U.S. patent application number 15/128253 was filed with the patent office on 2017-04-06 for system and method for automation of detection of stress patterns and equipment failures in hydrocarbon extraction and production.
The applicant listed for this patent is Schlumberger Technology Corporation. Invention is credited to Thibault Blanckaert, Yves-Marie Clet Robert Subervie.
Application Number | 20170096889 15/128253 |
Document ID | / |
Family ID | 54196425 |
Filed Date | 2017-04-06 |
United States Patent
Application |
20170096889 |
Kind Code |
A1 |
Blanckaert; Thibault ; et
al. |
April 6, 2017 |
SYSTEM AND METHOD FOR AUTOMATION OF DETECTION OF STRESS PATTERNS
AND EQUIPMENT FAILURES IN HYDROCARBON EXTRACTION AND PRODUCTION
Abstract
Process control system and method using data-stream
segmentation, segment modeling, and Bayesian belief networks for
analyzing sensor data are used to determine equipment events, such
as equipment failure and equipment stress conditions, that may lead
to equipment failure. Other systems and methods are disclosed.
Inventors: |
Blanckaert; Thibault;
(Houston, TX) ; Subervie; Yves-Marie Clet Robert;
(Houston, TX) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Schlumberger Technology Corporation |
Sugar Land |
TX |
US |
|
|
Family ID: |
54196425 |
Appl. No.: |
15/128253 |
Filed: |
March 27, 2015 |
PCT Filed: |
March 27, 2015 |
PCT NO: |
PCT/US2015/022867 |
371 Date: |
September 22, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61972075 |
Mar 28, 2014 |
|
|
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
E21B 47/06 20130101;
G05B 2219/45004 20130101; G05B 2219/45129 20130101; E21B 47/07
20200501; E21B 43/128 20130101; E21B 44/00 20130101; E21B 47/008
20200501; E21B 41/0085 20130101; G05B 19/4065 20130101 |
International
Class: |
E21B 47/00 20060101
E21B047/00; E21B 47/06 20060101 E21B047/06; E21B 43/12 20060101
E21B043/12 |
Claims
1. A method for detecting equipment failures or stress conditions
that may result in equipment failures in a process in the
hydrocarbon industry, where the hydrocarbon-industry-process is
subject to a change in a plurality of operating conditions each
monitored by at least one sensor providing a plurality of input
data streams, comprising: segmenting the input data streams such
that each segment of data points is modeled using a simple
mathematical model; using the segmentations and statistical
parameters associated with the segmentations and the underlying
data to compute probabilities associated with at least one
high-level inquiry in regard to the input streams thereby computing
probabilities for inquiry answers; and inputting the high-level
inquiry probabilities into a reasoning engine and operating the
reasoning engine to determine the probability of an equipment
event.
2. The method of claim 1 wherein the at least one high-level
inquiry is a determination of a probability values for basic
tendencies of an operating physical property relative to a
reference distribution.
3. The method of claim 2 wherein the basic tendencies are decrease
strongly, decrease, steady, increase, and increase strongly and the
probabilities for each of these is determined by comparing the
probability distribution according to the model against defined
thresholds in the reference distribution.
4. The method of claim 1 wherein the at least one high-level
inquiry is a determination of a probability of noise levels of an
operating physical property.
5. The method of claim 4 wherein noise levels stable, unstable, and
very unstable and probabilities for each of these is determined by
comparing the distribution for noise in the input sensor data
against pre-defined threshold values.
6. The method of claim 1 wherein the at least one high-level
inquiry is a determination of a probability of correlation values
between two operating physical properties.
7. The method of claim 6 wherein the correlation values are
negative correlation, positive correlation, and no correlation and
the probability of each of these is determined by comparing the
distribution of correlation against pre-defined thresholds.
8. The method of claim 1 wherein the reasoning engine comprises a
Bayesian belief network linking the high-level inquiry
probabilities to equipment event probabilities.
9. The method of claim 1 further comprising upon the reasoning
engine determining a probability of an equipment event exceeding a
predefined threshold, triggering an alarm.
10. The method of claim 1 wherein the hydro-carbon process is
artificial lift using an electric submersible pump or a progressive
cavity pump.
11. The method claim 10 wherein the sensor data includes at least
one sensor input selected from discharge pressure, intake pressure,
delta pressure, current draw, motor temperature, wellhead
temperature, wellhead pressure.
12. The method of claim 10 wherein the equipment event is one of
deadhead, low flow, gas ingestion, downhole mechanical failure, and
pump off.
13. A hydrocarbon process control system comprising: at least one
sensor measuring an operating property of a hydrocarbon process
controlled by the control system; a signal processing module for
segmenting an input stream from the at least one sensor and for
computing probabilities of answers to at least one high-level
inquiry in regard to the input stream from the at least one sensor;
and an expert system connected to the signal processing module and
operable to receive the probabilities for the answers to the at
least one high-level inquiry and operable to compute therefrom
probabilities of at least one equipment event.
14. The hydrocarbon process control system of claim 13 wherein the
at least one high-level inquiry is a determination of a probability
values for basic tendencies of an operating physical property
relative to a reference distribution.
15. The hydrocarbon process control system of claim 14 wherein the
basic tendencies are decrease strongly, decrease, steady, increase,
and increase strongly and the probabilities for each of these is
determined by comparing the probability distribution according to
the model against defined thresholds in the reference
distribution.
16. The hydrocarbon process control system of claim 13 wherein the
at least one high-level inquiry is a determination of a probability
of noise levels of an operating physical property.
17. The hydrocarbon process control system of claim 16 wherein
noise levels stable, unstable, and very unstable and probabilities
for each of these is determined by comparing the distribution for
noise in the input sensor data against pre-defined threshold
values.
18. The hydrocarbon process control system of claim 13 wherein the
at least one high-level inquiry is a determination of a probability
of correlation values between two operating physical
properties.
19. The hydrocarbon process control system of claim 18 wherein the
correlation values are negative correlation, positive correlation,
and no correlation and the probability of each of these is
determined by comparing the distribution of correlation against
pre-defined thresholds.
20. The hydrocarbon process control system of claim 13 wherein the
reasoning engine comprises a Bayesian belief network linking the
high-level inquiry probabilities to equipment event
probabilities.
21. The hydrocarbon process control system of claim 13 further
comprising upon the reasoning engine determining a probability of
an equipment event exceeding a predefined threshold, triggering an
alarm.
22. The hydrocarbon process control system of claim 13 wherein the
hydro-carbon process is artificial lift using an electric
submersible pump or a progressive cavity pump.
23. The hydrocarbon process control system claim 22 wherein the
sensor data includes at least one sensor input selected from
discharge pressure, intake pressure, delta pressure, current draw,
motor temperature, wellhead temperature, wellhead pressure.
24. The hydrocarbon process control system of claim 22 wherein the
equipment event is one of deadhead, low flow, gas ingestion,
downhole mechanical failure, and pump off.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present document is based on and claims priority to U.S.
Provisional Application Ser. No. 61/972,075, filed on Mar. 28,
2014, which is incorporated herein by reference in its
entirety.
