U.S. patent application number 15/685089 was filed with the patent office on 2019-02-28 for technique for estimating a state of a drilling apparatus or operation.
The applicant listed for this patent is Rowan Companies, Inc.. Invention is credited to Viswanath AVASARALA, Olivier LHOMMET.
Application Number | 20190063192 15/685089 |
Document ID | / |
Family ID | 65436911 |
Filed Date | 2019-02-28 |
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United States Patent
Application |
20190063192 |
Kind Code |
A1 |
LHOMMET; Olivier ; et
al. |
February 28, 2019 |
TECHNIQUE FOR ESTIMATING A STATE OF A DRILLING APPARATUS OR
OPERATION
Abstract
A method includes storing a first data structure associated with
estimating a state of a drilling apparatus or operation based on
data from a plurality of measurement devices. The method further
includes receiving measurement data associated with a subset of the
plurality of measurement devices. The method further includes, in
response to determining that the subset does not include one or
more measurement devices of the plurality of measurement devices,
transforming the first data structure into a second data structure
based on identities of the one or more measurement devices. The
second data structure is associated with estimating the state of
the drilling apparatus or operation based on data from the subset.
The method further includes, based on the measurement data and the
second data structure, generating an estimation of the state of the
drilling apparatus or operation and a confidence value associated
with the estimation.
Inventors: |
LHOMMET; Olivier; (Houston,
TX) ; AVASARALA; Viswanath; (Katy, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Rowan Companies, Inc. |
Houston |
TX |
US |
|
|
Family ID: |
65436911 |
Appl. No.: |
15/685089 |
Filed: |
August 24, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 47/00 20130101;
E21B 47/06 20130101; E21B 41/0092 20130101; E21B 3/02 20130101;
E21B 47/09 20130101; E21B 47/10 20130101; E21B 44/00 20130101 |
International
Class: |
E21B 41/00 20060101
E21B041/00; E21B 44/00 20060101 E21B044/00; E21B 47/09 20060101
E21B047/09; E21B 47/10 20060101 E21B047/10; E21B 47/06 20060101
E21B047/06; E21B 47/00 20060101 E21B047/00 |
Claims
1. A method comprising: storing a first data structure associated
with estimating a state of a drilling apparatus or operation based
on data from a plurality of measurement devices; receiving
measurement data associated with a subset of the plurality of
measurement devices associated with the drilling apparatus or
operation; in response to determining that the subset does not
include one or more measurement devices of the plurality of
measurement devices, transforming the first data structure into a
second data structure based on identities of the one or more
measurement devices, wherein the second data structure is
associated with estimating the state of the drilling apparatus or
operation based on data from the subset; and based on the
measurement data and the second data structure, generating: an
estimation of the state of the drilling apparatus or operation; and
a confidence value associated with the estimation.
2. The method of claim 1, further comprising presenting, via an
output device, a message based on the estimation, the message
including an audible component, a visual component, or a
combination thereof.
3. The method of claim 2, wherein the message includes a warning
associated with the estimation of the state.
4. The method of claim 1, wherein the plurality of measurement
devices includes a sensor, a measurement calculation device
configured to calculate a measurement, or a combination
thereof.
5. The method of claim 4, wherein the plurality of measurement
devices includes a device configured to calculate total depth, a
device configured to calculate bit depth, a torque sensor, a speed
sensor, a flow sensor, a pressure sensor, a weight on bit sensor, a
block position sensor, a hook load sensor, or a combination
thereof.
6. The method of claim 4, wherein the state includes a drilling
state, a drilling in cement state, a drilling while sliding state,
a tripping in state, a tripping out state, an in slips state, a
static state, a wash up state, a wash down state, a reaming in
state, a remaining out state, or a circulating state.
7. The method of claim 1, wherein the first data structure
indicates a first number of instances that a first combination of
measurement values corresponded to the state and a second number of
instances that a second combination of measurement values
corresponded to the state, and wherein transforming the first data
structure based on the identities of the one or more measurement
devices includes summing the first number and the second number in
response to determining that the first combination matches the
second combination when measurement values associated with the one
or more measurement devices are ignored.
8. The method of claim 1, further comprising generating the
measurement data by calculating change point values based on raw
data received from the subset.
9. The method of claim 1, wherein receiving the measurement data
includes receiving a dataset associated with the plurality of
measurement devices and, in response to determining that the one or
more measurement devices are unreliable, generating the measurement
data based on a portion of the dataset that is independent of the
one or more measurement devices.
10. The method of claim 9, further comprising determining that the
one or more measurement devices are unreliable based on user
input.
11. The method of claim 1, further comprising constructing the
first data structure by applying a deterministic model to
historical measurement data to determine a number of occurrences of
each of a plurality of states for each combination of values
corresponding to the plurality of measurement devices.
12. The method of claim 11, wherein the first data structure
corresponds to a p.times.n matrix, wherein p is a number of
distinct measurement data value combinations present in the
historical measurement data and n is a number of states included in
the plurality of states.
13. The method of claim 1, wherein generating the estimation
includes applying a k nearest neighbors algorithm to the second
data structure.
14. The method of claim 1, further comprising calculating the
confidence value based on an entropy value associated with
transforming the first data structure into the second data
structure.
15. A computer-readable storage device storing instructions that,
when executed by one or more processors, cause the one or more
processors to perform operations including: storing a first data
structure associated with estimating a state of a drilling
apparatus or operation based on data from a plurality of
measurement devices; receiving measurement data associated with a
subset of the plurality of measurement devices associated with the
drilling apparatus or operation; in response to determining that
the subset does not include one or more measurement devices of the
plurality of measurement devices, transforming the first data
structure into a second data structure based on identities of the
one or more measurement devices, wherein the second data structure
is associated with estimating the state of the drilling apparatus
or operation based on data from the subset; and based on the
measurement data and the second data structure, generating: an
estimation of the state of the drilling apparatus or operation; and
a confidence value associated with the estimation.
16. The computer-readable storage device of claim 15, wherein the
operations further include converting raw measurement data received
from the subset to binary data, ternary data, or a combination
thereof.
17. The computer-readable storage device of claim 16, wherein the
operations further include initiating transmission of a message to
another device, the message indicating the estimation.
18. The computer-readable storage device of claim 17, wherein the
message corresponds to an electronic mail message, to a short
message service message, a social networking service message, or a
combination thereof.
19. The computer-readable storage device of claim 15, wherein the
operations further comprise initiating transmission of control
signals to the drilling apparatus or operation based on the
estimation.
