U.S. patent application number 16/926200 was filed with the patent office on 2022-01-13 for channel detection system and method.
This patent application is currently assigned to HALLIBURTON ENERGY SERVICES, INC. The applicant listed for this patent is HALLIBURTON ENERGY SERVICES, INC. Invention is credited to Amit PADHI, Sonali Pattnaik.
Application Number | 20220010670 16/926200 |
Document ID | / |
Family ID | |
Filed Date | 2022-01-13 |
United States Patent
Application |
20220010670 |
Kind Code |
A1 |
PADHI; Amit ; et
al. |
January 13, 2022 |
CHANNEL DETECTION SYSTEM AND METHOD
Abstract
Systems, methods, and computer-readable media are provided for
detecting a channel behind casing and generating an image that
represents the channel. An example method can include receiving
data samples associated with at least one casing, each data sample
representing channel information behind a representative casing,
training a machine learning model using the data samples to
generate a mapping between waveform information in each of the data
samples and the channel information behind the representative
casing, receiving acoustic data from a tool, the acoustic data
representing a particular casing, and using the machine learning
model to analyze the acoustic data from the tool and determine one
of a presence and an absence of a channel behind the particular
casing at a plurality of depths.
Inventors: |
PADHI; Amit; (Cypress,
TX) ; Pattnaik; Sonali; (Houston, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
HALLIBURTON ENERGY SERVICES, INC |
Houston |
TX |
US |
|
|
Assignee: |
HALLIBURTON ENERGY SERVICES,
INC
Houston
TX
|
Appl. No.: |
16/926200 |
Filed: |
July 10, 2020 |
International
Class: |
E21B 47/002 20060101
E21B047/002; G01S 15/89 20060101 G01S015/89 |
Claims
1. A method comprising: receiving, by at least one processor, data
samples associated with at least one casing, each data sample
representing channel information behind a representative casing;
training, by the at least one processor, a machine learning model
using the data samples to generate a mapping between integral
amplitudes calculated from waveform information in each of the data
samples and the channel information behind the representative
casing, wherein at least one integral amplitude is calculated by
rectifying the waveform information, determining a peaks-based
envelope of the rectified waveform information, using the
peaks-based envelope to modulate a sinusoid signal, and integrating
a period of the sinusoid signal to obtain the integral amplitude;
receiving, by the at least one processor, acoustic data from a
tool, the acoustic data representing a particular casing;
calculating a plurality of integral amplitudes based on the
acoustic data from the tool; and using, by the at least one
processor, the machine learning model to analyze the calculated
plurality of integral amplitudes from the acoustic data and
determine one of a presence and an absence of a channel behind the
particular casing at a plurality of depths.
2. The method of claim 1, further comprising generating an
azimuthal cement bond depth channel image that represents the
presence and the absence of the channel behind the particular
casing at the plurality of depths.
3. The method of claim 2, wherein the channel image is a binary
two-dimensional image.
4. The method of claim 3, wherein the binary two-dimensional image
represents a size and an azimuthal location of the channel behind
the particular casing at the plurality of depths.
5. The method of claim 1, wherein the tool comprises at least one
array of receivers azimuthally arranged along a circumference of
the tool.
6. The method of claim 5, wherein the tool comprises a monopole
transmitter that transmits waves having a frequency less than
ultrasound frequencies.
7. The method of claim 5, wherein the tool comprises a monopole
transmitter, and wherein a ring of receivers is one of three feet
and five feet from the monopole transmitter.
8. The method of claim 1, wherein the machine learning model is a
regression model based on random forest.
9. A system comprising: an acoustic tool comprising at least one
sensor; at least one processor; and at least one computer-readable
storage medium having stored therein instructions, which when
executed by the at least one processor cause the system to: receive
data samples associated with at least one casing, each data sample
representing channel information behind a representative casing;
train a machine learning model using the data samples to generate a
mapping between integral amplitudes calculated from waveform
information in each of the data samples and the channel information
behind the representative casing, wherein at least one integral
amplitude is calculated by rectifying the waveform information,
determining a peaks-based envelope of the rectified waveform
information, using the peaks-based envelope to modulate a sinusoid
signal, and integrating a period of the sinusoid signal to obtain
the integral amplitude; receive acoustic data from the tool, the
acoustic data representing a particular casing; calculate a
plurality of integral amplitudes based on the acoustic data from
the tool; and use the machine learning model to analyze the
calculated plurality of integral amplitudes from the acoustic data
from the tool and determine one of a presence and an absence of a
channel behind the particular casing at a plurality of depths.
10. The system of claim 9, the at least one processor further to
generate an azimuthal cement bond depth channel image that
represents the presence and the absence of the channel behind the
particular casing at the plurality of depths.
11. The system of claim 10, wherein the channel image is a binary
two-dimensional image.
12. The system of claim 11, wherein the binary two-dimensional
image represents a size and an azimuthal location of the channel
behind the particular casing at the plurality of depths.
13. The system of claim 9, wherein the acoustic tool comprises at
least one array of receivers azimuthally arranged along a
circumference of the tool.
14. The system of claim 13, wherein the acoustic tool comprises a
monopole transmitter that transmits waves having a frequency less
than ultrasound frequencies.
15. The system of claim 13, wherein the acoustic tool comprises a
monopole transmitter, and wherein a ring of receivers is one of
three feet and five feet from the monopole transmitter.
16. The system of claim 9, wherein the machine learning model is a
regression model based on random forest.
17. A non-transitory computer-readable medium having instructions
stored thereon that, when executed by at least one processor, cause
the at least one processor to perform operations comprising:
receiving data samples associated with at least one casing, each
data sample representing channel information behind a
representative casing; training a machine learning model using the
data samples to generate a mapping between integral amplitudes
calculated from waveform information in each of the data samples
and the channel information behind the representative casing,
wherein at least one integral amplitude is calculated by rectifying
the waveform information, determining a peaks-based envelope of the
rectified waveform information, using the peaks-based envelope to
modulate a sinusoid signal, and integrating a period of the
sinusoid signal to obtain the integral amplitude; receiving
acoustic data from a tool, the acoustic data representing a
particular casing; calculating a plurality of integral amplitudes
based on the acoustic data from the tool; and using the machine
learning model to analyze the calculated plurality of integral
amplitudes from the acoustic data and determine one of a presence
and an absence of a channel behind the particular casing at a
plurality of depths.
