Note: Descriptions are shown in the official language in which they were submitted.
METHOD FOR MONITORING A WELL OR A RESERVOIR CONTAINING
A FLUID, AND APPARATUS FOR USING THE SAME
The present disclosure relates to the field of monitoring a well or reservoir
containing a fluid.
The present disclosure claims priority benefit from U.S. Provisional
Application No. 61/867,335 (filed August 19, 2013).
Distributed acoustic sensor (so-called "DAS") technology offers a way to
monitor oil and gas resources, as described in the document: 'Distributed
Acoustic
.. Sensing ¨ a new way of listening to your well/reservoir', K. Johannessen,
B.
Drakeley, M. Farhadiroushan, SPE 149602 (2012). The operating principal for
this
technology is based on interference effects in optical fiber that are
associated with
the optical time domain reflectometry, a description of which can be found in:
"Interferometric Optical Time-Domain Reflectometry for Distributed
Optical¨Fiber
Sensing", S. V. Shatalin, V. N. Treschikov and A. J. Rogers, Appl. Opt., vol.
37, no.
24, pp 5600-5603 (1998). The backscattered centres form low contrast Fabry-
Perot
interferometers, which are illuminated by optical pulses travelling along the
fiber. A
major limitation of many disturbance sensors based on this approach is that
they
are incapable of determining the full acoustic field ¨ namely the amplitude,
frequency and phase of the incident signal.
Improved DAS measurement techniques ¨have been developed with an
aim to remove this limitation for determination of the amplitude, frequency
and the
distance of an acoustic event. Such improved DAS techniques have been applied
for downhole measurements of flow, sound and seismic vibration, as described
in:
"Distributed Acoustic Sensing - A New Tool for Seismic Applications", T.
Parker,
S.V. Shatalin., M. Farhadiroushan, Y. I. Kamil, A. Gillies, D. Finfer, and G.
Efstathopoulos, 74th EAGE Conference & Exhibition incorporating SPE EUROPEC
2012, Y002 (June 2012).
There remains a need for new ways of exploiting data generated by such
improved DAS techniques, in particular in the field of monitoring a well or a
reservoir containing a fluid.
It is an object of the present subject disclosure to provide improved
systems and methods for monitoring a well or a reservoir containing a fluid.
According to one aspect of the present subject disclosure, an apparatus for
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monitoring a well or a reservoir containing a fluid is proposed. The proposed
apparatus for monitoring a well or a reservoir containing a fluid comprises:
an
optical fiber extending along the well or the reservoir; a light pulse
generator
connected to the optical fiber and adapted for sending light pulses down the
optical
fiber; an acoustic wave generator adapted for generating acoustic waves that
propagate in the fluid and exert pressure changes onto the optical fiber; a
sensor
connected to the optical fiber and adapted for detecting propagation of
acoustic
waves through measuring of modulation of light backscattered in the optical
fiber
generated by the pressure changes exerted onto the optical fiber; and a
processing
module adapted for determining a limit of acoustic wave propagation in the
fluid
based on acoustic wave propagation data generated by the sensor.
The proposed apparatus advantageously uses acoustic wave propagation
data generated through DAS data acquisition to process such data in order to
determine a limit of acoustic wave propagation. The processing may include
identifying specific acoustic wave propagation profiles, and determining a
limit of
acoustic wave propagation based on such profiles.
In some embodiments, the processing module is further adapted for
generating, based on acoustic wave propagation data generated by the sensor,
image data representing acoustic wave propagation over a predetermined period
of time, and for processing said image data using pattern recognition for
determining the limit of acoustic wave propagation.
In some embodiments, the processing module is further adapted for
determining an estimate of the annulus fluid level based on the determined
limit of
acoustic wave propagation in the fluid.
Therefore, the proposed scheme for determining a limit of acoustic wave
propagation based on DAS acquired data may be advantageously exploited to
obtain an estimate of the annulus fluid level so as to monitor the well or
reservoir,
as the case may be.
In some embodiments, the processing module is further adapted for
determining a plurality of limits of acoustic wave propagation in the fluid
over a
period of time based on acoustic wave propagation data generated by the
sensor,
and dynamically monitoring the annulus fluid level based on the determined
plurality of limits of acoustic wave propagation in the fluid.
In some embodiments, the acoustic wave generator is included in a pump
immersed in the well and the generated acoustic waves correspond to noise
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generated by the pump during operation.
In some embodiments, the processing module is further adapted for
monitoring the annulus fluid level above a pump immersed in the well.
Embodiments may include a pump of the electrical submersible pump
(ESP) type.
In some embodiments, the processing module comprises an interface for
receiving data captured by the sensor, a processor, and a memory operatively
connected to the processor and storing a computer program that, when executed,
causes the processor to determine a limit of acoustic wave propagation in the
fluid
based on acoustic wave propagation data received from the sensor through the
interface.
According to another aspect of the present subject disclosure, a method for
monitoring the level of fluid in the annulus of a well is proposed. The
proposed
method comprises: processing image data generated using distributed acoustic
sensing on an optical fiber extending along the well to determine at least one
acoustic wave propagation limit in the annulus; determining an estimate of the
annulus fluid level based on the determined at least one acoustic wave
propagation limit.
In some embodiments, the processing image data includes pattern
recognition image processing for determining the at least one acoustic wave
propagation limit.
In some embodiments, distributed acoustic sensing is used to determine a
plurality of acoustic wave propagation limits in the annulus over a period of
time,
and further comprising dynamic monitoring of the annulus fluid level over the
period of time based on the determined plurality of acoustic wave propagation
limits in the annulus.
According to yet another aspect of the present subject disclosure, a
method for monitoring a well or a reservoir containing a fluid, wherein an
optical
fiber extends along the well or the reservoir, is proposed. The method
comprises:
sending light pulses down the optical fiber; generating acoustic waves that
propagate in the fluid and exert pressure changes onto the optical fiber;
generating
image data representing acoustic wave propagation over a predetermined period
of time; and determining a limit of acoustic wave propagation in the fluid
based on
a processing of the generated image data.
In some embodiments, the proposed method further comprises:
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determining an estimate of the annulus fluid level based on the determined
limit of
acoustic wave propagation in the fluid.
