Acoustic emission characteristics in hydraulic fracturing of stratified rocks: A laboratory study

Table of contents :
Acoustic emission characteristics in hydraulic fracturing of stratified rocks: A laboratory study
1. Introduction
2. Materials and method
2.1. Experimental equipment
2.2. Specimen preparation
2.3. Experimental procedure
3. Results and discussion
3.1. AE count
3.2. AE energy
3.3. AE frequency
3.4. Crack classification
4. Conclusion
Declaration of Competing Interest
section13
Acknowledgement
References

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Powder Technology 371 (2020) 267–276

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Powder Technology journal homepage: www.elsevier.com/locate/powtec

Acoustic emission characteristics in hydraulic fracturing of stratified rocks: A laboratory study Zhizhong Jiang a,b, Quangui Li a,b,c,⁎, Qianting Hu a,b, Yunpei Liang a,b, Yangcheng Xu a,b, Le Liu a,b, Xiaobing Wu a,b, Xuelong Li a,b,c, Xiaoguang Wang a,b, Liangping Hu a,b, Faping Ling a,b a b c

State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China School of Resources and Safety Engineering, Chongqing University, Chongqing 400044, China State Key Laboratory Cultivation Base for Gas Geology and Gas Control (Henan Polytechnic University), China

a r t i c l e

i n f o

Article history: Received 30 November 2019 Received in revised form 1 April 2020 Accepted 14 May 2020 Available online 20 May 2020 Keywords: Acoustic emission Hydraulic fracturing Coalbed methane Peak frequency

a b s t r a c t Acoustic emission (AE) is a popular technique to monitor the process of rock failure during hydraulic fracturing for unconventional natural gas production. It contains abundant information that will be useful to study in-depth the nature of hydraulic fracturing. In this study, we focused on the AE count, energy, peak frequency, crack classification, and location recorded from four rock specimens subjected to a specific triaxial stress condition. We found the multi-frequency-response phenomenon of AE, and proposed the multi-frequency-response index to indicate the moment of the macrohydraulic crack formation. Furthermore, it was found that the power law distribution index of AE energy of non-stratified specimen was bigger than that of stratified specimens during hydraulic fracturing. The tensile crack dominated in all hydraulic fracturing tests. Our results are of significance for understanding hydraulic fracturing in stratified rocks. © 2020 Elsevier B.V. All rights reserved.

1. Introduction Stratified rocks are widely distributed in unconventional natural gas production sources, such as shales and coalbeds [1–3]. In order to enhance their permeability, and thus increase gas production, many techniques are employed, among which hydraulic fracturing is commonly applied [4–8]. Hydraulic fracturing induces failure and fracture of rock mass, which results in acoustic emission (AE) [9–13]. AE, which has been a subject of research on rock failure since the 1950s [14], is basically an elastic wave, which is similar to microseism in geophysics. AE events are associated with the initiation and propagation of hydraulic cracks [15,16]. Globally, scientists and engineers are trying to understand the intrinsic mechanism of rock mass fracture from AE characteristics, as well as to recognize these AE characteristics during hydraulic fracturing, as rocks with different physical properties [17–20] or under different stress conditions [21,22] will produce AE waves with varying characteristics during the damage process. Furthermore, the AE characteristics vary during different stages of hydraulic fracturing [15]. Therefore, by studying AE characteristics, the failure law of hydraulic fracturing can be indirectly revealed, with sufficient investigation of AE characteristics in the laboratory expected to improve the microseismic monitoring of hydraulic fracturing during field applications [23,24]. ⁎ Corresponding author at: State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044, China. E-mail address: [email protected] (Q. Li).

https://doi.org/10.1016/j.powtec.2020.05.050 0032-5910/© 2020 Elsevier B.V. All rights reserved.