FIELD
[0002] The present disclosure relates generally to process
automation, and more particularly but not by limitation, to
real-time detection of stress patterns and equipment failures based
on sensor data.
BACKGROUND
[0003] In many industries, automated processes are now used for
fabrication of products, monitoring operation of systems, designing
systems, interacting machinery with other objects and/or the like.
In such automated industrial processes, there is a broad latitude
of issues that may affect the process. These issues may cause a
halt and/or break down of the automated industrial process, may
degrade the operation of the automated industrial process, may
change the background, environment and/or the like the automated
industrial process is working in and so may change how the
automated industrial process works, what the automated industrial
process achieves, the goal of the automated industrial process
and/or the like.
[0004] One or more of the broad latitude of issues that may affect
the automated industrial process may arise during the automated
industrial process causing real time changes to the operation of
the automated industrial process. To mitigate such issues,
forward-looking models of the automated industrial process may be
analyzed and used to control the automated industrial process. Such
models may be determined from results from prior processes,
theoretically, experimentally and/or the like. Mitigation of such
issues may also be achieved by obtaining data from the automated
industrial process and/or the environment in which the automated
industrial process occurs and retroactively identifying the
existence of an issue.
[0005] Merely by way of example, in the hydrocarbon industry, the
process of drilling into a hydrocarbon reservoir may be impeded by
a wide variety of problems and may include
monitoring/interpretation of a considerable amount of data.
Accurate measurements of downhole conditions, downhole equipment
properties, geological properties, rock properties, drilling
equipment properties, fluid properties, surface equipment
properties and/or the like may be analyzed by a drilling crew to
minimize drilling risks, to make determinations as to how to
optimize the drilling procedure given the data and/or to
detect/predict the likelihood of a problem/decrease in drilling
efficiency and/or the like.
[0006] Similarly, in hydrocarbon exploration, hydrocarbon
extraction, hydrocarbon production, hydrocarbon transportation
and/or the like many conditions may be sensed and data gathered to
provide for optimizing and/or preventing/mitigating issues/problems
concerning the exploration, production and or transportation of
hydrocarbons. Hydrocarbons are a lifeblood of the modern industrial
society, as such, vast amounts of hydrocarbons are being
prospected, retrieved and transported on a daily basis. Associated
with this industry are an enormous amount of sensors gathering
enumerable amounts of data relevant to the exploration, production
and or transportation of hydrocarbons.
[0007] To provide for safe and efficient exploration, production
and or transportation of hydrocarbons this data may be processed.
While computers may be used to process the data, it is often
difficult to accurately process the incoming data for real-time
control of the hydrocarbon processes. As such, human operators are
commonly used to control the hydrocarbon processes and to make
decisions on optimizing, preventing risks, identifying faults
and/or the like based on interpretation of the raw/processed data.
However, optimization of a hydrocarbon process and/or mitigation
and detection of issues/problems by a human controller may often be
degraded by fatigue, high workload, lack of experience, the
difficulty in manually analyzing complex data and/or the like.
Furthermore, noisy data may have some impact on a human observer's
ability to take note of or understand the meaning occurrences
reflected in the data.
[0008] The detection of occurrences reflected in the data goes
beyond detection of issues and problems. Accurate analysis of
operating conditions may allow for an operator to operate the
industrial process at near optimal conditions. For example, in the
hydrocarbon industry, the bit-response to changes in parameters
such as drill-bit rotational speed and weight-on-bit (WOB) while
drilling into a hydrocarbon reservoir is very much affected by
changes in the lithological environment of drilling operations.
Accurate and real-time knowledge of a transition from one
environment to another, e.g., one formation to another, and
real-time analysis of how such environmental conditions impact the
effect that parameter changes are likely to have on bit-response
may greatly improve the expected rate of penetration (ROP).
[0009] Similarly, the constraints that limit the range of the
drilling parameters may change as the drilling environment changes.
These constraints, e.g., the rate at which cuttings are removed by
the drilling fluids, may limit the maximum permissible drilling
parameter values. Without accurate knowledge of these changes in
the constraints, a driller may not be fully aware of where the
constraints lie with respect to the ideal parameter settings and
for the sake of erring on the side of caution, which is natural
considering the dire consequences of drilling equipment failures
and drilling accidents, a driller may operate the drilling process
at parameters far removed the actual optimal parameters.
Considering that drilling, like many other processes associated
with the production and transport of hydrocarbons is an extremely
costly procedure, the operation of the drilling system at less than
optimal parameters can be extremely costly.
[0010] Similarly, accurate measurement of the direction (Toolface)
and curvature (Dogleg-Severity (DLS)) of a borehole is helpful for
a driller to accurately direct a drilling process to a target.
Measurements of these properties are typically taken at rather
infrequent intervals (e.g., every 30 to 90 feet) while the
drill-bit is off bottom and the drill string is stationary.
However, modern drilling equipment may provide for taking
directional measurements continuously while drilling.
Unfortunately, the measurements obtained while-drilling are
generally very noisy and difficult for a driller to interpret
because of the noise in the data.
[0011] Furthermore, the noise in the data tends to be amplified in
any direct computation of the Dogleg-Severity and Toolface from the
continuous surveys and the results are generally of such low
quality to be of little value to the drillers. As a result, the
while-drilling data is often not used in computation of
Dogleg-Severity, Toolface and/or the like and instead the
infrequent measurements, which require the drilling process to be
halted while the measurements are taken, are often still used to
determine drilling trajectory and/or the like.
[0012] In the hydrocarbon industry, as in other industries, event
detection systems have generally depended upon people, such as
drilling personnel, to manage processes and to identify occurrences
of events, such as a change in a rig state. Examples of rig state
detection in drilling may be found in the following references:
"The MDS System: Computers Transform Drilling", Bourgois, Burgess,
Rike, Unsworth, Oilfield Review Vol. 2, No. 1, 1990, pp.4-15; and
"Managing Drilling Risk" Aldred et al., Oilfield Review, Summer
1999, pp. 219.
[0013] With regard to the hydrocarbon industry, some very limited
techniques have been used for detecting a certain type of event,
i.e., possible rig states, such as "in slips", "not in slips",
"tripping in" or "tripping out". These systems take a small set of
rig states, where each rig state is an intentional drilling state,
and use probability analysis to retroactively determine which of
the set of intentional drilling states the rig has moved into.
Probabilistic rig state detection is described in U.S. Pat. No.
7,128,167, the entirety of which is hereby incorporated by
reference for all purposes. A system and method for online
automation is described in U.S. patent application Ser. No.
13/062,782, now U.S. Pat. No. 8,838,426, the entirety of which is
hereby incorporated by reference for all purposes.
[0014] Electrical Submersible Pumps (ESPs) are of common use in the
Oil and Gas industry. The physicality of the ESP behavior in regard
to operational conditions is well documented and understood. One of
the main reasons for pump failure is to operate the pump for too
long under stressful conditions that are often the result of human
error. For instance, problems may occur when a valve downstream of
an ESP is closed while the pump is still operating (for any
reason). Such actions produce a stress condition for the pump as
doing so would lead to operating the pump at zero efficiency with
no cooling fluid flowing past the motor. The lack of fluid flow can
therefore potentially quickly lead to burning failure of the ESP if
left unanswered. This specific phenomenon is oftentimes referred to
as "deadhead condition". Similarly, "gas ingestion," a situation in
which a pump is attempting to pump gas rather than liquid, and
"low-flow," a situation in which the pump is not producing adequate
flow, produce stress on the pumping equipment that may lead to
equipment failure.