20. An apparatus comprising: one or more processors; a storage
device storing a first data structure associated with estimating a
state of a drilling apparatus or operation based on data from a
plurality of measurement devices; and a memory storing instructions
that, when executed by the one or more processors, cause the one or
more processors to perform operations including: receiving
measurement data associated with a subset of the plurality of
measurement devices associated with the drilling apparatus or
operation; in response to determining that the subset does not
include one or more measurement devices of the plurality of
measurement devices, transforming the first data structure into a
second data structure based on an identities of the one or more
measurement devices, wherein the second data structure is
associated with estimating the state of the drilling apparatus or
operation based on data from the subset; and based on the
measurement data and the second data structure, generating: an
estimation of the state of the drilling apparatus or operation; and
a confidence value associated with the estimation.
Description
TECHNICAL FIELD
[0001] Many devices function Embodiments described herein generally
relate to estimating a drilling apparatus or operation state.
BACKGROUND ART
[0002] An operator of a drilling apparatus may perform various
actions based on a state of the drilling apparatus or an operation
associated with the drilling apparatus as reported by a monitoring
system. The monitoring system may receive sensor data from a
plurality of sensors associated with the drilling apparatus and
apply the sensor data to a model to determine the state of the
drilling apparatus or operation. The model may be dependent on
receiving data from each of the plurality of sensors. However, in
some circumstances, the monitoring system may receive data from
fewer than all of the plurality of sensors.
SUMMARY
[0003] The following presents a simplified summary of the disclosed
subject matter in order to provide a basic understanding of some
aspects of the subject matter disclosed herein. This summary is not
an exhaustive overview of the technology disclosed herein. It is
not intended to identify key or critical elements of the invention
or to delineate the scope of the invention. Its sole purpose is to
present some concepts in a simplified form as a prelude to the more
detailed description that is discussed later.
[0004] According to one embodiment, a method includes storing a
first data structure associated with estimating a state of a
drilling apparatus or operation based on data from a plurality of
measurement devices. The method further includes receiving
measurement data associated with a subset of the plurality of
measurement devices associated with the drilling apparatus or
operation. The method further includes, in response to determining
that the subset does not include one or more measurement devices of
the plurality of measurement devices, transforming the first data
structure into a second data structure based on identities of the
one or more measurement devices. The second data structure is
associated with estimating the state of the drilling apparatus or
operation based on data from the subset. The method further
includes, based on the measurement data and the second data
structure, generating an estimation of the state of the drilling
apparatus or operation and a confidence value associated with the
estimation.
[0005] In another embodiment, a computer-readable storage device
stores instructions, that when executed by one or more processors
cause the one or more processors to perform operations including
storing a first data structure associated with estimating a state
of a drilling apparatus or operation based on data from a plurality
of measurement devices. The operations further include receiving
measurement data associated with a subset of the plurality of
measurement devices. The operations further include, in response to
determining that the subset does not include one or more of the
plurality of measurement devices, transforming a first data
structure into a second data structure based on identities of the
one or more measurement devices. The second data structure is
associated with estimating the state of the drilling apparatus or
operation based on data from the subset. The operations further
include, based on the measurement data and the second data
structure, generating an estimation of the state of the drilling
apparatus or operation and a confidence value associated with the
estimation.
[0006] In another embodiment, an apparatus includes one or more
processors and a storage device storing a first data structure
associated with estimating a state of a drilling apparatus or
operation based on data from a plurality of measurement devices.
The apparatus further includes a memory storing instructions that,
when executed by the one or more processors, cause the one or more
processors to perform operations including receiving measurement
data associated with a subset of the plurality of measurement
devices. The operations further include, in response to determining
that the subset does not include one or more of the plurality of
measurement devices, transforming the first data structure into a
second data structure based on identities of the one or more
measurement devices. The second data structure is associated with
estimating the state of the drilling apparatus or operation based
on data from the subset. The operations further include, based on
the measurement data and the second data structure, generating an
estimation of the state of the drilling apparatus or operation and
a confidence value associated with the estimation.
BRIEF DESCRIPTION OF DRAWINGS
[0007] For a more complete understanding of this disclosure,
reference is now made to the following brief description, taken in
connection with the accompanying drawings and detailed description,
wherein like reference numerals represent like parts.
[0008] FIG. 1A is a schematic diagram of an embodiment of a
computing system for estimating a state of a device or operation by
transforming a data structure.
[0009] FIG. 1B is a schematic diagram of a specific example of the
computing system for estimating a state of a drilling apparatus or
operation by transforming a data structure.
[0010] FIG. 2 is a diagram of a process of transforming a data
structure.
[0011] FIG. 3 is a diagram of a process for generating measurement
data that may be used to estimate a state of a device or
operation.
[0012] FIG. 4 is a diagram illustrating a process of generating a
data structure that may be transformed to estimate a state of a
device or operation.
[0013] FIG. 5 is a flowchart illustrating a method of estimating a
state of a device or operation by transforming a data
structure.
[0014] FIG. 6 is a block diagram illustrating an embodiment of a
computing system for use with techniques described herein.
DESCRIPTION OF EMBODIMENTS
[0015] In the following description, for purposes of explanation,
numerous specific details are set forth in order to provide a
thorough understanding of the embodiments disclosed herein. It will
be apparent, however, to one skilled in the art that the disclosed
embodiments may be practiced without these specific details. In
other instances, structure and devices are shown in block diagram
form in order to avoid obscuring the disclosed embodiments.
References to numbers without subscripts or suffixes are understood
to reference all instance of subscripts and suffixes corresponding
to the referenced number. Moreover, the language used in this
disclosure has been principally selected for readability and
instructional purposes, and may not have been selected to delineate
or circumscribe the inventive subject matter, resort to the claims
being necessary to determine such inventive subject matter.
Reference in the specification to "one embodiment" or to "an
embodiment" means that a particular feature, structure, or
characteristic described in connection with the embodiments is
included in at least one embodiment.
[0016] The terms "a," "an," and "the" are not intended to refer to
a singular entity unless explicitly so defined, but include the
general class of which a specific example may be used for
illustration. The use of the terms "a" or "an" may therefore mean
any number that is at least one, including "one," "one or more,"
"at least one," and "one or more than one." The term "or" means any
of the alternatives and any combination of the alternatives,
including all of the alternatives, unless the alternatives are
explicitly indicated as mutually exclusive. The phrase "at least
one of" when combined with a list of items, means a single item
from the list or any combination of items in the list. The phrase
does not require all of the listed items unless explicitly so
defined.