AE characteristics, such as count, energy, frequency, location, have been extensively studied, among which the AE count feature is the most studied. In general, the more the AE count the greater the energy of AE events, and the larger the scale of coal/rock fractures. AE count characteristics demonstrate good Kaiser effect in a homogeneous medium [14], and Felicity effect in a heterogeneous medium [25]. AE count often goes through several “sudden increase and calm” stages during loading tests of coal and rock (heterogeneous medium) [22]. Hydraulic fracturing of coal and rock is a mechanical process. AE count presents an obvious increase when coal or rock is destroyed by water injection [12]. In addition, in high stress regions, AE count induced by failure of coal or rock mass is more than that in low stress regions [15,26–28]. Ishida et al. [9] found that the AE count increased dramatically and the cracks expanded rapidly with the sudden drop in pressure after the injection of highpressure water into granite. Li et al. [29] reported that the AE count during the stage of breakdown was far more than that during other stages. This phenomenon was also reported in other studies [15,30–32]. AE waveform characteristics are studied to identify failure modes, such as tension failure, shear failure, or their combination. There are two methods to identify failure modes: moment tensor analysis [33] and analyzing the relationship between RA and AF, which is obtained from AE data [34,35]. AE frequency is an important parameter of AE signal, which can indirectly reflect the material properties. While studying composite fiber damage, AE frequency presents good zoning characteristics with different materials

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of the PCI-2 workstation (Physical Acoustics Corporation, USA) and RS54B sensors (Beijing Softland Times Scientific & Technology Co. Ltd., China). Parameters set for the AE acquisition workstation and sensors are listed in Tables 1 and 2. The PDT, HDT, and HLT in Table 1 are the peak definition time, hit definition time, and hit lockout time, respectively. The correct setting of PDT will ensure correct identification of rise time and peak amplitude detection. The correct setting of HDT will ensure that an AE signal in the data structure is reflected in the system as the one and only one hit. The correct setting of HLT will avoid false detection of signal attenuation and improve the speed of data acquisition. The triaxial stress environment and placement of AE sensors are illustrated in Fig. 2. The value of the principal stress is set as σv = 5.0 MPa, σH = 7.0 MPa, and σh = 4.5 MPa, which is half the ratio of diggings in the southwest of China. Sensors were evenly distributed on the four surfaces of the specimen.

[36,37]. AE frequency distribution is regular in rock uniaxial compression failure [37–39]. For example, the frequency band can reflect the failure type of AE source. Different rock materials emit AEs of different frequencies when they are loaded to failure [22]. Yao et al. [40] found that the energy of high-frequency AE is smaller than that of low-frequency AE when rock failure occurs by uniaxial compression. Cai et al. [41] found that high-frequency AE corresponds to smallscale cracks, whereas low-frequency AE corresponds to large-scale cracks in the numerical simulation of underground cavern excavation. Uniaxial compression experiments have shown that frequency of AE signals following coal failure is lower than that of rock [15,17]. These studies provide a primary reference for the investigation of AE signal frequency during hydraulic fracturing. Still, AE characteristic and evolution are rarely investigated in stratified rocks such as coalbed seams. Mechanism of fracture initiation and propagation are complex in stratified coalbed reservoirs with different properties. It is difficult to perform hydraulic fracturing in these stratified rocks [42]. Ding et al. [43] have studied hydraulic fracturing in a stratified formation, which was made of similar materials. They also analyzed the AE count with the hydraulic fracturing pressure. However, they did not analyze the AE frequency and energy, and the crack classification, which are crucial to understand mechanisms of fracture propagation. In this paper, we aim to obtain relationships between multiple AE characteristics and injection pressure of hydraulic fracturing. We will analyze the influence of stratified formation on fracturing pressure and AE. To this end, the piezoelectric ceramic AE sensors are used to record the AE signals generated during hydraulic fracturing of four kinds of specimens under a true triaxial stress conditions.

