U.S. patent application number 17/581753 was filed with the patent office on 2022-06-16 for detecting operational anomalies for continuous hydraulic fracturing monitoring.
The applicant listed for this patent is Seismos, Inc.. Invention is credited to Jerry M. Harris, Daniel Moos, Youli Quan.
Application Number | 20220186605 17/581753 |
Document ID | / |
Family ID | |
Filed Date | 2022-06-16 |
United States Patent
Application |
20220186605 |
Kind Code |
A1 |
Quan; Youli ; et
al. |
June 16, 2022 |
DETECTING OPERATIONAL ANOMALIES FOR CONTINUOUS HYDRAULIC FRACTURING
MONITORING
Abstract
A method for detecting operating anomalies during hydraulic
fracturing includes inducing tube waves in a well during pumping a
hydraulic fracture treatment. At least one of pressure and time
derivative of pressure in the well is measured. The measured at
least one of pressure and time derivative of pressure is
transformed into the cepstrum domain. An operational anomaly is
detected by determining a change in cepstral quefrency
corresponding to a two-way travel time of the tube waves and
resonances in the well.
Inventors: |
Quan; Youli; (Houston,
TX) ; Moos; Daniel; (Palo Alto, CA) ; Harris;
Jerry M.; (Stanford, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Seismos, Inc. |
Austin |
TX |
US |
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Appl. No.: |
17/581753 |
Filed: |
January 21, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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PCT/US2020/043175 |
Jul 23, 2020 |
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17581753 |
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62877476 |
Jul 23, 2019 |
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International
Class: |
E21B 47/06 20060101
E21B047/06; E21B 43/26 20060101 E21B043/26 |
Claims
1. A method for detecting operating anomalies during hydraulic
fracturing, comprising: inducing tube waves in a well during
pumping a hydraulic fracture treatment; measuring at least one of
pressure and time derivative of pressure in the well; transforming
the measured at least one of pressure and time derivative of
pressure into the cepstrum domain; and detecting an operational
anomaly by determining a change in cepstral quefrency corresponding
to a two-way travel time of the tube waves in the well.
2. The method of claim 1 wherein the change in cepstral quefrency
comprises a maximum value of quefrency.
3. The method of claim 1 wherein the change in cepstral quefrency
comprises a minimum value of quefrency.
4. The method of claim 1 wherein the change in cepstral quefrency
comprises a sum of a maximum and a minimum value of quefrency.
5. The method of claim 1 wherein the change in cepstral quefrency
comprises at least one of peak width, rise time and time
offset.
6. The method of claim 1 wherein the inducing tube waves comprises
changing a rate of pumping the hydraulic fracture treatment so as
to induce water hammer.
7. The method of claim 1 wherein the inducing tube waves comprises
imparting pressure changes into the well.
8. The method of claim 1 wherein on determining the operational
anomaly, a warning is communicated to a system operator, the method
further comprising performing a mitigation activity corresponding
to the determined anomaly.
9. The method of claim 8 wherein the mitigation activity includes
comprises changing at least one parameter of a hydraulic fracture
treatment.
10. The method of claim 9 wherein the at least one mitigation
parameter comprises at least one of proppant concentration,
proppant density, proppant amount, proppant particle size
distribution, proppant particle shape, fluid type/composition,
fluid viscosity, fluid viscosity change rate, fluid pumping rate,
fluid temperature, fluid chemical composition, chemical additives,
co-injection of energized gases in both liquid and gas phases,
injection of petroleum distillates, pH of injection fluid, fluid
pumping pressure, diverter type, perforation location, number of
perforations, angle of perforations, size of perforations, depth of
perforations, plug type, and stage length.
11. The method of claim 9 wherein the monitoring and mitigation
steps are controlled by a microcomputer.
12. The method of claim 9 wherein a machine learning algorithm is
applied to identify types of pumping problems and suggest
solutions.
13. The method of claim 1 wherein a visual tracking is provided to
a system operator.
14. The method of claim 1 wherein the operational anomaly comprises
screenout.
15. A non-transitory computer readable medium comprising logic
operable to cause a computer to perform actions comprising:
accepting as input to the computer, signals resulting from inducing
tube waves in a well during pumping a hydraulic fracture treatment
and measuring at least one of pressure and time derivative of
pressure in the well; transforming the measurements of at least one
of pressure and time derivative of pressure into the cepstrum
domain; and detecting an operational anomaly by determining a
change in cepstral quefrency corresponding to a two-way travel time
of the tube waves in the well.
16. The non-transitory computer readable medium of claim 15 wherein
the change in cepstral quefrency comprises a maximum value of
quefrency.
17. The non-transitory computer readable medium of claim 15 wherein
the change in cepstral quefrency comprises a minimum value of
quefrency.
18. The non-transitory computer readable medium of claim 15 wherein
the change in cepstral quefrency comprises a sum of a maximum and a
minimum value of quefrency.
19. The non-transitory computer readable medium of claim 15 wherein
the change in cepstral quefrency comprises at least one of peak
width, rise time and time offset.
20. The non-transitory computer readable medium of claim 15 wherein
the inducing tube waves comprises changing a rate of pumping the
hydraulic fracture treatment so as to induce water hammer.
21. The non-transitory computer readable medium of claim 15 wherein
the inducing tube waves comprises imparting pressure changes into
the well.
22. The non-transitory computer readable medium of claim 15 further
comprising logic operable to cause the computer to, on determining
the operational anomaly, communicating a warning to a system
operator.
23. The non-transitory computer readable medium of claim 22 further
comprising logic operable to cause the computer to calculate a
mitigation parameter to correct the operational anomaly.
24. The non-transitory computer readable medium of claim 23 wherein
the at least one mitigation parameter comprises at least one of
proppant concentration, proppant density, proppant amount, proppant
particle size distribution, proppant particle shape, fluid
type/composition, fluid viscosity, fluid viscosity change rate,
fluid pumping rate, fluid temperature, fluid chemical composition,
chemical additives, co-injection of energized gases in both liquid
and gas phases, injection of petroleum distillates, pH of injection
fluid, fluid pumping pressure, diverter type, perforation location,
number of perforations, angle of perforations, size of
perforations, depth of perforations, plug type, and stage
length.