[0017] As used herein, the term "computing device" may refer to a
device that includes, but is not limited to a single computer,
host, server, laptop, and/or mobile device.
[0018] As used herein, the term "network device" may refer to any
device that is capable of communicating and transmitting data to
another device across any type of network.
[0019] As used herein, the term "computing system" may refer to a
single electronic computing device or network device that includes,
but is not limited to a single computer, virtual machine, virtual
container, host, server, laptop, and/or mobile device. The term
"computing system may also refer to a plurality of electronic
computing devices and/or network devices working together to
perform the function described as being performed on or by the
computing system.
[0020] As used herein, the term "medium" refers to one or more
non-transitory physical media that together store the contents
described as being stored thereon. Embodiments may include
non-volatile secondary storage, read-only memory (ROM), and/or
random-access memory (RAM).
[0021] As used herein, the term "application" refers to one or more
computing modules, programs, processes, workloads, threads and/or a
set of computing instructions executed by a computing system.
Example embodiments of an application include software modules,
software objects, software instances and/or other types of
executable code.
[0022] Sequences of method steps presented herein are provided as
examples and are not meant to be limiting. Thus, methods may be
performed in an order alternative to that illustrated in the
figures and described herein. To illustrate, a method described as
including steps "A" and "B" may be performed with "A" either
preceding or following "B," unless a specific order is indicated.
Further, "A" and "B" may be performed concurrently.
[0023] Systems and methods described herein enable estimating a
state of a device or operation (e.g., a drilling apparatus or
operation). In particular, the state may be estimated based on data
received from an arbitrary number of measurement devices (e.g.,
sensors, measurement calculation devices, or a combination
thereof). For example, the disclosed systems and methods enable
estimation of a state of a drill or drilling operation based on
measurement data corresponding to less than all of a set of
measurement devices used to construct an estimation model.
Accordingly, the disclosed systems and methods enable state
estimation in cases where one or more of the plurality of
measurement devices fails to provide usable data or in cases where
the drill is equipped with fewer than all of the plurality of
measurement devices. Further, the disclosed systems and methods
enable calculation of a confidence value and/or a diminished
confidence value associated with an estimation of a state. The
diminished confidence value indicates a degree of uncertainty in
the estimation due to lack of data from one or more measurement
devices.
[0024] In operation, the systems and methods transform a first data
structure (e.g., a matrix) into a second data structure in response
to one or more measurement devices of a plurality of measurement
devices failing to provide usable data. The first data structure is
associated with estimating the state of the drill or drilling
operation based on data from the plurality of measurement devices,
and the second data structure is associated with estimating the
state of the drill or drilling operation based on data from a
subset of the plurality of measurement devices that provide usable
data (e.g., less than all of the plurality). The transformation is
based on identities of one or more measurement devices that fail to
provide usable data. In a particular example, the first data
structure includes values indicating counts of instances that
various combinations of sensor values corresponded to various
states. Transforming the data structure includes summing sensor
values for combinations of sensor values that are equivalent when
sensor values corresponding to the one or more measurement devices
are ignored. The systems and methods further determine entropy
(e.g., uncertainty) associated with the summed sensor values.
Entropy is greater when the ignored sensor values are more highly
indicative of different states than when the ignored sensor values
are less indicative of different states.
[0025] The systems and methods use the second data structure to
generate an estimation of the state of the drill or drilling
operation based on the data from less than all of the plurality of
measurement devices. Further, the systems and methods generate a
confidence value and/or a diminished confidence value based on the
entropy. In some implementations, the estimation includes
probabilities associated with a plurality of states. For example,
the estimation may indicate that there is a first probability that
a drill or drilling operation state corresponds to a first state
and a second probability that the drill or drilling operation state
corresponds to a second state. Accordingly, the systems and methods
may be used to estimate a state of a drilling device or operation
without imputing missing measurement data or storing different
models for different combinations of measurement devices
[0026] Referring to FIG. 1A, a diagram illustrating a system 100
for estimating a state of a device (e.g., a drilling apparatus) or
operation is shown. The system 100 includes a computing device 102
and a drilling device 114. The computing device 102 may include a
server computer, a mobile device, or any other type of computing
device. The drilling device 114 may operate in a plurality of
states. While illustrated as distinct, the computing device 102 and
the drilling device 114 may be components of a single apparatus.
For example, the computing device 102 may correspond to a
controller of a drilling apparatus. Components of the computing
device 102 and the drilling device 114 are described below.
[0027] Referring to the computing device 102, the computing device
102 includes a processor 104. While illustrated as a single
component, the processor 104 may correspond to one or more
processor devices. The processor 104 may correspond to a central
processor unit. The computing device 102 further includes a memory
device 106 and a storage device 109. The memory device 106 and the
storage device 109 may include a computer readable storage device,
such as a non-volatile random access memory (RAM), a static RAM, a
dynamic RAM, a hard disk drive, a solid state drive, another type
of storage media, or a combination thereof.
[0028] The computing device 102 further includes an input/output
device, such as a display device 108. The display device 108 may
correspond to a liquid crystal display (LCD), a light emitting
diode (LED) display, or another type of display device or to a
display controller, such as a graphics card. The computing device
102 further includes an audio device 110. The audio device 110 may
correspond to a speaker or to an audio controller, such as a sound
card. The computing device 112 further includes a network interface
112. While illustrated as a single component, the network interface
112 may correspond to one or more interface devices. The network
interface 112 may include a wired interface, a wireless interface,
or a combination thereof. It should be noted that the configuration
of the computing device 102 illustrated in FIG. 1A is provided as
an example. Alternative embodiments may include more or fewer
components. For example, alternative embodiments of the computing
device 102 may not include one or more of the display device 108,
the audio device 110, and the network interface 112.
[0029] Referring to the drilling device 114, the drilling device
114 includes a plurality of N measurement devices. In the
illustrated example, the N measurement devices include a first
measurement device 116, a second measurement device 118, and an Nth
measurement device 120. Each of the plurality of measurement
devices may be configured to monitor one or more conditions
associated with the drilling device 114 and generate raw
measurement data accordingly. While illustrated as three
measurement devices, the plurality of measurement devices may
include 2 or more measurement devices. As used herein a measurement
device may correspond to a sensor or to a measurement calculation
device. Measurement calculation devices include devices that derive
measurements based on data (e.g., from a sensor, a clock, a
counter, etc.). A measurement device may be a distinct module or
may be a component of another device. In embodiments in which the
drilling device 114 corresponds to a drilling device, the plurality
of measurement devices may include a total depth calculation
device, a bit depth calculation device, a torque sensor, a speed
sensor, a flow sensor, a pressure sensor, a weight on bit sensor, a
block position sensor, a hook load sensor, or a combination
thereof.