In some embodiments, the proposed method further comprises:
determining a plurality of limits of acoustic wave propagation in the fluid
over a
period of time, and dynamically monitoring the annulus fluid level based on
the
determined plurality of limits of acoustic wave propagation in the fluid.
In some embodiments, the proposed method further comprises: monitoring
the annulus fluid level above a pump immersed in the well or reservoir.
According to other aspects, disclosed is a computer-readable storage
medium storing computer-executable instructions for monitoring a well or a
reservoir, the computer executable instructions comprising instructions for
implementing any of the methods disclosed herein for monitoring a well or a
reservoir.
According to yet other aspects, disclosed is a computer program product
comprising computer program code tangibly embodied in a computer readable
medium, said computer program code comprising instruction to, when provided to
a computer system and executed, cause said computer to perform any of the
methods disclosed herein for monitoring a well or a reservoir.
According to further aspects of the present disclosure, disclosed is a non-
transitory computer-readable storage medium. The computer-readable storage
medium can store a computer program that, when executed, causes an apparatus
comprising a processor operatively connected with a memory, to perform any of
the methods disclosed herein for monitoring a well or a reservoir.
It should be appreciated that the present invention can be implemented
and utilized in numerous ways, including without limitation as a process, an
apparatus, a system, a device, and as a method for applications now known and
later developed.
The present subject disclosure will be better understood and its numerous
objects and advantages will become more apparent to those skilled in the art
by
reference to the following drawings, in conjunction with the accompanying
specification, in which:
Fig. 1 illustrates an example DAS system according to an embodiment;
Fig. 2 illustrates the configuration of a test well setup used for performing
DTS and DAS data acquisition according to an embodiment;
Fig. 3 illustrates DAS and DTS acquisition data acquired on an example
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test well setup;
Fig. 4 illustrates an example DAS spectrogram generated using a raw FFT
analysis from pump shut-in to rate 1 operation;
Fig. 5 illustrates a zoom of the spectrogram of Fig. 4 on the 0-50 Hz
frequency band;
Fig. 6 illustrates another example DAS spectrogram generated using a
fine-tuned FFT analysis from pump shut-in to rate 1 operation;
Fig. 7 illustrates another example DAS spectrogram generated using a
fine-tuned FFT analysis from rate 1 to rate 2 operations;
Fig. 8 illustrates an example DAS acoustic signal propagation during well
shut-in;
Fig. 9 illustrates an example DAS acoustic signal propagation during stable
rate 1 operations;
Fig. 10 illustrates an example DAS acoustic signal propagation during
stable rate 2 operations;
Fig. 11a and 11b illustrate example DAS acoustic signal propagation
images during various stages of operation of a pump in a well on which the
invention implemented for annulus fluid level monitoring according to an
embodiment;
Fig. 12 is an example graph that may be used for annulus fluid level
monitoring with BHP pump according to an embodiment;
Fig. 13 is an example DTS image data usable for annulus liquid level
interface determination according to an embodiment;
Fig. 14 illustrates an example reservoir/well monitoring system configured
to use distributed acoustic sensor technology according to an embodiment;
Fig. 15 illustrates an example architecture of the DAS module shown on
Fig. 14. according to an embodiment;
Fig. 16 shows an example architecture of a well/reservoir monitoring
system according to an embodiment;
Fig. 17a, 17b, 170, and 17d illustrate example DAS acquisition images with
corresponding frequency analysis and classification processing according to an
embodiment.
Figures 4, 5, 6, 7, 8, 9, 10, 11a and 11b show frequency spectrum
amplitude values represented by different grey shades. Those exemplary
amplitude values are assumed to be normalized around a zero value.
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On Figure 13 measured temperature values in Celsius are also
represented by different grey shades.
The advantages, and other features of the components disclosed herein,
will become more readily apparent to those having ordinary skill in the art
form.
The following detailed description of certain preferred embodiments, taken in
conjunction with the drawings, sets forth representative embodiments of the
subject technology, wherein like reference numerals identify similar
structural
elements.
In addition, it should be apparent that the teaching herein can be embodied
in a wide variety of forms and that any specific structure and/or function
disclosed
herein is merely representative. In particular, one skilled in the art will
appreciate
that an aspect disclosed herein can be implemented independently of any other
aspects and that several aspects can be combined in various ways.
The present disclosure is described below with reference to functions,
engines, block diagrams and flowchart illustrations of the methods, systems,
and
computer program according to one or more embodiments. Each described
function, engine, block of the block diagrams and flowchart illustrations can
be
implemented in hardware, software, firmware, middleware, microcode, or any
suitable combination thereof. If implemented in software, the functions,
engines,
blocks of the block diagrams and/or flowchart illustrations can be implemented
by
computer program instructions or software code, which may be stored or
transmitted over a computer-readable medium, or loaded onto a general purpose
computer, special purpose computer or other programmable data processing
apparatus to produce a machine, such that the computer program instructions or
software code which execute on the computer or other programmable data
processing apparatus, create the means for implementing the functions
described
herein.
Embodiments of computer-readable media includes, but are not limited to,
both computer storage media and communication media including any medium
that facilitates transfer of a computer program from one place to another. As
used
herein, a "computer storage media" may be any physical media that can be
accessed by a computer. Examples of computer storage media include, but are
not
limited to, a flash drive or other flash memory devices (e.g. memory keys,
memory
sticks, key drive), CD-ROM or other optical storage, DVD, magnetic disk
storage or
other magnetic storage devices, memory chip, RAM, ROM, EEPROM, smart cards,
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or any other suitable medium from that can be used to carry or store program
code
in the form of instructions or data structures which can be read by a computer
processor. Also, various forms of computer-readable media may transmit or
carry
instructions to a computer, including a router, gateway, server, or other
transmission device, wired (coaxial cable, fiber, twisted pair, DSL cable) or
wireless
(infrared, radio, cellular, microwave). The instructions may comprise code
from any
computer-programming language, including, but not limited to, assembly, C,
C++,
Visual Basic, HTML, PHP, Java, Javascript, Python, and bash scripting.
Additionally, the word "example" as used herein means serving as an
example, instance, or illustration. Any aspect or design described herein as
"example" is not necessarily to be construed as preferred or advantageous over
other aspects or designs.
