensor
signal and electric noise. Cables with 50-Ohm BNC connectors at both ends were used to
transmit signals between the pre-amplifier and the data acquisition system. These cables
also supply 28V DC power to the preamplifiers. The transmission signal and the acoustic
emission waveforms were stored using the Vallen AMSY-5 system with 16 bits of
amplitude resolution and 10 MHz sampling rate. The Vallen AMSY-5 data acquisition
system is equipped with 18 channels and 9 Gb buffer memory for temporary storage.
Figure 3.3 shows the general process flow for Acoustic Emissions monitoring.
Sensor calibration was performed using a lead break test to determine the accuracy of
localization. Pool mode of trigger was used to assemble the events. In this mode, once
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Figure 3.2: PZT AE transducer setup adapted from [27]
Figure 3.3: AE measurement chain adapted from [27]
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the first transducer receives the waveform, it triggers all other transducers to start
recording at the same time. This allows for measuring the difference in the time of
arrival at each transducer from the first trigger. Hyperbolic triangulation method,
described in Chapter 1, was used to calculate the source location.
3.3.2Borehole Pressurizing System
The pressurizing system consisted of a TELEDYNCE ISCO hydraulic pump (model
100 DM). The pump has a maximum pressurizing capability of 10,000 psi. The flow
rate range for the pump is 0.00001-25 ml/min. It has a flow accuracy of 0.3% from the
set point and a standard pressure accuracy of 0.5%. The fluid used in the hydraulic pump
was water. High-pressure steel tubing capable of withstanding 10000-psi pressure was
used to transport fluid, to and from the pump. The diameter of the tubing was 1/8th inch.
An industrial pressure sensor from Sensotec, Super TJE, was installed to measure
pressure inside the borehole, and to digitize the pressure signals. The pressure transducer
has a wide range of pressure measurement from 10psi-7500 psi with an accuracy of
0.05%. The pressure transducer was calibrated before testing, to convert the voltage in
mV to pressure in psi. The output of the Sensotecpressure transducer was input to the
Vallen AMSY-5 AE data acquisition system. The Vallen AMSY-5 system has the
capability to record external parametric data, which facilitated the recording of borehole
pressure and integrating it with the acoustic emission data in the same data set. This also
provided the same time stamping of the borehole pressure as that of the acoustic emission
data. This proved valuable in correlating the AE activity with the changes in pressure. A
pressure gauge was also installed in the pressure line along with the pressure transducer
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as a backup to record the rock failure pressure. Care was taken at every connection to
prevent the leaking of the fluid.
The rock slabs under test have 1-inch thickness. A -inch borehole was drilled in
each rock sample as described previously. An impermeable cylindrical rubber jacket
with outer diameter slightly more than the borehole was pressed inside the borehole. The
rubber jacket was about 0.25 inch thick and 2.5 inches long. The rubber jacket extended
0.75 inch on each side of the slab. A 6-inch steel tube with 0.125-inch outer diameter and
0.0625-inch inner diameter was placed inside the rubber jacket. This tube was perforated
with a hole in the middle for bleeding the fluid inside the jacket. The hole in the tube was
aligned to be approximately in the middle of the block thickness. The tube extended
symmetrically on both sides of the slab. End caps were used on both sides to seal the
rubber bladder. The steel tube extends beyond the end caps. O-rings were used on both
sides of the end caps to prevent leaking. 90-degree elbows were connected on both sides
of the tube using collets. One end of the tube with a 90-degree connector was connected
to the tubing carrying fluid to and from the pump. The other end of the tube with a 90-
degree connector was sealed off. This allowed the fluid to flow only into the rubber
bladder and pressurize the borehole. The entire assembly was bled prior to the testing to
get rid of any air bubbles in the pump or the tubes. The borehole pressurizing assembly
is shown inFigure 3.4.