[0015] These stress patterns (e.g., deadhead, gas ingestion, and
low-flow) have known signatures on the different measurements taken
downhole or at the surface of an ESP-equipped well (bottom hole
gauge pressure and temperature, surface well head pressure and
temperature, etc.). Hitherto, the typical way of detecting deadhead
(and other stress conditions and downhole equipment failures) is
through a surveillance engineer cognitively recognizing a signature
on the different channels of a monitoring system (parameter A
increasing, while B is decreasing etc.). However, with a myriad of
data-streams arriving quickly and simultaneously, it is often very
difficult for the surveillance engineer to make the connection
between the data values from the sensors and the potential stress
or failure condition.
[0016] While the different ESP stress patterns signatures are
known, some prior art systems detect stress patterns
programmatically. One prior art technology uses fuzzy logic;
another applies linear regression to the relevant operating
properties (taking into account the last X measurement points) to
combine the relevant operating property values into a stress
condition corresponding to a known pattern. This approach is most
of the time done in a deterministic way; in other words, stress
conditions are recognized if they meet the signature programmed in
the employed expert system. One thought is that today in the
industry a focus is attached to the detection of failure events
(broken shaft, gas lock or other) while much less is done on the
actual stress conditions leading to a failure.
[0017] In the hydrocarbon industry there are ever more and better
sensors for sensing data related to the exploration, extraction,
production and/or transportation of the hydrocarbons, for example,
in the artificial lift domain, which uses electrical submersible
pumps or progressive cavity pumps. To better control/automate
processes related to the exploration, extraction, production and/or
transportation of the hydrocarbons and/or to better
process/interpret the data for human controllers/operators of the
processes related to the exploration, extraction, production and/or
transportation of the hydrocarbons the sensed data associated with
the processes may be quickly and effectively handled.
[0018] Furthermore, there is still a desire to improve the
automation of the detection of these stress patterns and failure
conditions so as to quickly detect stress patterns so that
equipment failure may be avoided or to mitigate the cost of
equipment failure.
SUMMARY
[0019] Embodiments of the present disclosure provide systems and
methods for real-time/online interpretation/processing of data
associated with a hydrocarbon related procedure to provide for
real-time automation/control of the procedure. In an embodiment of
the present disclosure the data is segmented and the
segments/changepoints between segments are analyzed so that the
data can be processed and provide for the operation/control of the
hydrocarbon related procedure.
[0020] In one aspect the technology disclosed herein includes a
method for detecting equipment failures or stress conditions that
may result in equipment failures in a process in the hydrocarbon
industry, where the hydrocarbon-industry-process is subject to a
change in a plurality of operating conditions each monitored by at
least one sensor providing a plurality of input data streams,
comprising segmenting the input data streams such that each segment
of data points is modeled using a simple mathematical model, using
the segmentations and statistical parameters associated with the
segmentations and the underlying data to compute probabilities
associated with at least one high-level inquiry in regard to the
input streams thereby computing probabilities for inquiry answers,
and inputting the high-level inquiry probabilities into a reasoning
engine and operating the reasoning engine to determine the
probability of an equipment event. The method may advantageously be
applied to artificial lift operations employing electrical
submersible pumps (ESP) or progressive cavity pump.
[0021] In another aspect the technology disclosed herein includes a
hydrocarbon process control system comprising at least one sensor
measuring an operating property of a hydrocarbon process controlled
by the control system, a signal processing module for segmenting an
input stream from the at least one sensor and for computing
probabilities of answers to at least one high-level inquiry in
regard to the input stream from the at least one sensor, and an
expert system connected to the signal processing module and
operable to receive the probabilities for the answers to the at
least one high-level inquiry and operable to compute therefrom
probabilities of at least one equipment event. Such a control
system may advantageously be applied to artificial lift operations
employing electrical submersible pumps (ESP) or progressive cavity
pump.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The present disclosure is described in conjunction with the
appended figures.
[0023] FIG. 1 is a schematic diagram illustrating a drilling system
including an online automation/control system, in accordance with
an embodiment of the present disclosure.
[0024] FIG. 2 shows detail of a processor for processing data to
automate hydrocarbon processes, for example, oilfield drilling
processes as shown in FIG. 1, according to one embodiment of the
present disclosure.
[0025] FIG. 3 is a graph illustrating changes in volume of a mud
pit employed in a drilling operation including two distinct changes
in volume indicative of a change in operating condition during a
wellbore drilling process, which change may be used in a processor
for processing data to automate hydrocarbon processes according to
one embodiment of the present disclosure.
[0026] FIGS. 4A-D illustrate inclination and azimuth measurements
obtained during a portion of a directional drilling operation which
change may be used in a processor for processing data to automate
hydrocarbon processes according to one embodiment of the present
disclosure.
[0027] FIG. 5 is a three-dimensional graph illustrating differences
in the linear response in a drill bit model, the drill bit
comprising polycrystalline diamond compact cutters (hereinafter a
"PDC bit"), for two different lithologies which change may be used
in a processor for processing data to automate hydrocarbon
processes according to one embodiment of the present
disclosure.
[0028] FIG. 6 is a flow-diagram illustrating an embodiment of the
present disclosure for obtaining segmentations of data streams that
may include changepoints.
[0029] FIG. 7 is an illustration of a tree data structure showing
four-levels of data modeling corresponding to four data points and
weights associated with the various segmentations illustrated
therein, according to one embodiment of the present disclosure.
[0030] FIG. 8 is a block diagram of a software architecture for one
embodiment of the present disclosure for using a changepoint
detector described herein in conjunction with a process control
program.
[0031] FIGS. 9A-B illustrate possible segmentations for the
inclination and azimuth measurements of FIGS. 4A-D, according to
one embodiment of the present disclosure.
[0032] FIGS. 10A-C are graphs illustrating the output calculated by
the changepoint detector for determining the probability of a kick
from the data stream shown in FIG. 3, according to one embodiment
of the present disclosure.
[0033] FIG. 11 is a flow-chart illustrating the operation of the
changepoint detector to determine the probability of a ramp having
a value greater than a given threshold, according to one embodiment
of the present disclosure.
[0034] FIG. 12 is a data-flow illustration showing the output of
the changepoint detector acting as an input to a Bayesian Belief
Network (BBN) to use that output in conjunction with a change in
rig state output to draw an inference as to whether a kick has
occurred, according to one embodiment of the present
disclosure.
[0035] FIG. 13 is a graph illustrating the relationship between
rate-of-penetration (ROP) as a function of weight-on-bit (WOB) and
drill-bit-rotational speed (RPM), which relationship may be used in
a processor for processing data to automate hydrocarbon processes
according to one embodiment of the present disclosure.
[0036] FIG. 14 is the graph of FIG. 13 having drilling process
constraints super-imposed thereon to define a safe operating
window, which window may be analyzed/used in a processor for
processing data to automate hydrocarbon processes according to one
embodiment of the present disclosure.