[0030] The plurality of measurement devices may be configured to
generate a dataset describing one or more conditions of the
drilling device 114, an environment of the drilling device 114, or
a combination thereof. For example, the first measurement device
116 may be configured to generate first raw measurement data 122,
the second measurement device 118 may be configured to generate
second raw measurement data 124, and the Nth measurement device 120
may be configured to generate Nth raw measurement data 126. In some
examples, the plurality of measurement devices may generate the
dataset by periodically recording measurements at sample times
(e.g., every second). The measurements may correspond to a wide
range of values. Accordingly, for each sample time, there may be
relatively many different possible combinations of measurement
values that may be included in the dataset.
[0031] The computing device 102 may be configured to determine a
state of the drilling device 114 (or an operation associated with
the drilling device 114) based on output of the plurality of
measurement devices. For example, the storage device 109 may store
a first data structure 134 usable by the processor 104 to estimate
a state of the drilling device 114 (or the operation) based on data
from the plurality of measurement devices. The first data structure
134 is described further below with respect to FIGS. 2 and 4.
[0032] The computing device 102 may be unable to use a portion of
measurement data associated with the plurality of measurement
devices. In the illustrated example of FIG. 1A, the computing
device 102 receives the first raw measurement data 122 and the
second raw measurement data 124 but does not receive the Nth raw
measurement data 126 (e.g., because of a disruption in
communication between the computing device 102 and the Nth
measurement device 120). In other examples, the Nth measurement
device 120 may fail to generate the Nth raw measurement data 126
(e.g., because of a malfunction of or damage to the Nth measurement
device 120). In other examples, the computing device 102 may
receive the Nth raw measurement data 126 (e.g., receive the entire
dataset from the plurality of measurement devices) but be unable to
use the Nth raw measurement data 126. For example, the computing
device 102 may receive an indication that the Nth raw measurement
data 126 is unreliable. The indication may be input by a user of
the computing device 102, generated automatically by the Nth
measurement device 120 or the drilling device 114, or a combination
thereof. In practice, the computing device 102 may be unable to use
data from any combination of the plurality of measurement devices
for any combination of reasons. For example, the computing device
102 may be unable to use the Nth raw measurement data 126 because
the computing device 102 may fail to receive the Nth raw
measurement data 126. The computing device 102 may further be
unable to use Nth-1 raw measurement data because an Nth-1
measurement device failed. The computing device 102 may further be
unable to use an Nth-2 raw measurement data because of an
indication that the Nth-2 raw measurement data is unreliable.
[0033] In response to determining that a portion of the dataset
received from the plurality of measurement devices is unusable
(e.g., is unreliable or not received), the processor 104 transforms
the first data structure 134 based on identities of one or more
measurement devices corresponding to the unusable portion of the
dataset. In the example of FIG. 1A, the processor 104 transforms
the first data structure 134 into a second data structure 136 in
response to not receiving the Nth raw measurement data 126 from the
Nth measurement device 120. The processor 104 may initiate storage
of the second data structure 136 in the storage device 109. In some
examples, the processor 104 may overwrite the first data structure
134 with the transformed second data structure 136. Generation of
the second data structure 136 is described in more detail with
reference to FIG. 2 below.
[0034] In addition to generating the second data structure 136, the
processor 104 may transform a usable (e.g., received and not
indicated as unreliable) portion of the dataset received from the
plurality of measurement devices. In the example illustrated in
FIG. 1A, the processor 104 transforms the first raw measurement
data 122 and the second raw measurement data 124 into measurement
data 132. The measurement data 132 may include values selected from
a relatively smaller range of values (e.g., as compared to the
values of the dataset). In some examples, values of the measurement
data 132 may be binary, ternary, or a combination thereof. To
illustrate, a binary value may indicate values of on or off, below
or above a threshold value, or the like. A ternary value may
indicate a value of decreasing, constant, or increasing.
Accordingly, for each sample time, there may be relatively fewer
different possible combinations of measurement values that may be
included in the measurement data 132 (e.g., as compared to the
dataset). Raw measurement data from different measurement devices
may be transformed into measurement data according to a common
technique or different techniques. Transformation of data is
described further below with reference to FIG. 3.
[0035] The processor 104 may apply the measurement data 132 to the
second data structure 136 to generate an estimation 138 of the
state of the drilling device 114 (or the operation associated with
the drilling device 114) and a confidence value 139, as described
below with reference to FIG. 2. In examples where the drilling
device 114 corresponds to a drilling device, the state may
correspond to a drilling state, a drilling in cement state, a
drilling while sliding state, a tripping in state, a tripping out
state, an in slips state, a static state, a wash up state, a wash
down state, a reaming in state, a reaming out state, a circulating
state, or the like.
[0036] Based on the estimation 138, the processor 104 may initiate
presentation of one or more messages. The one or more messages may
include a visual message 142, an audio message 144, a network
message 146 or a combination thereof. For example, the processor
104 may initiate presentation of the visual message 142 via the
display device 108. The visual message 142 may include a graphic
and/or a video indicating the estimation 138, indicating the
confidence value 139 and/or a diminished confidence value
associated with the estimation 138, indicating instructions for
responding to the estimation 138, announcing a warning, or a
combination thereof. As another example, the processor 104 may
initiate presentation of the audio message 144 via the audio device
110. The audio message 144 may indicate the estimation 138,
indicate instructions for responding to the estimation 138,
announce a warning, or a combination thereof. As another example,
the processor 104 may initiate transmission of the network message
146 via the network interface 112. The network message 146 may
correspond to an electronic mail message, a social media message, a
short message service message, an automated telephone call, another
type of message, or a combination thereof. The network message 146
may include audio content, visual content, or a combination
thereof. The network message 146 may indicate the estimation 138,
indicate instructions for responding to the estimation 138,
announce a warning, or a combination thereof. In some examples, the
processor 104 may initiate presentation of the one or messages
based further on the confidence value 139 associated with the
estimation 138. For example, the processor 104 may initiate
presentation of the one or more messages based on the estimation
138 in response to the confidence value 139 satisfying a confidence
threshold. Generation of the confidence value is explained further
below with reference to FIG. 2.