18. The non-transitory computer-readable medium of claim 17, the
operations further comprising generating an azimuthal cement bond
depth channel image that represents the presence and the absence of
the channel behind the particular casing at the plurality of
depths.
19. The non-transitory computer-readable medium of claim 18,
wherein the channel image is a binary two-dimensional image.
20. The non-transitory computer-readable medium of claim 19,
wherein the binary two-dimensional image represents a size and an
azimuthal location of the channel behind the particular casing at
the plurality of depths.
Description
TECHNICAL FIELD
[0001] The present technology pertains to detecting channels behind
casing, and more specifically generating images that represent the
channels.
BACKGROUND
[0002] Interzonal channeling can cause problems such as water
production and depletion of gas drive mechanism, among other
problems. It is important to determine the presence of channels and
their locations to alleviate their impact on hydrocarbon
production. Factors such as mud in a wellbore may affect existing
solutions. The conventional solutions have not been accurate or
robust enough to rely upon resulting in problems that cannot be
adequately addressed.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] In order to describe the manner in which the above-recited
and other advantages and features of the disclosure can be
obtained, a more particular description of the principles briefly
described above will be rendered by reference to specific
embodiments thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only exemplary embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0004] FIG. 1A is a schematic diagram of an example logging while
drilling (LWD) wellbore operating environment, in accordance with
some examples;
[0005] FIG. 1B is a schematic diagram of an example downhole
environment with a conveyance, in accordance with some
examples;
[0006] FIG. 2 is a block diagram of an example channel detection
system which may be implemented to detect channels behind casing
and generate images, in accordance with some examples;
[0007] FIG. 3 is a block diagram of an example acoustic tool
associated with the system, in accordance with some examples;
[0008] FIG. 4 is a graph of example waveform derived attribute data
used to train a machine learning model, in accordance with some
examples;
[0009] FIG. 5 is another graph of example waveform derived
attribute data used to train the machine learning model, in
accordance with some examples;
[0010] FIG. 6 is an image representing an actual channel size and
location, in accordance with some examples;
[0011] FIG. 7 is another image representing a predicted channel
size and location, in accordance with some examples;
[0012] FIG. 8 is a flowchart of an example method for detecting a
channel behind casing and generating an image that represents the
channel, in accordance with some examples;
[0013] FIG. 9 is another flowchart of an example method for
determining an integral amplitude attribute used to detect the
channel, in accordance with some examples;
[0014] FIG. 10 is a flowchart of an example method for detecting a
channel behind casing and generating an image that represents the
channel, in accordance with some examples;
[0015] FIG. 11 is a schematic diagram of an example computing
device architecture, in accordance with some examples.
DETAILED DESCRIPTION
[0016] Various embodiments of the disclosure are discussed in
detail below. While specific implementations are discussed, it
should be understood that this is done for illustration purposes
only. A person skilled in the relevant art will recognize that
other components and configurations may be used without parting
from the spirit and scope of the disclosure.
[0017] Additional features and advantages of the disclosure will be
set forth in the description which follows, and in part will be
obvious from the description, or can be learned by practice of the
herein disclosed principles. The features and advantages of the
disclosure can be realized and obtained by means of the instruments
and combinations particularly pointed out in the appended claims.
These and other features of the disclosure will become more fully
apparent from the following description and appended claims, or can
be learned by the practice of the principles set forth herein.
[0018] It will be appreciated that for simplicity and clarity of
illustration, where appropriate, reference numerals have been
repeated among the different figures to indicate corresponding or
analogous elements. In addition, numerous specific details are set
forth in order to provide a thorough understanding of the
embodiments described herein. However, it will be understood by
those of ordinary skill in the art that the embodiments described
herein can be practiced without these specific details. In other
instances, methods, procedures and components have not been
described in detail so as not to obscure the related relevant
feature being described. The drawings are not necessarily to scale
and the proportions of certain parts may be exaggerated to better
illustrate details and features. The description is not to be
considered as limiting the scope of the embodiments described
herein.
[0019] Disclosed are systems, methods, and computer-readable
storage media for detecting channels behind casing, and generating
images that represent the channels. The channels may be detected
using a monopole acoustic measurement tool having one or more
azimuthally arranged receiver rings along the circumference of the
tool. The receivers may collect waveforms and determine integral
amplitudes based on the waveform data. The data may be used to
determine azimuthal location and a size of a channel in a casing by
using a machine learning model at a number of depths of interest. A
two dimensional image or map can be generated that represents the
location and size of the channel in the casing at the number of
depths of interest.
[0020] According to at least one aspect, an example method for
detecting channels behind casing, and generating images that
represent the channels is provided. The method can include
receiving, by at least one processor, data samples associated with
at least one casing, each data sample representing channel
information behind a representative casing, training, by the at
least one processor, a machine learning model using the data
samples to generate a mapping between waveform information in each
of the data samples and the channel information behind the
representative casing, receiving, by the at least one processor,
acoustic data from a tool, the acoustic data representing a
particular casing, and using, by the at least one processor, the
machine learning model to analyze the acoustic data from the tool
and determine one of a presence and an absence of a channel behind
the particular casing at a plurality of depths.
[0021] According to at least one aspect, an example system for
detecting channels behind casing, and generating images that
represent the channels is provided. The system can include one or
more processors and at least one computer-readable storage medium
having stored therein instructions which, when executed by the one
or more processors, cause the system to receive data samples
associated with a at least one casing, each data sample
representing channel information behind a representative casing,
train a machine learning model using the data samples to generate a
mapping between waveform information in each of the data samples
and the channel information behind the representative casing,
receive acoustic data from an acoustic tool, the acoustic data
representing a particular casing, and use the machine learning
model to analyze the acoustic data from the tool and determine one
of a presence and an absence of a channel behind the particular
casing at a plurality of depths.
[0022] According to at least one aspect, an example non-transitory
computer-readable storage medium for detecting channels behind
casing, and generating images that represent the channels is
provided. The non-transitory computer-readable storage medium can
include instructions which, when executed by one or more
processors, cause the one or more processors to receive data
samples associated with a at least one casing, each data sample
representing channel information behind a representative casing,
train a machine learning model using the data samples to generate a
mapping between waveform information in each of the data samples
and the channel information behind the representative casing,
receive acoustic data from a tool, the acoustic data representing a
particular casing, and use the machine learning model to analyze
the acoustic data from the tool and determine one of a presence and
an absence of a channel behind the particular casing at a plurality
of depths.