The inventive concepts and features disclosed herein related to the
monitoring of well or reservoir are described hereinafter in the non-limiting
context
of embodiments in wells, including wells with a complex configuration such as
the
well configuration illustrated in Figure 2. However, such specific context is
not
meant to limit the various features described herein, which are applicable to
other
types of wells or reservoirs.
The operating principle of the Distributed Acoustic Sensing (DAS)
technology is illustrated in Figure 1. Figure 1 shows a DAS device (10)
connected
to an optical fiber (11) through an optical interface (13). The DAS device
(10)
includes a light pulse generator (12) connected to the fiber (11) through the
interface (13) and adapted for generating a pulse of light (16) (for example,
a laser
pulse) which travel down the optical fiber (11). A small amount of the light
is
naturally backscattered (15) in the fiber (11) and collected by a sensor unit
(14) of
the DAS device (10) through the interface (13). The DAS device (10) utilizes
an
optoelectronics architecture that measures the modulation of the backscattered
light (15).
Also shown on Figure 1 is an acoustic wave generator (17) adapted for
generating acoustic waves (18) that propagate towards the optical fiber (11)
and
exert pressure and/or strain changes onto the fiber (11), resulting in
vibrations
thereof. The DAS sensor unit (14) measures these pressure changes at a rate of
up to several kilohertz, so can be used to measure the acoustic field
generated by
the generator (17).
The deployment and use of the so-called Distributed Temperature Sensing
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(DTS) technique has proven advantageous for well and reservoir monitoring
applications. However, for multiphasic fluids cases, there are still
limitations on
achieving reliable quantitative temperature interpretations in horizontal
wells. In
such wells, these limitations are mainly due to the close Joule Thomson
effects for
oil and water, and the lack of temperature gradient. Most of the time, a
standalone
temperature acquisition is not enough to get a reliable and quantitative rate
interpretation.
Additional acquisitions such as the acquisition of Distributed Acoustic
Sensing (DAS) data can help to better constrain the temperature
interpretation.
The DAS can be used in water injectors to precisely localize the injection
zones,
and acoustic/noise data can play an important role in the reduction of
uncertainties
on temperature interpretation.
The present disclosure describes a DAS data acquisition in an example
complex well configuration illustrated on Figure 2: a horizontal, multi
lateral
producer equipped with a pump, for example an ESP (Electrical Submersible
Pump). In this example well, DAS acquisitions and interpretations, which were
initially considered as a complement to DTS acquisitions in order to achieve
more
reliable DTS interpretation from the reservoir zones, can be performed so as
to
monitor the well according to the subject disclosure.
However the invention is not limited to specific well configuration or
reservoir configuration, and may be implemented for the monitoring of various
wells or reservoirs containing a fluid.
Example Well Configuration
Figure 2 shows the configuration of a test well setup (50) in which tests
were performed, and provides an example well configuration in which the
present
invention can be implemented. The pilot was performed in an offshore dry tree
well
in the Gulf of Guinea. The well is made of two branches: (1) an open hole
lateral
branch (51a) and (2) an horizontal branch (51b), for example of approximately
1000 meters in length and producing a viscous oil (28 API) carbonate
reservoir,
through 4 reservoirs zones completed with sand screens (52a, 52b, 52c, 52d)
isolated with blank pipes and swell packers (53a, 53b, 53c, 53d).
The well (50) is equipped with an ESP (54) to ensure an average oil
production rate around 800 bbl/d. In addition, to monitor the whole main
branch, a
DTS (55) may be deployed on a tail pipe attached below the ESP and running to
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the toe inside the sand screens, together with four Bragg grating based
optical
pressure/temperature (P/T) gauges (56a, 56b, 56c, 56d) which may be deployed
at
each sand screen zone. In the illustrated well, DTS data may be acquired from
a
double ended multimode fiber (not shown on the figure), while DAS data may be
acquired from one or several DAS units, for example two DAS units (such as the
ones described above with respect to Fig. 1) respectively connected to two
single
mode fibers linking the optical P/T gauges. Two fibers (for example single
mode
fibers) may indeed be used and respectively connected to DAS devices (or
units)
so as to perform DAS data acquisition through two acquisition channels.
The optical fiber(s) used for the DAS data acquisition may be any optical
fiber suitable for DAS data acquisition in view of the application considered,
and
the invention is not limited to any specific type of optical fiber or else any
specific
equipment or scheme for the DAS data acquisition.
Well Monitoring History
The example test well illustrated in Fig. 2 was first equipped with an early
DTS and optical P/T gauge system at the end of 2005. A temperature
interpretation
was performed in 2006 from a DTS acquisition during well clean-up operations
in
November 2005, with some difficulties due to bad data quality. The
interpretation
showed that 80% of the production was coming from the toe of the drain and 20%
from the heel.
A second DTS acquisition was performed in January 2008. A new
quantitative interpretation concluded on opposite results: 20% of the
production
was coming from the toe and 80% from the heel.
The 2005 and 2008 DTS data acquisitions were performed on demand.
Due to the lack of continuous DTS monitoring, no preference could be given to
2005 or 2008 interpretations, as the well behaviour could have changed within
this
period.
In 2010, following a work-over to replace the ESP, the well was equipped
with a new DTS and optical P/T acquisition system. One DTS trace per day was
recorded and stored on site. The latest DTS interpretations showed more or
less
the same production split as the 2008 one.
Therefore, in order to obtain a more reliable production distribution
diagnosis, simultaneous DTS & DAS acquisition was performed in December
2012.
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Design and reality of DTS and DAS Acquisition Program
In oil & gas applications, quantitative DTS interpretations are typically
performed to analyze the temperature profile changes of a well and link these
changes to fluid production or injection. Two main phenomena impact the
temperature profile of a well: (1) introduction of a fluid of a different
temperature
into the wellbore; and (2) temperature variation due to the Joule-Thomson
effects
directly associated with the pressure drawdown experienced by fluids as they
pass
from the reservoir to the wellbore.
The main difficulty for interpretations in horizontal wells is the lack of
geothermal gradient inside the well leading to a constant reservoir
temperature
along the drain. Temperature variations inside the drain during production are
then
mainly controlled by Joule-Thomson effect.