25. The non-transitory computer readable medium of claim 24 further
comprising logic operable to cause the computer to implement a
machine learning algorithm to identify types of pumping problems
and suggest solutions.
26. The non-transitory computer readable medium of claim 15 wherein
the operational anomaly comprises screenout.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] Continuation of International Application No.
PCT/US2020/043175 filed on Jul. 23, 2020. Priority is claimed from
U.S. Provisional Application No. 62/877,476 filed on Jul. 23, 2019.
Both the foregoing applications are incorporated herein by
reference in their entirety.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0002] Not Applicable
NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT
[0003] Not Applicable.
BACKGROUND
[0004] This disclosure relates to the field of hydraulic fracturing
of subsurface reservoir formations. More specifically, the
disclosure relates to measuring properties of resonances and tube
waves propagating in a well during fracture treatment to diagnose
possible difficulties in a fracturing operation.
[0005] Hydraulic fracturing is a completion method designed to
improve the productivity of wells by enhancing the fluid connection
of the well to a subsurface reservoir. Fractures are created by
injection of high pressure fluids, with carefully controlled
injection rates and fluid properties (e.g., viscosity, density, and
compliance) and with various added solutes, e.g., acids or solids,
and quartz or other controlled-size particulates (proppant). During
pumping, typical measurements made at the surface include fluid
flow rate, proppant and chemical concentrations, and pressure. Such
measurements may be digitally sampled to provide "pumping data" at
digital sample frequencies of at most a few samples per second.
Along with the information regarding the amounts of fluid and
proppant pumped, these measurements often constitute the sole and
primary means of monitoring what is occurring in the well and the
reservoir.
[0006] Surface equipment and wellbore components have maximum safe
operating pressures. Thus predicting the risk of flow restrictions
in the borehole is important to avoid exceeding these maximum
pressures to prevent damage to equipment or environment.
[0007] Operational problems such as difficulties delivering fluid
at the required pumping rate) can be revealed by sudden and
unexpected increases pressure. However, using current methods and
data it can be difficult to quickly identify the source of such
problems and thereby to select the appropriate solutions. Without
additional information, identifying excessive pumping pressure is
not always successful or timely enough to avoid problems. Other
failures can occur such as leaks, isolation failures, etc., that
are not revealed by pressure response until they are detected may
cause a shutdown or failure. Moreover, occurrence of such problems
may have significant negative impact on ultimate hydrocarbon
production and recovery because the well may suffer reduced fluid
production from the created hydraulic fracture network. Therefore,
the ability to monitor the well and reservoir system to detect and
characterize adverse events prior to failure would be important and
valuable both to avoid costly downtime and also to achieve desired
productivity improvements.
[0008] An example of a problem that can occur during fracturing
operations is a screenout. A screenout occurs due to the buildup of
solids, typically pumped proppant, in a portion of the flow path
that impedes fluid motion and causes a rapid increase in pressure
during continued fluid injection. The solids can build up anywhere
in the system: in the well, the near-well region of fractures in
the reservoir formation, or within the fractures themselves either
close to or far from the well. When screenout occurs, pumping is
halted while remedial operations are carried out, such as a
"flowback" of several wellbore volumes, to remove the proppant
(solids) from the well to re-establish a pathway for subsequent
fracture fluid injection to complete the fracturing process. If the
onset of proppant blockage in the near-well region and of proppant
build-up at the bottom of the well could be detected during pumping
operations (i.e., in real time) it would allow operators to stop
injecting proppant before significant build-up occurs, thus
enabling flushing out the build-up, and possibly avoiding the
time-consuming and expensive process of having to remove such
accumulated proppant inside the well when pumps are forced to shut
down due to excessive pressure.
[0009] Methods known in the art rely on simple pressure monitoring
during hydraulic fracturing treatment. A limitation to using
pressure data alone to diagnose potential issues as they develop is
that it is difficult to differentiate among various explanations
for a particular pressure anomaly. For example, an increase in
pressure could be due to a restriction anywhere within the system
downstream of the pumps, including within or near the well. Such an
increase could also occur at a distance from the well due to a
restriction within a fracture being extended away from the well
into the reservoir. Thus, it is desirable to have a method for
detecting changes in the well or in the near-field of the well that
can also differentiate those effects from changes at a distance
from the well that produce similar indicators in pumping data, for
example, increases in pressure. Earlier problem identification may
allow for a higher chance of a successful recovery or outright
avoidance of the problem.
[0010] Vibrational energy is created by fluid pumping and by the
motion of fluids and solids, and this energy propagates throughout
the well and tubing and interacts with the surroundings. This
vibrational energy is efficiently propagated within the well, often
in the form of guided waves, or tube waves, which are sensitive to
properties of the well and the near-well region including of any
connected fractures, and is relatively unaffected by properties
including of the fracture system at a larger distance from the
well. This vibrational energy is known to excite resonances in the
wellbore and wellbore-fracture-system.
[0011] The characteristics of the vibrational energy, e.g., its
frequency components and amplitudes, arrival times of pulses, and
pulse shapes, are affected by the properties of the system
including of the connection between the well and the evolving
fracture system. However, this vibrational energy is seldom
recorded and rarely used. Such is the case firstly, because this
energy occurs at frequencies higher than can be measured with
conventional fracture pumping data acquisition systems with
sampling rates of no more than a few samples per second and
secondly, because detecting coherent energy or signals that allows
characterization of the well system is difficult due to the
continuous presence of pumping and other noise.
[0012] Additionally, use of (exploiting) this data to monitor and
react to identified system changes requires a rapid,
near-real-time, robust means of analyzing and delivering the
information about system characteristics to system operators.
Multiple indicators are required to avoid false alerts and to
provide information about the severity and likely time to
occurrence of possible future events. Systems with the ability to
acquire and display this data would be valuable because such
systems would enable monitoring changes during operations to
identify upcoming problems, to mitigate those problems by changing
operations to prevent their occurrence, and to treat them while
monitoring the treatments. This allows for continued, uninterrupted
operation and successful delivery of hydraulic fracturing
productivity improvements.
SUMMARY
[0013] One aspect of the present disclosure is a method for
detecting operating anomalies during hydraulic fracturing. A method
according to this aspect includes inducing tube waves in a well
during pumping a hydraulic fracture treatment. At least one of
pressure and time derivative of pressure in the well is measured.