[0023] In some aspects, the systems, methods, and non-transitory
computer-readable storage media described above can include
generating an azimuthal cement bond depth channel image that
represents the presence and the absence of the channel behind the
particular casing at the plurality of depths. The channel image may
be a binary two-dimensional image. In addition, the binary
two-dimensional image can represent a size and an azimuthal
location of the channel behind the particular casing at the
plurality of depths. The machine learning model can be based on
random forest, among other algorithms.
[0024] Additionally, the tool can be at least one array of
receivers azimuthally arranged along a circumference of the tool.
The array of receivers can determine an integral amplitude for
waveform data obtained by the at least one array of receivers. In
one example, the tool may have eight arrays of receivers that are
azimuthally arranged. Each array may have thirteen receivers. At an
offset of three feet from the monopole source, there may be a ring
of eight receivers. Similarly, at an offset of five feet from
monopole source there may be a ring of receivers.
[0025] As follows, the disclosure will provide a more detailed
description of the systems, methods, computer-readable media and
techniques herein for detecting channels behind casing, and
generating images that represent the channels. The disclosure will
begin with a description of example systems and environments, as
shown in FIGS. 1A-7. A description of example methods and
technologies for detecting channels behind casing, and generating
images that represent the channels, as shown in FIGS. 8, 9, and 10
will then follow. The disclosure concludes with a description of an
example computing system architecture, as shown in FIG. 11, which
can be implemented for performing computing operations and
functions disclosed herein. These variations shall be described
herein as the various embodiments are set forth.
[0026] The disclosure now turns to FIG. 1A, which illustrates a
schematic view of a logging while drilling (LWD) wellbore operating
environment 100 in accordance with some examples of the present
disclosure. As depicted in FIG. 1A, a drilling platform 102 can be
equipped with a derrick 104 that supports a hoist 106 for raising
and lowering a drill string 108. The hoist 106 suspends a top drive
110 suitable for rotating and lowering the drill string 108 through
a well head 112. A drill bit 114 can be connected to the lower end
of the drill string 108. As the drill bit 114 rotates, the drill
bit 114 creates a wellbore 116 that passes through various
formations 118. A pump 120 circulates drilling fluid through a
supply pipe 122 to top drive 110, down through the interior of
drill string 108 and orifices in drill bit 114, back to the surface
via the annulus around drill string 108, and into a retention pit
124. The drilling fluid transports cuttings from the wellbore 116
into the retention pit 124 and aids in maintaining the integrity of
the wellbore 116. Various materials can be used for drilling fluid,
including oil-based fluids and water-based fluids.
[0027] Logging tools 126 can be integrated into the bottom-hole
assembly 125 near the drill bit 114. As the drill bit 114 extends
the wellbore 116 through the formations 118, logging tools 126
collect measurements relating to various formation properties as
well as the orientation of the tool and various other drilling
conditions. The bottom-hole assembly 125 may also include a
telemetry sub 128 to transfer measurement data to a surface
receiver 132 and to receive commands from the surface. In at least
some cases, the telemetry sub 128 communicates with a surface
receiver 132 using mud pulse telemetry. In some instances, the
telemetry sub 128 does not communicate with the surface, but rather
stores logging data for later retrieval at the surface when the
logging assembly is recovered.
[0028] Each of the logging tools 126 may include one or more tool
components spaced apart from each other and communicatively coupled
with one or more wires and/or other media. The logging tools 126
may also include one or more computing devices 134 communicatively
coupled with one or more of the one or more tool components by one
or more wires and/or other media. The one or more computing devices
134 may be configured to control or monitor a performance of the
tool, process logging data, and/or carry out one or more aspects of
the methods and processes of the present disclosure.
[0029] In at least some instances, one or more of the logging tools
126 may communicate with a surface receiver 132 by a wire, such as
wired drillpipe. In other cases, the one or more of the logging
tools 126 may communicate with a surface receiver 132 by wireless
signal transmission. In at least some cases, one or more of the
logging tools 126 may receive electrical power from a wire that
extends to the surface, including wires extending through a wired
drillpipe.
[0030] Referring to FIG. 1B, an example system 140 for downhole
line detection in a downhole environment with a conveyance can
employ a tool having a tool body 146 in order to carry out logging
and/or other operations. For example, instead of using the drill
string 108 of FIG. 1A to lower tool body 146, which may contain
sensors or other instrumentation for detecting and logging nearby
characteristics and conditions of the wellbore 116 and surrounding
formation, a wireline conveyance 144 can be used. The tool body 146
can include a resistivity logging tool. The tool body 146 can be
lowered into the wellbore 116 by wireline conveyance 144. The
wireline conveyance 144 can be anchored in the drill rig 145 or a
portable means such as a truck. The wireline conveyance 144 can
include one or more wires, slicklines, cables, and/or the like, as
well as tubular conveyances such as coiled tubing, joint tubing, or
other tubulars.
[0031] The illustrated wireline conveyance 144 provides support for
the tool, as well as enabling communication between tool processors
148A-N on the surface and providing a power supply. In some
examples, the wireline conveyance 144 can include electrical and/or
fiber optic cabling for carrying out communications. The wireline
conveyance 144 is sufficiently strong and flexible to tether the
tool body 146 through the wellbore 116, while also permitting
communication through the wireline conveyance 144 to one or more
processors 148A-N, which can include local and/or remote
processors. Moreover, power can be supplied via the wireline
conveyance 144 to meet power requirements of the tool. For
slickline or coiled tubing configurations, power can be supplied
downhole with a battery or via a downhole generator.
[0032] Disclosed herein are systems and methods for detecting
channels behind casing and using a machine learning based approach
and generating images that represent the channels behind the
casing. An image may be an azimuthal cement bond depth image that
may be generated using one or more receiver rings azimuthally
arranged along a circumference of a monopole acoustic measurement
tool. There may any number of receiver rings located at different
offsets to generate the image. The method for detecting channels
may use a portion of a waveform as detected by the acoustic
measurement tool. Conventionally, an E1 peak in the waveform data
may have been used. However, the systems and methods discussed
herein provide a more robust image that is less affected by noise.
In addition, the systems and methods discussed herein utilize
machine learning to avoid effects of factors affecting data that
cannot be corrected effectively (e.g., mud effects and receiver
azimuthal responses at higher frequencies used for cement
evaluation).