In the test well setup a DTS acquisition has been designed in order to
emphasize this Joule-Thomson effect with an initial shut in period to
establish a
good geothermal baseline temperature, followed by two different rates, leading
to
two different temperature disturbances. This was achieved by varying the ESP
pump frequency.
During startup and the ESP frequency changes, high resolution and
simultaneous data sampling from DTS and DAS was designed as illustrated in
Figure 3, which shows an example DTS and DAS acquisition scheme with ESP
frequency changes. Time was allocated between rate changes to allow the well
to
reach steady state production, within practical time limitations imposed by
operational requirements. Another factor that had to be considered was that
the
platform is normally unmanned and crew would have to return, by boat, to
another
offshore accommodation facility every evening.
The design of the test acquisition was split into 6 stages
Stage 1: Well is shut-in with a continuous DTS acquisition at a 1 trace/min
acquisition frequency
Stage 2: Simultaneous DTS & DAS acquisitions at the end of the shut-in
period (3hr5) and after the start up of the ESP (2hr5)
Stage 3: DTS acquisition at 1 trace/min for rate 1
Stage 4: Simultaneous DTS & DAS acquisitions at the end of rate 1 (3hr5)
and after the ESP frequency change (2hr5)
Stage 5: DTS acquisition at 1 trace/min for rate 2
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Stage 6: Simultaneous DTS & DAS acquisitions once well stabilization
occurred for rate 2
The acquisition was performed as planned until the end of stage 4, but five
unexpected ESP shutdowns occurred during stage 5. Stage 6 was then been
performed without a fully stabilized well. Figure 3 illustrates the 3 DTS and
DAS
acquisition periods (periods 2, 4, and 6 on the Figure) performed during ESP
frequency changes. Bottom hole flowing pressure variations are shown by the
dotted-line curve.
Results from DAS acquisition
Figure 3 shows DAS data acquired in the test well during stages 2, 4 and 6
described above with two DAS units (also interchangeably referred to herein as
"DAS devices") respectively connected to two single mode optical fibers
connected
to Pressure/Temperature gauges.
The measurements may be investigated, that is, acquired data (herein
referred to as "DAS data set") may be processed, in order to determine: (1)
the
optical performance; (2) the ESP acoustic signature: frequency vs. time ¨
spectrograms; (3) the acoustic wave propagation at different ESP frequencies
i.e.
rates; and/or (4) a flow analysis above and below the ESP pump.
(1) DAS spectrograms:
Frequencies generated around the ESP pump during various phases of its
operation (for example, during the start-up, and/or during rates changes) may
be
determined through a Fast Fourier Transform algorithm (FFT analysis), to
perform
acoustical and vibrational diagnostics. This frequency analysis may be
performed
on the entire DAS data set so as to generate image data representing ESP
frequency response changes during operation.
Different tonal frequency components of an example ESP frequency
response along with harmonics can be observed in Figures 4, 5, and in Figure
6.
Figures 4, 5, and 6 show examples of DAS spectrogram corresponding to
the initial 2-hour-period of the ESP pump operation at a first rate (Rate1)
including
the pump turn-on. Figure 4 shows an example spectrogram for a time period
which
includes the ESP start-up time, in the [0-500Hz] frequency band, therefore
corresponding to a raw FFT analysis, while Figure 5 is a zoom on the graph
shown
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on Figure 4 for the [0-50Hz] frequency band. Figure 6 shows another example
spectrogram for a time period of approximately 2 hours in the [0-100Hz]
frequency
band, therefore corresponding to a fine tuned FFT analysis,
These graphs capture the initial start-up of the pump which can be seen
from the sharply increasing tonal frequency components of the ESP acoustic
signature. Once the ESP reaches its operating frequency these detected
frequency
components stabilize and it can be seen that the ESP pump exhibits a
fundamental
pump frequency of 22Hz. A fundamental frequency for a centrifugal pump is
defined as the "self-excited vibration" and corresponds to rotor instability.
This
"self-excited vibration" is most commonly associated with radial journal
bearings,
annular seals, and hydraulic impeller-casing interaction. In case of vertical
pumps a
typical "self-excited vibration" lies with 0.5x running speed. DAS analysis is
able to
detect this "self-excited vibration" around 22Hz along with a surface
frequency of
46 Hz.
Irregularities in the operating frequencies can indicate defects or problems
with the ESP pump.
Figure 7 shows a different example DAS spectrogram, covering three
periods of DAS data acquisition, among which two periods of stable operation
of
the ESP pump at a first rate (referred to as "Rate1") followed by the
transition
period between Rate1 and a second rate (referred to as "Rate2"). It can be
seen
that during the transition between rates that the surface frequency increases
from
46 to 50Hz, but also an increase of the fundamental frequency. This change in
the
fundamental frequency between rates shows that it is directly linked to the
ESP
operating frequency. The tonal component is not recorded by the ESP log at
surface but DAS shows that it is relatively high amplitude suggesting the
source of
this signal is generating a large amount of energy. This confirms that
investigations
are required to localize the source of this fundamental frequency and see if
this can
be detrimental for the ESP pump life duration.
(2) Acoustic wave propagation at different ESP frequencies i.e. rates:
In an embodiment, the DAS data set may be processed for performing an
acoustic signal analysis in order to detect waves' propagation directions.
This
analysis may be performed for various time periods covering different stages
of
operating the well. For example, the following periods may be considered:
(a) Well shut-in
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(b) Pump operating at a first rate (Rate 1)
(c) Pump operating at a second rate, different from the first rate (Rate 2)
2.a Acoustic wave propagation during well shut-in
Figure 8 shows two images which respectively represent acoustic wave
propagation profiles along each of the two optical fibers (Fiber 1 and Fiber
2) at
well depth spanning from approximately 750 meters to 1600 meters. Shown on the
left hand side of Figure 8 is a schematic view of the well architecture, more
specifically of the pipe 100 inside diameter 101 (ID) and pipe outside
diameter 102
(OD) thereof. The two horizontal stripes103 indicate the location of the pump
(in
this embodiment, an ESP) along the pipe. The horizontal dashed line 104
indicates
the location of a pressure/temperature gauge (P/T gauge), which as shown on
the
figure is located under the ESP.