The measured at least one of pressure and time derivative of
pressure is transformed into the cepstrum domain. An operational
anomaly is detected by determining a change in cepstral quefrency
corresponding to a two-way travel time of the tube waves in the
well.
[0014] In some embodiments, the change in cepstral quefrency
comprises a maximum value of quefrency.
[0015] In some embodiments, the change in cepstral quefrency
comprises a minimum value of quefrency.
[0016] In some embodiments, the change in cepstral quefrency
comprises a sum of a maximum and a minimum value of quefrency.
[0017] In some embodiments, the change of cepstral quefrency
comprises a change in relative quefrency corresponding to a two-way
travel time of a maximum and a minimum value of a quefrency. The
lag time between the maximum and minimum can change, or the
relative order of the maximum and minimum can switch from the
maximum leading (following) to the maximum lagging (leading) as the
operation progresses.
[0018] In some embodiments, the inducing tube waves comprises
changing a pump rate so as to induce water hammer.
[0019] In some embodiments, the inducing tube waves comprises
imparting pressure pulses into the well.
[0020] In some embodiments, the pressure pulses may be a frequency
or amplitude modulated series, various shape (triangle, sawtooth,
sine . . . ) swept frequencies, single frequency pulses, or single
impulses.
[0021] In some embodiments, the inducing tube waves comprises
pumping a fracture treatment into the well.
[0022] In some embodiments, on determining the operational anomaly,
a warning is communicated to a system operator, the method further
comprising performing a mitigation activity corresponding to the
determined anomaly.
[0023] In some embodiments, the mitigation activity includes
changing at least one hydraulic fracture treatment parameter of a
fracture treatment.
[0024] In some embodiments, the at least one parameter comprises at
least one of proppant concentration, proppant density, proppant
amount, proppant particle size distribution, proppant particle
shape, fluid type/composition, fluid viscosity, fluid viscosity
change rate, fluid pumping rate, fluid temperature, fluid chemical
composition, chemical additives (e.g., viscosifiers or acids),
co-injection of energized gases (nitrogen, CO.sub.2, propane,
methane) in both liquid and gas phases, injection of petroleum
distillates, or pH of injection fluid (acid/base), fluid pumping
pressure, diverter type (if any), perforation schema (perforation
location, number of perforations, angle of perforations, size of
perforations, depth of perforations), plug type, and stage
length.
[0025] In some embodiments, the monitoring and mitigation steps are
controlled by a microcomputer.
[0026] A non-transitory computer readable medium according to
another aspect of this disclosure includes logic operable to cause
a computer to perform actions. The actions comprise accepting as
input to the computer, signals resulting from inducing tube waves
in a well during pumping a hydraulic fracture treatment and
measuring at least one of pressure and time derivative of pressure
in the well; transforming the measurements of at least one of
pressure and time derivative of pressure into the cepstrum domain;
and detecting an operational anomaly by determining a change in
cepstral quefrency corresponding to a two-way travel time of the
tube waves in the well.
[0027] In some embodiments, the change in cepstral quefrency
comprises a maximum value of quefrency.
[0028] In some embodiments, the change in cepstral quefrency
comprises a minimum value of quefrency.
[0029] In some embodiments, the change in cepstral quefrency
comprises a sum of a maximum and a minimum value of quefrency.
[0030] In some embodiments, the change in cepstral quefrency
comprises at least one of peak width, rise time and time
offset.
[0031] In some embodiments, the inducing tube waves comprises
changing a rate of pumping the hydraulic fracture treatment so as
to induce water hammer.
[0032] In some embodiments, the inducing tube waves comprises
imparting pressure changes into the well.
[0033] Some embodiments further comprise logic operable to cause
the computer to, on determining the operational anomaly,
communicating a warning to a system operator.
[0034] Some embodiments further comprise logic operable to cause
the computer to calculate a mitigation parameter to correct the
operational anomaly.
[0035] In some embodiments, the at least one mitigation parameter
comprises at least one of proppant concentration, proppant density,
proppant amount, proppant particle size distribution, proppant
particle shape, fluid type/composition, fluid viscosity, fluid
viscosity change rate, fluid pumping rate, fluid temperature, fluid
chemical composition, chemical additives, co-injection of energized
gases in both liquid and gas phases, injection of petroleum
distillates, pH of injection fluid, fluid pumping pressure,
diverter type, perforation location, number of perforations, angle
of perforations, size of perforations, depth of perforations, plug
type, and stage length.
[0036] Some embodiments further comprise logic operable to cause
the computer to implement a machine learning algorithm to identify
types of pumping problems and suggest solutions.
[0037] In some embodiments, the operational anomaly comprises
screenout.
[0038] Other aspects and possible advantages will be apparent from
the description and claims that follow.
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] FIG. 1 shows a possible well data acquisition setup
according to the present disclosure.
[0040] FIG. 2 shows input pressure data x(t) or pressure time
derivative data (dx/dt) transformed into a power spectrum
X.sup.2(f). Each column in the lowermost panel in FIG. 2 is the
power spectrum of x(t) within a short time window. The peaks in in
the power spectrum correspond to resonance events.
[0041] FIG. 3 shows the power spectrum of FIG. 2 data transformed
into the cepstrum domain. Each column in the lowermost panel is the
cepstrum of x(t) in a short time window. The cepstral peak around 7
seconds starting at about 3 minutes represents reflection from
fractures near the fracture zone perforations in the well. This
reflection is a main event that may be tracked and used in a method
according to the present disclosure.
[0042] FIGS. 4a, 4b and 4c show simplified models for various
borehole conditions, e.g., sand or other proppant accumulating
adjacent to perforations with FIG. 4a showing an open boundary
condition, FIG. 4b showing a partially open boundary condition, and
FIG. 4c showing a closed boundary condition. Note that sand or
other proppant can accumulate adjacent to perforations on the well
side of a well casing or liner, or on the formation side of the
casing or liner, and the accumulated sand or other proppant may
partially or completely fill the well.
[0043] FIG. 5 shows three typical cepstral peaks obtained from the
lowermost panel in FIG. 3 at 100, 115, and 135 min. When the
borehole bottom (fracturing region) is under a closed boundary
condition, the cepstral peak is positive as shown at line 501. If
the borehole bottom becomes open, the cepstral peak changes to
negative (black line 502). A partially closed boundary condition
response contains both a positive and a negative cepstral peak as
shown at line 503. The actual depth of a plug in the wellbore can
also be used to estimate the tube wave velocity from the two way
travel time.