[0037] FIG. 15 is a screen shot of a graphic user's interface
displaying drilling data collected during a drilling operation,
straight line models corresponding to a segmentation, the safe
operating window corresponding to the current segmentations,
current drilling parameters used, and recommended parameters to
optimize rate of penetration, according to one embodiment of the
present disclosure.
[0038] FIG. 16 is a flow-chart illustrating the operation of the
changepoint detector to determine recommended parameters in an ROP
optimizer, according to one embodiment of the present
disclosure.
[0039] FIG. 17 is a three-dimensional graph illustrating azimuth
and inclination of a wellbore through a three-dimensional space,
which data may be used in a processor for processing data to
automate hydrocarbon processes according to one embodiment of the
present disclosure.
[0040] FIG. 18 is a flow-chart illustrating the use of a
changepoint detector in determining real-time estimates for dogleg
severity and toolface from azimuth and inclination data collected
during a drilling operation, according to one embodiment of the
present disclosure.
[0041] FIG. 19 is a high-level schematic drawing illustrating
examples of sensor data that may be supplied from a downhole ESP
assembly and related surface equipment as well as from a pump
variable frequency controller to a control system.
[0042] FIG. 20 is a graph illustrating changes in operating
properties during a low-flow event.
[0043] FIG. 21 is a graph illustrating changes in operating
properties during a deadhead event.
[0044] FIG. 22 is a graph illustrating changes in operating
properties during a gas ingestion event.
[0045] FIG. 23 is a schematic illustration of a control system
connected to various sensors, as described above in conjunction
with FIG. 19.
[0046] FIG. 24 is a flowchart illustrating the high-level
operations performed by the signal processing module 301 and the
expert system.
[0047] FIG. 25 is a flowchart illustrating operation of the basic
tendency question. Based on the segmentation, a section of data
from the past that is considered normal values for the operation
property is selected for reference.
[0048] FIG. 26 is a graph showing distributions of the value for
the operations property in this normal section as well as for the
portion of the data stream being evaluated.
[0049] FIG. 27 is a graph that illustrates determination of
probabilities to answer alternatives for the correlation
question.
[0050] FIG. 28 illustrates a two-layer Bayesian belief network.
[0051] FIG. 29 illustrates one example of a Bayesian belief network
in which two intermediate nodes--Energy Consumption and Impeller
Inactive--are included.
[0052] FIG. 30 is a portion of a Bayesian belief network linking
PumpWorn and ImpellersInactive events to a
DischargerPressureVariations high-level question.
[0053] FIG. 31 is an example Bayesian belief network that may be
used to calculate probabilities for equipment events in an
artificial lift operation that includes an electric submersible
pump or a progressive cavity pump.
[0054] In the appended figures, similar components and/or features
may have the same reference label. Further, various components of
the same type may be distinguished by following the reference label
by a dash and a second label that distinguishes among the similar
components or by appending the reference label with a letter. If
only the first reference label is used in the specification, the
description is applicable to any one of the similar components
having the same first reference label irrespective of the second
reference label or appended letter.
DETAILED DESCRIPTION
[0055] In the following detailed description, reference is made to
the accompanying drawings that show, by way of illustration,
specific embodiments in which the disclosure may be practiced.
These embodiments are described in sufficient detail to enable
those skilled in the art to practice the disclosure. It is to be
understood that the various embodiments of the disclosure, although
different, are not necessarily mutually exclusive. For example, a
particular feature, structure, or characteristic described herein
in connection with one embodiment may be implemented within other
embodiments without departing from the spirit and scope of the
disclosure. In addition, it is to be understood that the location
or arrangement of individual elements within each disclosed
embodiment may be modified without departing from the spirit and
scope of the disclosure. The following detailed description is,
therefore, not to be taken in a limiting sense, and the scope of
the present disclosure is defined only by the appended claims,
appropriately interpreted, along with the full range of equivalents
to which the claims are entitled. In the drawings, like numerals
refer to the same or similar functionality throughout the several
views.
[0056] It should also be noted that in the description provided
herein, computer software is described as performing certain tasks.
For example, we may state that a changepoint detector module
performs a segmentation of a data stream by following a described
methodology. That, of course, is intended to mean that a central
processing unit executing the instructions included in the
changepoint detector (or equivalent instructions) would perform the
segmentation by appropriately manipulating data and data structures
stored in memory and secondary storage devices controlled by the
central processing unit. Furthermore, while the description
provides for embodiments with particular arrangements of computer
processors and peripheral devices, there is virtually no limit to
alternative arrangements, for example, multiple processors,
distributed computing environments, web-based computing. All such
alternatives are to be considered equivalent to those described and
claimed herein.
[0057] It should also be noted that in the development of any such
actual embodiment, numerous decisions specific to circumstance may
be made to achieve the developer's specific goals, such as
compliance with system-related and business-related constraints,
which will vary from one implementation to another. Moreover, it
will be appreciated that such a development effort might be complex
and time-consuming but would nevertheless be a routine undertaking
for those of ordinary skill in the art having the benefit of this
disclosure.
[0058] In this disclosure, the term "storage medium" may represent
one or more devices for storing data, including read only memory
(ROM), random access memory (RAM), magnetic RAM, core memory,
magnetic disk storage mediums, optical storage mediums, flash
memory devices and/or other machine readable mediums for storing
information. The term "computer-readable medium" includes, but is
not limited to portable or fixed storage devices, optical storage
devices, wireless channels and various other mediums capable of
storing, containing or carrying instruction(s) and/or data.
[0059] A technology is presented herein that automates the
detection of signal signatures that are indicative of equipment
stress and failure events. This technology uses a segmentation
algorithm to segment data series and to model the data series. The
results from the segmentation are used to answer several high-level
inquiries, such as tendencies, noise level, and correlation between
signals. Each high-level inquiry has associated therewith several
answers. Probabilities for each of the answers is computed and the
probabilities are input into a reasoning engine, for example, a
Bayesian belief network, for determining the probabilities of
equipment events, such as stress patterns and equipment
failure.
[0060] The segmentation algorithm is described herein in the
context of a drilling operation. However, it is applied in some
embodiments to hydrocarbon extraction and production, in
particular, to artificial lift using electric submersible pumps and
progressive cavity pumps.
[0061] Accordingly, to set the stage for the explanation of the
segmentation algorithm, FIG. 1 shows a drilling system 10 using
changepoint detection in the control of the drilling apparatus,
according to one embodiment of the present disclosure. As depicted,
a drill string 58 is shown within a borehole 46. The borehole 46 is
located in the earth 40 having a surface 42. The borehole 46 is
being cut by the action of a drill bit 54. The drill bit 54 is
disposed at the far end of the bottomhole assembly 56 that is
itself attached to and forms the lower portion of the drill string
58.
[0062] The bottomhole assembly 56 contains a number of devices
including various subassemblies. According to an embodiment of the
present disclosure, measurement-while-drilling (MWD) subassemblies
may be included in subassemblies 62. Examples of typical MWD
measurements include direction, inclination, survey data, downhole
pressure (inside the drill pipe, and outside or annular pressure),
resistivity, density, and porosity. The subassemblies 62 may also
include is a subassembly for measuring torque and weight on
bit.