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Figure 3.4: Borehole pressurizing assembly
3.3.3Fracture Mapping and Data Visualization
Post the fracturing test, a fracture mapping is required to compare the AE localization
results to the actual fracture geometry. AMicroscribe3D digitizer was used to map the
fracture geometry. The articulated arm of the tool has multiple degrees of freedom,
which makes it easy to reach all the regions of the fracture. In this technique, a reference
point or origin is chosen on the rock using the stylus of the digitizer tool. The stylus of
the tool is then slid all over the fracture surface while maintaining constant contact with
the rock to get xyz coordinates of the points on the fracture. A higher number of data
points on the fracture facilitates high resolution imaging of the fracture. The xyz co-
ordinates are recorded in an excel file. ThePara Viewsoftware was used to visualize the
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fracture and if any error was observed, the fracture was remapped. 3D block models of
the rock samples were made using the Pro-Engineer Wildfire 4software. The borehole
and stress concentrators were also modeled for better visualization.
The block model, fracture model and the acoustic localization locations were
combined using theAE analysis software, developed at TerraTek. The block model, with
the mapped fracture for the TerraTek sandstone rock slab, is shown inFigure 3.5.
Figure 3.5: Block model with mapped fracture
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3.4Experimental Setup
6 inch by 6 inch by 1 inch slabs were cut for the test from each type of rock. Two
different geometries were used for the fracturing tests. A 0.5 inch borehole was drilled in
the center of the Carbon Tan sample. Water was used while drilling, to lubricate, cool
the drill bit and prevent the fracturing of the rock. The rock was dried in a furnace at 620
F for 24 hours. Two diametrically opposite stress concentrators were scribed inside the
surface of the borehole to initiate fractures. The TerraTek sandstone sample was drilled
with a 0.5 inch offset borehole. A single stress concentrator was cut on the longer side
inside the surface of the borehole to initiate the fractures. A diamond coated wire saw
was used to cut the 3mm stress concentrator. Aluminum disks were attached to the rocks
along the thickness using epoxy and sensors were coupled to these aluminum plates using
putty. The borehole pressurizing assembly was installed on the sample. The actual test
setup with the transducers mounted on the sample is shown inFigure 3.6.
Figure 3.6: Test setup
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CHAPTER 4
EXPERIMENTAL PROCEDURE AND RESULTS
4.1Centre Borehole Fracturing Test
A 6 inch x 6 inch x 1 inch Carbon Tan slab was used for this test. The borehole was
inflated using a cylindrical bladder at a controlled injection rate of 2cc/min until 200 psi
and then at the rate of 0.02cc/min until the failure. The rock fractured at approximately
1500 psi. Posttest, the fractures were mapped. The block model with the sensor positions
and mapped fracture is shown inFigure 4.1.The postfracture image of the slab is shown
inFigure 4.2.
Figure 4.1: Block model with mapped fracture
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Figure 4.2: Postfracture image of the Carbon Tan slab
4.1.1Stress Distributions during Pressurization
Finite element modeling was conducted using the actual geometry and rock properties,
to better understand the distribution of stress concentration in the sample during wellbore
pressurization. The results were computed using COMSOL version 3.5. The following
results have also been discussed in [28]. Figure 4.3 and Figure 4.4 show the direction
and magnitudes of the principal stresses, compressive and tensile. Figure 4.3 shows a
color map of minimum principal stress and Figure 4.4 shows a color map of maximum
principal stress, during initial pressurization of the wellbore. The wellbore is subjected to
the maximum tensile hoop stresses, and the maximum compressive radial stresses. The
geomechanics conventions, in which tension is negative and compression is positive,
have been used to plot these results. In these figures, green represents unstressed
conditions, blue is tension, and red is compression.