[0037] In some implementations, the processor 104 may further
initiate transmission of one or more control signals to the
drilling device 114 based on the estimation 138, the confidence
value 139, or a combination thereof. For example, the processor 104
may initiate transmission of one or more signals to deactivate the
drilling device 114, to activate the drilling device 114, or to
adjust operation of the drilling device 114. To illustrate, the
processor 104 may initiate transmission of one or more signals to
the drilling device 114 to control a drilling speed or a drill
depth associated with the drilling device 114.
[0038] Thus, the system 100 may enable estimation of and reaction
to a state of the drilling device 114 or an operation associated
with the drilling device 114 even in cases where expected
measurement data is not received or is not usable. By transforming
the first data structure 134 into the second data structure 136,
the processor 104 may generate the estimation 138 without imputing
the missing Nth raw measurement data 126. Since the processor 104
may determine the estimation 138 without filling in the missing Nth
raw measurement data 126, the processor 104 may determine a
confidence value and/or a diminished confidence value associated
with the estimation 138 associated with missing the Nth raw
measurement data 126. Accordingly, the computing device 102 may
represent an improvement to techniques for state estimation.
Further, the processor 104 may generate the estimation 138 without
accessing a model designed specifically for cases where the
computing device 102 does not receive the Nth raw measurement data
126. Therefore, the computing device 102 may store fewer models in
the storage device 109 in order to estimate the state of the
drilling device 114 (or the operation associated with the drilling
device 114). Accordingly, demand on the storage device 109 may be
decreased and functioning of the computing device 102 may be
improved.
[0039] Referring to FIG. 1B, a diagram illustrating a specific
example of the system 100 in which the drilling device 114
corresponds to a drilling apparatus 180. As illustrated in FIG. 1B,
the drilling apparatus 180 includes a top drive 184 and a bit 186.
In other examples, the drilling apparatus 180 may include more or
fewer components. Additionally, components of the drilling
apparatus 180 may be arranged in a different manner than is
illustrated in FIG. 1B.
[0040] The top drive 184 is configured to rotate the bit 186 so
that the bit 186 may bore into a material, such as earth. The top
drive 184 and/or the bit 186 may be communicatively coupled to
measurement devices. For example, the top drive 184 may be
communicatively coupled to a rotations per minute (RPM) sensor 184
and a torque sensor 185. The bit 186 may be communicatively coupled
to a weight on bit sensor 187. The RPM sensor 184 is configured to
generate RPM data 192 indicating RPM of the top drive 184 over
time. The RPM sensor 184 is configured to transmit (e.g.,
wirelessly transmit) the RPM data 192 to the computing device 102.
The torque sensor 185 is configured to generate torque data 190
indicating torque force between the top drive 184 and the drill bit
186 over time. The torque sensor 185 is configured to transmit
(e.g., wirelessly transmit) the torque data 190 to the computing
device 102. The weight on bit sensor 187 is configured to generate
weight data 194 indicating a weight force experienced by the bit
186 over time. The weight on bit sensor 187 is configured to
transmit (e.g., wirelessly transmit) the weight data 194 to the
computing device 102.
[0041] The RPM sensor 184, the torque sensor 185, and the weight on
bit sensor 187 may correspond to the first measurement device 116,
the second measurement device 118, and the Nth measurement device
120 of FIG. 1A. Accordingly, the first data structure 134 may be
usable by the processor 104 to estimate a state of the drilling
apparatus 180 (or a drilling operation associated with the drilling
apparatus 180) based on data from the RPM sensor 184, the torque
sensor 185, and the weight on bit sensor 187. As explained above,
during operation, the computing device 102 may not receive usable
data from any combination of the RPM sensor 184, the torque sensor
185, and the weight on bit sensor 187. In the illustrated example
of FIG. 1B, the computing device 102 does not receive the weight
data 194. Accordingly, the processor 104 generates the measurement
data 132 based on the torque data 190 and the RPM data 192, as
explained above with reference to FIG. 1A. Further, the processor
104 transforms the first data structure 134 into the second data
structure 136 based on an identity of the weight on bit sensor 187,
as explained above with reference to FIG. 1A. The second data
structure 136 is associated with estimating the state of the
drilling apparatus 180 (or the operation associated with the
drilling apparatus 180) based on data from the RPM sensor 184 and
the torque sensor 185. The processor 104 may proceed to generate
and react to the estimation 138 and the confidence value 139, as
explained above with reference to FIG. 1A. Thus, FIG. 1B
illustrates a system for estimating a state of a drilling apparatus
or an operation associated with the drilling apparatus.
[0042] Referring to FIG. 2, a diagram 200 illustrating
transformation of a data structure is shown. The diagram 200
depicts a first data structure 202 and a second data structure 204.
The first data structure 202 may correspond to the first data
structure 134 and the second data structure 204 may correspond to
the second data structure 136. Generation of data structures (e.g.,
the first data structure 134, the first data structure 202, etc.)
from historical data is described further below with reference to
FIG. 4. The first data structure 202 may correspond to a p.times.n
matrix Q, where p is a number of distinct measurement data value
combinations that occur in historical data used to generate the
first data structure 202 and n is a number of distinct states of a
device (e.g., the drilling device 114) or an operation.
Accordingly, each row i of the first data structure 202 may be
associated with a different vector of measurement data values and
each column j may be associated with a different state. Each value
Q.sub.ij of the first data structure 202 may correspond to a number
of instances in the historical data that the vector of measurement
data values associated with the row i is labeled as corresponding
to the state associated with the column j.
[0043] Each vector of measurement data values associated with each
row i may be formatted [i.sub.s1, . . . i.sub.sn], where i.sub.s1
is a value in the vector associated with row i that corresponds to
a first measurement device ("s.sub.1"). In response to determining
that data from one or more of the measurement devices is not usable
(e.g., not received or unreliable), a processor (e.g., the
processor 104) transforms the first data structure 202, based on
identities of the one or more measurement devices. In the
illustrated example, the processor transforms the first data
structure 202 by collapsing rows (e.g., summing rows) that
correspond to vectors of measurement data values that match when
measurement data values corresponding to the one or more
measurement devices are ignored. In the illustrated example, the
processor receives measurement data 203 indicating a vector [1, -1,
?] indicating that measurement device s.sub.3 corresponds to
unusable data. Accordingly, the processor determines whether any
rows of the first data structure 202 are collapsible when i.sub.s3
is ignored. As illustrated in FIG. 2, a first vector corresponding
to a first row of the first data structure 202 has values of [0, 1,
0]. A second vector corresponding to a second row of the first data
structure 202 has values of [0, 1, 1]. When the values
corresponding to measurement device s.sub.3 are ignored, both the
first vector and the second vector have values of [0, 1].