[0044] FIG. 6 shows a time-lapse cepstral scatter plot. The data
points are time-coded with time-lapse also shown by arrows. Black
is earlier time and lighter gray is later time. The data points
around 90-105 minutes fall into the first cluster, shown at 601, in
which the cepstral peaks have dominantly negative values. In the
second cluster, shown at 602, around 109-120 minutes, the cepstral
peaks have both positive and negative values. The third cluster,
shown at 603, around 130-150 minutes is dominated by a single
positive cepstral peak.
[0045] FIG. 7 shows a discriminant function x+y=0. The x-axis
represents the minimum value of the cepstral peaks, and the y-axis
represents the maximum value of the cepstral peaks. As may be
observed in FIGS. 5 and 6, the minimum value x is mostly negative
and the maximum value y is likely to be positive. Then, x+y=0 is a
useful linear discriminant function [Duda 2001] to classify the
data.
[0046] FIGS. 8a, 8b and 8c show a combined treatment chart (FIG.
8a), discriminant function f (FIG. 8b), and cepstrum plot (FIG. 8c)
with all information time-aligned for pumping of a stage.
[0047] FIG. 9 shows an example of screenout detection by using both
an x+y plot (left) and x vs. y scatter plot (right).
[0048] FIG. 10 shows a flowchart of an example embodiment of a
process according to this disclosure.
[0049] FIGS. 11a, 11b and 11c show a synthetic demonstration--a
model parameter of conductivity is in FIG. 11a and simulated
time-lapse tube wave data in FIG. 11b; time (i) increases from left
to right. Each i.sup.th column in FIG. 11b is a time series x.sub.i
(t), and X.sub.i.sup.2 (f) FIG. 11c is the power spectrum.
[0050] FIGS. 12a, 12b and 12c show the time-lapse discriminant
chart and cepstrum of the simulated data. The i.sup.th column in
FIG. 12c is the cepstrum c.sub.i(t), where .tau. is frequency. The
event at approximately 7.2 s is a reflection from plug-fractures
region in the borehole bottom. The min and max values of this event
are tracked and displayed in (12b).
[0051] FIG. 13 shows three typical cepstral peaks cut from FIG. 12c
at 2.5 min, 12.5 min, and 23 min, corresponding to times when the
simulated data illustrate the response of an open, partially
closed, and closed boundary condition, respectively.
[0052] FIG. 14 shows the time-lapse cepstral scatter plot generated
from the simulated data in 12b, where horizontal axis is cepstral
min and vertical axis is cepstral max. Darker dots are earlier
times.
[0053] FIG. 15 shows an example embodiment of a computer system
that may be used in some embodiments.
DETAILED DESCRIPTION
[0054] A method according to the present disclosure may use a
variety of signals, including vibrational energy induced by fluid
pumping and other operations in a well. Such induced vibrational
energy may include "passive signals", which may otherwise be
treated as noise, along with any "active data" (that is, energy
which is produced by changes in operations such as pumping rate
changes or valve position changes, or deliberately by active
processes, e.g., perforation shot firing or pressure pulsing) as
input data to monitor the changes in the hydraulic fracturing
system. Gr t et al. [Gret 2006] give an example that uses noise
(coda waves) to monitor a mining environment. For purposes of a
method according to the present disclosure, changes in pump rate or
valve position should be of such nature as to induce tube waves in
the well, e.g., by causing the change in pump rate and/or valve
position to be sufficiently rapid so as to induce water hammer. In
other implementations, a pressure pulse generator may be coupled to
the well. Such pressure pulse generator should induce tube waves in
the well. It is believed that for purposes of a method according to
the present disclosure, signals induced by changes in pump rate
and/or valve position will be sufficient.
[0055] The description below uses specific examples but is not
necessarily the only intended or possible implementation or use of
the disclosed method. The leading indicators of possible pumping
problems described here are identifiable and present before a
noticeable change in pressure, which represents the state of the
art prior to the present disclosure, is detected.
[0056] FIG. 1 is a schematic diagram of an example well data
acquisition system that may be used in some embodiments. The system
(100) comprises components associated with a well including fluid
pump(s) 101, sensors such as hydrophones or pressure transducers
102, a data acquisition and processing apparatus 103, a cased or
open well 104, plug or wellbore bottom 106, fracture network 107,
and perforations 108. A nearby well 109 may be present in the area
of interest. A water hammer pulse 105 may be generated either by
the pumps 101 such as by a change in the rate of pumping, or a
pressure pulse may be generated by other means as will be described
below. Some pulses are the ones generated inherently as part of
fluid pumping. Such pulses could be considered "passive" and
generally preferred measurement as no additional pressure pulse
source is necessary. The pulse(s) will travel and reflect along the
well. Nonintrusive sensors, such as pressure transducers,
accelerometers, and hydrophone(s), may be disposed in a location on
or near the top of the well (e.g., the wellhead) to measure
pressure, pressure time derivative and/or particle motion data
continuously before, during, and after pumping of a fracture
treatment stage. Similar measurements may be made at other points
along the well and surface equipment where pressure pulses or
pumping noise are detectable.
[0057] Signals, such as pressure (p) and pressure time derivative
(dp/dt) shown in FIG. 2, which may include both active and passive
data, are recorded in this example and may be processed in real
time or near real time to extract usable resonances and other
events to detect anomalies in the fracture-wellbore system.
[0058] To better understand a method according to the present
disclosure, the following explanation presents the basis of such
method. Let Eq. 1 represent the input data (e.g., pressure time
derivative signals) as measured in the well near the wellhead or
other suitable location.
x(t)=dp/dt (Eq. 1)
[0059] The input data power spectrum can be calculated by a Fourier
Transform (FT) (Eq. 2).
X.sup.2(f)=|FT[x(t)]|.sup.2 (Eq. 2)
[0060] FIG. 2 in the lowermost panel shows a moving window FT
(spectrogram, or waterfall plot) of the input pressure or pressure
time derivative signals. Peaks or resonance events can be observed
in the lowermost panel. As a matter of technical principle, one may
directly use the spectrogram such as shown in FIG. 2 to detect
changes in a resonant system. However, for certain situations, it
is desirable to further transform the power spectrum into a form
that allows easier event detection.