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Figure 4.3: Radial stress concentrations prior to fractureinitiation
Figure 4.4: Tangential stress concentrations prior to fracture initiation
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Figure 4.5 and Figure 4.6 show the evolution of the stress concentrations as the
fracture grows from the wellbore. Although these simulations are conducted assuming a
homogeneous medium and are correct at a macroscopic scale, they are not correct for the
microscopic scale of the real rock. The granular, discontinuous nature of sedimentary
rocks introduces stress concentrations at the grain contacts, and makes these locations
susceptible to localized grain crushing, if overstressed. With this in mind, the presence of
acoustic emissions can be anticipated to be associated with both the macroscopic
fracturing in the general direction of fracturing, and acoustic emissions associated to
localized grain crushing, near the wellbore and in the regions of high compression (red,
orange and yellow). Because of the small area of contact, considerable stress
concentrations may develop at the grain level as a result of small loads applied at the rock
boundaries.
Figure 4.7 andFigure 4.8 show the stress concentrations as the fracture approaches the
sample external boundaries. As before, green represents unstressed conditions, yellow
and red represent compression. The region adjacent to the fracture (the shadow zone) is
unstressed, and the tensile hoop stresses redistribute themselves away from the wellbore
region and along the boundary of the sample opposite to the direction of fracture
propagation. These results suggest that some degree of tensile microcracking
(debonding) and associated acoustic emissions may occur along these regions (blue). In
real rocks, this effect can be accentuated because of the higher stress concentrations at the
grain contact level.
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Figure 4.5: Radial stress concentrations during fracture initiation
Figure 4.6:Tangential stress concentrations during fracture initiation
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Figure 4.7: Radial stress concentrations during fracture propagation
Figure 4.8: Tangential stress concentrations during fracture propagation
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4.1.2 AE Results
Acoustic emissions were detected using eight P-wave PZT transducers and the
transient waveforms were digitized and recorded using the Vallen AMSY-5 AE data
acquisition system. An autocalibration process as described in Chapter 2 was performed
to verify good sensor coupling and measure the velocity in the Carbon Tan rock. The
amplitudes measured by all transducers during the autocalibration are shown in Figure
4.9. This indicates good coupling of the transducers to the test sample. The calculated
velocity of wave propagation in the Carbon Tan sample is shown inFigure 4.10.
As mentioned earlier in Chapter 2, the following assumptions were made for event
localization:
Figure 4.9: Amplitudes measured by each transducer during ultrasonic transmission
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Figure 4.10: Velocity of wave propagation in Carbon Tan rock
The velocity of wave propagation in the rock sample is
a)
Uniform throughout the sample and
b) Isotropic, or equal in all directions
c) Stress-independent and does not change with induced fractures.
Taking into consideration the above-mentioned assumptions, the slope relationship
should correspond to the absolute value of the velocity measured. In the autocalibration
process, each sensor transmits an ultrasonic pulse, which is received by seven sensors.
Thus, 72 waveforms were received using the ultrasonic transmissions. Onset times were
picked on these 72 waveforms automatically using the amplitude threshold-crossing
method. The distances between each transducer were measured. After analysis, a linear
relationship between the onset time and distance was observed, as shown in Figure 4.10.
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The corresponding velocity of wave propagation is 2205.1 m/s. The calculated value of
R2 was 0.985, which is acceptable. This velocity was used for AE localization
calculations. AE locations were calculated in Vallen Visual AE software, using
hyperbolic triangulation, as described in Chapter 1. These results were extracted and
visualized using TerraTek AE analysissoftware.
The cylindrical rubber jacket inside the borehole was pressurized using a TELEDYNE
ISCO 500Dsyringe pump at a constant flow rate. Acoustic emissions were detected well
before as well as after the failure of the rock. The amplitude of acoustic events and the
borehole pressure versus time is shown inFigure 4.11. Peak in AE detection is observed
just before the failure. Figure 4.12 shows all localized AE events during the entire test
without any filtering. The localization results show that AE events are located near the
actual fracture and significantly away from it.
In order to understand the AE activity recorded during the fracturing test, the results
are divided into pressure intervals. The mapped fracture is shown in all the figures for
reference. In all the AE visualizations for this thesis, colors of the AE hypocenters
represent time domain, blue being the initial events and red being the last. During the
initial pressurization of the borehole, very few events were detected before reaching 500-
psi borehole pressure. AE event locations indicate broad spatial distribution of events
during 0-500 psi borehole pressure, as shown inFigure 4.13.