[0063] The subassemblies 62 may generate signals related to the
measurements made by the subassemblies 62. The signals from the
subassemblies 62 may be processed in processor 66. After
processing, the information from processor 66 may be communicated
to communication assembly 64. The communication assembly 64 may
comprise a pulser, a signal processor, an acoustic processor and/or
the like. The communication assembly 64 converts the information
from processor 66 into signals that may be communicated as pressure
pulses in the drilling fluid, as signals for communication through
an optic fibre, a wire and/or the like, or signals for wireless or
acoustic communication and/or the like. Embodiments of the present
disclosure may be used with any type of sensor associated with the
hydrocarbon industry and with any type of telemetry system used
with the sensor for communicating data from the sensor to the
online changepoint detector, according to one embodiment of the
present disclosure.
[0064] The subassemblies in the bottomhole assembly 56 can also
include a turbine or motor for providing power for rotating and
steering drill bit 54. In different embodiments, other telemetry
systems, such as wired pipe, fiber optic systems, acoustic systems,
wireless communication systems and/or the like may be used to
transmit data to the surface system.
[0065] The drilling rig 12 includes a derrick 68 and hoisting
system, a rotating system, and a mud circulation system. The
hoisting system which suspends the drill string 58, includes draw
works 70, fast line 71, crown block 75, drilling line 79, traveling
block and hook 72, swivel 74, and deadline 77. The rotating system
includes kelly 76, rotary table 88, and engines (not shown). The
rotating system imparts a rotational force on the drill string 58
as is well known in the art. Although a system with a kelly and
rotary table is shown in FIG. 1, those of skill in the art will
recognize that the present disclosure is also applicable to top
drive drilling arrangements. Although the drilling system is shown
in FIG. 1 as being on land, those of skill in the art will
recognize that the present disclosure is equally applicable to
marine environments.
[0066] The mud circulation system pumps drilling fluid down the
central opening in the drill string. The drilling fluid is often
called mud, and it is typically a mixture of water or diesel fuel,
special clays, and other chemicals. The drilling mud is stored in
mud pit 78. The drilling mud is drawn in to mud pumps (not shown),
which pump the mud through stand pipe 86 and into the kelly 76
through swivel 74 which contains a rotating seal.
[0067] The mud passes through drill string 58 and through drill bit
54. As the teeth of the drill bit grind and gouges the earth
formation into cuttings the mud is ejected out of openings or
nozzles in the bit with great speed and pressure. These jets of mud
lift the cuttings off the bottom of the hole and away from the bit
54, and up towards the surface in the annular space between drill
string 58 and the wall of borehole 46.
[0068] At the surface the mud and cuttings leave the well through a
side outlet in blowout preventer 99 and through mud return line
(not shown). Blowout preventer 99 comprises a pressure control
device and a rotary seal. The mud return line feeds the mud into
separator (not shown) which separates the mud from the cuttings.
From the separator, the mud is returned to mud pit 78 for storage
and re-use.
[0069] Various sensors are placed on the drilling rig 10 to take
measurement of the drilling equipment. In particular hookload is
measured by hookload sensor 94 mounted on deadline 77, block
position and the related block velocity are measured by block
sensor 95 which is part of the draw works 70. Surface torque is
measured by a sensor on the rotary table 88. Standpipe pressure is
measured by pressure sensor 92, located on standpipe 86. Additional
sensors may be used to detect whether the drill bit 54 is on
bottom. Signals from these measurements are communicated to a
central surface processor 96. In addition, mud pulses traveling up
the drillstring are detected by pressure sensor 92.
[0070] Pressure sensor 92 comprises a transducer that converts the
mud pressure into electronic signals. The pressure sensor 92 is
connected to surface processor 96 that converts the signal from the
pressure signal into digital form, stores and demodulates the
digital signal into useable MWD data. According to various
embodiments described above, surface processor 96 is programmed to
automatically detect the most likely rig state based on the various
input channels described. Processor 96 is also programmed to carry
out the automated event detection as described above. Processor 96
may transmit the rig state and/or event detection information to
user interface system 97 which is designed to warn the drilling
personnel of undesirable events and/or suggest activity to the
drilling personnel to avoid undesirable events, as described above.
In other embodiments, interface system 97 may output a status of
drilling operations to a user, which may be a software application,
a processor and/or the like, and the user may manage the drilling
operations using the status.
[0071] Processor 96 may be further programmed, as described below,
to interpret the data collected by the various sensors provided to
provide an interpretation in terms of activities that may have
occurred in producing the collected data. Such interpretation may
be used to understand the activities of a driller, to automate
particular tasks of a driller, to provide suggested course of
action such as parameter setting, and to provide training for
drillers.
[0072] In the hydrocarbon industry it is often desirable to
automate, semi-automate and/or the like operations to remove,
mitigate human error, to increase speed and/or efficiency, allow
for remote operation or control, lessen communication obstacles
and/or the like. Moreover, in the hydrocarbon industry sensors are
commonly deployed to gather data to provide for monitoring and
control of the systems related to hydrocarbon capture and/or the
like.
[0073] In the process of drilling a borehole a plurality of sensors
are used to monitor the drilling process--including the functioning
of the drilling components, the state of drilling fluids or the
like in the borehole, the drilling trajectory and/or the
like--characterize the earth formation around or in front of the
location being drilled, monitor properties of a hydrocarbon
reservoir or water reservoir proximal to the borehole or drilling
location and/or the like.
[0074] To analyze the multitude of data that may be sensed during
the drilling process, averaging or the like has often been used to
make statistical assumptions from the data. Such averaging analysis
may involve sampling sensed data periodically and then
statistically analyzing the periodic data, which is in effect a
looking backwards type analysis. Averaging may also involve taking
frequent or continuous data and making assessments from
averages/trends in the data.
[0075] Most analysis of data captured in the hydrocarbon industry
is moving window analysis, i.e., a window of data is analyzed using
the same assumptions/as a whole without consideration as to whether
a change has occurred requiring separate analysis of different
portions of the window of data. If small data windows are selected
to try and avoid/mitigate the effect of changes on the data being
analyzed, the small windows often give rise to large amounts of
"noise" in the data. To avoid the moving window problem, Kalman
filters have been used, however such filters can smooth out effects
of changes, especially abrupt changes, on the data, and may provide
for incorrect analysis of substantially steady state data in which
changes are not occurring. In embodiments of the present
disclosure, real-time analysis of the data is provided by
identifying and/or processing changepoints in the data.
[0076] FIG. 2 shows further detail of processor 96, according to
some embodiments of the disclosure. Processor 96 may include one or
more central processing units 350, main memory 352, communications
or I/O modules 354, graphics devices 356, a floating point
accelerator 358, and mass storage such as tapes and discs 360. It
should be noted that while processor 96 is illustrated as being
part of the drill site apparatus, it may also be located, for
example, in an exploration company data center or headquarters. It
should be noted that many architectures for processor 96 are
possible and that the functionality described herein may be
distributed over multiple processors. All such embodiments are
considered equivalents to the architecture illustrated and
described here.