[0061] One example of this type of transform is autocorrelation.
Another example of such transform is a cepstrum. A cepstrum is the
result of taking the inverse Fourier transform of the logarithm of
the estimated spectrum of a signal. An inverse Fourier Transform
may be used to transform the data into the cepstrum domain (Eq. 3),
where .tau. is the cepstrum quefrency with the unit of time.
c(.tau.)=IFT.sup.-1[log(X.sup.2(f))] (Eq. 3)
[0062] FIG. 3 in the uppermost and middle panels shows the same
input signals as explained with reference to FIG. 2. The lowermost
panel in FIG. 3 shows the moving window cepstrum transform (Eq. 3)
of the input pressure signals. Reflections or echo events will
often appear as peaks in the cepstrum domain, e.g., the event
around 7 seconds in quefrency is a reflection from plug-fractures,
and the weaker event around 4 seconds is a reflection from a casing
and/or liner joint.
[0063] FIGS. 4a, 4b and 4c illustrate three examples of well
conditions, which may characterize the bottom of a fractured well
as open boundary condition (FIG. 4a), partially open (or partially
closed) boundary condition (FIG. 4b), and closed boundary condition
(FIG. 4c). In FIGS. 4a, 4b and 4c, a change in boundary condition
may be related to buildup of sand or other proppant in the well; in
other circumstances sand or other proppant may accumulate close to
the well (i.e., in the formation or fractures near the wellbore)
with relatively little accumulation within the well itself. During
normal pumping operations of hydraulic fracturing treatments, the
well is open, and proppant and fluid can easily flow through the
casing/liner perforations and into the fractures which are being
extended into the formation as shown in FIG. 4a.
[0064] Sand or proppant accumulation near casing or liner
perforations can result in the boundary condition changing from
open to more closed, i.e., partially open as shown in FIG. 4b. In
some cases, accumulation of proppant might be more extreme,
creating a poor hydraulic conductivity between the well and
fractures (low permeability value). Such poor hydraulic
conductivity affects the boundary condition. An example of this
phenomenon is shown in FIG. 4c, wherein proppant solids fill the
well and block the perforations. Blockages may occur in the well,
or close to the well in the fractures or perforations, which may be
detected as changes in the character of tube wave reflection power
spectrum or in the cepstrum of the tube wave reflections. Blockages
occurring at a larger distance from the well may not be detectable
using tube wave reflections; however they may be detected by other
means, for example, an increase in pressure required to maintain a
constant rate of fluid injection. For an operator, an open,
hydraulically unimpeded wellbore is desirable so pumping a
treatment can be completed on schedule at planned rates and
proppant loadings.
[0065] The tube wave reflections in the pressure data carry
information about well conditions; specifically, about the objects
that generate the tube wave reflections. In particular, a
perforation-fracture peak in the cepstrum transform of the pressure
signals can provide information about conditions at the bottom of
the well where the tube waves are reflected by the combined
reflectivity of the perforations, fractures, and a plug or other
well components which isolate deeper sections of the well from the
section of the well that is being fractured.
[0066] The relative amplitudes and absolute magnitudes of the
positive and negative peaks in the cepstrum transform of the
pressure signals will depend on the condition of the well bottom.
When the cepstral peak has a more positive amplitude, the wellbore
is "closed", while a larger negative amplitude of the cepstral peak
indicates an "open" wellbore. A partially open or partially closed
well bottom condition will have an intermediate relative amplitude
ratio depending on conditions in the well. The lag between the
maximum and minimum can change, or the relative temporal order of
the maximum and minimum can switch from the maximum leading then
following, to the maximum lagging and then leading as the fracture
treatment pumping operation progresses.
[0067] FIG. 5 shows three cepstral waveforms obtained from the
cepstrum transform shown in FIG. 3c at 100, 115, and 135 min. It
can be observed in FIG. 5 that cepstral peaks at a time consistent
with acoustic tube wave travel time from the top of the well to the
perforations and back exhibit significantly different waveforms due
to boundary condition changes over time. When hydraulic
connectivity between borehole and fractures is low:
[0068] the borehole bottom is "closed",
[0069] the acoustic impedance of the combined
perforation/fracture/plug system is greater than the acoustic
impedance of the well, and
[0070] the cepstral peak is positive (curve 501, "Closed" @135
min'' in FIGS. 3 and 5). The open boundary condition, detected by a
negative cepstral peak (curve 502, "Open" @100 min" in FIG. 5):
[0071] indicates a good connection between the well and fractures,
and
[0072] the acoustic impedance of the combined
perforation/fracture/plug system is smaller than the acoustic
impedance of the wellbore.
[0073] Partially closed boundary condition can be inferred from a
cepstrum transform with limited variation between the positive and
negative cepstral peaks, and usually lies between the open and
closed boundary condition cepstral peak values (curve 503,
"Partially closed@115" min in FIG. 5).
[0074] As described above, one example of an attribute that can be
monitored in real-time is a positive or negative cepstral peak
amplitude relative to a zero value, such as shown in FIG. 5.
However, other attributes can be extracted from the cepstral peaks
for the purpose of well condition detection. One such attribute is
a time delay between a negative or positive peak, e.g., of curve
503, which may be defined as positive if the positive cepstral peak
occurs later than the negative peak, or defined as negative if the
positive cepstral peak occurs earlier than the negative peak.
Additional attributes may include a ratio of positive to negative
cepstral peak amplitude relative to a mean cepstral amplitude; or a
difference in cepstral amplitude.
[0075] A scatter plot shown in FIG. 6, which has a cepstral
positive peak on one axis (y), a cepstral negative peak on the
other axis (x) plotted with respect to time, can reveal clusters
that correspond to all three different boundary conditions. The
scatter plot also may provide the advantage of clustering the data
points visually. Using color or grayscale coding to track the time
at which the x and y values occur may provide information regarding
how the well the condition changes with respect to time. In some
embodiments, each time point may be represented by a different size
dot or other unique symbol, or by other attributes that would
indicate relative timing of each datum. Each symbol's attributes
may change depending on the length of time since the data were
acquired; for example, dots may become increasingly transparent and
eventually disappear. The rate of change of the attribute may be
controlled by the user.
