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SPWLA 53rd

Annual Logging Symposium, June 16-20, 2012

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BOREHOLE ACOUSTIC REFLECTION SURVEY (BARS) FROM

MODERN, DIPOLE ACOUSTIC LOGS FOR HIGH-RESOLUTION

SEISMIC-BASED FRACTURE ILLUMINATION AND IMAGING

A. D. Grae1, G. A. Ugueto2 C., J. A. Roberts3, H. Yamamoto4, T. Oliver5 and G. Martinez6

Copyright 2012, held jointly by the Society of Petrophysicists and Well Log Analysts (SPWLA) and the submitting authors

This paper was prepared for presentation at the SPWLA 53rd Annual Logging Symposium held in Cartagena, Colombia, June 16-20, 2012.

ABSTRACT

Understanding the impact of natural fractures in unconventional plays has been limited by the difficulty of

describing fractures and intergrading this information from different scales. On one end of the scale, we have

information from seismic that can allow the visualization of large fault systems or highlight areas of high tectonic

displacement. On the other end of the spectrum, the data provide from wells, image logs, core and production can

allow one to map and even characterize the fractures that intersect with the wellbore. What has been missing is data

that allows bridging of the gap between the large scale, as provided from seismic, to the meso- and micro-

information provided by logs and core. However, with modern borehole acoustic tools, meticulous data acquisition

and adaptive processing algorithms, we can generate a borehole acoustic reflection survey (BARS), thereby creating

a high-resolution seismic image of fractures around the well. To accomplish this, the components of the acoustic

waveform data that escape the wellbore area and are reflected off the fractures are separated and processed using

new, innovative processing methods. This paper discusses the tool and subsequent processing that enables the

application of this technology in unconventional reservoirs. Moreover, the paper then describes the integration and

verification of this data with seismic, borehole image data, and conventional core. Finally, the paper lays out

conclusions and data acquisition modifications to better leverage the complimentary BARS data.

INTRODUCTION

As oil and gas exploration and development moves further into unconventional plays, natural fracture detection and

characterization has become more important. Some plays can leverage these natural fractures to increase a well’s

productivity, while in other plays the fractures can be a hindrance, acting as loss zones or creating high leakoff

during fracture stimulation. Approaches exist for detecting natural fractures. Large fractures or fracture networks

can be imaged by traditional seismic data, while image, acoustic and other logs can also be used to identify

fractures that intersect the wellbore. When core is acquired and fractures are intersected, core analysis can allow

one to characterize the fractures in high detail. These methods each provide complementary information about

fractures, although on different scales and to different degrees. Each method provides limited information and a

method for integrating the data from the large scale, as provided by traditional seismic, to the meso- and micro-

information provided by logs and core, is needed.

1 Shell Exploration & Production Company, 4582 S. Ulster St., Suite 500, Denver, CO 80237, USA; Email:

[email protected] 2 Shell Exploration & Production Company, 4582 S. Ulster St., Suite 500, Denver, CO 80237, USA; Email:

[email protected] 3 Schlumberger, 6501 S. Fiddler’s Green Circle, Suite 400, Denver, CO 80238, USA; Email:[email protected] 4 Schlumberger, 2-2-1 Fuchinobe, Chuo, Sagamihara, Kanagawa 252-0206, Japan; Email: [email protected] 5 Schlumberger, 6501 S. Fiddler’s Green Circle, Suite 400, Denver, CO 80238, USA; Email: [email protected]

6 Schlumberger, 6501 S. Fiddler’s Green Circle, Suite 400, Denver, CO 80238, USA; Email: [email protected]

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The idea of using sonic waveform data to generate a high-resolution, seismic around the borehole was first discussed

by Hornby (1989). Although Hornby’s paper focused on imaging reflectors crossing the wellbore, borehole acoustic

reflection surveys (BARS) initially focused on imaging structural features around the borehole to provide a more

detailed scale to the seismic surveys. Later, in the 1990s, this method was used to image fractures as described by

Yamamoto et al. (1999). Then, the ability to image fractures away from the borehole has become a more important

tool as exploration has progressed into unconventional reservoirs. Microresistivity, ultrasonic, and Stoneley

methods of fracture and mobility detection are all limited to the area immediately around the borehole. Using BARS

extends the area of investigation for fracture detection to 15 ft or more, depending on the rock and angle of

reflectors. This distance away from the borehole moves the depth of investigation out past the damaged area caused

by drilling the well and gives a better understanding of the reservoir’s fracture system in an undisturbed portion of

the reservoir.