[0077] Data collected by various sensors in industrial processes
are often very noisy. Such noise may cause real-time human
interpretation of the data near impossible. Furthermore,
calculations based on individual datapoints may amplify the effect
of the noise.
[0078] FIGS. 3 through 5 are illustrations of various examples of
data that may be encountered in the process of drilling wells in
the exploration for subterranean resources such as oil, gas, coal,
and water.
[0079] FIG. 3 shows pit volume data 215 changing with time in a
process of drilling a wellbore 46. In the process of drilling a
wellbore 46, a drilling fluid called mud is pumped down the central
opening in the drill pipe and passes through nozzles in the drill
bit 54. The mud then returns to the surface in the annular space
between the drill pipe 58 and the inner-wall of the borehole 46 and
is returned to the mud pit 78, ready for pumping downhole again.
Sensors measure the volume of mud in the pit 78 and the volumetric
flow rate of mud entering and exiting the well. An unscheduled
influx of formation fluids into the wellbore 46 is called a kick
and is potentially dangerous. The kick may be detected by observing
that flow-out is greater than flow-in and that the pit volume has
increased.
[0080] In FIG. 3, a pit volume data signal 215 is plotted against a
time axis 220. The pit volume data signal 215 is measured in [m3]
and illustrated on a volume axis 210. During the drilling process,
a kick may be observed in the data at around t=1300 and t=1700 time
on the time axis 220. The kick is identifiable in the pit volume
data signal 215 as a change in the gradient of the pit volume data
signal 215. It is desirable to detect these kicks automatically and
to correlate the occurrence of kicks with other events taking place
in the drilling operation, e.g., changes in rig state.
[0081] FIGS. 4A-D are graphs illustrating inclination 401 and
azimuth 403 measurements obtained during a portion of a directional
drilling operation. Inclination 401 and azimuth 403 measurements
are useful to a driller in adjusting the drilling operation to
arrive at particular target formations. The driller uses these
measurements to predict whether the desired target is likely to be
intersected and may take corrective actions to parameters such as
weight-on-bit and drilling-rotational-speed to cause the drilling
trajectory to change in the direction of the target if
necessary.
[0082] As may be seen in FIGS. 4A-D both the continuous inclination
data channel 401 and the continuous azimuth data channel have
rather noisy data. Yet examination of the data reveals certain
trends illustrated by the segmented straight lines superimposed on
the raw data in FIGS. 4C & 4D, respectively. For example, in
the inclination data 401b, the data seems to follow a ramp from
depth .apprxeq.1.016.times.10.sup.4 to depth
.apprxeq.1.027.times.10.sup.4, followed by a step to depth
.apprxeq.1.0375.times.10.sup.4, and another ramp to
.apprxeq.1.047.times.10.sup.4. For determination of the curvature
of the well ("dogleg severity") and direction of the curvature
("toolface"), models may be used to reflect these steps and ramps
rather than using any one data point in the data stream.
Conversely, models may be used rather than the traditional way of
taking stationary measurements at 30 foot or 90 foot intervals
because calculations based models based on the steps and ramp
models of the data may be used in real-time, do not require taking
the drilling operation off-bottom, and may provide dogleg severity
and toolface calculations at relatively short intervals.
[0083] FIG. 5 is yet another graphical illustration of how changes
in lithology may affect drilling operations, in this case, the bit
response of a PDC (Polycrystalline diamond compact) bit in the
three-dimensional space defined by weight-on-bit ("WOB"),
depth-of-cut ("DOC"), and torque. The expected bit response in that
space is described in Detournay, Emmanuel, Thomas et al., Drilling
Response of Dragbits: Theory and Experiment, International Journal
of Rock Mechanics & Mining Sciences 45 (2008): 1347-1360. The
bit response tends to have three phases with respect to the WOB
applied. Each phase has a relatively linear bit response.
[0084] In a first phase 501, with low WOB applied, very low depth
of cut is achieved. At low WOB most of the interaction between the
bit 54 and rock occurs at the wear flats on the cutters. Neither
the rock surface nor the wear flat will be perfectly smooth, so as
depth of cut increases the rock beneath the contact area will fail
and the contact area will enlarge. This continues until a critical
depth of cut where the failed rock fully conforms to the geometry
of the wear flats and the contact area grows no larger. Next, a
second phase 503 corresponds to an intermediate amount of WOB. In
this phase 503, beyond a critical depth of cut, any increase in WOB
translates into pure cutting action.
[0085] The bit incrementally behaves as a perfectly sharp bit until
the cutters are completely buried in the rock and the founder point
is reached. The third phase 505 is similar to the first phase 501
in that little is gained from additional WOB. The response past the
founder point depends on how quickly the excess WOB is applied.
Applied rapidly, the uncut rock ahead of the cutters will contact
with the matrix body of the bit and act in a similar manner to the
wear flats in Phase I, so depth of cut will increase slightly with
increasing WOB. Applied slowly, the cuttings may become trapped
between the matrix and the uncut rock, so depth of cut may decrease
with increasing WOB. Drillers may operate near the top of the
second phase with the optimal depth of cut achieved without wasting
additional WOB.
[0086] Depth of cut per revolution can be estimated by dividing ROP
by RPM, so real-time drilling data can be plotted in the three
dimensional {WOB, bit torque and depth of cut} space as illustrated
in FIG. 5. As the bit drills into a new formation, the response
will change abruptly and the points will fall on a new line. The
plotted line 507 illustrates a model of the bit response for a
first formation corresponding to collected data points 509. On the
other hand, data points 511 correspond to data collected in a
different formation from the data points 509. If the second set
(511) correspond to data encountered after the first set (509), a
change in formation and ancillary operating conditions may have
occurred.
[0087] A straight line in three dimensions has four unknown
parameters, two slopes and the intersection with the x-y plane,
i.e., WOB-torque plane in this case. These parameters could be
estimated with a least squares fit to a temporal or spatial sliding
window, e.g., last five minutes or last ten feet of data, but this
would provide very poor fits in the vicinity of formation
boundaries. For example, in FIG. 5, plotting a straight line
through both the points of the first set (509) and points of the
second set (511) would yield bizarre model parameters.
[0088] The PDC bit models have successfully been applied in the
field by manual inspection of the data and breaking it up into
homogeneous segments, e.g., in FIG. 5, a straight line is fitted to
the data points 509 only and a second straight line (not shown) may
be fitted to the data points 511 only, thereby avoiding the
cross-class polluted estimates produced by a moving window. While
in a simplified example as illustrated in FIG. 5 it is possible to
visually see that the data points 511 and the data points 509
lie/occur on different lines, with real world data, this is a
labour intensive process that hitherto has prevented application of
the PDC bit model in controlling drilling systems/procedures.
[0089] Consider now again FIGS. 4A & 4B. As discussed
hereinabove, the data may be segmented into three different
segments and each segment having associated therewith a model
particularly useful for modeling the data in that segment. In one
embodiment of the disclosure, the data is modeled using either ramp
or step functions, for example, using the least squares algorithm,
and these models are evaluated using Bayesian Model Selection.