2.2. Specimen preparation The experimental materials were gypsum powder, Portland cement, and crushed stone (diameter ≤ 8 mm). The material proportions with different uniaxial compressive strengths were obtained as demonstrated previously [44,45]. The uniaxial compressive strength of rock was 14.34 MPa, whereas that of coal was 1.98 MPa. Four specimens corresponding to different engineering conditions were prepared (Fig. 3 a). The four specimens were all made with a 300 × 300 × 300-mm3 mold, and numbered as S1, S2, S3, and S4 (Fig. 3 b). Among them, S1 simulates a single coal seam; S2 simulates a single rock seam; and S3 and S4 are stratified samples with two coal seams and three rock seams. All interfaces are naturally formed without any special treatment. All rock seams in S3 and S4 were 50-mm thick. Both coal seams in S3 had a thickness of 75 mm. The upper coal seam in S4 was 50-mm thick, whereas the lower was 100 mm. A 20-mm diameter borehole was drilled in the middle of each specimen. The length of the borehole in S1 and S2 was 155 mm, whereas in S3 and S4 it was 255 mm. A metal pipe was inserted into the borehole and a water outlet was arranged at the bottom of the metal pipe. The external and internal diameters of the metal pipe were 15 and 8 mm, respectively. The external wall of the metal pipe was made rough to increase friction with the borehole wall and prevent sliding during fracturing. Epoxy resin was used to seal the hole to ensure the metal pipe is tightly attached to the hole wall. The sealing lengths of S1 and S2 were 145 mm, and those of S3 and S4 were 50 mm.

2. Materials and method 2.1. Experimental equipment A schematic diagram of the true triaxial hydraulic fracturing experimental system used in this work is presented in Fig. 1. The system mainly consists of a true triaxial stress loading subsystem, an AE and pressure monitoring subsystem, and a pump subsystem. The true triaxial subsystem could simulate in situ stress by loading independently in three mutually perpendicular directions, with each of the maximum pressure being able to reach 70 MPa. The maximum pump rate was 100 ml/min. An eight-channel high-frequency AE instrument was used to collect the AE signals during hydraulic fracturing. The AE instrument consisted

Loading frame

Oil pump

HF hole

Water pump

Jack

Specimen AE sensor

Preamplifier

AE sensor

Oil pump

Jack

AE collecng instrument

Fig. 1. True triaxial hydraulic fracturing and acoustic emission monitoring system. There is the two-dimension diagram, three jacks are actually employed for true triaxial stress.

Z. Jiang et al. / Powder Technology 371 (2020) 267–276 Table 1 Parameters of the PCI-2 AE system.

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Table 3 Sensors coordinates.

Preamp gain (dB)

Frequency range (kHz)

Sampling frequency (103 s−1)

PDT (μs)

HDT (μs)

HLT (μs)

Signal threshold (dB)

40

100– 400

10,000

50

200

300

45

Table 2 Parameters of the sensor. Scale (mm)

Working temperature (°C)

Hit limit

Peak sensitivity (10−6 Pa)

Frequency range (kHz)

8 × 21.9

−20–130

10,000

−650

100–400

2.3. Experimental procedure Firstly, the prepared specimen was put into the loading frame. AE sensors were installed as illustrated in Fig. 2. The ceramic contact surface of the AE sensor was in close contact with the specimen surface through Vaseline (coupling agent). After connecting the line of AE instrument, AE parameters specified in Table 1 and Table 3 were set, and the stability of AE system was tested to ensure the experiment effects. Meanwhile, the fracturing pipeline was connected, with its gas tightness tested in advance. Secondly, stresses were independently applied to the specimen in three directions using flat jacks with 0.01 MPa per second increasing,

Sensors No.

x (mm)

y (mm)

z (mm)

Sensors No.

x (mm)

y (mm)

z (mm)

1 2 3 4

0 0 75 225

75 225 75 225

225 75 0 0

5 6 7 8

300 300 225 75

75 225 75 225

75 225 300 300

simulating the stress conditions existing in the field specified in Fig. 2. Enough red-dyed water was prepared for injection to investigate the cracks. Finally, the water pump and AE system were turned on simultaneously to inject water into the borehole and collect AE signals. The pump rate was set as 100 ml/min. After the injected water flowed out of the specimen surface, the water pump was closed, marking the completion of the AE signals acquisition program. 3. Results and discussion Each specimen was successfully treated by hydraulic fracturing, which resulted in obvious cracks. As illustrated in Fig. 4, there were marked tracks of fluid (red-dyed water) flowing out of the fractured specimens and cracks in the sections across the specimens. The directions of the cracks varied between the four specimens. The direction of cracks can be categorized as either horizontal or vertical. The horizontal trend was seen in S1 and S4, whereas the vertical trend was seen in S2 and S3. These differences might be caused by the irregularity of the

Fig. 2. Triaxial stress condition and AE sensors layout diagram.