Accordingly, the processor sums the first row and the second row to
transform the first data structure 202 into the second data
structure 204.
[0044] Once the second data structure 204 is generated, the
processor may estimate a state of a device or an operation based on
the measurement data 203 and the second data structure 204. In some
implementations, the processor may use a k-nearest neighbors
algorithm to estimate the state. As part of the k-nearest neighbors
algorithm, the processor may identify a set R of vectors, where R
is a subset of the set of measurement data value vectors
corresponding to rows of the second data structure 204 and each
vector in R is within a threshold distance (e.g., Euclidian
distance) from the vector indicated by the measurement data 203
(e.g., [1, -1]). In some examples of the k-nearest neighbors
algorithm, the processor may recalculate R using an increased
threshold distance in response to a number of vectors included in R
failing to satisfy a threshold number of vectors.
[0045] In an illustrative example, the threshold distance may be
equal to 2. The processor may determine that a Euclidian distance
between the vector indicated by the measurement data 203 and a
vector corresponding to a first row of the second data structure
204 is equal to {square root over ((1-0).sup.2+(-1-1).sup.2)}=
{square root over (5)}>2. Accordingly, the processor may
determine that the vector corresponding to the first row of the
second data structure 204 is not to be included in R. The processor
may further determine that a Euclidian distance between the vector
indicated by the measurement data 203 and a vector corresponding to
a second row of the second data structure 204 is equal to {square
root over ((1-1).sup.2+(-1-0).sup.2)}=1<2. Accordingly, the
processor may determine that the vector corresponding to the second
row of the second data structure 204 is to be included in R. The
processor may further determine that a Euclidian distance between
the vector indicated by the measurement data 203 and a vector
corresponding to a third row of the second data structure 204 is
equal to {square root over ((1-1).sup.2+(-1-1).sup.2)}=2=2.
Accordingly, the processor may determine that the vector
corresponding to the second row of the second data structure 204 is
to be included in R.
[0046] Once the processor has calculated R, the processor may
identify a collection of rows of the second data structure 204 that
correspond to the vectors in R. For each state, the processor may
calculate a probability (e.g., a confidence value) of the device
(e.g., the drilling device 114) or operation being in the state by
summing a column that corresponds to the state in the collection of
rows and dividing by a sum of all of the values included in the
collection of rows. The processor may generate an estimation (e.g.,
the estimation 138) based on which state is determined to have a
highest probability.
[0047] To illustrate, the processor may identify the second row and
the third row of the second data structure 204 as the collection of
rows corresponding to the vectors in R. The processor may calculate
a probability that the state of the device or operation is a first
state by dividing a sum of a first column of the collection by a
sum of a total of the collection. Accordingly, the probability that
the state of the device or operation is the first state may be
equal to
2 + 1 2 + 1 + 3 + 2 + 4 + 3 = 3 15 = .2 . ##EQU00001##
The processor may calculate a probability that the state of the
device or operation is a second state by dividing a sum of a second
column of the collection by the sum of a total of the collection.
Accordingly, the probability that the state of the device or
operation is the second state may be equal to
3 + 2 2 + 1 + 3 + 2 + 4 + 3 = 5 15 .apprxeq. .33 . ##EQU00002##
The processor may calculate a probability that the state of the
device or operation is a third state by dividing a sum of a third
column of the collection by a sum of a total of the collection.
Accordingly, the probability that the state of the device or
operation is the third state may be equal to
4 + 3 2 + 1 + 3 + 2 + 4 + 3 = 7 15 .apprxeq. .47 . ##EQU00003##
Since the third state has a highest probability (e.g., because
0.47>0.33>0.2), the processor may generate a estimation
indicating that the device is in the third state. In some examples,
the processor further determines a degree of diminished confidence
in response to determining that R includes a vector that is a
result of collapsing rows in the first data structure 202. The
processor may determine the degree of diminished confidence based
on entropy values associated with collapsing the rows of the first
data structure 202. Entropy may increase relatively more when the
rows that are collapsed to generate the vector strongly indicate
different states (e.g., because data from an associated ignored
measurement device is highly linked to distinguishing between
states). Entropy may increase relatively less when the rows that
are collapsed do not strongly indicate different states (e.g.,
because data from the associated ignored measurement device is not
highly linked to distinguishing between states). The probability
associated with a state (e.g., the confidence value) may be
discounted by the diminished confidence value. In some
implementations, the processor is configured to generate an error
message (e.g., indicating an unidentifiable state) in response to
determining that no confidence value satisfies a confidence
threshold.
[0048] Therefore, FIG. 2 illustrates how a first data structure may
be transformed into a second data structure based on available
measurement data and how the second data structure may be used to
estimate a state of a device or operation. The process described
with reference to FIG. 2 may enable a system to estimate the state
of the device or operation even when data associated with arbitrary
combinations of measurement devices is unreliable.
[0049] Referring to FIG. 3, a diagram 300 illustrating processes
for transforming raw measurement data is shown. The diagram 300
depicts a data conversion process 304 that may be performed by a
processor, such as the processor 104 of FIG. 1. FIG. 3 depicts the
data conversion process 304 converting raw measurement data 302
into measurement data 318. The raw measurement data 302 may
correspond to the first raw measurement data 122 and the second raw
measurement data 124. The measurement data 318 may correspond to
the measurement data 132. As explained above, raw measurement data,
such as the raw measurement data 302, may correspond to a series of
periodically recorded measurements selected from a wide range of
possible values.
[0050] The data conversion process 304 may include a plurality of
sub-processes. In the illustrated example, the sub-processes
include an outlier filter process 308, a smoothing process 310, a
normalization process 312, a change point detection process 314,
and a lag filter process 316. Alternative examples of the data
conversion process 304 may include different combinations of
sub-processes. The sub-processes are described in more detail
below.
[0051] The outlier filter process 308 may receive the raw
measurement data 302 (e.g., the first raw measurement data 122 and
the second raw measurement data 124). The processor executing the
outlier filter process 308 may generate processed measurement data
by removing outlier values from the raw measurement data 308. In
some examples, the outlier filter process 308 utilizes a median
absolute deviation algorithm to remove outliers from the raw
measurement data 124.
[0052] The smoothing process 310 may receive the processed data
from the outlier filter process 308 and generate smoothed data by
removing high frequency noise from the processed data. In some
examples, the smoothing process 310 utilizes a Gaussian kernel
based smoothing algorithm. The Gaussian kernel may, for example,
be
K ( x , x ' ) = exp ( - || x - x ' || 2 2 .sigma. 2 ) .