Fig. 8 shows that no acoustic wave propagation (although one could have
expected some due to cross flow for example) occurs along the fiber during the
shut-in period (vertical stripes on the fiber 2 are an artefact of the image
pixel
decimation and not the data).
2.b Rate1 wave propagation
Figure 9 shows the detected acoustic wave propagation profiles along
each of the two optical fibers (Fiber 1 and Fiber 2) at well depth spanning
from
approximately 750 meters to 1600 meters, once the pump is turned on and
operates at a substantially stable first rate ("Rate 1") during a first period
of time
("Rate 1 period"). As on Fig. 8, two images respectively corresponding to an
acoustic wave propagation profile based on DAS data captured with respect to
Fiber 1 and Fiber 2 are shown on Figure 9.
According to an embodiment of the proposed process the two images
shown on Fig. 9 may be analyzed and interpreted as follows, with respect to
the
stable Rate 1 period:
There are strong propagating acoustic waves travelling up and down the
well, which may be identified through recognition of the presence of diagonal
lines
indicating pressure disturbances moving up and down the well.
All detected propagating acoustic energy is originating from the location of
the ESP. This suggests the detected signal is the noise generated by the ESP
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propagating up and down the well.
The acoustic signal propagates to a much greater distance above the ESP
than below it.
A limit of acoustic wave propagation 105a, 105b, located on the images of
Fig. 9 above the ESP (at approximately 1120 meters), can be determined where
the acoustic waves reflect and travel back down towards the well 107a, 107b.
This
is thought to correspond to a coupling effect i.e. a fluid interface within
the annulus.
A background noise profile 106a, 106b, similar to the ones observed on the
images of Fig. 8 corresponding to a well shut-in case, can be identified above
the
limit of acoustic wave propagation 105a, 105b.
In another embodiment, a limit of acoustic wave propagation 105a, 105b
can be determined as corresponding to the interface between the acoustic
waves'
propagation profile 107a, 170b identified based on recognition of the presence
of
diagonal lines indicating pressure disturbances moving up and down the well,
and
a background noise profile 106a, 106b where such diagonal lines cannot be
detected.
As indicated above, although Figures 7 and 8 show example image data
representing acoustic wave propagation profile changes over a predetermined
period of time for two fibers (Fiber 1 and Fiber 2), corresponding to the
example
and non-limiting case of two DAS acquisition channels, the present disclosure
is
not limited to any specific number of DAS acquisition channels or any specific
DAS
acquisition scheme.
Some waves propagate below the ESP ¨ this propagation likely results
from the noise from the ESP or from some fluids dropping down below the pump
during the transient period to reach a stable rate. However, the first
interpretation
related to the ESP noise is the most probable.
DTS data interpretation can show that, for the example well configuration in
which tests were performed, most of the fluid is produced from the heel of the
well
(from 1370 meter measured depth (mMD) to 1470 mMD). Due to the proximity of
the producing zone to the ESP, the noise generated by the ESP may possibly be
hiding the noise due to the production.
2.c Rate2 wave propagation
Figure 10 shows the detected acoustic wave propagation profiles along
each of the two optical fibers once the pump is turned on and operates at a
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substantially stable second rate ("Rate 2") during a second period of time
("Rate 2
period").
The stable Rate2 period allows the same interpretations as those made
above for the Rate1 period but with a depth limit for wave propagation at a
depth of
1150m, a little deeper as compared to Rate1.
Wave propagation for Annulus fluid level monitoring:
In an embodiment of the proposed process, a plurality of acoustic signal
analyses are performed at different times in order to investigate possible
changes
of coupling above the ESP during transient periods, for instance from shut-in
to the
stable Rate1.
The result of such plurality of acoustic signal analyses is that acoustic
signal reflections can be detected and interpreted as fluid interfaces.
Reference is now made to Figures 11a and 11b, which each shows 3
images representing respective detected acoustic wave propagation profiles
along
an optical fiber, for the following 6 example time periods: (1) well shut in;
(2)
transitory between pump turned on and pump operating at a first rate ("Rate
1"); (3)
pump operating at Rate 1, 2 hours, 1 min, 53s after pump turn on; (4) pump
operating at Rate 1, 15 hours, 19 min, 55s after pump turn on; (5) pump
operating
at Rate 1, 17 hours, 21 min, 54s after pump turn on; and (6) pump operating at
Rate 1, 20 hours, 54 min, 33s after pump turn on.
Also shown on the 6 images of Figures lla and llb is a determined ESP
pump depth level, as well as, except for the first one (well shut in), the
annulus
liquid level (that is, a limit of acoustic wave propagation in the fluid)
determined as
a result of digital image processing of the image.
The respective acoustic wave propagation limits determined for each of the
5 images representing acoustic wave propagation data for an ESP pump operating
at a first rate Rate 1 at different times after being turned-on are as
follows: (2)
approximate liquid level = 884 m; (3) approximate liquid level = 955 m; (4)
approximate liquid level = 1100 m; (5) approximate liquid level = 1115 m; and
(6)
approximate liquid level = 1135 m. Therefore, determined acoustic wave
propagation limits are linked to different fluid levels within the annulus. In
other
words, the determined annulus liquid level evolves with time. Such an analysis
over different time periods allows dynamic monitoring of the annulus fluid
level with
time and is consistent with the Bottom Hole Pressure (BHP) history as
illustrated in
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Figure 12. On Figure 12, the DAS-detected annulus level (referred to as "DAS
level" on the figure) is plotted in crosses, against the BHP history and the
ESP
pump surface log frequency.
As illustrated above the proposed process provides for both direct and
dynamic annulus fluid level monitoring in a well, for example to monitor that
such
fluid level stays above an ESP pump. This dynamic measurement is very
important
in early diagnosis of any gas locking event within the well. This can extend
the life
of the ESP pump by ensuring enough submergence i.e. to optimize the ESP
frequency to generate an optimal drawdown.
A correlation between the bottomhole pressure and DTS can also be
observed, as shown in Figure 13, where BHP is overlaid on DTS as a black line.
The DTS interface detection is, however, much weaker and harder to detect in
real
time, making the DAS technique proposed herein more robust. In addition, the
fact
that the interface is observed at the same level as identified by DAS does
provide
confidence that the interpreted acoustic effect is indeed related to the
annular liquid
level.