In this paper, we discuss a single-well example of a BARS, and its results compare with other, complementary

methods of fracture detection and evaluation. To begin with, the paper introduces the basics of the underlying

theory. Second, the paper discusses the specific data acquisition practices carried out in this example, followed by

processing techniques. Next, the paper discusses three specific cases within this well where the BARS data

contributed to our understanding. In the first case, the BARS data was used to aid in classifying encountered

fractures as either being natural or drilling induced. In the second case, the BARS data was used to support the

geological interpretation of some fractures and provided evidence that the fractures were, in fact, extensive. The

third case shows how the BARS data highlighted a likely fractured zone that might not otherwise have been

observed. Finally, the paper is summarized, with conclusions and actions taken by the asset to address the

deficiencies observed in the three case studies.

THEORY

When an acoustic logging tool fires a monopole source, a compressional wave propagates in all directions from that

point. In traditional sonic waveform processing and interpretation, the focus is associated with the waveforms that

refract along the borehole towards the receivers. This traditional analysis only uses a portion of the information

available in the waveform acquired by a state of the art sonic tool. Conversely, when performing a BARS, the focus

of the analysis resides on the rest of the information contained in this waveform. This consists of the reflections

from acoustic impedance boundaries and mode-converted waves generated as compressional or shear waves across

acoustic impedance boundaries.

When imaging an event that crosses the borehole, the geometry of the tool as well as the location and geometry of

the event relative to the tool govern the reflected waveforms present in the data acquired. If both the transmitter and

receivers are located above the event, the generated reflections received by the tool originate from the updip side of

the event; the reflections from the downdip side of the event do not return to the tool (Hornby 1989). When the

transmitter and receiver are below the event, the reflections measured by the tool originate from the downdip side of

the event, and the updip reflections propagate away from the borehole without being detected by the tool. Figure 1

demonstrates this with a cartoon of the wave-form ray paths (Hornby 1989). When the reflector is located between

the transmitter and receiver, compressional-to-shear wave (PtS) conversions are seen on the updip side of the event,

and shear-to-compressional wave (StP) conversions are seen on the downdip side of the event, as demonstrated in

Figure 2 (Yamamoto 1999). Using the knowledge of how the reflected waves behave, in comparison to refracted

waves, the signals can be separated. Then, information from the reflected and mode-converted waves can be used to

image acoustic impedance boundaries that cross the borehole or that are situated within the depth of investigation for

that particular case.

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ACQUISITION

The BARS was acquired using a state-of-the-art acoustic tool, with standard transmitter-receiver (T-R) spacing on

wireline. The acoustic tool was logged twice over the interval of interest: one pass acquired standard acoustic data

for azimuthal anisotropy processing (about 1400 ft/hr), and the second pass was a special acquisition for BARS and

was logged at 850 ft/hr. The BARS acquisition pass consisted of firing three monopole sources sequentially,

recording 104 waveforms per monopole per depth, for a total 312 waveforms per 6-in. depth interval. The upper

and lower monopole waveforms include 737 samples recorded at a 20-µs sample interval. The far monopole

waveforms include 782 samples at a 20-µs sample interval. In addition to the acoustic and BARS data, other high-

tier logs were acquired to characterize the reservoir.

PROCESSING

The raw data from the far monopole acquisition did not show any clear reflectors, but it did show strong arrivals of

the refracted compressional, shear, and Stoneley waveforms (see Figure 3-A). After applying a band-pass filter to

remove low- and high-frequency noise, some reflectors could clearly be seen in Figure 3-B, but the stronger

response from the compressional, shear, and Stoneley arrivals still dominated the waveforms.

To remove the direct arrivals of the compressional, shear, and Stoneley waveforms, two methods were used. The

first method was removal of these events by using a borehole modes filter. The borehole modes filter performs

wave-field separation using a hierarchical filter in either common shot or common receiver gathers. Both gathers

were processed to obtain the PtS and StP mode conversions The PtS and StP mode conversions are used to

determine fractures near the wellbore as described by Yamamoto et al. (1999).