Fig. 3. Specimens for test. (a) specimen image, (b) profile map of interior structure of specimens, brown presents epoxy resin (sealing length), red presents injection fluid, arrows presents interface between injection fluid and specimen. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Fig. 4. Crack formation after hydraulic fracturing.

specimen surface, which led to asymmetrical loading that resulted in the deviation of principal stress orientation. In addition, these differences might be caused by the load stress difference among the three orthogonal direction. Small stress difference in this tests is apt to increase the uncertainty of direction of crack's propagation. Some crack branches were also noted in the stratified specimens S3 and S4, indicating that more crack networks formed in the stratified specimen than in the non- stratified specimen. 3.1. AE count Damage and fracture of rock mass induce AE activity. In the waveform of AE signal, the number of times the amplitude crosses the threshold value is called “ringing count”, which is also called “AE count”. The more the AE count, the more severe the damage inside the rock mass. In the test involving S1, the injection pressure reached its maximum value of 9.32 MPa at 270 s and then dropped immediately (Fig. 5), indicating that the specimen was broken down. At the precise moment of breakdown, AE counts of C1, C5, and C6 rose steeply, especially for C6 which increased from 56 to 836; by contrast, all remaining channels responded poorly. This difference between the channels might be due to prior loading of true triaxial stress, which could change the interface connecting the specimen with the AE sensor. After breaking down S1, the injection pressure dropped and then rose again; however, it continued to fluctuate for over 200 s. During the pressure fluctuation period, the AE count of C1 increased simultaneously, suggesting that crack was produced and continued to propagate and enlarge through the specimen cube. Meanwhile, the AE count of C5 and C6, which was sensitive at the moment of breakdown, hardly increased, implying that the contact of these channels with the specimen was switched off due to the displacement and movement of S1 during breakdown. At the end of the injection process of S1, the pressure remained at about 4.19 MPa (45% of breakdown pressure) and the AE cumulative counts reached 1255. In the test involving S2, by synthesizing injection pressure curve and AE cumulative counts evolution, S2 was found to have experienced a relatively intense failure at 348 s (marketed in Fig. 5). At this point, the pressure reached 5.46 MPa and dropped immediately to 4.48 MPa, and the AE count of C1 and C4 presented an obvious increase. However, the pressure continued to increase and became higher beyond this point; meanwhile, the AE activity had not calmed down. This suggested that although a crack might have formed inside, it did not yet penetrate

the specimen cube. The specimen broke down at 475 s, at which point the injection pressure dropped again, beyond which it never rose again. The breakdown pressure of S2 was 6.39 MPa, and pressure remained at about 5.71 MPa (89% of breakdown pressure) in the end. The maximum AE cumulative counts were 1963. It is worth noting that the pressure had fluctuated for over 385 s from 90 s to 475 s, which was extremely longer than that observed in S1. This longer period of pressure fluctuation suggests that the crack produced propagated gradually rather than instantaneously. This result is also supported by the overall trend of gradual increase in the AE count. In the test involving S3, the breakdown pressure was 4.39 MPa (Fig. 5). The injection pressure decreased linearly for about 35 s after the moment of breakdown. The rate of descent within the 35 s was relatively less than those of S1 and S2, which presented a sharp drop. These results indicate that although some cracks might have formed, causing the fluid to leak, these were still too small to allow a massive flow. In addition, following breakdown, AE activity was detected by only C2 and C6. At 710 s, all the AE count of all channels increased enormously (C5 was broken), with a considerable fall in pressure. When came to 710 s, injection fluid of 1183.3 ml (equal to 4.4% volume of S3 specimen) had been injected into S3 specimen. While the porosity of all specimens was less than 1%. Based on these results, we inferred that a snap likely occurred in the specimen, which would cause the fluid to leak out of the specimen. The injection pressure finally remained around 3.14 MPa until the pump was turned off. Among all AE channels, the evolution of C6 agreed well with the injection pressure history. In the test involving S4, breakdown did not occur until 1276 s (peak pressure, 8.04 MPa; Fig. 5). Before breakdown, perturbation of injection pressure was very drastic and remained the same for about 236 s. During the period, the AE cumulative counts simultaneously grew the fastest. This result suggested that 236 s was required for macrocrack initiation and penetration through S4. Before drastic perturbation of injection pressure, an S-style change in pressure was noted. Initially, the fluid was injected to fill the volume of the injection hole, which caused the pressure to grow slowly. Once the injection hole was full, the pressure grew approximately linearity. When the pressure growth trend shifted to nonlinearity, fluid leakage began, and the slope of the pressure curve decreased, indicating micro cracks were forming before breakdown, and these micro cracks could continuously let fluid diffusion in the specimen. Similar to Ishida et al. [29,34], the AE counts before and after a sharp drop in pressure during hydraulic fracturing were far more