Referring to the figures, Fig. 14 illustrates an example reservoir/well
monitoring system 200 configured to use distributed acoustic sensor technology
in
accordance with the present disclosure. The reservoir/well monitoring system
200
includes a distributed acoustic sensor (DAS) module 201 connected to an
optical
fiber 202 extending along the well 203 (for example an oil & gas well, and
more
specifically a deep seawater oil & gas well), preferably through one of the
packers
207a, 207b which link the tubing of the well 203 to the casing thereof. In the
example shown on the Figure 14, the packer 207a may be designed to include a
feed-through for the optical fiber 202 used for DAS acquisition. The DAS
module
201 includes a light pulse generator (not shown on the figure) connected to
the
optical fiber 202 and adapted for sending light pulses down the optical fiber
202. In
this embodiment, a pump 204, preferably an electric submerged pump (ESP), is
inserted in the well 203 and contributes to the extraction of the oil or gas
from the
well. The pump is preferably located in a region of the well where it is
immersed in
a fluid 206 contained in the well. In this embodiment, the fluid 206 is a
liquid
composed of a mixture of water, oil, and gas. An acoustic wave generator 205
can
be inserted in the well, which can be in some embodiments connected to the
fiber
202. The acoustic wave generator 205 is adapted for generating acoustic waves
that propagate in the fluid and exert pressure changes onto the optical fiber.
In
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some embodiments, the system 200 is provided with a plurality of acoustic wave
generators such as the generator 205, for example to be located along the
optical
fiber 202 inserted in the well 203. In other embodiments, the acoustic wave
generator 205 is provided by the pump 204 itself, with the acoustic waves
corresponding to the noise generated by the pump as described above. In other
embodiments, one or several acoustic wave generators 205 are provided without
the pump 204 being placed in the well.
The acoustic wave generator 205 may also be or include a speaker, and in
particular a hydro speaker adapted for immersion in a fluid, or a
piezoelectric
device, and a combination of those may be used in embodiments where a
plurality
of acoustic wave generators 205 are used. Such devices may also be used on top
of the pump 204 in order to ensure a noise level which is high enough for
operating
distributed acoustic sensing, including when the pump 204 is not running (or,
said
otherwise, not in operation). This may help for instance ensuring that the
pump is
indeed submersed in liquid before starting operation thereof. In such
instances, the
pump serves as acoustic wave generator and is complemented by an additional
acoustic wave generator device.
The DAS module 201 also include a sensor (not shown on the figure)
connected to the optical fiber 202, and adapted for detecting propagation of
acoustic waves through measuring of modulation of light backscattered in the
optical fiber 202 generated by the pressure changes exerted onto it, as
explained
above. The sensor generates data which is processed by a DAS engine (not
shown on the figure) of the DAS module 201. The DAS engine comprises a
processing module adapted for determining a limit of acoustic wave propagation
in
the fluid based on acoustic wave propagation data generated by the sensor. In
an
embodiment, the processing module includes a processor, which may be any
suitable microprocessor, ASIC, and/or state machine. The processing module may
also comprise, or may be in communication with, computer storage media, such
as, without limitation, data memory, capable of storing computer program
instructions or software code that, when executed by the processor, cause the
processor to perform the elements described herein. Data generated by the
sensor
may be stored in a DAS database memory, operatively connected to the
processor, which may be any computer storage medium connected to the DAS
engine and operable with one or more associated database management systems
to facilitate management of data stored in respective databases and associated
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hardware.
It will be appreciated that the reservoir/well monitoring system 200 shown
and described with reference to Fig. 14 is provided by way of example only.
Numerous other architectures, operating environments, and configurations are
possible. Other embodiments of the system may include fewer or greater number
of components, and may incorporate some or all of the functionality described
with
respect to the system components shown in Fig. 14.
Fig. 15 shows an example architecture of the DAS module 201. The DAS
module 201 is a computer system which includes a DAS database memory 210, an
acoustic wave propagation image database memory 211, a DAS data acquisition
engine 212, a DAS data processing engine 213. The DAS module 201 also
includes as described above a sensor 214 connected in operation to an optical
fiber through an interface 215. In the architecture illustrated on Fig. 15,
all of the
DAS database memory 210, acoustic wave propagation image database memory
211, DAS data acquisition engine 212, sensor 214, interface 215, and DAS data
processing engine 213 are operatively connected with one another through a
control engine 216.
In an embodiment, the DAS data acquisition engine 212 manages the DAS
data acquisition through the sensor 214, which is adapted for detecting
propagation of acoustic waves according to DAS technology. As described above,
the acquired DAS data may include acoustic wave propagation data. Acquired
DAS data may be stored in the DAS database memory 210. The DAS data
processing engine 213 processes acquired DAS data stored in the DAS database
memory 210 and generates, based thereon, image data representing acoustic
wave propagation over a predetermined period of time. The predetermined period
of time may in an embodiment be chosen equal to a value in an time interval
spanning from lOs to 100s, and preferably equal to 50s. The image data
generated
by the DAS data processing engine 213 will typically generate image data that
include images representing acoustic wave propagation corresponding to a
predetermined location or section of the well/reservoir to be monitored.
Preferably,
the generated image data may include images representing acoustic wave
propagation at the vicinity of an acoustic source (e.g. a pump in an oil & gas
well)
used for DAS data acquisition.
In some embodiments corresponding to DAS data acquisition in an oil &
gas well in which a pump is immersed, the generated image data includes images
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representing acoustic wave propagation at the vicinity of the pump. The image
data
generated by the DAS data processing engine 213 may be stored in the acoustic
wave propagation image database memory 211.
In an embodiment, the control engine 216 includes a processor, which may
be any suitable microprocessor, ASIC, FPGA, and/or state machine. According to
various embodiments, one or more of the computers can be configured as a multi-
processor computer having multiple processors for providing parallel
computing.
The control engine 216 may also comprise, or may be in communication with,
computer storage media, capable of storing computer program instructions or
software code that, when executed by the processor, cause the processor to
perform the elements described herein. The DAS database memory 210 and
acoustic wave propagation image database memory 211 may be any computer
storage medium connected to the control engine 216 and operable with one or
more associated database management systems to facilitate management of data
stored in respective databases and associated hardware.