Since the mode-converted waves only image a small area around the borehole (approximately 4 ft in this case), a

second set of filters was run to extend the imaging distance from the wellbore. The second filter that was applied to

the raw data corresponded to a velocity filter. This filter is applied by aligning the waveforms in common offset

domain and applying a media filter, after the transit time determined from each slowness. This filter is generally

applied three times, starting with the latest arriving borehole mode (see Figure 3-C). The resulting dataset was used

to obtain the compressional-to-compressional (PrP) and shear-to-shear (SrS) reflections, which contained

information about the fractures at a greater distance from the borehole (12 to 15 ft).

The longest spacing receivers from the far monopole (7 to 13 ft) displayed the highest signal-to-noise ratio in

imaging the reflectors; therefore, only these receivers were used as an input for a generalized radon transfer (GRT)

migration for each reflection type (Miller et al. 1987). The seven receivers on each azimuth were then stacked to

improve signal-to-noise ratio. Meanwhile, the eight azimuths for each reflection mode were focused to generate an

image on both sides of the borehole (Haldorsen et al. 2006). The PtS and StP mode conversions were placed on the

updip and downdip sides of the borehole (Yamamoto 1999). Next, the images were fit to the well trajectory and

merged into a single image of the area surrounding the borehole, as demonstrated in Figure 4.

WELL AND PLAY BACKGROUND AND CASE STUDIES

This study focuses on a light, tight oil, shale play, where natural fractures are a key enabler to economic production.

Because of this play concept, 3D seismic data was analyzed, and a well was subsequently planned and drilled to

intersect a likely faulted zone. A slightly deviated (generally 10° or less) pilot well was drilled with nitrified, oil-

base mud to minimize the risk of loss events while drilling through the faulted, underpressured, and possibly

depleted target zone. Core was acquired over two intervals to understand rock properties and for fracture

characterization. After achieving total depth (TD), the production interval of approximately 1,200 ft measured depth

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(MD) was subsequently logged with four runs. The first run was a standard logging suite, including single-arm

caliper, cable-head tension, natural gamma ray, induction, compensated neutron porosity, and bulk density. The

second run consisted of two passes of a dipole acoustic tool, cable cable-head tension, and a natural gamma ray,

with data acquired as previously discussed in the acquisition section. The third and fourth runs consisted of an

imaging tool engineered for oil-base mud, with an elemental spectroscopy tool and an acoustic imaging tool.

The following three cases explain our findings and how BARS contributed to our understanding of the encountered

events.

Case study one – In the first, most interesting case, BARS was used to dissuade us from considering many fractures

from our geologic model. Over hundreds of feet (approximately 500 ft MD), various data sets pointed to a high

concentration of fractures, with a subsection of the logs demonstrating our observations (see Fig. 5). Increased

acoustic-derived slowness anisotropy (SLO ANI), Stoneley-derived fracture mobility (ST PERM), and fracture

width (Wf) all indicated the presence of fractures at the wellbore area. Furthermore, sonic waveform dispersion

analysis displayed a nontraditional behavior of the flexural waveforms. Although the fast and slow shear (red and

blue circles in Figure 6) were crossing over due to the presence of a hoop stress caused by present day stress and the

rock sensitivity, the slowness difference at low frequencies (1500-3500 Hz in Figure 6) suggest that fractures

extended into the formation.

Meanwhile, the image logs seemed to provide evidence of a large swarm of fractures. The induction and acoustic

image logs (see Fig. 5), showed a cluster of fractures with similar dip over this interval. The induction image,

shaded on a dynamic scale (Induction Image – Dyn), shows both bedding boundaries, as light and dark, nearly

horizontal features, and fractures, seen as highly resistive, white sinusoidal features cutting across bedding. Because

of the limited coverage of the pads, the fractures were difficult to characterize with the induction image alone, yet

the acoustic image, shaded by amplitude (Acoustic Image – Amp), also showed these fractures cutting across bed

boundaries. However, the fractures on the acoustic image did not form complete sinusoids and would actually be

mapped with different dips. Being dubious of the fracture swarm, we turned to the BARS data for a reality check.

Over the interval in question, the BARS interpretation located very few events. The BARS image is provided for

lower half of the heavily fractured 500 ft interval in Fig. 7, showing a couple of events but lacking evidence pointing

to a swarm of fractures. The outlined depth interval highlighting the 40 ft section from Fig. 5 shows no significant

events extending away from the wellbore, providing further evidence that the fractures observed over the interval

were not geologically extensive features. Further investigation into drilling parameters, geomechanical properties

and cores led us to conclude that these fractures were, in fact, drilling induced.