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271

Fig. 5. Injection pressure history and AE count accumulation of all channels.

than at other times in our experiments, especially in S1 and S3 (Fig. 5). By contrast, we find that the injection pressure sometimes experiences a long, slow fluctuation process (Fig. 5), and the AE count increases rapidly. This is due to the fact that microcracks have been produced in the specimen all the time; however, they did not penetrate the outer side of the specimen. Once the crack propagation breaks through the outer surface of the specimen, the pressure will decrease dramatically. It is worth affirming that the more intense the pressure fluctuation, the greater the scale and the longer the length of the cracks formed, although the cracks did not penetrate the specimen. Therefore, the faster the increase in AE count, the larger the crack size. The sudden rapid growth of AE counts indicates that large-scale cracks are being generated. Unexpectedly, the response of each AE sensor is different in the four tests. The AE cumulative counts were not equal at each time point; in fact, they varied significantly. Besides, few channels agreed well with

the injection pressure history throughout except C1 and C4. Thus, in the following sections, data from C1 and C4 were mainly analyzed. 3.2. AE energy The AE energy statistics of the four test are summarized in Table 4, in which S2 has the highest energy value of mean, sum, and maximum, whereas S1 has the lowest values, which agreed well with the strength of the specimens. The strength of S3 and S4 was calculated by an equivalent approach [46]. In addition, by comparing the four specimens, it was found that the AE energy was larger if rock stratum was broken. Therefore, the magnitude of AE energy is likely to indicate the type of ruptured stratum. The AE energy of brittle materials is known to obey the power law distribution [47]. To investigate the power law distribution index, we analyzed the energy data in double logarithmic

Table 4 AE energy statistics. Specimen

Strength (Mpa)

Mean (aJ)

Standard Deviation (aJ)

Sum (aJ)

Minimum (aJ)

Median (aJ)

Maximum (aJ)

S1 S2 S3 S4

1.98 14.34 5.61 2.78

690.61 2120.58 1017.20 935.02

1013.31 18,084.43 1639.33 1313.84

147,099.55 897,007.20 545,219.76 476,860.84

14.61 6.59 23.42 6.55

318.16 420.66 472.41 415.35

6937.00 367,434.00 16,399.00 9017.00

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The probability plot [48] is a graphical technique to assess whether or not a data set follows a given distribution such as the log-normal. The data are plotted against a theoretical distribution in such a way that the points should form approximately a straight line (cf. the reference line in Fig. 8). Departures from the reference line indicate departures from the specified distribution. As Fig. 8 illustrates, the first few points and the last few points show some departure from the reference line (identified below the reference line). Nevertheless, the trend of percentile coincided with the reference line on the whole, which indicated that the AE energy was in accordance with the log-normal distribution in the four specimens tested. 3.3. AE frequency

Fig. 6. Fit of power law distribution index.