It will be appreciated that the DAS module 201 shown and described with
reference to Fig. 15 is provided by way of example only. Numerous other
architectures, operating environments, and configurations are possible. Other
embodiments of the system may include fewer or greater number of components,
and may incorporate some or all of the functionality described with respect to
the
system components shown in Fig. 15. Accordingly, although the DAS database
memory 210, acoustic wave propagation image database memory 211, DAS data
acquisition engine 212, DAS data processing engine 213, sensor 214, interface
215, and control engine 216 are illustrated as part of the DAS module 201, no
restrictions are placed on the location and control of components 210 - 216.
In
particular, in other embodiments, components 210 - 216 may be part of
different
entities or computing systems.
Fig. 16 shows an example architecture of a well/reservoir monitoring
system 220 according to an embodiment of the present subject disclosure. The
well/reservoir monitoring system 220 is also computer system which includes a
DAS image data database memory 221, a DAS image data processing engine 222,
a data memory 223 and a control engine 224. In the architecture illustrated on
Fig.
16, all of the DAS image data database memory 221, DAS image data processing
engine 222, and data memory 223 are operatively connected with one another
through the control engine 216. In addition, Fig. 16 shows an example DAS
image
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data processing engine 222 which comprises an image processing pattern
recognition engine 225, an acoustic wave limit determination engine 226, and a
fluid level monitoring engine 227.
In an embodiment, the DAS image data processing engine 222 retrieves
from the DAS image data database memory 221 image data representing acoustic
wave propagation over a period of time (referred to hereinafter as "data
analysis
window") generated based on data acquired using DAS technology. The data
analysis window may be chosen equal to a few seconds, for example 5 seconds.
Such image data may correspond in an embodiment to DAS image data generated
by the example DAS module 201 shown on Figure 15. The DAS image data
processing engine 222 processes such image data to determine a limit of
acoustic
wave propagation in the fluid contained in the monitored well/reservoir. Such
processing is managed by the acoustic wave limit determination engine 226, and
includes in an embodiment an image pattern recognition processing which may be
provided in the example architecture shown on Fig. 16 by the image processing
pattern recognition engine 225.
In an embodiment, the DAS image data processing engine 222 is
configured to use a determined limit of acoustic wave propagation in the fluid
for
determining an estimate of the annulus fluid level in the well/reservoir. Such
processing is managed by the fluid level monitoring engine 227, and also
includes
in an embodiment an image pattern recognition processing which may be provided
in the example architecture shown on Fig. 16 by the image processing pattern
recognition engine 225.
In another embodiment, the DAS image data processing engine 222 is
configured to determine a plurality of limits of acoustic wave propagation in
the fluid
contained in the well/reservoir over a period of time, and to dynamically
monitor the
annulus fluid level based on the determined plurality of limits of acoustic
wave
propagation in the fluid contained in the well/reservoir. The determination of
each
or a plurality of the limits of acoustic wave propagation may use image
pattern
recognition processing as described above.
Data generated by the processing performed by the DAS image data
processing engine 222 may be stored in the data memory 223.
In an embodiment, the control engine 224 includes a processor, which may
be any suitable microprocessor, ASIC, FPGA, and/or state machine. According to
various embodiments, one or more of the computers can be configured as a multi-
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processor computer having multiple processors for providing parallel
computing.
The control engine 224 may also comprise, or may be in communication with,
computer storage media, capable of storing computer program instructions or
software code that, when executed by the processor, cause the processor to
perform the elements described herein. The DAS image data database memory
221 and data memory 223 may be any computer storage medium connected to the
control engine 224 and operable with one or more associated database
management systems to facilitate management of data stored in respective
databases and associated hardware.
It will be appreciated that the well/reservoir monitoring system 220 shown
and described with reference to Fig. 16 is provided by way of example only.
Numerous other architectures, operating environments, and configurations are
possible. Other embodiments of the system may include fewer or greater number
of components, and may incorporate some or all of the functionality described
with
respect to the system components shown in Fig. 16. Accordingly, although the
DAS image data database memory 221, DAS image data processing engine 222,
data memory 223, image processing pattern recognition engine 225, acoustic
wave
limit determination engine 226, fluid level monitoring engine 227, and control
engine 224 are illustrated as part of the well/reservoir monitoring system
220, no
restrictions are placed on the location and control of components 221 - 227.
In
particular, in other embodiments, components 221 - 227 may be part of
different
entities or computing systems.
Figures 17a, 17b, 17c and 17d show image data with image processing
data according to an exemplary embodiment. Shown on the left hand-side of
figures 17a, 17b, 17c, and 17d is image data representing acoustic wave
propagation over a data analysis window spanning approximately 5 seconds
generated based on data acquired using DAS technology. The vertical axis
corresponds in those figures to the depth (in meters).
For each horizontal line of the images of figures 17a, 17b, 17c, and 17d, a
Fourier analysis is performed so as to generate frequency spectrum data for
each
depth acquisition in the well/reservoir over the data analysis time window.
For
example, assuming that the time domain data was sampled with a frequency
sampling of 1 KHz, a 500 point Fast Fourier Transform may be performed over
the
time domain data so as to generate a frequency spectrum spanning a bandwidth
of
0 to 500 Hz.
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Preferably, the data analysis time window will be chosen sufficiently short
so that the acquired image data are of a size such that the data processing
time
can be maintained at a predetermined level. Also, the time window may be
chosen
short enough so as to obtain a snapshot type view of the fluid level, for
instance in
case a dynamic monitoring of a well/reservoir is performed. At the same time,
a
sufficient amount of data in the time domain is required for a meaningful
frequency
analysis. The selected data analysis time window may therefore be the result
of a
compromise, depending on the type of monitoring performed on the
well/reservoir.
Shown on the right hand-side of figures 17a, 17b, 17c, and 17d is the
spectrum data 300a, 300b, 300c, 300d generated from respective time data 301a,
301b, 301c, 301d shown on the left hand-side of the figures. In this example,
the
frequency axis spans a 0 - 500 Hz bandwidth, and dark portions of the spectra
correspond to low frequency values, while light portions correspond to high
frequency values.