[0033] The system may utilize eight azimuthal receivers including
one fixed source-receiver offset or multiple receivers' offset
waveform data at a number of depths and may apply preprocessing
steps such as resampling and de-trending on the data. The system
may then determine an amplitude based attribute (e.g., integral
amplitude) for each of the eight receivers. Each of the receivers
may be an array of receivers. If the system uses multiple
source-receiver offsets, the ring generated attributes may be
stacked or combined together to provide more robust calculations.
The system may analyze the attributes to determine an azimuthal
location and an angular extent of a channel at each depth using a
machine learning regression model. Multiple machine learning models
may be generated for differing casings and mud specifications and a
particular machine learning model may be selected for a particular
casing and mud being analyzed.
[0034] In one example, the machine learning models may be trained
with data that may be collected from controlled laboratory
environments and/or simulation data. As a result, each machine
learning model may be used to determine channel size and location
using a particular tool. The system may determine the channel size
and azimuthal location and generate an azimuthal cement bond image
that may indicate a presence or the absence of a channel. The image
may indicate the presence or absence in the image using a binary
mapping. As a result, the image may provide a two-dimensional
attribute map that is based on amplitude attributes from the data
that may be interpolated and smoothed. The system may be used to
detect narrow channels because the machine learning models may
convert lower magnitude casing wave amplitude attributes to a
corresponding channel specification (e.g., a channel size and
location).
[0035] The machine learning regression based models may be trained
with sonic tool measurements to predict size and location of a
channel behind casing. The data may be numerically simulated data
and used to train the machine learning algorithm with tool measured
waveform data. As a result, the system may be used to provide proof
and evidence of the presence of channels with monopole acoustic
tools.
[0036] Interzonal channeling may cause problems such as water
production and depletion of the gas drive mechanism, among others.
As a result, detecting the presence of channels and determining
their azimuthal location may be important to alleviate impacts on
hydrocarbon production.
[0037] Conventional solutions may provide high resolution
evaluations of the state of cement behind casing using ultrasonic
data. A radial bond logging tool may provide cement bond using E1
peak data from sonic data collected using receivers. However, the
images are not reliable or as accurate as the system discussed
herein. The system is able to utilize a number of azimuthally
distributed receivers to generate azimuthal images from sonic data
using monopole transmitter tools. E1 peaks may be affected by
receiver responses that vary azimuthally at relatively higher
frequencies. In addition, mud may affect amplitudes. These factors
may affect the conventional images such that they are not
reliable.
[0038] The system discussed herein may detect channels with more
confidence than conventional solutions that may be prone to effects
of noise. As a result, the system may be used to provide an
azimuthal ultrasonic cement evaluation with a lower
frequency/deeper reading sonic frequency result.
[0039] In one example, one or more source-receiver offsets may be
used to generate an azimuthal cement bond image. For a chosen
source-receiver offset, at each depth, the system may obtain eight
azimuthally distributed fixed source-receiver offset waveform data.
The waveforms may be resampled. As an example, they may be
resampled at five microseconds instead of ten microseconds and
background trends may be removed.
[0040] A time window may be defined by identifying a beginning of a
casing wave in the waveform data. The time window may be fixed as
long as the casing pipe specifications do not change. Once the time
window is defined, an amplitude based attribute (e.g., integral
amplitude) may be determined for each of the eight waveforms.
Determination of the integral amplitude for a waveform data may be
accomplished by determining a peaks based envelope of the rectified
waveform and using the envelope to modulate a sinusoid signal. A 30
kHz signal may be used as an example. A first period of the
modulated signal may be integrated to obtain an integral amplitude
attribute. Eight values may then be used as features for a machine
learning regression model to predict channel size and azimuthal
location.
[0041] The process may be repeated for a number of depths to
produce a two-dimensional channel distribution map image. More than
one source-receiver offset may be used such as one receiver three
feet away and another receiver five feet away. Individually
generated amplitude attributes may be combined and stacked together
to reduce noise. The channel map may be generated from the multiple
receiver data using the trained machine learning model.
[0042] The machine learning model may be trained to predict channel
size and location provided the eight integral amplitude values from
the receivers. The machine learning model may be based on random
forest and/or another machine learning algorithm such as support
vector machine (SVM) and neural networks. As an example, 10,000
data samples may be used to train the random forest machine
learning model. As an example, a random forest may be an ensemble
learning method for classification and regression. It may operate
by constructing a multitude of decision trees while training on
provided data. The random forest may output a mean prediction of
the individual trees. Random forests may be less prone to bias and
overfitting as opposed to other machine learning algorithms. The
random forest algorithm may be trained using data (e.g., eight
integral amplitude values used as features and corresponding
channel sizes and locations as realizations of the dependent
variables) and generated under controlled conditions for various
casing sizes and mud conditions. The training may be used to
produce a direct mapping between measurement distribution and
channel specification. As a result, the machine learning model may
be used to collect data and predict a channel in real-time. In one
example, the random forest is trained with data collected by the
sonic tool under controlled conditions and then utilized to predict
the channel with actual data in real-time.
[0043] FIG. 2 illustrates a channel detection system 200. The
channel detection system 200 can be implemented for detecting
channels behind casing, and generating images that represent the
channels as described herein. In this example, the channel
detection system 200 can include compute components 202, waveform
collection engine 204, imaging engine 206, a storage 208, and a
tool or device 212. In some implementations, the channel detection
system 200 can also include a display device 210 for displaying
data and graphical elements such as images, videos, text,
simulations, and any other media or data content.
[0044] The tool 212 may be a monopole acoustic measurement tool or
device that includes one or more receiver rings azimuthally
arranged along the circumference of the tool. There may be any
number of rings (e.g., eight) located at different offsets to
receive and collect waveform data that may be used to generate an
image.
[0045] The channel detection system 200 can be part of, or
implemented by, one or more computing devices, such as one or more
servers, one or more personal computers, one or more processors,
one or more mobile devices (for example, a smartphone, a camera, a
laptop computer, a tablet computer, a smart device, etc.), and/or
any other suitable electronic device. In some cases, the one or
more computing devices that include or implement the channel
detection system 200 can include one or more hardware components
such as, for example, one or more wireless transceivers, one or
more input devices, one or more output devices (for example,
display device 210), one or more sensors (for example, an image
sensor, a temperature sensor, a pressure sensor, an altitude
sensor, a proximity sensor, an inertial measurement unit, etc.),
one or more storage devices (for example, storage system 208), one
or more processing devices (for example, compute components 202),
etc.
[0046] As previously mentioned, the channel detection system 200
can include compute components 202. The compute components can be
used to implement the waveform collection engine 204, the imaging
engine 206, and/or any other computing component. The compute
components 202 can also be used to control, communicate with,
and/or interact with the storage 208 and/or the display device 210.