The density of acoustic events localized near the borehole and the stress concentrator
increased as the borehole is pressurized from 500-750 psi, as seen inFigure 4.14.A small
number of localized AE events are also observed away from the borehole (Figure 4.14).
With progressive increase in the borehole pressure, acoustic events start localizing around
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Figure 4.11 AE amplitude and borehole pressure versus time
the borehole, in the direction of the stress concentrator and at an angle to it. The AE
events during 750 to 1000 psi borehole pressure are shown inFigure 4.15.
The rate of acoustic emission increases rapidly as the borehole is pressurized from
1000 to 1250 psi. Figure 4.16 shows the AE event locations during 1000 to 1250 psi
borehole pressure. The results indicate that AE events are located around the wellbore,
possibly associated to compressive grain failure, and in the direction of the stress
concentrators, where the fracture is expected to grow, as anticipated by the FEM analysis.
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Figure 4.12 All localized events during fracturing test
Figure 4.13: Acoustic events during 0-500psi borehole pressure
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Figure 4.14: Acoustic events during 500-750psi borehole pressure
Figure 4.15: Acoustic events during 750-1000psi borehole pressure
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The distributions of AE events just before failure, between 1200-1495 psi borehole
pressures, are shown in Figure 4.17.The distribution of events strongly maps the final
distribution of the fractures prior to failure. This most likely indicates the development of
an incipient fracture prior to rapid fracturing and detachment.
The rock failed by fracturing at approximately 1500 psi. Elastic strain energy stored
during wellbore pressurization facilitates the rapid propagation of fractures to the sample
boundaries. The rapid propagation considerably reduced the number of events that were
captured.
Figure 4.18 shows the acoustic events located during and after the failure of the rock.
The distribution of AE events during actual fracturing and detachment (1495 psi
failure) is similar to the results prior to fracturing and detachment (1200-1495 psi).
Rapid combination of fracture propagation and unloading both contribute to AE event
generation; however, fracturing with less unloading gives better mapping of the fracture.
The results of this test have also been discussed briefly in [28] and show the evolution
of localized AE events during different stages of wellbore pressurization. Figure 4.19
shows the same results but organized/filtered as a function of the amplitude of the
localized AE, high, medium and low. Higher amplitude events are also higher confidence
events. The AE with highest amplitude map the fractures quite closely. Based onFigure
2.10,the accuracy of these results is approximately 0.5 cm.
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Figure 4.16: Acoustic events during 1000-1250psi borehole pressure
Figure 4.17: Acoustic events during 1250-1495psi borehole pressure
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Figure 4.18: Acoustic events during and after the catastrophic failure
Figure 4.19:Images of localized AE of high, medium and low amplitudes are shown.
The high amplitude events map the detached fractures closely
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4.2Offset Borehole Fracturing Test
A 6 inch by 6 inch by 1 inch TerraTek sandstone sample was used for this test. The
borehole was inflated by injecting fluid into a cylindrical bladder using a manual pressure
generator. A single stress concentrator was notched inside the surface of the borehole to
initiate the fracture. The reason for drilling the offset borehole was to initiate and
propagate a fracture over a longer distance, to capture a maximum number of acoustic
events during fracture propagation.
Acoustic emissions were not detected at the very beginning of pressurization. With an
increase in borehole pressure, an increase in acoustic emissions was observed. A peak in
the number of acoustic emissions was observed just before visible failure of the rock. The
fracture was designed to initiate at the stress concentrator, but the block fractured at two
locations behind the borehole, as shown in Figure 4.20. The detached fracture was
mapped and is shown in all the figures for reference. The frequency of acoustic emissions
against time is shown in Figure 4.21.The amplitude of acoustic events against time is
shown inFigure 4.22.
During the initial pressurization of the borehole, a large number of AE events
localized around the stress concentrator, as well as away from it, as shown inFigure 4.23.