[0076] By viewing the data in different time windows, or causing a
sliding time window to be applied so that colors or gray scale
shading change and data appear and disappear as the time window
moves, it is possible to create a moving visualization of the
dynamic variations in well boundary conditions. The speed/rate of
change of position on the scatter plot may be used as an indicator
of the onset severity of an event and the importance of acting to
change operational conditions to avoid unwanted events such as
screenout, and may also provide information regarding where the
change that causes the event in the well and surrounding volume is
occurring. The display as in FIG. 6 can be used to quickly and
easily identify the state of the wellbore system with respect to
possible pumping problems.
[0077] Since passive data are usually noisy and thus have
relatively poor quality, the evaluation of attributes must be
robust. Cepstral Min (or x) and Max (or y) as shown in FIG. 6 are
two robust attributes and can be selected from the peak of
waveforms. Thus it is desirable to extract attributes of the
detected tube wave reflection events and to display those
attributes in other ways such as a two-dimensional time-lapse
cepstral scatter plot using the data points (x, y) obtained as
shown in FIG. 3. Additionally, FIG. 6 is also an example of
time-lapse cepstral scatter plot of cepstral peaks with the darker
dots indicating the earlier time and lighter dots later time during
pumping operation. The progression is also shown from t=90 minutes
through the end with arrows parallel to the measurement points. The
data points around 90-105 minutes fall into the first cluster,
shown at 601, in which the cepstral peaks have dominantly negative
values. In the second cluster, shown at 602, around 109-120 min,
the data points have both positive and negative values. The third
cluster, shown at 603, around 130-150 minutes is dominated by a
positive cepstral peak. A user viewing the plot at various times
can be looking on changes during pumping and be forewarned if the
peak starts shifting as from 602 to 603.
[0078] Other attributes of the cepstral transform of the signals
within the time window may also be extracted from the data, such as
is known by those skilled in the art of signal analysis. These
include various shape attributes, for example, slopes, peak rise
time, width, half-power width, and ratio of peak amplitude to
width. These can also be plotted on plots such as FIG. 6, either on
the x- or y-axis, in color or gray scale, or by varying dot size or
shape.
[0079] FIG. 7 shows an example embodiment of a cepstral scatter
plot using the observations from FIG. 6. A discriminant function
x+y=0 is shown in FIG. 7, which may be a useful linear discriminant
function to classify the data [Duda 2001].
[0080] FIGS. 8a, 8b and 8c show combined data plots using the same
input data as used in FIG. 3 during passive and active recording.
The pressure, flow rate, and proppant concentration data are shown
in FIG. 8a. In the combined plot FIG. 8b, x+y is displayed as a new
attribute for comparison to other available time-lapse data, such
as pressure, flow rate, proppant concentration, and moving window
cepstrum.
[0081] Cepstral attributes min (x) and max (y) are displayed in
FIG. 9b along with moving window cepstrum FIG. 9a, which is a zoom
in of the data shown at the bottom of FIG. 8. It can be observed
that the sum x+y increases and crosses zero from 105 to 130 min.
The scatter plot shown in FIG. 6 confirms there are three clusters
(or three distinguishable borehole conditions) during this period.
FIG. 9 is a composite plot using the plots in FIGS. 8a, 8b, 8c and
FIG. 6 for an analysis of the pressure and reflection data from 110
min to 130 min. Both the x+y plot (left) and x vs. y scatter plot
(right) may be used to detect and predict a screen-out. A fracture
screen-out occurred after 130 min (Closed boundary condition). A
screen-out precursor is detected around 110 min (Partially Open
boundary condition). Other possible applications of this scatter
plot include plug slippage detection, casing breach detection,
borehole object tracking, and ball seating state detection. In
particular, a seated ball will close the plug and result in a
closed boundary condition while the cepstral peak is positive. The
condition in this example is open, prior to the screenout precursor
and screenout onset, and the cepstral peak is more negative than
positive.
[0082] To implement a method according to the present disclosure,
please refer to FIG. 10. FIG. 10 is a flow chart describing an
example implementation of a method according to the present
disclosure (add hydraulic fracturing supporting operations, e.g.,
pumping, perforating, etc.) in the following steps:
[0083] At 1001, acquire pressure data or pressure time derivative
data continuously with a sensor at or near the wellhead (other
locations for acquiring pressure or pressure time derivative data
are possible, including using a hydrophone string or an optical
fiber in the wellbore); During hydraulic fracturing treatment, the
sensor(s) are connected and their signals recorded, and the
following actions are taken continuously as the data are
recorded;
[0084] At 1002, transform the time domain pressure or pressure time
derivative measurements to the power spectrum X.sup.2(f), e.g., by
a moving window Fourier Transform (FT); In a microcomputer, the
recorded time domain pressure data may be continuously windowed and
transformed using a Fourier Transform (FT).
[0085] At 1003, transform log[X.sup.2(f)] to the cepstrum domain
c(T) by Inverse FT, see Eqs. 2-3; Cepstrum transform is
continuously generated;
[0086] At 1004, track cepstral peaks in the c(T) domain to pick
tube wave reflection events; select, visually or by foreknowledge,
a time window within which the plug/bottom tube wave reflection
peak occurs and then track the cepstrum transform of the pressure
data within that time window. The foregoing window changes slowly
since tube wave propagation velocity changes are small and well
length changes are also small.
[0087] At 1005, extract cepstral Min and Max from the tube wave
reflection events.
[0088] At 1006, display the above extracted Min, and display
Min+Max vs. time-lapse to monitor boundary condition changes; other
displays of these quantities are possible. Points may be added in
real time;
[0089] At 1007, use a moving window scatter plot to classify the
boundary conditions; According to FIG. 5, 6, or 7, the
borehole-fracture system boundary condition can be classified as
the one of those described with reference to FIGS. 4a, 4b and
4c.
[0090] At 1008, notify the system operator of the borehole-fracture
system boundary status; keep the operator apprised of the ongoing
status of the wellbore using a display or other machine-human
interface device, for example, using plots such as those in FIG. 9.
Provide the operator with ongoing alerts for anomalies, trends,
open and closed wellbore-fracture system condition, etc.