coordinates and fitted these with linearity as shown in Fig. 6. These lines fitted well with energy value, which ranged from hundreds to thousands. The opposite number of slope of the fitting line was equivalent to the power law distribution index. As shown in Fig. 6, the power law distribution index of nonstratified specimen (S1 and S2) was bigger than that of stratified specimens (S3 and S4). This suggested that the stratified specimen was more unstable than the non- stratified specimen. Furthermore, the slope of S1 was equal to that of S2, which indicates no influence of the strength on the power law distribution index. To investigate the distribution of all energy data, we calculated the probability of energy and plotted the probability histogram (Fig. 7). We found that the distribution obeyed log-normal distribution fitted curves in Fig. 7.

The peak frequency is the key parameter which is subsequently acquired from the AE detection, indicating the vibration frequency of material damage and fracture. The peak frequency is calculated corresponding to the maximum of FFT (fast Fourier transform) of AE amplitude. Sometimes the peak frequency is also called the main frequency, which refers to the main frequency of the AE sensor. The main frequencies of the sensors used in this experiment were 100– 400 kHz; therefore, the AE peak frequency was filtered out to this range (Fig. 9). To investigate the evolution of peak frequency associated with the injection pressure, peak frequencies, pressures, and energies were plotted (Fig. 9). The AE events with higher energy were considered to identify the failure stages of hydraulic fracture in this experiment. The peak frequencies of these events with higher energy were cataloged and classified into trees bands (Fig. 10). When combined these peak frequencies correspond to the failure stages of hydraulic fracture. The events in band A were correspond to the stage of intense pressure fluctuation, events in band B to the stage of pressure fluctuation after the peak pressure, and events in band C to all stages. Furthermore, as shown in Fig. 9, a relatively wide range of peak frequency was noted between 100 and 300 Hz when the injection pressure fluctuated sharply and the low-frequency events increased at the same time. For the time being, this phenomenon of peak frequency range broadening will be termed “multifrequency response (MFR).” MFR

Fig. 7. Energy probability distribution. The thin curve above bars is the fitted standard log-normal distribution curve.

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Fig. 8. Log-normal probability plot of energy. The reference line represents percent of absolute log-normal probability. The closer data point (percent) is to the reference line, the more it agrees with the log-normal distribution, vice versa.

characteristics are associated with the size of frequency range, AE number, and AE density in time scale. The larger the frequency range, the more the AE events and the smaller the frequency difference between them, the more sufficient the MFR. The MFR index (MFRI) was assumed to represent the value positively correlated with the degree of MFR. It is assumed that there are n AE events in t ~ t + Δt during hydraulic fracturing, and their frequencies are fi (i = 1, 2, 3, …, n). The frequency bandwidth of MFR is the difference between max fi and min fi in t ~ t + Δt. Therefore, that the frequency bandwidth can be written as L f ¼ maxð f i Þ− minð f i Þ

ð1Þ

The frequency difference between adjacent events is li = fi+1 − fi, (i = 1,2,3, …, n−1), and the standard deviation of li is

Stdevðli Þ ¼

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP  2 u n−1 t i¼1 li −li n−2

ð2Þ

where Stdev(li) denotes the standard deviation of li. The smaller the Stdev(li), the more evenly the AE events are distributed in Lf, and the larger the Lf, the more sufficient the degree of MFR. Therefore, the MFRI can be expressed as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u uðn−2Þ½ maxð f i Þ− minð f i Þ2 MFRIðtÞ ¼ u 2 t Pn−1  li −li i¼1

Fig. 9. AE peak frequency characteristics during the hydraulic fracturing process.

ð3Þ

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Fig. 10. AE peak frequency range deviation according to AE event data.