The colors of the dots indicate time and the size indicates the amplitude of the events.
Figure 4.24 shows the results of AE localizations with increased wellbore pressure.
These results indicate that prior to fracture propagation and detachment, AE events
localize first near the stress concentrator and then, just before the fracturing pressure,
they localize along the general direction where fracture is expected to happen. This again
indicates a possible development of an incipient fracture prior to rapid unstable growth.
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Figure 4.20: Sensor positions on the sample with mapped fracture
Figure 4.21:Frequency of Acoustic Emissions against time
AE/second
Time (seconds)
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Figure 4.22: Amplitude of acoustic events against time
Figure 4.23: Localized AE events during initial pressurization of borehole
Amplitude(dB)
Time (seconds)
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However, in this test, at 5500-psi bore pressure, a catastrophic fracture occurred at two
locations. Elastic strain energy stored during wellbore pressurization facilitated the rapid
propagation of fractures to the sample boundaries. This significantly reduced the number
events that were captured during rapid fracture propagation. Localized acoustic
emissions during and after the failure are shown in Figure 4.25. It can be seen that the
highest density of events are located in the direction of the stress concentrator where the
failure was anticipated. In addition, almost no AE events are located in the region where
the rock failed by fracturing.
There was no obvious fracture visible in the direction of the stress concentrator after
fracturing and detachment, as shown inFigure 4.26. A CT scan was performed on the
slab post the fracturing job. CT scan results revealed a fracture initiating at the stress
concentrator and propagating in the direction of the stress concentrator, as shown in
Figure 4.27. All localized acoustic emissions during the entire test are shown inFigure
4.28. The highest densities of AE events are observed near the microscopic fracture
revealed by the CT scan. The unstable rapid fracturing happens at the speed of sound and
releases less acoustic energy.
The AE results indicate that the incipient, nondetached fracture is mapped more
predominantly than the detached fracture. The incipient fracture happens at a low speed
and is accompanied with high dissipation of acoustic energy. The results illustrate how
the fracturing process is generated, which is visually difficult to perceive. From Figure
4.28,it can be seen that a significant number of AE events are also located away from the
actual microscopic fracture revealed by the CT scan. These events are possibly grain
failure events in the rock matrix.
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Figure 4.24: Localized acoustic emission events precatastrophic fracture
Figure 4.25: Localized acoustic emission events during and postfracture
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Figure 4.26: Postfracture image of the test sample.
Figure 4.27: Postfracture CT image of the test sample
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Figure 4.28: All localized events during testing
The fracturing tests were recorded using a video camera to observe and record the
fracturing process. Figure 4.29 shows a captured video frame during the fracturing. It
can be seen that there is no evident fracture on the longer side of the borehole where the
fracture was designed to initiate (1). The fracture behind the wellbore has already taken
place (2) and the rock is completely detached in this location. The fracture on the closer
side is still not detached completely (3), which indicates that the fracture (3) happens as
an after effect of fracture (2). This interesting snapshot of the fracturing process provides
an important insight into the sequence and speed of fracturing. The fracturing process
was captured in only one frame of the video. The video recording was done at
60frames/second. It is estimated that the unstable fracture lasted approximately 1/60th
of
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Figure 4.29: Snapshot during the fracturing process
a second. The fracture length is approximately 3 inches. Based on this video frame,
recording the rate of fracture propagation is calculated as 180 in/sec.
4.3Conclusions
Borehole fracturing experiments were conducted on two different rock samples of
identical geometry, but with different locations of the borehole. Both the tests were
conducted under stress free conditions. Acoustic emissions were monitored continuously
as the borehole was stressed and the rock samples were fractured.
Finite element analysis was done to understand the localized stress distributions
during the fracturing process and to anticipate the location of AE events in time. Results
indicate presence of AE events around the wellbore during wellbore pressurization, along
the fracture and away from the fracture along the tensile stress zone, which forms away
from the fracture as the fracture propagates. AE results verify this qualitatively.