Case study two - The next case focuses on fractures that were observable in both the image logs and cores, but were

sub-seismic resolution. The acoustic-log-derived fracture indicators were heavily influenced by the previously

described swarm of induced fractures, and it was not possible to separate the effects of present-day stresses from

natural fractures. However, both image logs showed indications of other fracture types. Figure 8 shows the

induction image, shaded on a dynamic scale (Induction Image – Dyn) and the acoustic image, shaded by amplitude

(Acoustic Image – Amp), along with duplicates of each image type. The features labeled (A) and (B) in Fig. 8, were

dipping in a different direction to the drilling-induced fractures. Note how both images show the fractures as having

a different dip, while the acoustic image shows that the fracture (B) is more than just a single plane.

Because we had cored this particular interval, we were able to observe these fractures in the core. Figure 8 shows

cutouts of the high-resolution core photos. The top core photo, labeled (A) in the log, is a 1 ft image of the core,

showing parting of the core at the fracture plane. Meanwhile, the lower core image is a 2 ft photo, showing a swarm

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of fractures, consistent with the previously described image logs. Finally, core analysis indicated that these fractures

were naturally occurring and not caused by the drilling process.

While the image logs and core allowed us to classify these fractures, most wells will not have such datasets, and the

BARS data could allow us to highlight the significance of these fractures away from the wellbore. Figure 9 shows

the BARS image data, with the depth interval from Figure 8 outlined and the two fractures labeled (A) and (B).

Note that both these events persist away from the wellbore. The upper event, (A), is seen to extend at least 2.5 ft

from the wellbore. However, this BARS data should not be used to constrain the fracture to such a short length and

may be affected by a change in acoustic impedance either in the fracture or matrix away from the wellbore

decreasing the reflection coefficient of the fracture or a change in fracture aperture away from the wellbore.

Meanwhile, the larger, lower, event, labeled (B), not only has greater amplitude, but also extends to at least the

depth of investigation of this survey—between 12 and 15 ft.

This second event is significant, as the image logs, core photos, and BARS data show but the event was not

observable on the 3D seismic survey, as seen in Figure 10. This figure is a cutout image of a 3D seismic survey

cross section intersecting the wellbore, showing the well path as a black, nearly vertical line and various fractures

mapped by the team’s geologists, with this interval indicated by a black box. Thus, the BARS data set is able to

map significant fractures that extend away from the wellbore at a scale not resolvable by seismic.

Case study three - The final case is an example where some of BARS reflections agreed with identifiable fractures,

while others do not. Figure 11 displays the BARS image for this interval, covering approximately 200 ft of vertical

length, with an outlined subsection of approximately 70 ft. This image has two reflections clusters (C) and (D).

Displayed in Figure 12 are the static display for the inductive image (Induction Image – Stat), dynamic display for

the inductive image (Induction Image – Dyn), which is repeated with various fracture picks added, the interpreted

mineralogy in weight percentage from the elemental spectroscopy tool (MIN WT PER) and the interpreted stress

anisotropy, derived from the acoustic log measurements.

Close integration of the data indicate that the deeper cluster, (D), correspond to multiple fractures clearly identifiable

in the image logs. Meanwhile, the mineralogy data showed the interval was likely to be more brittle, as it the weight

percentage of calcite increased relative to the surrounding lithology, and a lower minimum horizontal stress, as

computed from acoustic measurements.

In contrast, cluster (C) show strong reflections but no identifiable fractures in the image logs. These low angle

reflectors, could be the related to bedding, especially given the image results.

CONCLUSIONS

The presence of fractures in the formation affects drilling, completion, and production decisions, particularly in

unconventional reservoirs. The proper characterization of fractures results in a better understanding of the

unconventional reservoir, providing knowledge to help optimize the drilling, completion, and production processes.

The use of traditional wireline methods leaves some uncertainty about fracture classification, especially in oil-base

mud environments. By using data from a state-of-the art acoustic tool in a different way, we are able to expand our

understanding of the fractures in and around the borehole by creating a BARS.