Using formula (3), MFRI at each time from t to t + Δt can be calculated. Mathematically, it takes Δt → 0 to get the MFRI of time t. As Δt is associated with the AE event rate, by setting t = 1 s, the results were calculated (Fig. 11). From an MFRI view point (Fig. 11), it is clear that a relationship exists between AE peak frequency and injection pressure evolution. MFRI is prominent with a drop in injection pressure. With the appearance of large MFRI, AE activities increase extensively and macrofractures are formed. This result is consistent with the principle of vibration mechanics [49], because the formation of cracks is the result of the convergence and penetration of multiple microcracks. Before the microcracks penetrated the specimen, the rock mass yielded locally in many places, forming microvoids. Prior to the penetration of voids, the rock mass was relatively stable, the strength was good and consistent everywhere, and the peak frequency of AE was relatively single. However, when the voids start to penetrate, cracks start to form, and numerous microvoid walls are generated instantaneously. The rock mass deformation increased subsequently, which caused rock mass instability and deteriorated its strength. Therefore, the AE frequency became unstable, lowfrequency events occurred, and MFRI increased. Thus, we could

infer that macrofracture was produced once in S1, thrice in S2, and twice in S3 and S4. Therefore, the MFRI method can be an efficient approach to identify the occurrence of macrofracture. 3.4. Crack classification Crack mechanism is an important feature of material failure. In AE testing, the AF–RA method is widely used to fast classify crack model including tensile crack and shear crack [50], where AF is AE count divided by duration, and RA is rise time divided by amplitude. Thus, it is important that the threshold set is accurate and appropriate to obtain the true rise time and duration. Ignoring the deviation from AE system, we could find that tensile crack comprised over 94% of the cracks in all tests, with the remaining being shear cracks (Fig. 12). The domination of tensile cracks demonstrates that most factures which contributed to injection pressure acted as the tensile stress inside the specimens. This result was well in accordance with the tensile theory [51] of hydraulic fracture. Unfortunately, the result of AE location in this four tests was not very encouraging. As shown in Fig. 13, what location acquired in the profile depart invariably from the crack marked red induced by hydraulic

Fig. 11. MFRI during hydraulic fracturing.

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Fig. 12. Crack classification by the AF–RA method.

Fig. 13. AE location, with S2 as an example. To fit with the crack, the events located in the cross section were extracted.

fracturing treatment. This inconsistency may be due to the anisotropy of the specimens [52], namely, AE velocity was not a constant in every direction and also in every moment of the injection period. Furthermore, first-arrival detections, which require manual correction, were all dependent on automation of the AE system, resulting in inevitable location errors. This discrepancy presented some challenges for evaluation of hydraulic fracturing. Therefore, it is necessary to come up with a new and reliable location method. 4. Conclusion Hydraulic fracturing under four coal seam conditions was simulated in a true triaxial stress environment, and their AE data were collected, analyzed, and discussed in depth. Some meaningful insights are

summarized as follows: 1) low-frequency AE events increased and the phenomenon of MFR occurred when the pressure curve fluctuated sharply. The MFRI was positively correlated with the formation of macrocracks in hydraulic fracturing. 2) a sudden rapid growth of AE counts indicated the generation of large-scale cracks. The AE count increased sharply when the pressure curve fluctuated sharply; by contrast, the AE count increased steadily when the pressure curve fluctuated slightly. 3) AE energy was more sensitive to formation conditions. AE energy tend to be larger if the rock stratum was broken. Concerning the magnitudes, AE energy was in accordance with lognormal distribution in this four-sample test. The AE energy value varied from hundreds to thousands and fitted well with power law distribution; the power law distribution index of non- stratified specimen was bigger than that of stratified specimens during hydraulic fracturing, suggesting that the stratified specimen was more unstable than the nonstratified specimen. 4) The tensile crack, classified by the AF–RA method, dominated in all tests, indicating that most factures which contributed to injection pressure acted as the tensile stress inside specimens. Overall, hydraulic fracturing of coalbed methane is a complex process, some encouraged perspectives were summarized with a few typical experiments provided in this paper. The only drawback is that not all AE sensors could completely record the critical signals of fracturing, and the AE location had errors due to heterogeneity of specimens. To obtain more information about AE signal characteristics during hydraulic fracturing in coal seam gas reservoirs, more experimental studies should be carried out using a reliable location method.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This paper was supported by the National Major Science and Technology Projects of China (No. 2016ZX05045004, No.

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