##EQU00004##
[0053] The normalization process 312 may receive the smoothed data
from the outlier filter process 308 and generate normalized data
based on the smoothed data. In some examples, the normalization
process 312 may utilize a max-min normalization algorithm so that
the normalized data has a range of (0, 1). The max-min
normalization algorithm may be based on the following equation:
Z i = x i - min ( x ) max ( x ) - min ( x ) , ##EQU00005##
where x are values in the smoothed data and z are values in the
normalized data.
[0054] The change point detection process 314 may receive the
normalized data from the normalization process 312 and identify
change points in the normalized data. In some implementations, the
change point detection process 314 may utilize a median shift
detector algorithm to detect change points in the normalized data.
Once change points are identified, the change point detection
process 314 generates the measurement data 318 based on the change
points. The measurement data 318 may correspond to a lower
dimensional representation of the raw measurement data 302. For
example, the measurement data 318 may use a ternary value to denote
whether a trend between two consecutive change points is
increasing, decreasing or staying constant rather than including
each data point included in the raw measurement data 302. In
addition or in the alternative, the measurement data 318 may
include binary values indicating whether a particular component is
on or off. Since values of the measurement data 318 are drawn from
fewer dimensions (e.g., 3 or 2) than the raw measurement data 302,
the measurement data 318 may have fewer possible combinations of
values as compared to the raw measurement data 302. Thus, FIG. 3
illustrates a process that may be used to generate a simplified
version of raw measurement data.
[0055] Referring to FIG. 4, a diagram 400 illustrating processes
for generating a data structure based on raw historical data is
shown. The process illustrated in FIG. 4 may be used to generate a
first data structure 416 based on historical raw measurement data
402. The first data structure 416 may correspond to the first data
structure 134 of FIG. 1 or to the first data structure 202 of FIG.
2. For example, the processor 104 may perform the process of FIG. 4
to generate the first data structure 134. Alternately another
device may perform the process of FIG. 4 to generate the first data
structure 134 and then transmit the first data structure 134 to the
computing device 102.
[0056] The process of FIG. 4 includes performing the data
conversion process 304, as described with reference to FIG. 3, on
the historical raw measurement data 402 to generate historical
measurement data 406. Accordingly, vectors of the historical
measurement data 406 may have fewer possible combinations of values
as compared to the historical raw measurement data 402.
[0057] The process of FIG. 4 further includes applying a label
process 408 to the historical measurement data 406 to generate
labeled historical measurement data 412. The label process 408 may
assign a label indicating a state of a device (e.g., the drilling
device 114) or operation to each vector of measurement data values
in the historical measurement data 406 using a deterministic model.
In some examples, the deterministic model may correspond to a
decision tree.
[0058] The process of FIG. 4 further includes building the first
data structure 416 based on the labeled historical measurement data
using a training process 414. For example, the training process 414
may construct a p.times.n matrix Q corresponding to the first data
structure 416. P may be a number of different vectors of
measurement data values that occur in the labeled historical
measurement data 412 and n may be a number of different states of
the device (e.g., the drilling device 114) or operation. Each value
Q.sub.ij of Q may correspond to a number of instances that a
measurement data vector corresponding to row i is labeled as the
state corresponding to column j. Since the matrix Q includes rows
corresponding to measurement data vectors that occur in the labeled
historical data 412, the matrix Q may be smaller and less complex
than if a row for every possible measurement data vector were
included in Q. For example, because a first measurement data value
associated with total depth never indicates a decrease at the same
time a second measurement data value associated with bit depth
indicates an increase in the labeled historical data 412, the Q
matrix may not include rows associated with measurement data
vectors for that combination of values. Additionally, since the
labeled historical measurement data 412 includes fewer possible
combinations of measurement data values than the historical raw
measurement data 402, the size of the Q matrix may be further
reduced as compared to a matrix generated based on the historical
raw measurement data 402.
[0059] Thus, the process illustrated in FIG. 4 may be used to
generate a data structure usable to estimate a state of a device or
operation. The data structure may be smaller as compared to other
data structures. Accordingly, the data structure may consume less
storage space in a memory device. Further a processor using the
data structure to estimate a state of the device may perform fewer
calculations due to a smaller size of the data structure.
[0060] Referring to FIG. 5, a flowchart illustrating a method 500
of estimating a state of a device or operation is shown. The method
500 may be performed by the processor 104 of FIG. 1. The method 500
includes storing a first data structure associated with estimating
a state of a drilling apparatus or operation based on data from a
plurality of measurement devices, at 502. For example, the
processor 104 may store the first data structure 134 in the memory
device 106 and/or the storage device 109.
[0061] The method 500 further includes receiving measurement data
associated with a subset of the plurality of measurement devices
associated with the drilling apparatus or operation, at 504. For
example, the processor 104 may receive the measurement data 132 (or
the first raw measurement data 122 and the second raw measurement
data 124) associated with the first measurement device 116 and the
second measurement device 118 (e.g., a subset of the first
measurement device 116-the Nth measurement device 120).
[0062] In response to determining that the subset does not include
one or more measurement devices of the plurality of measurement
devices, the method 500 includes transforming the first data
structure into a second data structure based on identities of the
one or more measurement devices, at 506. The second data structure
is associated with estimating the state of the drilling apparatus
or operation based on data from the subset. For example, the subset
of measurement devices from which the processor 104 receives data
may not include the Nth measurement device 120 (e.g., because the
Nth raw measurement data 126 was not received or because the Nth
raw measurement data 126 was indicated as unreliable). In response
to determining that the subset does not include the Nth measurement
device 120, the processor 104 may transform the first data
structure 134 into the second data structure 136, as described with
reference to FIG. 2. The first data structure 134 may be associated
with estimating a state of the drilling device 114 based on data
from each of the first measurement device 116, the second
measurement device 118, and the Nth measurement device 120 (e.g.,
the plurality of measurement devices). The second data structure
136 may be associated with estimating the state of the drilling
device 114 based on the first measurement device 116 and the second
measurement device 118 (e.g., the subset of measurement
devices).
[0063] The method 500 further includes, based on the measurement
data and the second data structure, generating an estimation of the
state of the drilling apparatus or operation and a confidence value
associated with the estimation, at 508. For example, the processor
104 may generate the estimation 138 based on the measurement data
132 and the second data structure 136. Further, the processor 104
may generate the confidence value 139 associated with the
estimation 138. Accordingly, the method 500 may enable estimation
of a state of a device when measurement data associated with
estimating the state is not received or is unreliable. The method
500 may consume fewer storage resources as compared to techniques
involving storing unique estimation models for each combination of
received measurement data. Further, the method 500 may enable
determination of confidence values, as compared to techniques that
rely on imputation of lost measurement data.