The compute components 202 can include electronic circuits and/or
other electronic hardware, such as, for example and without
limitation, one or more programmable electronic circuits. For
example, the compute components 202 can include one or more
microprocessors, one or more graphics processing units (GPUs), one
or more digital signal processors (DSPs), one or more central
processing units (CPUs), one or more image signal processors
(ISPs), and/or any other suitable electronic circuits and/or
hardware. Moreover, the compute components 202 can include and/or
can be implemented using computer software, firmware, or any
combination thereof, to perform the various operations described
herein.
[0047] The waveform engine 204 can be used to receive data samples
associated with training a machine learning algorithm such as
random forest. As an example, the waveform engine 204 may receive
approximately 10,000 data samples to train the random forest. The
data samples may be collected by the tool 212 and/or may be
synthetically generated or simulated. The data samples may
represent waveforms or casing waves that may be generated under
controlled conditions for various casing sizes and mud conditions.
As a result, the machine learning algorithm may be trained with the
data samples to produce a direct mapping between the data samples
and channel specifications such as channel size and location.
[0048] The waveform engine 204 can be used to determine integral
amplitudes for the azimuthal receivers associated with the tool
212. The trained model may then be used to predict channels using
the integral amplitudes from sonic waveform data at depths of
interest.
[0049] Based on the predicted channel information, the imaging
engine 206 may generate a two dimensional image or map that
indicates the presence and/or absence of channels. As an example,
the two dimensional image may indicate a predicted channel size and
location. The image may indicate where the channel is located in
the azimuth in degrees and depth, among other information.
[0050] The storage 208 can be any storage device(s) for storing
data. In some examples, the storage 208 can include a buffer or
cache for storing data for processing by the compute components
202. Moreover, the storage 208 can store data from any of the
components of the memory tool activation and control system 200.
For example, the storage 208 can store input data used by the
channel detection system 200, outputs or results generated by the
channel detection system 200 (for example, data and/or calculations
from the waveform collection engine 204, the imaging engine 206,
etc.), user preferences, parameters and configurations, data logs,
documents, software, media items, GUI content, and/or any other
data and content.
[0051] While the channel detection system 200 is shown in FIG. 2 to
include certain components, one of ordinary skill in the art will
appreciate that the channel detection system 200 can include more
or fewer components than those shown in FIG. 2. For example, the
channel detection system 200 can also include one or more memory
components (for example, one or more RAMs, ROMs, caches, buffers,
and/or the like), one or more input components, one or more output
components, one or more processing devices, and/or one or more
hardware components that are not shown in FIG. 2.
[0052] FIG. 3 shows an example acoustic tool 300 associated with
the system according to an example. A monopole source is shown at
302. As shown in FIG. 3, a two-dimensional view of the tool shows
three visible arrays 310 of the tool 300. As an example, eight such
azimuthally arranged arrays 310 having 45 degrees of separation may
be present on the tool 300. As noted above, each array 310 may have
thirteen receivers 312, although only eight receivers are shown in
each array 310 in FIG. 3. The other five arrays of receivers are
not visible in this two-dimensional view of the tool 300. The
design of the arrays is not limited to this arrangement and may
differ. As an example, at a particular offset, there may be a ring
of receivers 304. Each ring of receivers may include eight
receivers, or another number of receivers. A ring of receivers 306
may be at one offset, e.g., three feet from the source. A ring of
receivers 308 may be at a second offset, e.g., five feet from the
source. There may be a spacing between each ring of receivers,
e.g., 0.5 feet.
[0053] FIG. 4 is a graph 400 of example waveform data used to train
a machine learning model according to an example. FIG. 4 shows a
graph of noise free data from a numerical simulation including data
collected from each receiver and an associated amplitude. The plot
shown includes the integral amplitudes for eight azimuthal
receivers in the presence of a ninety degree channel in the cement
and it may be generated using finite difference simulations.
[0054] The data shown represents a plot of integral amplitudes with
a ninety degree channel in cement located at one hundred and twenty
degrees with respect to the reference axis. The waveform data may
be determined using finite difference code. As a result, the
integral amplitudes may be determined from controlled experiment
data similarly for training and integral amplitudes collected from
data may be used with the trained machine learning model to predict
channels.
[0055] FIG. 5 is a graph 500 of example waveform data used to train
the machine learning model according to an example. FIG. 5 shows a
graph of noise corrupted data including data collected from each
receiver and an associated amplitude. The plot shown in FIG. 5
indicates the integral amplitudes that are multiplied with an
artificial azimuthal response and adding 10% random noise. In other
words, the data shown represents a plot of integral amplitudes with
a ninety degree channel in cement located at one hundred and twenty
degrees with respect to the reference axis. The values may be
multiplied with an artificial azimuthal receiver response function
and as shown random noise may be added. As a result, the waveform
data may be determined using finite difference code.
[0056] FIG. 6 is an image 600 representing an actual channel size
and location according to an example. FIG. 6 shows an actual
channel image for a synthetic test. As shown in FIG. 6, the first
section 602 represents a channel and a second section 604
represents an absence of the channel. FIG. 6 indicates a size and
location of the channel with respect to a depth level index and an
azimuth in degrees.
[0057] FIG. 7 is an image 700 representing a predicted channel size
and location as generated by the system 200 according to an
example. FIG. 7 shows a predicted channel image as determined by
the machine learning random forest model that may be trained with
the data. Again, a first section 702 represents a channel and a
second section 704 represents an absence of the channel. FIG. 7
indicates a size and location of the channel with respect to a
depth level index and an azimuth in degrees. Thus, the random
forest regression algorithm may be trained with data (e.g.,
integral amplitudes) that may be affected by various factors and a
corresponding channel specification (e.g., location and size).
Alternatively, a different attribute other than integral amplitudes
may be used. The trained model may be used to accurately predict
channels from data in the field that may be affected by factors
such as mud conditions and receiver behavior.
[0058] Having disclosed some example system components and
concepts, the disclosure now turns to FIG. 8, which illustrates an
example method 800 for detecting a channel behind casing and
generating an image that represents the channel. For the sake of
clarity, the method 800 is described in terms of the channel
detection system 200, as shown in FIG. 2, configured to practice
the method. The steps outlined herein are exemplary and can be
implemented in any combination thereof, including combinations that
exclude, add, or modify certain steps.
