Acustic Emission

8/11/2019 Acustic Emission

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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



(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


(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



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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)

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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)

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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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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 4.18: 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 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

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Acustic Emission