[0064] Referring now to FIG. 6, a block diagram illustrates a
computing device 600 that may be used for implementing the
techniques described herein in accordance with one or more
embodiments. For example, the computing device 600 illustrated in
FIG. 6 could represent a client device or a physical server device.
In some implementations, the computing device 600 corresponds to
the computing device 102 of FIG. 1. As shown in FIG. 6, the
computing device 600 can include one or more input/output devices,
such as a network communication unit 608 that could include a wired
communication component and/or a wireless communications component,
which can be coupled to processing element 602. The network
communication unit 608 can utilized any of a variety of
standardized network protocols, such as Ethernet, TCP/IP, to name a
few of many protocols, to effect communications between devices and
comprise one or more transceiver(s) that utilize the Ethernet,
power line communication (PLC), WiFi, and/or other communication
methods.
[0065] The computing system 600 includes a processing element 602
that contains one or more hardware processors, where each hardware
processor may have a single or multiple processor cores. In one
embodiment, the processing element 602 may include at least one
shared cache that store data (e.g., computing instructions) that
are utilized by one or more other components of processing element
602. For example, the shared cache may be locally cached data
stored in a memory for faster access by components of the
processing elements 602. In one or more embodiments, the shared
cache may include one or more mid-level caches, such as level 2
(L2), level 3 (L3), level 4 (L4), or other levels of cache, a last
level cache (LLC), or combinations thereof. Examples of processors
include, but are not limited to a central processing unit (CPU) a
microprocessor. Although not illustrated in FIG. 6, the processing
element 602 may also include one or more other types of hardware
processing components, such as graphics processing units (GPU),
application specific integrated circuits (ASICs),
field-programmable gate arrays (FPGAs), and/or digital signal
processors (DSPs).
[0066] FIG. 6 illustrates that memory 604 may be operatively
coupled to processing element 602. Memory 604 may be a
non-transitory medium configured to store various types of data.
For example, memory 604 may include one or more memory devices that
comprise a non-volatile storage device and/or volatile memory.
Volatile memory, such as random access memory (RAM), can be any
suitable non-permanent storage device. The non-volatile storage
devices can include one or more disk drives, optical drives,
solid-state drives (SSDs), tap drives, flash memory, read only
memory (ROM), and/or any other type memory designed to maintain
data for a duration time after a power loss or shut down operation.
In certain instances, the non-volatile storage device may be used
to store overflow data if allocated RAM is not large enough to hold
all working data. The non-volatile storage device may also be used
to store programs that are loaded into the RAM when such programs
are selected for execution. In the illustrated example, the memory
604 stores state estimation instructions 612. The state estimation
instructions 612 may be executable by the processor 602 to perform
any of the operations of methods described with respect to FIGS.
1-5.
[0067] Persons of ordinary skill in the art are aware that software
programs may be developed, encoded, and compiled in a variety
computing languages for a variety software platforms and/or
operating systems and subsequently loaded and executed by
processing element 602. In one embodiment, the compiling process of
the software program may transform program code written in a
programming language to another computer language such that the
processing element 602 is able to execute the programming code. For
example, the compiling process of the software program may generate
an executable program that provides encoded instructions (e.g.,
machine code instructions) for processor 602 to accomplish
specific, non-generic, particular computing functions.
[0068] After the compiling process, the encoded instructions may
then be loaded as computer executable instructions or process steps
to processing element 602 from storage (e.g., memory 604) and/or
embedded within the processing element 602 (e.g., cache).
Processing element 602 can execute the stored instructions or
process steps in order to perform instructions or process steps to
transform the computing device into a non-generic, particular,
specially programmed machine or apparatus. Stored data, e.g., data
stored by a storage device, can be accessed by processing element
602 during the execution of computer executable instructions or
process steps to instruct one or more components within the
computing device 600.
[0069] A user interface 610 can include a display, positional input
device (such as a mouse, touchpad, touchscreen, or the like),
keyboard, or other forms of user input and output devices. The user
interface 610 can be coupled to processor element 602. Other output
devices that permit a user to program or otherwise use the
computing device can be provided in addition to or as an
alternative to network communication unit 608. When the output
device is or includes a display, the display can be implemented in
various ways, including by a liquid crystal display (LCD) or a
cathode-ray tube (CRT) or light emitting diode (LED) display, such
as an OLED display. Persons of ordinary skill in the art are aware
that the computing device 600 may comprise other components well
known in the art, such as measurement devices, powers sources,
and/or analog-to-digital converters, not explicitly shown in FIG.
6. For ease of discussion, FIG. 6 explanation of these other
components well known in the art.
[0070] At least one embodiment is disclosed and variations,
combinations, and/or modifications of the embodiment(s) and/or
features of the embodiment(s) made by a person having ordinary
skill in the art are within the scope of the disclosure.
Alternative embodiments that result from combining, integrating,
and/or omitting features of the embodiment(s) are also within the
scope of the disclosure. Where numerical ranges or limitations are
expressly stated, such express ranges or limitations may be
understood to include iterative ranges or limitations of like
magnitude falling within the expressly stated ranges or limitations
(e.g., from about 1 to about 10 includes, 2, 3, 4, etc.; greater
than 0.10 includes 0.11, 0.12, 0.13, etc.).
[0071] Use of the term "optionally" with respect to any element of
a claim means that the element is required, or alternatively, the
element is not required, both alternatives being within the scope
of the claim. Use of broader terms such as comprises, includes, and
having may be understood to provide support for narrower terms such
as consisting of, consisting essentially of, and comprised
substantially of. Accordingly, the scope of protection is not
limited by the description set out above but is defined by the
claims that follow, that scope including all equivalents of the
subject matter of the claims. Each and every claim is incorporated
as further disclosure into the specification and the claims are
embodiment(s) of the present disclosure.
[0072] It is to be understood that the above description is
intended to be illustrative, and not restrictive. For example, the
above-described embodiments may be used in combination with each
other. Many other embodiments will be apparent to those of skill in
the art upon reviewing the above description. The scope of the
invention therefore should be determined with reference to the
appended claims, along with the full scope of equivalents to which
such claims are entitled. It should be noted that the discussion of
any reference is not an admission that it is prior art to the
present invention, especially any reference that may have a
publication date after the priority date of this application.
* * * * *