[0091] At 1009, provide a record of the borehole-fracture system
behavior; record and display historical trends, evolution (e.g., as
shown in FIG. 9). Identify and alert of anomalies. The anomaly type
can be related to the cepstrum location in time and amplitude. Rate
of change indicates severity of the onset event.
[0092] At 1010, which is optional, use historical data, machine
learning, or artificial intelligence to improve the delivery of
alerts and mitigation recommendations; machine learning based on
previous events or near-screenouts and positive resolution can help
recommend to the operator a course of action to mitigate adverse
effects (high pressure, screenouts, etc.)
[0093] At 1011, adjust fracture treatment parameters in real-time
to mitigate adverse effects; This step can be automated in a
microcomputer. The operator will adjust treatment parameters in
real-time. For example, the operator may reduce proppant loading,
reduce pumping rate, change fluid properties to flush out the
wellbore and/or fractures of excess sand to establish a proper
wellbore-reservoir connection. This will show as a more "open
boundary condition" indicator.
[0094] The process described with reference to 1001-1011 may be
repeated throughout the hydraulic fracturing treatment.
[0095] An example embodiment of a data display may be as shown FIG.
8 or 9, but other ways to visually depict the well bottom or entire
wellbore system condition are possible.
[0096] Alerts, as described with reference to 1008 may be generated
indicating types of the anomaly, severity, uncertainty, and
possible mitigating actions such as reducing flow rate and proppant
concentration, or pump shut-down. The alerts include types of the
anomaly, severity, uncertainty, and possible mitigating actions
(either artificial intelligence-generated or hard-programmed given
certain conditions) such as reducing flow rate and proppant
concentration, or shutting pump down.
[0097] Implicit in the flowchart description of FIG. 10 is that the
information is provided to the operator and can be acted upon and a
well treatment adjusted accordingly in real-time.
[0098] If mitigation of an adverse condition is warranted, perform
such mitigation as deemed appropriate (e.g. reduce rate, reduce
proppant loading, etc. and continue the above actions to monitor
progress and whether the mitigation approach is working. If the
chosen mitigation approach is not working, the operator may choose
to adjust additional parameters, including but not limited to
modifying proppant concentration, proppant density, proppant
amount, proppant particle size distribution, proppant particle
shape, fluid type/composition, fluid viscosity, fluid viscosity
change rate, fluid pumping rate, fluid temperature, fluid chemical
composition, chemical additives (e.g., viscosifiers or acids),
co-injection of energized gases (nitrogen, CO.sub.2, propane,
methane) in both liquid and gas phases, injection of petroleum
distillates, or pH of injection fluid (acid/base), fluid pumping
pressure, and diverter type (if any). Additional mitigation,
although on a follow up stage may include changes in perforation
schema (perforation location, number of perforations, angle of
perforations, size of perforations, depth of perforations), plug
type, and stage length.
[0099] A method according to present disclosure may provide a
record of behavior. It will also capture learnings and apply
Artificial Intelligence/Machine Learning (AI/ML) to enhance
delivery of advice and alerts, for example.
[0100] The methodology can be automated and implemented in an
apparatus that autonomously performs the above described steps, the
monitoring function and event-flagging (at 1007-1009). Moreover, a
system can be designed to learn and perform mitigation and
monitoring activities automatically and autonomously--either based
on simple rules or based on machine learning.
[0101] A synthetic data example is described below to demonstrate
and verify a screen-out detection method presented in this
disclosure. A borehole model was created based on the real data
example shown in FIG. 2. There are two sections in the borehole
model. The top part is a 3078 m long casing, and the lower part is
a 2317 m long liner. The inner diameters of the casing and liner
are 0.1186 m and 0.1086 m, respectively. To simulate a screen-out,
we assume the borehole boundary condition changes from open to
closed, i.e., the conductivity of the fracture-wellbore connection
changes from very high to extremely low in 30 minutes. In the
simulation the conductivity changes linearly in log scale from 60
to 0.06 (.times.10.sup.-12 m.sup.3). Here conductivity is defined
as the product of permeability and fracture width.
[0102] FIGS. 11a, 11b and 11c show a modeled hydraulic conductivity
(FIG. 11a), the simulated time-lapse tube wave data in the time
domain (FIG. 11b) and in the frequency domain (FIG. 11c). Events in
FIG. 11b are tube wave reflections within the well. It can be
observed that the waveforms of the reflections change as hydraulic
conductivity decreases. If the conductivity is high (e.g., at time
before 7 min), successive reflections reverse polarities, for
example, the event at 8-10 seconds is negative (black) then
positive (white) and the event at 16-17 seconds is positive then
negative at early time-lapse, then all events become positive then
negative at late time-lapse when conductivity is low. The events in
FIG. 11b can also be thought of as resonances. The resonant
frequencies decrease as the hydraulic conductivity changes from
large to small. The resonant amplitudes have a sharp drop near 12
min when the screen-out occurs. Although it is possible to detect
the screen-out with resonances, according to the present disclosure
it is more suitable to use cepstrum to detect the screen-out.
[0103] Cepstrum calculated using an inverse Fourier transform,
i.e., Eq. 3. FIGS. 12a, 12b and 12c show, respectively, the model
parameters, the cepstrum min and min+max, and the time-lapse
cepstrum. The event around 7.2 s in FIG. 12c is a cepstral peak
that represents the reflection from the plug-fractures near
borehole bottom; the time corresponds to the 2-way time of the tube
wave in the wellbore, which allows estimating this time from the
well length and tube wave velocity. The cepstral peak changes from
negative (dark) to positive (bright) as conductivity decreases. The
polarity change is slow at first, rapid over the interval near 12
minutes, when screen-out occurs, and then slower after the
transition. The min and max values of the cepstral peak are traced
and displayed in FIG. 12b in the forms of cepstral min and cepstral
min+max. Cepstral min+max=0 may be used as a threshold to flag the
screen-out.
[0104] In order to view the details of the cepstrum, three time
indications, around 2.5, 12.5, and 23 min in FIG. 12c, are
extracted and plotted in FIG. 13. Those three indications represent
open, partially closed, and closed boundary conditions,
respectively. It can be clearly observed that the open boundary
condition (or large conductivity) results in a negative cepstral
peak, and the closed boundary condition results in a positive peak.
The polarity of the cepstral peaks, therefore, may be a robust
indicator for the boundary condition that is directly related to
screen-out.