Acoustic activity is observed before, during as well as after the failure. The rapid
unstable fracture lasts approximately 1/60th
of a second, and hence, very few acoustic
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emissions are located during the failure. The results show that prior to failure and
detachment, AE events localize near the region where the fracture eventually develops.
Posttest CT scan verifies the development of an incipient fracture prior to failure and
detachment. In addition, the incipient nondetached fracturing gives rise to the highest
densities of AE events. Further, the events with highest amplitude have higher accuracy
by the relative closeness to the actual fracture. Thus, using amplitude filtering is a good
method to identify macroscopic fracturing events. The AE results also show that the
velocity model used for localizations is reasonable.
To better understand the rock matrix effect, i.e., the presence of AE events away from
the fracture, posttest microstructure analysis is required. The microstructure analysis of
TerraTek sandstone is described in Chapter 5.
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CHAPTER 5
ROCK MICROSTRUCTURE ANALYSIS
5.1Introduction
Rock fabric affects the AE characteristics significantly [44] and hence, it is crucial to
understand the influence of rock microstructure. The parameters like AE rate are highly
sensitive to arrangement of fabric in rocks [44]. In order to understand the location of
AE events, it is critical to understand the fracture-related damage and identify the sources
of acoustic emissions. Microstructure analysis was performed to understand the rock
matrix effect and investigate the cause of unexpected failure in the TerraTek sandstone
rock during the fracturing test. The classification of the TerraTek sandstone and the petro
graphic analysis are described in this chapter. Also discussed in this chapter is the
relation between the rock failure and acoustic emissions.
5.2Rock Classification
Sandstones are classified based on the textural and mineral composition. Several
schemes have been published to classify sandstones based on these aspects as they
provide the most insight into the genesis of the rock [45]. The classification based on the
mineralogical composition of Quartz, feldspar and rock fragments is commonly used, and
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5.3Petrographic Analysis
The rock fabric was studied by petro graphic analysis of thin sections taken in
different orientations. This allowed precise description of the mineralogy, grain size and
shape. John Petriello, at TerraTek, did petro graphic analysis. The detailed understanding
of the petrology of the rock is helpful in understanding the acoustic behavior of the rock.
The mineralogy of the TerraTek sandstone in its pristine form as observed in vertical
orientation is listed inTable 5.1
Table 5.1: Petrographic analysis of vertical section
Sample ID TTSS-1
Lithology Subarkose
Max Grain Size m sand (~300 microns)
Detrital Grain Types Q + F silt and sand
Dominant Matrix
Composition
grain-supported
Detrital Clays dispersed clays, clays stained with hematite,
pseudomatrix clays, rare micas, speculation of illite and
mixed-layer illite-smectite, kaolinite
Biotic Grains none observed
Accessory Grains tourmaline (light blue grains), rare zircons
Authigenic Minerals iron-oxide cements (hematite), common chert
Pore Types intergranular porosity, minor secondary and fracture
porosity
Petrographic Comments Scattered oxide cements and oxide crystals. Generally a
clay-poor sandstone. Not many carbonates.
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The thin section taken in horizontal orientation was analyzed for grain shape, grain
size and grain contacts. The median grain size is 0.14 mm with a range of 0.03 mm to
0.50 mm. The grain size distribution is shown inFigure 5.2.Several types of porosities
were observed in the sandstone. Intergranular pores were dominant, followed by minor
secondary and fracture porosity. The sandstone matrix is dominantly grain supported in
composition with mostly long and concavo-convex contacts. Tangential and point
contacts between the grains are uncommon. This indicates low stress concentrations at
grain boundaries, less grain crushing, and less AE events generation. The sandstone is
quartz rich (~70.7%) and contains 4.7% Feldspar. The grains in this sandstone are
dominantly ranging to subangular, well-rounded and angular grains.