Bayesian Model Selection is discussed in detail in Deviderjit Sivia
and John Skilling, Data Analysis: A Bayesian Tutorial (OUP Oxford,
2ed. 2006), the entire contents of which is incorporated herein by
reference. Thus, for each segment of each segmentation, a model
that is either a ramp or a step is assigned and the corresponding
segmentations are assigned a weight indicative of how well the
segmentation and associated models conform to the data stream as
compared to other segmentations.
[0090] In embodiments of the present disclosure, online data
analysis may be provided by treating incoming data as being
composed of segments between which are changepoints. The
changepoints may be identified by the data analysis to provide for
detection in changes in the automated industrial process. In
certain aspects, a plurality of sensors or the like may provide a
plurality of data channels that may be segmented into homogeneous
segments and data fusion may be used to cross-correlate, compare,
contrast or the like, changepoints in the incoming data to provide
for management of the automated industrial procedure.
[0091] In an embodiment of the present disclosure, the data may be
analyzed in real-time to provide for real-time detection, rather
than retrospective, detection of the changepoint. This real-time
detection of the changepoint may be referred to as online
analysis/detection. In an embodiment of the present disclosure, the
data from one or more sensors may be fitted to an appropriate model
and from analysis of the incoming data with regard to the model
changepoints may be identified. The model may be derived
theoretically, from experimentation, from analysis of previous
operations and/or the like.
[0092] As such, in an embodiment of the present disclosure, data
from an automated industrial process may be analyzed in an online
process using changepoint modeling. The changepoint models divide a
heterogeneous signal, in an embodiment of the present disclosure
the signal being data from one or more sources associated with the
hydrocarbon related process, into a sequence of homogeneous
segments. The discontinuities between segments are referred to as
changepoints.
[0093] Merely by way of example, an online changepoint detector in
accordance with an embodiment of the present disclosure, may model
the data in each homogeneous segment as a linear model, such as a
ramp or step, with additive Gaussian noise. Such models are useful
when the data has a linear relationship to the index. In some
embodiments, more complex models may be employed, e.g.,
exponential, polynomial and trigonometric functions. As each new
sample (set of data) is received, the algorithm outputs an updated
estimate of the parameters of the underlying signal, e.g., the mean
height of steps, the mean gradient of ramps and the mean offset of
ramps, and additionally the parameters of the additive noise (for
zero-mean Gaussian noise, the parameter is the standard deviation
or the variance, but for more general noise distributions other
parameters such as skewness or kurtosis may also be estimated).
[0094] If so desired, a changepoint may be designated where the
noise parameters are found to have changed. In some embodiments of
the present disclosure, the tails of a distribution are may be
considered in the analysis, as when analyzing the risk of an event
occurring the tails of the distribution may provide a better
analytical tool than the mean of the distribution. In an embodiment
of the present disclosure, the changepoint detector may be used to
determine a probability that the height/gradient/offset of the
sample is above/below a specific threshold.
[0095] A basic output of the changepoint detector may be a
collection of lists of changepoint times and a probability for each
list. The most probable list is thus the most probable segmentation
of the data according to the choice of models: G1, . . . , Gj.
[0096] The segmentation of the signal may be described using a tree
structure (see FIG. 7) and the algorithm may be considered as a
search of this tree. At time 0 (before any data has arrived) the
tree may contain a single root node, R. At time 1 the root node
spawns J leaves, one leaf for each of the J segment models--the
first leaf represents the hypothesis that the first data point is
modeled with G.sub.1, the second leaf hypothesises is G.sub.2, etc.
At subsequent times, the tree grows by each leaf node spawning J+1
leaves, one for each model and an extra one represented by 0, which
indicates that the data point at the corresponding time belongs to
the same model segment as its parent. For example, if G.sub.1 were
a step model and G.sub.2 l were a ramp, a path through the tree
from the root to a leaf node at time 9 might be:
[0097] R 1 0 0 0 0 0 2 0 0
where this would indicate that the first six samples were generated
by a step and that the remaining four samples were generated by a
ramp.
[0098] Over time the tree grows and it is searched using a
collection of particles each occupying a distinct leaf node. The
number of particles may be chosen by the user/operator and around
20-100 is may be sufficient, however other amounts of particles may
be used in different aspects of the present disclosure. Associated
with a particle is a weight, which can be interpreted as the
probability that the segmentation indicated by the path from the
particle to the root (as in the example above) is the correct
segmentation. The objective of the algorithm is to concentrate the
particles on leaves that mean the particle weights will be
large.
[0099] FIG. 6 is a flow-diagram illustrating an embodiment of the
present disclosure for obtaining segmentations of data streams that
may include changepoints. The segmentation process for determining
changepoints and associated models successively builds a tree data
structure, an example of which is illustrated in FIG. 7, wherein
each node in the tree represents different segmentations of the
data. The tree is also periodically pruned to discard
low-probability segmentations, i.e., segmentations that have a poor
fit to the data.
[0100] The segmentations are initialized by establishing a root
node R 701. Next a data point is received from one or more input
streams 703. In response the segmentation process spawns child
segmentations 705, that reflect three different alternatives,
namely, a continuation of the previous segment, a new segment with
a first model, or a new segment with a second model (while we are
in this example describing an embodiment with two models, ramp and
step, in some embodiments additional models may be included). In an
embodiment of the present disclosure, illustrated and described
herein, the alternative models are ramp and step functions. As the
root node does not represent any model, the first generation in the
tree, reflecting the first data point, may start a new segment
which is either a ramp, which is represented in the tree as 1, or a
step, which is represented in the tree as 2.
[0101] In the example given above, the particle R 1 0 0 0 0 0 2 0 0
would produce three new child nodes with corresponding
particles:
[0102] R 1 0 0 0 0 0 2 0 0 0
[0103] R 1 0 0 0 0 0 2 0 0 1
[0104] R 1 0 0 0 0 0 2 0 0 2
The first of which indicates a continuation of the step segment
that begins with the 7th data point, the second, a new ramp, and
the third, a new step.
[0105] Models are then created by fitting the data in the new
segments to the designated models for the segments and models
corresponding to existing segments are refit 706. For example, if a
new ramp segment is to be created for a new child particle, the
data in the segment is fit to that ramp. Naturally, when a new
segment is created, the corresponding model that is assigned is
merely a function that puts the model value through the new data
point. However, for existing segments in which the segment
encompasses a plurality of data points, the model parameters, e.g.,
the parameters defining the gradient and offset of a ramp, are
re-evaluated. Some form of linear regression technique may be used
to determine the linear function to be used to model the data in
the segment as a ramp or step.
[0106] The segmentations produced are next evaluated, 707, using
Bayesian Model Selection or the like to calculate weights
indicative of how good a fit each segmentation is for the
underlying data.
[0107] After the segmentations, creation of model functions, and
corresponding models have been evaluated, i.e., having had weights
assigned thereto, the tree is pruned by removing some particles
from future consideration and to keep the particle population size
manageable 709. The weights of the remaining particles are
normalized 711.