The first case study shows how BARS determined the existence of only drilling-induced fractures, while the

interpretation of borehole image misclassified these fractures as naturally occurring. The second case study shows

how BARS imaged a fracture seen in conventional core and a fault seen in conventional image log and core. The

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third case study shows an example where some of BARS reflections agreed with identifiable fractures, while others

do not.

Using the knowledge obtained from the three case studies, future borehole data acquisition can be planned. From

these studies it is apparent that running a single oil-based imaging tool does not provide enough borehole coverage

to properly image fractures in this formation; to increase borehole coverage, two oil-based imaging tools can be run

in combination. In addition, a state-of-the-art acoustic tool can be used to acquire both standard acoustic logs and

BARS data, to help classify fractures by expanding the depth of investigation into the formation. The

complementary information we have discussed in this paper will aid in future identification of fractures, especially

when conventional core and seismic are not available.

ACKNOWLEDGMENTS

Many thanks are extended to those in Shell who made this work possible, including Matt Holman, Amgad Younes,

John Bickley, Jeff Rogers, Carolyn Fleming, Mark Chapin, Jen Bobich, Michael Ehiwario, and Jill Savage.

Moreover, we appreciate the support of personnel in Schlumberger who supported this work, including Tom

Bratton, Chad Timken, Yulia Faulkner, Takeshi Endo, and Nobuyasu Hirabayashi.

Finally, thanks are extended to ION Geophysical for permission to use an image from their data (see Figure 10).

REFERENCES

Haldorsen, J., Voskamp, A., Throsen, R., Vissapragada, B., Williams, S., Fejerskov, M., 2006, Borehole acoustic

reflection survey for high resolution imaging: 76th Annual International Meeting, SEG Extended Abstracts, 314-

318.

Hornby, B., 1989, Imaging near-borehole of formation structure using full-waveform sonic data: Geophysics, 54,

747–757.

Miller, D., Oristaglio, M., and Beylkin, G., 1987, A new slant on seismic imaging: Migration and integral geometry:

Geophysics, 54, 943–964.

Yamamoto, H., Haldorsen, J., Mikada, H., Watanabe, S., 1999, Fracture imaging from sonic reflections and mode

conversions: 69th Annual International meeting, SEG Extended Abstracts, 148–151.

ABOUT THE AUTHORS

Abram D. Grae is a Development Petrophysicist with Shell Exploration & Production Company in Denver,

Colorado, USA, focusing on unconventional data acquisition techniques. He is currently earning a Doctor of

Philosophy in Petroleum Engineering at the Colorado School of Mines. He joined Shell International in 2004,

supporting deepwater Brazil, Nigeria, GoM, Egypt and Australia exploration and development. His previous

degrees include a Master (2004) and Bachelor (2002) in Mechanical Engineering from The University of Texas at

Austin. He is a member of Society of Petroleum Engineers (SPE), Denver Well Logging Society (DWLS), and

SPWLA.

Gustavo A. Ugueto C. is the Senior Staff Petrophysical Engineer with Shell Exploration & Production Company in

Denver. In 1982 received a degree in Geological Engineering from Universidad Central de Venezuela and began his

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work as Petrophysical Engineer at Petroleos de Venezuela S. A. He joined Shell International in 1988 and has

worked as Petrophysical Engineer and Section Head of Petrophysics in The Hague, London and Nigeria. In 1996 he

became Discipline Head of Petrophysics in Brunei before transferring to USA in 1997. After working in several

exploration and development projects in Deep Water Gulf of Mexico, he is currently works in Denver on the

development of Tight Sands and Shales Reservoirs in the Rockies. In 2010, he became Global Discipline Principal

Technical Expert for Data Acquisition and Special Processing.

Jonathan A. Roberts is a Petrophysicist specializing in sonic processing and interpretation at Schlumberger Data and

Consulting Services in Denver, Colorado, USA. He graduated from Colorado School of Mines in 2003, earning a

Bachelor in Geophysical Engineering. He is a member of Denver Well Logging Society (DWLS).

Hiroaki Yamamoto is Measurement Evaluation group leader in the Sonic Product Line at Schlumberger K.K.

Technology Center, Japan. He graduated from Kyoto University in 1985 with a M. Eng. in Engineering Geophysics.

He is a member of Society of Economic Geologists (SEG), Society of Exploration Geophysics of Japan (SEGJ), and

SPWLA.