The petrology of the TerraTek sandstone observed in horizontal orientation of a thin
section is listed inTable 5.2
Figure 5.2: Grain size distribution in TerraTek Sandstone
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Table 5.2: Petrographic analysis of vertical thin section (by John Petrello, TerraTek)
TTSS
Sandstone Classification Subarkose
Median Grain Size (mm) 2.86 (0.14 mm)
Grain Sorting Coefficient ()
(Inclusive Graphic Std Deviation,
Folk [1974])
0.58 (0.67 mm)
Grain Rounding1 SR > SA > WR and A
Grain Contacts2 L > CC > T > P
Framework Mineralogy Q91F6R3
% Quartz 70.7
% Feldspar 4.7
% Rock Fragments 2.3
% Accessory Minerals 2.0
% Clays or Matrix 11.0
% Secondary Cements 2.0
% Modal Porosity 7.3
Clays/Cements Matrix consisting of hemtatite and
clays, partially mixed in places, in
other places more hematitic in
nature. Scattered micas. Common
chert cement, likely converted
detrital quartz
Dominant Pore Types Primary intergranular porosity most
prominent, minor fracture porosity
and dissolution porosity
Additional Comments Tourmaline, rare zircons,
dominantly quartz sand and silt,
feldspars evident through twinning,
common chert fragments
1: SR = subrounded, SA = subangular, WR = well rounded, A = angular
2: L = long, CC = concavo-convex, T = tangential, P = point
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5.4Thin Sections
5.4.1Thin Section Regions
The locations of the thin sections were selected based on the results of AE locations.
The AE results during the offset borehole fracturing test were discussed in depth in
Chapter 4. The chosen locations and dimensions of the thin sections are shown inFigure
5.3. Table 5.3 lists the dimensions and orientations of all the thin sections. Six thin
sections were prepared from the TerraTek sandstone sample in the selected regions to
understand the fracture-related damage near the fracture and away from the fracture. Thin
section 1, was chosen to understand the rock fabric in pristine state as it was far away
from the fracture process zone. Thin section 2 and 3 were chosen in the same orientation
as section 1 but were closer to the borehole. Thin section 4 was chosen to observe the
damage along the incipient microscopic fracture propagation path. Thin section 5 was
chosen to detect any weakness around the borehole causing the unexpected fracture, and
to observe the rock damage around the fracture. Thin section 6 was analyzed to detect
any rock damage away from the fracture process zone. The detailed observations of the
thin sections are described in the following sections.
5.4.2Vertical Thin Sections
The structure of the rock, or the rock fabric, was found to be homogeneous in all
vertical thin sections. Thin section 1 was taken far away from the fracture process zones.
No damage was seen in this section. Thin sections 2 and 3 were closer to the borehole. In
thin sections 2 and 3, very few grain failure events were observed. Figure 5.4 shows an
example grain crushing observed in section 2. The orientation of the grain failure cannot
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Figure 5.3: Location of thin sections
(all measurements in inch)
Table 5.3:Details of all thin sections
Thin Section Number Orientation Size
TS 1 Vertical Z (0.8) in x X (1.6) in
TS 2 Vertical Z (0.9) in x X (1.5) in
TS 3 Vertical Z (0.8) in x X (1.6) in
TS 4 Horizontal X (1.6) in x Y ( 2.2) in
TS 5 Horizontal Y (1.7) in x X (2.0) in
TS 6 Horizontal Y (1.8) in x X (1.5) in
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Figure 5.4: Grain crushing in vertically oriented thin section
be visualized using the vertical thin sections. For this purpose, horizontal thin sections
were prepared and analyzed.
5.4.3Horizontal Thin Sections
Plain Polarized light and cross-polarized light images were taken using high resolution
microscope. The observations in thin section 4 show that a microscopic fracture initiated
at the stress concentrator and propagated in the desired direction. Figure 5.5 shows a
plain polarized light image of the fracture initiation. The fracture seen is a nondetached
fracture and is less than a grain size in width. Grain crushing and grain sliding was
observed along the fracture path. Several crushed grains along the fracture propagation
path are shown inFigure 5.6. The fracture branched several times along the path, as
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Figure 5.5: Microscopic fracture initiating at the stress concentrator
shown inFigure 5.7. This indicates that the nondetached fracture develops slowly as a
function of loading, dissipates a large amount of energy (grain crushing), creates a larger
surface area along its propagation path and generates a large amount of AE energy. It is
observed that the branching of the fracture branching zone can be as small as < 1 mm.