[0059] At step 802, the channel detection system 200 can determine
integral amplitude for one or more azimuthal receivers to generate
training data. The training data may be synthetic data and/or data
that represents known channels behind casing. At step 804, the
channel detection system 200 can train one or more random forest
models using the training data. At step 806, the channel detection
system 200 can test prediction accuracy of the random forest
models. At step 808, the channel detection system 200 can use the
random forest models to predict channels from integral amplitudes
measured from sonic waveform data at depths of interest. At step
810, the channel detection system 200 can generate a channel image
and/or a channel map that represents the absence and/or presence of
a channel behind casing based on the waveform data. The channel
image may be a two-dimensional image or another type of image.
[0060] FIG. 9 illustrates another method 900 for determining an
integral amplitude attribute. For the sake of clarity, the method
900 is described in terms of the channel detection system 200, as
shown in FIG. 2, configured to practice the method. The steps
outlined herein are exemplary and can be implemented in any
combination thereof, including combinations that exclude, add, or
modify certain steps.
[0061] At step 902, the channel detection system 200 can de-trend
and/or resample waveform data from one or more receivers of the
tool. The waveform data may be sampled at too high of a frequency
and some of the data may be discarded or not considered. At step
904, the channel detection system 200 can determine peak based
envelopes of the rectified waveform data. At step 906, the channel
detection system 200 can modulate the sinusoid signal with an
envelope. At step 908, the channel detection system 200 can extract
a first period of modulated signal and integrate to obtain an
integral amplitude attribute.
[0062] FIG. 10 illustrates a method 1000 for detecting a channel
behind casing and generating an image that represents the channel.
For the sake of clarity, the method 1000 is described in terms of
the channel detection system 200, as shown in FIG. 2, configured to
practice the method. The steps outlined herein are exemplary and
can be implemented in any combination thereof, including
combinations that exclude, add, or modify certain steps.
[0063] At step 1002, the channel detection system 200 can receive
data samples associated with at least one casing, each data sample
representing channel information behind a representative casing. At
step 1004, the channel detection system 200 can train a machine
learning model using the data samples to generate a mapping between
waveform information in each of the data samples and the channel
information behind the representative casing. At step 1006, the
channel detection system 200 can receive acoustic data from the
tool 212. The acoustic data may represent a particular casing. At
step 1008, the channel detection system 200 can use the machine
learning model to analyze the acoustic data from the tool 212 and
determine of a presence and an absence of a channel behind the
particular casing at a plurality of depths. At step 1010, the
channel detection system 200 can generate an azimuthal cement bond
depth channel image that represents the presence and the absence of
the channel behind the particular casing at the plurality of
depths.
[0064] The channel detection system 200 can generate an azimuthal
cement bond depth channel image that represents the presence and
the absence of the channel behind the particular casing at the
plurality of depths. The channel image may be a binary
two-dimensional image. In addition, the binary two-dimensional
image can represent a size and an azimuthal location of the channel
behind the particular casing at the plurality of depths. The
location may be an azimuthal location. The machine learning model
can be based on random forest, among other algorithms.
[0065] Additionally, the tool 212 can be at least one array of
receivers azimuthally arranged along a circumference of the tool.
The array of receivers can determine an integral amplitude for
waveform data obtained by the at least one array of receivers.
Additionally, a ring of receivers can be one of three feet and five
feet from the casing.
[0066] Having disclosed example systems, methods, and technologies
for detecting a channel behind casing and generating an image that
represents the channel, the disclosure now turns to FIG. 11, which
illustrates an example computing device architecture 1100 which can
be employed to perform various steps, methods, and techniques
disclosed herein. The various implementations will be apparent to
those of ordinary skill in the art when practicing the present
technology. Persons of ordinary skill in the art will also readily
appreciate that other system implementations or examples are
possible.
[0067] As noted above, FIG. 11 illustrates an example computing
device architecture 1100 of a computing device which can implement
the various technologies and techniques described herein. For
example, the computing device architecture 1100 can implement the
system 200 shown in FIG. 2 and perform various steps, methods, and
techniques disclosed herein. The components of the computing device
architecture 1100 are shown in electrical communication with each
other using a connection 1105, such as a bus. The example computing
device architecture 1100 includes a processing unit (CPU or
processor) 1110 and a computing device connection 1105 that couples
various computing device components including the computing device
memory 1115, such as read only memory (ROM) 1120 and random access
memory (RAM) 1125, to the processor 1110.
[0068] The computing device architecture 1100 can include a cache
of high-speed memory connected directly with, in close proximity
to, or integrated as part of the processor 1110. The computing
device architecture 1100 can copy data from the memory 1115 and/or
the storage device 1130 to the cache 1112 for quick access by the
processor 1110. In this way, the cache can provide a performance
boost that avoids processor 1110 delays while waiting for data.
These and other modules can control or be configured to control the
processor 1110 to perform various actions. Other computing device
memory 1115 may be available for use as well. The memory 1115 can
include multiple different types of memory with different
performance characteristics. The processor 1110 can include any
general purpose processor and a hardware or software service, such
as service 1 1132, service 2 1134, and service 3 1136 stored in
storage device 1130, configured to control the processor 1110 as
well as a special-purpose processor where software instructions are
incorporated into the processor design. The processor 1110 may be a
self-contained system, containing multiple cores or processors, a
bus, memory controller, cache, etc. A multi-core processor may be
symmetric or asymmetric.
[0069] To enable user interaction with the computing device
architecture 1100, an input device 1145 can represent any number of
input mechanisms, such as a microphone for speech, a
touch-sensitive screen for gesture or graphical input, keyboard,
mouse, motion input, speech and so forth. An output device 1135 can
also be one or more of a number of output mechanisms known to those
of skill in the art, such as a display, projector, television,
speaker device, etc. In some instances, multimodal computing
devices can enable a user to provide multiple types of input to
communicate with the computing device architecture 1100. The
communications interface 1140 can generally govern and manage the
user input and computing device output. There is no restriction on
operating on any particular hardware arrangement and therefore the
basic features here may easily be substituted for improved hardware
or firmware arrangements as they are developed.
[0070] Storage device 1130 is a non-volatile memory and can be a
hard disk or other types of computer readable media which can store
data that are accessible by a computer, such as magnetic cassettes,
flash memory cards, solid state memory devices, digital versatile
disks, cartridges, random access memories (RAMs) 1125, read only
memory (ROM) 1120, and hybrids thereof. The storage device 1130 can
include services 1132, 1134, 1136 for controlling the processor
1110. Other hardware or software modules are contemplated. The
storage device 1130 can be connected to the computing device
connection 1105. In one aspect, a hardware module that performs a
particular function can include the software component stored in a
computer-readable medium in connection with the necessary hardware
components, such as the processor 1110, connection 1105, output
device 1135, and so forth, to carry out the function.