[0105] FIG. 14 shows cepstral scatter plot, where the horizontal
axis is cepstral min and the vertical axis is cepstral max. The
location in this scatter plot reveals the condition of the wellbore
connection, as detailed in FIG. 7. The scatter locations can help
reveal different boundary conditions. FIG. 14. is an example of
relatively faster speed of change in boundary condition compared to
FIG. 6 where the change in boundary conditions from partially
closed to closed occurred in about 20 min. For the situation
revealed in FIG. 6, the operator could have recovered by reducing
the injection rate and/or proppant concentration in order to
recover the operation to normal condition without shutting down the
pumps. If the onset was much faster, the operator would have to
shut down the pump or drop rate and proppant concentration more
aggressively to avoid a screenout. Synthetic data, such as shown in
FIGS. 11-14, may be generated to simulate various events that may
occur in a wellbore, and the behaviors used to train or program a
machine to perform autodetection of event severity and
rapidity.
[0106] The results of the synthetic data example and the real data
example are consistent. Both real data and synthetic data show that
(1) the cepstral peaks change from positive to negative when a
screen-out occurs, (2) cepstral min and max are two robust
features, and (3) the time-lapse cepstral scatter plot is a useful
tool to view the course of a screen-out. In some cases, changes
that occur rapidly might be handled differently than changes that
occur slowly.
[0107] FIG. 15 shows an example computing system 1500 in accordance
with some embodiments that may be used to implement a method
according to the disclosure. The computing system 1500 may be an
individual computer system 1501A or an arrangement of distributed
computer systems. The individual computer system 1501A may include
one or more analysis modules 1502 that may be configured to perform
various tasks according to some embodiments, such as the tasks
explained with reference to FIG. 15. To perform these various
tasks, the analysis module 1502 may operate independently or in
coordination with one or more processors 1504, which may be
connected to one or more storage media 1506. A display device 1505
such as a graphic user interface of any known type may be in signal
communication with the processor 1504 to enable user entry of
commands and/or data and to display results of execution of a set
of instructions according to the present disclosure.
[0108] The processor(s) 1504 may also be connected to a network
interface 1508 to allow the individual computer system 1501A to
communicate over a data network 1510 with one or more additional
individual computer systems and/or computing systems, such as
1501B, 1501C, and/or 1501D. Note that computer systems 1501B, 1501C
and/or 1501D may or may not share the same architecture as computer
system 1501A, and may be located in different physical locations,
for example, computer systems 1501A and 1501B may be at a well
drilling location, while in communication with one or more computer
systems such as 1501C and/or 1501D that may be located in one or
more data centers on shore, aboard ships, and/or located in varying
countries on different continents.
[0109] A processor may include, without limitation, a
microprocessor, microcontroller, processor module or subsystem,
programmable integrated circuit, programmable gate array, or
another control or computing device.
[0110] The storage media 1506 may be implemented as one or more
computer-readable or machine-readable storage media. Note that
while in the example embodiment of FIG. 15 the storage media 1506
are shown as being disposed within the individual computer system
1501A, in some embodiments, the storage media 1506 may be
distributed within and/or across multiple internal and/or external
enclosures of the individual computing system 1501A and/or
additional computing systems, e.g., 1501B, 1501C, 1501D. Storage
media 1506 may include, without limitation, one or more different
forms of memory including semiconductor memory devices such as
dynamic or static random access memories (DRAMs or SRAMs), erasable
and programmable read-only memories (EPROMs), electrically erasable
and programmable read-only memories (EEPROMs) and flash memories;
magnetic disks such as fixed, floppy and removable disks; other
magnetic media including tape; optical media such as compact disks
(CDs) or digital video disks (DVDs); or other types of storage
devices. Note that computer instructions to cause any individual
computer system or a computing system to perform the tasks
described above may be provided on one computer-readable or
machine-readable storage medium, or may be provided on multiple
computer-readable or machine-readable storage media distributed in
a multiple component computing system having one or more nodes.
Such computer-readable or machine-readable storage medium or media
may be considered to be part of an article (or article of
manufacture). An article or article of manufacture can refer to any
manufactured single component or multiple components. The storage
medium or media can be located either in the machine running the
machine-readable instructions, or located at a remote site from
which machine-readable instructions can be downloaded over a
network for execution.
[0111] It should be appreciated that computing system 1500 is only
one example of a computing system, and that any other embodiment of
a computing system may have more or fewer components than shown,
may combine additional components not shown in the example
embodiment of FIG. 15, and/or the computing system 1500 may have a
different configuration or arrangement of the components shown in
FIG. 15. The various components shown in FIG. 15 may be implemented
in hardware, software, or a combination of both hardware and
software, including one or more signal processing and/or
application specific integrated circuits.
[0112] Further, the acts of the processing methods described above
may be implemented by running one or more functional modules in
information processing apparatus such as general purpose processors
or application specific chips, such as ASICs, FPGAs, PLDs, or other
appropriate devices. These modules, combinations of these modules,
and/or their combination with general hardware are all included
within the scope of the present disclosure.
[0113] References cited in the present disclosure: [0114] [1] Gr t,
A., Snieder, R, U Ozbay, 2006, Monitoring in situ stress changes in
a mining environment with coda wave interferometry: Geophysical
Journal International [0115] [2] Dunham, E., Zhang, J., Quan Y.,
Harris, J., and Kaitlyn Mace, 2017, Hydraulic fracture conductivity
inferred from tube wave reflections: SEG Annual Meeting. [0116] [3]
Parkhonyuk, S., Fedorov, Kabannik, A., Korkin, R., Nikolaev, M.,
and Tsygulev, I., 2018, Measurements While Fracturing: Nonintrusive
Method of Hydraulic Fracturing Monitoring, Presented at the SPE
Hydraulic Fracturing Technology Conference & Exhibition held in
The Woodlands, Tex., SPE-189886-MS [0117] [4] Duda, R., Hart, P,
and Stork, D., 2001, Pattern Classification, 2nd Edition: Wiley
Interscience.
[0118] Although only a few examples have been described in detail
above, those skilled in the art will readily appreciate that many
modifications are possible in the examples. Accordingly, all such
modifications are intended to be included within the scope of this
disclosure as defined in the following claims.
* * * * *