Tamara Oliver is a Petrophysicist with the Data and Consulting group in Denver, Colorado, USA. She graduated

from Colorado School of Mines in 2006 with a Bachelor in Metallurgy and Materials Engineering.

Gabriela A. Martínez is a Petrophysicist working on acoustics processing with the Data Service group of the Data

and Consulting Segment in Denver, Colorado, USA. She graduated from Texas Tech University in 2003 with a

Doctorate in Geology and is a member of American Association of Petroleum Geologists (AAPG) and Rocky

Mountain Section of the Society for Sedimentary Geology (RMS-SEPM).

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FIGURES

Fig. 1 Reflected wave paths from transmitter to receiver for tool positions above and below an acoustic impedance

boundary (Hornby 1989).

Fig. 2 Mode-converted wave paths from transmitter to receiver (Yamamoto 1999).

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(A) Raw signal (no reflectors are visible)

(B) Signal after with 2-25 khz band pass filter applied (some reflectors are visible)

(C) Signal after removal of compressional & shear arrivals (more visible reflectors)

Fig. 3 Signal from one receiver, in common offset domain, at different processing stages.

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Fig. 4 Combined migrated image of reflections and mode converted data showing a strong reflection from a dipping

reflector. The vertical scale is in TVD with depths marked every 20 feet, the horizontal scale is in feet away from a

fixed point. The well is represented as the vertical white line running through the center of the image.

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Fig. 5 Log display, with head tension, caliper, and natural gamma ray (track 1); measured depth in feet (track 2);

array resistivity (track 3); neutron porosity and bulk density (track 4); compressional and shear sonic (track 5);

Stoneley-derived fracture width and slowness anisotropy (track 6); Stoneley-derived fracture permeability (track 7);

dynamic scaled induction image (track 8); acoustic image scaled by amplitude (track 9); and mapped induced

fractures (track 10). Note the arrows are indicating a few examples of the drilling induced fractures that are picked

in track 10.

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Fig. 6 Dispersion plot showing the fast shear (red circles) crossing over with the slow shear (blue circles). The

dominate cause of this crossover is a difference in stresses between the near wellbore and the far field.

Fig. 7 BARS image, with the area for case study one outlined with a black box. The vertical scale is in TVD with

depths marked every 20 feet, the horizontal scale is in feet away from a fixed point. The well is represented as the

vertical white line running through the center of the image. Note the event above this section is not discussed in this

paper, while the events below are discussed in case study two.

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Fig. 8 Log display, with head tension, caliper and natural gamma ray (track 1); measured depth in feet (track 2);


dynamic scaled induction image (tracks 6–7); acoustic image scaled by amplitude (tracks 8–9); mapped tectonic

and induced fractures (track 10); and core photos in separate boxes. Observed natural fractures are mapped (A)

and (B) while two drilling induced fractures, similar to those in Figure 5, are pointed out with arrows.

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Fig. 9 BARS image with the area for case study two outlined by a box, and the two reflectors discussed labeled (A)

and (B). The vertical scale is in TVD with depths marked every 20 feet, the horizontal scale is in feet away from a

fixed point. The well is represented as the vertical white line running through the center of the image. Note the

event labeled (1) was another fracture that was observed on image logs, though it is not discussed in this paper.

Fig. 10 3D seismic cross section showing the well path, some offset observable fractures and the interval in case

study 2, without any seismically resolvable fractures. Note the upper seismically mapped fracture on this cross-

(1)

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section, labeled (1), was in an interval not logged, while the lower seismically mapped fracture, labeled (2), was

visible with BARS and is not discussed in this paper.

Fig. 11 BARS image, with the area for case study three, outlined by a black box. The vertical scale is in TVD with

depths marked every 10 feet, the horizontal scale is in feet away from a fixed point. The well is represented as the

vertical white line running through the center of the image.

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Fig. 12 Log display, with head tension, caliper and natural gamma ray (track 1); measured depth in feet (track 2);


dynamic scaled induction image (track 6); dynamic scaled induction image (track 7); dynamic scaled induction

image repeated, with some fractures picked (track 8); interpreted natural fractures and their dip (track 9);

interpreted induced, incomplete fractures, bedding and their corresponding dip (track 10); interpreted mineralogy

(track 11); and minimum stress anisotropy (track 12).

(D)

(C)

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