Figure 5.8 shows a scan of the thin section with the uncertainties in localizing the highest
and lowest amplitude events. The highest amplitude events have accuracy < 5mm, and
the microscopic fracture branching observed is < 1mm. Therefore, localizing these grain
level failures, including fracture branching, is outside the resolution of AE localization,
and hence, is not possible.
The observations in thin section 4 helped in determining the fracture process zone and
understanding the presence of AE events in the neighborhood of that region.
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Figure 5.6: Crushed grains along the microscopic fracture propagation path
(arrows indicate crushed grains)
Figure 5.7: Branching of the microscopic fracture
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Figure 5.8: Scanned image of thin section shown with possible errors in localization
The rock detached during fracturing was glued back using epoxy and then, thin
section 5 was cut. Thin section 5 was chosen to understand the unexpected failure that
happened behind the well bore. Figure 5.9 shows a close microscopic view of the glued
fracture and the rock surrounding it. In this case, the fracture mostly appears to cut
around the grains in contrast to the microscopic fracture observed in thin section 4, which
cut through the grains on several occasions. This fracture exhibits a low surface area,
which is typical of a brittle fracture process that takes places at high speed and releases a
reduced amount of acoustic energy. Finally, no failure was observed in thin section 6
that was taken away from the fracture.
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Figure 5.9: Close view of the macroscopic fracture
5.5Relation of Rock Damage to AE
Thin section 1 did not show any damage to the microstructure and no AE hypocenters
were located in that region. Microstructure analysis of thin section 4 illustrates the
mechanism of microfracture initiating at the stress concentrator and propagating in the
direction of the stress concentrator. Several grain crushing and grain sliding events were
observed along the fracture propagation path. AE results indicate a high density of
events located in the location of the microfracture. Fracture branching was also observed
at several locations along the fracture propagation path, which explains the distribution of
AE hypocenters in the vicinity of the microscopic fracture. Thin section 6, which was
taken away from the fracture process zone, did not show any obvious damage in the
microstructure but AE results show a few events (~70) located in this region.
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5.6Conclusions
Six thin sections were made on the sandstone rock sample to understand the fracture-
related damage in rock. The results confirm the formation of an incipient microscopic
fracture before the unstable fracture propagation. Incipient fracturing happens slowly as
a function of loading, dissipates large amounts of energy (by intragranular grain crushing
and grain sliding), generating a large amount of AE energy during the process. The sub-
millimeter microscopic fracture branching events were outside the resolution of acoustic
emission event localization and could not be identified by the acoustic measurements.
The acoustic events located away from the fracture, though believed to be real, are hard
to detect and are not visible in the thin sections. However, it should be considered that
the test rock sample was 1 inch thick and the thickness of the thin section was only 50
microns. Hence, only 0.2 % of the entire thickness was analyzed to identify the events
occurring away from the fracture and in the rock matrix. It can be concluded,
1. The slow fracturing process causes grain crushing, grain sliding, and microscopic
fracture branching.
2. The fracture process zone is identified with thin sections. Microcracking within
the rock matrix effects is not.
3. Matrix effects are disseminated in volume, so they are hard to pick up.
4. A large number of thin section and a detailed analysis is required to identify the
sources of acoustic emissions in the rock matrix.
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Independent of the method of first arrival detection, acoustic emission localization was
calculated using the hyperbolic triangulation method.
Two slab samples of 6 inch by 6 inch length and 1 inch thickness, of Carbon Tan
and TerraTek sandstone, were prepared for testing. In addition, 0.5 inch boreholes were
dril