Taking the depth - frequency image of figure 17a as an example, the low
frequency peaks 303a, 303b, 303c, 303d seen on the left hand-side bottom part
of
the image have been interpreted as the frequency image of a pump 304a, 304b,
304c, 304d in the well/reservoir. On the other hand the depth areas 305a,
305b,
305c, 305d that may be interpreted as corresponding to acoustic wave
propagation
in the fluid in the time data 301a, 301b, 301c, 301d show a different
frequency
profile 306a, 306b, 306c, 306d with peaks in the medium low part of the
spectrum.
The depth areas 307a, 307b, 307c, 307d that may be interpreted as
corresponding
to noise in the fluid in the time data 301a, 301b, 301c, 301d show a frequency
profile 308a, 308b, 308c, 308d with low peaks disseminated on almost the
entire
spectrum bandwidth.
The depth - frequency image generated through the Fourier analysis of
each line of the DAS image data is then processed using a classification
algorithm,
for example a Kohonen classification algorithm or a nearest neighbor
classification
algorithm, in order to distinguish those different frequency spectrum profiles
from
each other so as to distinguish the depth areas corresponding to the fluid
level
from areas corresponding to noise or a pump, as the case may be.
The classification algorithm is performed on the depth - frequency image
data, with an initial learning stage, for which a first depth - frequency
image is use
(for example, the depth - frequency image of Fig. 17 a).
The outcome of this learning stage is a predetermined number C of classes
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corresponding to the C most represented frequency spectrum profiles among the
frequency spectrum profiles that can be found in the learning depth ¨
frequency
image. In the example shown on figure 17a, the number C of classes has been
chosen equal to 5, so that the classification algorithm learning stage has
determined 5 frequency spectrum profiles to which the frequency profiles found
in
the spectra of Fig. 17a are the closest. Those 5 classes are represented on
the
figure by different hatchings or fillings on the narrow areas 302a located in-
between the time data 301a and the frequency data 300a.
The learning stage is performed at least once, so that the C classes used
for the classification stages be defined. In an embodiment, the image used for
the
class learning may be checked, e.g. by an operator, so as to ensure that the
areas
of interest are indeed present in the image. Once the C classes are defined,
the
classification image processing is reduced to a classification stage, and the
learning stage is no longer necessary to complete the classification
processing on
a new image.
Fig. 17b, Fig. 17c, and Fig. 17d are examples of images processed through
only a classification stage, based on the 5 classes defined by the learning
stage
performed on the first image shown on Fig. 17a. Preferably, the acquisition
and
processing parameters of all four images are identical or substantially
identical. For
example, the images are generated based on a single well/reservoir
configuration
(in the example shown in the figures a well with an ESP pump), for a
predetermined data analysis time window and a given FFT processing.
Shown on Fig. 17a ¨ Fig. 17d is an indication of the class (among the 5
predetermined classes) to which each frequency spectrum (corresponding to a
line
in the time data area of each figure) has been associated. For example, the
class
with horizontal stripe corresponds to the depth area 307a, 307b, 307c, 307d
interpreted as representing noise, the two classes with oblique hatching
(oriented
left and right) correspond to the depth area 305a, 305b, 305c, 305d
interpreted as
representing acoustic wave propagation in the liquid, the class with small
dots
filling corresponds to the depth area 304a, 304b, 304c, 304d interpreted as
representing the pump.
In an embodiment, if several classes are shown to correspond to a single
area of interest (e.g. noise, liquid over the pump, pump, liquid under the
pump),
such may be merged into one class, so as to better distinguish the areas of
interest
from one another. For example, the two classes with oblique hatching (oriented
left
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and right) corresponding to the depth area 305a, 305b, 305c, 305d interpreted
as
representing acoustic wave propagation in the liquid, may be merged into a
single
class, as illustrated on Fig. 17c. The annulus fluid level may then be
determined by
calculating the depth interval corresponding to such merged class, or by
determining the boundary between such merged class and the class corresponding
to a noise area.
While the invention has been described with respect to preferred
embodiments, those skilled in the art will readily appreciate that various
changes
and/or modifications can be made to the invention without departing from the
spirit
or scope of the invention. In particular, the invention is not limited to
specific
embodiments regarding the apparatus for monitoring a well or a reservoir and
may
be implemented using various architecture or components thereof without
departing from its spirit or scope.
In addition, it should be understood that, while the invention has been
described with respect to preferred embodiments, the invention may be used for
the monitoring of devices inserted in a well, such as, for example and as
described
herein, pumps, such as electrical submersible pumps (ESP), the monitoring of
vibrations, of fluid level in a well or a reservoir (for instance the amount
in which a
pump, or another liquid pulling/lifting device, such as a gas lift device, is
submerged), of a gas rate at a pump level, of gas split in the annular, of gas
lock
and/or temperature level of devices inserted in a well, such as pumps... Such
monitoring may be used for dynamic or non-dynamic tuning and optimization of
devices inserted in a well, for example and without limitations with respect
to their
lifespan, for the maximization of drawdown, the optimization of maintenance of
such devices, and/or the maximization of the HSE (Hygiene Securite
Environnement, french for Hygiene Safety and Environmental issues) level when
operating such devices. Such tuning, optimization or shut in shut off
operations
may be performed automatically or manually according to the well fluid level
monitored.
Although this invention has been disclosed in the context of certain
preferred embodiments, it should be understood that certain advantages,
features
and aspects of the systems, devices, and methods may be realized in a variety
of
other embodiments. Additionally, it is contemplated that various aspects and
features described herein can be practiced separately, combined together, or
substituted for one another, and that a variety of combination and
subcombinations
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of the features and aspects can be made and still fall within the scope of the
invention. Furthermore, the systems and devices described above need not
include
all of the modules and functions described in the preferred embodiments.
Information and signals described herein can be represented using any of
a variety of different technologies and techniques. For example, data,
instructions,
commands, information, signals, bits, symbols, and chips can be represented by
voltages, currents, electromagnetic waves, magnetic fields or particles,
optical
fields or particles, or any combination thereof.
Depending on the embodiment, certain acts, events, or functions of any of
the methods described herein can be performed in a different sequence, may be
added, merged, or left out altogether (e.g., not all described acts or events
are
necessary for the practice of the method). Moreover, in certain embodiments,
acts
or events may be performed concurrently rather than sequentially.