[0071] For clarity of explanation, in some instances the present
technology may be presented as including individual functional
blocks including functional blocks comprising devices, device
components, steps or routines in a method embodied in software, or
combinations of hardware and software.
[0072] In some embodiments the computer-readable storage devices,
mediums, and memories can include a cable or wireless signal
containing a bit stream and the like. However, when mentioned,
non-transitory computer-readable storage media expressly exclude
media such as energy, carrier signals, electromagnetic waves, and
signals per se.
[0073] Methods according to the above-described examples can be
implemented using computer-executable instructions that are stored
or otherwise available from computer readable media. Such
instructions can include, for example, instructions and data, which
cause or otherwise configure a general purpose computer, special
purpose computer, or a processing device to perform a certain
function or group of functions. Portions of computer resources used
can be accessible over a network. The computer executable
instructions may be, for example, binaries, intermediate format
instructions such as assembly language, firmware, source code, etc.
Examples of computer-readable media that may be used to store
instructions, information used, and/or information created during
methods according to described examples include magnetic or optical
disks, flash memory, USB devices provided with non-volatile memory,
networked storage devices, and so on.
[0074] Devices implementing methods according to these disclosures
can include hardware, firmware and/or software, and can take any of
a variety of form factors. Typical examples of such form factors
include laptops, smart phones, small form factor personal
computers, personal digital assistants, rackmount devices,
standalone devices, and so on. Functionality described herein also
can be embodied in peripherals or add-in cards. Such functionality
can also be implemented on a circuit board among different chips or
different processes executing in a single device, by way of further
example.
[0075] The instructions, media for conveying such instructions,
computing resources for executing them, and other structures for
supporting such computing resources are example means for providing
the functions described in the disclosure.
[0076] In the foregoing description, aspects of the application are
described with reference to specific embodiments thereof, but those
skilled in the art will recognize that the application is not
limited thereto. Thus, while illustrative embodiments of the
application have been described in detail herein, it is to be
understood that the disclosed concepts may be otherwise variously
embodied and employed, and that the appended claims are intended to
be construed to include such variations, except as limited by the
prior art. Various features and aspects of the above-described
subject matter may be used individually or jointly. Further,
embodiments can be utilized in any number of environments and
applications beyond those described herein without departing from
the broader spirit and scope of the specification. The
specification and drawings are, accordingly, to be regarded as
illustrative rather than restrictive. For the purposes of
illustration, methods were described in a particular order. It
should be appreciated that in alternate embodiments, the methods
may be performed in a different order than that described.
[0077] Where components are described as being "configured to"
perform certain operations, such configuration can be accomplished,
for example, by designing electronic circuits or other hardware to
perform the operation, by programming programmable electronic
circuits (e.g., microprocessors, or other suitable electronic
circuits) to perform the operation, or any combination thereof.
[0078] The various illustrative logical blocks, modules, circuits,
and algorithm steps described in connection with the examples
disclosed herein may be implemented as electronic hardware,
computer software, firmware, or combinations thereof. To clearly
illustrate this interchangeability of hardware and software,
various illustrative components, blocks, modules, circuits, and
steps have been described above generally in terms of their
functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present application.
[0079] The techniques described herein may also be implemented in
electronic hardware, computer software, firmware, or any
combination thereof. Such techniques may be implemented in any of a
variety of devices such as general purpose computers, wireless
communication device handsets, or integrated circuit devices having
multiple uses including application in wireless communication
device handsets and other devices. Any features described as
modules or components may be implemented together in an integrated
logic device or separately as discrete but interoperable logic
devices. If implemented in software, the techniques may be realized
at least in part by a computer-readable data storage medium
comprising program code including instructions that, when executed,
performs one or more of the method, algorithms, and/or operations
described above. The computer-readable data storage medium may form
part of a computer program product, which may include packaging
materials.
[0080] The computer-readable medium may include memory or data
storage media, such as random access memory (RAM) such as
synchronous dynamic random access memory (SDRAM), read-only memory
(ROM), non-volatile random access memory (NVRAM), electrically
erasable programmable read-only memory (EEPROM), FLASH memory,
magnetic or optical data storage media, and the like. The
techniques additionally, or alternatively, may be realized at least
in part by a computer-readable communication medium that carries or
communicates program code in the form of instructions or data
structures and that can be accessed, read, and/or executed by a
computer, such as propagated signals or waves.
[0081] Other embodiments of the disclosure may be practiced in
network computing environments with many types of computer system
configurations, including personal computers, hand-held devices,
multi-processor systems, microprocessor-based or programmable
consumer electronics, network PCs, minicomputers, mainframe
computers, and the like. Embodiments may also be practiced in
distributed computing environments where tasks are performed by
local and remote processing devices that are linked (either by
hardwired links, wireless links, or by a combination thereof)
through a communications network. In a distributed computing
environment, program modules may be located in both local and
remote memory storage devices.
[0082] It will be appreciated that for simplicity and clarity of
illustration, where appropriate, reference numerals have been
repeated among the different figures to indicate corresponding or
analogous elements. In addition, numerous specific details are set
forth in order to provide a thorough understanding of the
embodiments described herein. However, it will be understood by
those of ordinary skill in the art that the embodiments described
herein can be practiced without these specific details. In other
instances, methods, procedures and components have not been
described in detail so as not to obscure the related relevant
feature being described. Also, the description is not to be
considered as limiting the scope of the embodiments described
herein. The drawings are not necessarily to scale and the
proportions of certain parts have been exaggerated to better
illustrate details and features of the present disclosure.
[0083] In the above description, terms such as "upper," "upward,"
"lower," "downward," "above," "below," "downhole," "uphole,"
"longitudinal," "lateral," and the like, as used herein, shall mean
in relation to the bottom or furthest extent of the surrounding
wellbore even though the wellbore or portions of it may be deviated
or horizontal. Correspondingly, the transverse, axial, lateral,
longitudinal, radial, etc., orientations shall mean orientations
relative to the orientation of the wellbore or tool. Additionally,
the illustrate embodiments are illustrated such that the
orientation is such that the right-hand side is downhole compared
to the left-hand side.
[0084] The term "coupled" is defined as connected, whether directly
or indirectly through intervening components, and is not
necessarily limited to physical connections. The connection can be
such that the objects are permanently connected or releasably
connected. The term "outside" refers to a region that is beyond the
outermost confines of a physical object. The term "inside" indicate
that at least a portion of a region is partially contained within a
boundary formed by the object. The term "substantially" is defined
to be essentially conforming to the particular dimension, shape or
other word that substantially modifies, such that the component
need not be exact. For example, substantially cylindrical means
that the object resembles a cylinder, but can have one or more
deviations from a true cylinder.
[0085] The term "radially" means substantially in a direction along
a radius of the object, or having a directional component in a
direction along a radius of the object, even if the object is not
exactly circular or cylindrical. The term "axially" means
substantially along a direction of the axis of the object. If not
specified, the term axially is such that it refers to the longer
axis of the object.
[0086] Although a variety of information was used to explain
aspects within the scope of the appended claims, no limitation of
the claims should be implied based on particular features or
arrangements, as one of ordinary skill would be able to derive a
wide variety of implementations. Further and although some subject
matter may have been described in language specific to structural
features and/or method steps, it is to be understood that the
subject matter defined in the appended claims is not necessarily
limited to these described features or acts. Such functionality can
be distributed differently or performed in components other than
those identified herein. The described features and steps are
disclosed as possible components of systems and methods within the
scope of the appended claims.
[0087] Moreover, claim language reciting "at least one of" a set
indicates that one member of the set or multiple members of the set
satisfy the claim. For example, claim language reciting "at least
one of A and B" means A, B, or A and B.
[0088] Statements of the disclosure include:
[0089] Statement 1: A method comprising receiving, by at least one
processor, data samples associated with at least one casing, each
data sample representing channel information behind a
representative casing, training, by the at least one processor, a
machine learning model using the data samples to generate a mapping
between waveform information in each of the data samples and the
channel information behind the representative casing, receiving, by
the at least one processor, acoustic data from a tool, the acoustic
data representing a particular casing, and using, by the at least
one processor, the machine learning model to analyze the acoustic
data from the tool and determine one of a presence and an absence
of a channel behind the particular casing at a plurality of
depths.
[0090] Statement 2: A method according to Statement 1, further
comprising generating an azimuthal cement bond depth channel image
that represents the presence and the absence of the channel behind
the particular casing at the plurality of depths.
[0091] Statement 3: A method according to any of Statements 1 and
2, wherein the channel image is a binary two-dimensional image.
[0092] Statement 4: A method according to any of Statements 1
through 3, wherein the binary two-dimensional image represents a
size and an azimuthal location of the channel behind the particular
casing at the plurality of depths.
[0093] Statement 5: A method according to any of Statements 1
through 4, wherein the tool comprises at least one array of
receivers azimuthally arranged along a circumference of the
tool.
[0094] Statement 6: A method according to any of Statements 1
through 5, further comprising determining an integral amplitude for
waveform data obtained by the at least one array of receivers.
[0095] Statement 7: A method according to any of Statements 1
through 6, wherein a ring of receivers is one of three feet and
five feet from the casing.
[0096] Statement 8: A method according to any of Statements 1
through 7, wherein the machine learning model is based on random
forest.
[0097] Statement 9: A system comprising an acoustic tool comprising
at least one sensor, at least one processor, and at least one
computer-readable storage medium having stored therein
instructions, which when executed by the at least one processor
cause the system to: receive data samples associated with at least
one casing, each data sample representing channel information
behind a representative casing, train a machine learning model
using the data samples to generate a mapping between waveform
information in each of the data samples and the channel information
behind the representative casing, receive acoustic data from the
tool, the acoustic data representing a particular casing, and use
the machine learning model to analyze the acoustic data from the
tool and determine one of a presence and an absence of a channel
behind the particular casing at a plurality of depths.
[0098] Statement 10: A system according to Statement 9, the at
least one processor further to generate an azimuthal cement bond
depth channel image that represents the presence and the absence of
the channel behind the particular casing at the plurality of
depths.
[0099] Statement 11: A system according to any of Statements 9 and
10, wherein the channel image is a binary two-dimensional
image.
[0100] Statement 12: A system according to any of Statements 9
through 11, wherein the binary two-dimensional image represents a
size and an azimuthal location of the channel behind the particular
casing at the plurality of depths.
[0101] Statement 13: A system according to any of Statements 9
through 12, wherein the acoustic tool comprises at least one array
of receivers azimuthally arranged along a circumference of the
tool.
[0102] Statement 14: A system according to any of Statements 9
through 13, the at least one processor further to dynamically sense
in real-time, by the at least one sensor, the first indication to
transition from the powered-off state to the low power standby
state.
[0103] Statement 14: A system according to any of Statements 8
through 13, the at least one processor further to determine an
integral amplitude for waveform data obtained by the at least one
array of receivers.
[0104] Statement 15: A system according to any of Statements 9
through 14, wherein a ring of receivers is one of three feet and
five feet from the casing.
[0105] Statement 16: A system according to any of Statements 9
through 15, wherein the machine learning model is based on random
forest.
[0106] Statement 17: A non-transitory computer-readable storage
medium comprising instructions stored on the non-transitory
computer-readable storage medium, the instructions, when executed
by one more processors, cause the one or more processors to perform
operations including: receiving data samples associated with at
least one casing, each data sample representing channel information
behind a representative casing, training a machine learning model
using the data samples to generate a mapping between waveform
information in each of the data samples and the channel information
behind the representative casing, receiving acoustic data from a
tool, the acoustic data representing a particular casing, and using
the machine learning model to analyze the acoustic data from the
tool and determine one of a presence and an absence of a channel
behind the particular casing at a plurality of depths.
[0107] Statement 18: A non-transitory computer-readable storage
medium according to Statement 17, the operations further comprising
generating an azimuthal cement bond depth channel image that
represents the presence and the absence of the channel behind the
particular casing at the plurality of depths.
[0108] Statement 19: A non-transitory computer-readable storage
medium according to any of Statements 17 and 18, wherein the
channel image is a binary two-dimensional image.
[0109] Statement 20: A non-transitory computer-readable storage
medium according to any of Statements 17 through 19, wherein the
binary two-dimensional image represents a size and an azimuthal
location of the channel behind the particular casing at the
plurality of depths.
[0110] Statement 21: A system comprising means for performing a
method according to any of Statements 1 through 8.
* * * * *