[0108] Having evaluated the segmentations of the input data stream,
the segmentations and corresponding models may be used in a process
control program or in a further data analysis program 713. The use
of the segmentations and corresponding models may take several
forms. For example, the remaining segmentations may each be used to
evaluate the input data in the calculation of a quantity used to
compare against a threshold value for the purpose of alerting of a
condition to which some corrective action should be taken. In such
a scenario, a weighted average (weighted by the weights associated
with each segmentation) may be computed to determine the
probability that the condition has or has not occurred. This
probability may either be used to trigger an action or suggest an
action, or as input into further condition analysis programs.
[0109] FIG. 8 is a block diagram illustrating a possible software
architecture using changepoint detection as described herein. A
changepoint detector module 901 and a process control program 903
may both be stored on the mass storage devices 360 of computer
system 96 used to receive and analyze sensor data obtained from a
drilling operation, and for control of the drilling operation. The
changepoint detector module 901 contains computer instructions
processable by the CPU 350 to provide calculations as described
herein, for example, the process flow set forth in FIG. 6. These
instructions cause the CPU 350 to receive data from a data stream
905 from one of the various sensors on the drilling rig, or other
industrial process.
[0110] The input data is processed by the CPU 350 according to
instructions of a segmentation module 907 to produce segmentations
909 of the data as described herein. These segmentations contain
segments defined by intervals of an index of the data stream, and
models associated with those segments. The segments are fed into a
calculation module to provide a result from the changepoint
detector 901 that in turn is an input to the process control
program 903. The result may be a probability of an event having
occurred or some other interpretation of the input data (e.g.,
toolface or dogleg severity), or even a recommended action (e.g.,
suggested change in drillbit rotational speed or weight on bit to
obtain better rate of penetration).
[0111] A more detailed view of FIG. 7, which is a graphical
depiction of the segmentation tree 801 and weights 803 associated
with the active particles after four time indexes, is now provided.
As noted above, to arrive at a segmentation, the changepoint
detector 901 uses a system of particles and weights. From, Time 0
(which is represented by the root node R) to Time 1, two particles
("1" and "2") are spawned (705); the first one ("1") representing a
step and the second ("2") representing a ramp. At Time 2 (and each
subsequent time index), each of the currently active particles
spawns three particles, the first representing no change ("0"), the
second representing a step ("1") and the third representing a ramp
("2"), thus producing the particles 10, 11, 12, 20, 21, and 22.
This continues for each time index and at Time 4 the tree has grown
to 54 particles. For each active particle, i.e., a particle that
was spawned at the latest index and that has not been removed
through pruning (709), a weight is determined (707 and 711). These
weights are illustrated graphically in FIG. 7 in the weight bar
chart 803. The weights are used to prune the tree 801 by removing
the lowest weight particles when the number of particles exceed a
preset maximum.
[0112] As noted in the discussion of FIG. 6, when the weights for
the remaining active particles have been determined and normalized,
the resulting segmentations are used in conjunction with a control
program 713.
[0113] Consider by way of example again the inclination 401a and
azimuth 403a input streams from FIGS. 4A-D. FIGS. 9A-B are
illustrations of changepoints identified by the changepoint
detector 901 and the associated models. For example, in the
inclination stream 401b, the changepoint detector 901 identifies
changepoints 405 and 407, in addition to changepoints at the start
and end of the data set. Similarly, in the azimuth data stream
403b, the changepoint detector 901 identifies changepoints 409 and
411. For the inclination stream, the changepoint detector 901 has
fit a ramp for the segment up to the first changepoint 405,
followed by a step up to the second changepoint 407, and finally a
ramp for the data following the second changepoint 407. On the
other hand, for the azimuth datastream 403b, the changepoint
detector 901 has fit three successive ramps, each having different
gradient.
[0114] As the above paragraphs illustrate, there are many processes
relating to the drilling of a hydrocarbon well or operation of any
other hydrocarbon related procedure in which data that is
indicative of operating environment is subject to difficult
interpretation due to noise or other factors, yet where that data
and changes in the operating environment that the data reflects may
have some effect on how an operator of the drilling of the
hydrocarbon well or operation of the hydrocarbon related procedure
would set parameters for optimal process performance or where the
such data, if modeled accurately, may be very useful in automation
of aspects of the creation/operation of the hydrocarbon well.
[0115] We now turn to three examples of the use of the changepoint
detector 901 in conjunction with a control program 903.
[0116] In a first example, the changepoint detector 901 is used to
determine kicks encountered in a drilling operation. In the process
of drilling a wellbore, a drilling fluid called mud is pumped down
the central opening in the drill pipe and passes through nozzles in
the drill bit. The mud then returns to the surface in the annular
space between the drill pipe and borehole wall and is returned to
the mud pit, ready for pumping downhole again. Sensors measure the
volume of mud in the pit and the volumetric flow rate of mud
entering and exiting the well. An unscheduled influx of formation
fluids into the wellbore is called a kick and is potentially
dangerous. The kick may be detected by observing that flow-out is
greater than flow-in and that the pit volume has increased.
[0117] FIG. 3 is a graphical depiction of pit volume data changing
with time in a process of drilling a wellbore. In FIG. 3, a pit
volume data signal 215 is plotted against a time axis 220. The pit
volume data signal 215 is measured in cubic meters (m.sup.3) and
illustrated on a volume axis 210. The pit volume signal is
indicative of kicks at two locations, at around t=1300 and t=1700.
For the sake of discussion, suppose that during the drilling
process, a kick was manually detected for the second of these at
around the t=1700 time on the time axis 220 and that the increase
in pit volume at t=1300 is due to a connection of drilling pipe.
The kick is identifiable in the pit volume data signal 215 as a
change in the gradient of the pit volume data signal 215.
[0118] FIGS. 10A-C illustrate the application of the changepoint
detector 901 to the pit-volume data of FIG. 3 (for the convenience
of the reader, FIG. 3 is replicated as FIG. 10A), in accordance
with an embodiment of the disclosure. FIG. 10B is a graphical
illustration of the output from the changepoint detector 901. The
changepoint detector 901 processes homogeneous segments of the pit
volume data 215 from FIG. 10A. Using these homogeneous segments the
changepoint detector 901 produces an output signal indicative of
the probability 225 that a ramp in the pit volume data 215 has a
gradient greater than 0.001 m.sup.3/s. The probability 225 is
plotted against the time axis 220 and a probability axis 227 that
provides for a zero to unity probability.
[0119] FIG. 11 is a flow-chart illustrating the operation of the
changepoint detector 901 to determine the probability of a ramp
having a value greater than a given threshold. Applying the method
described in conjunction with FIGS. 6 and 7, the changepoint
detector 901 determines possible segmentations and assigns weights
to these segmentations 101. In the example of FIG. 10, 101 would
have arrived at a number of segmentations, likely including
segmentations that indicate steps from t=800 to t=1280 and a ramp
from t=1280 to t=1300. Because such a segmentation would have a
good fit to the data, that segmentation would have a very high
weight.
[0120] Next the calculation module 911 uses the segmentations to
calculate a desired probability value, 103. In the present example,
that probability is the probability of the ramp of the pit volume
data exceeds a given threshold, namely, for the purposes of the
example, 0.001 m.sup.3/s. That result is obtained by calculating
the gradient from the models corresponding to each active
segmentation, 105, and computing a weighted average over those
results based on the weight associated with each segmentation. If
one of the possible segmentations under consideration represented a
continuation of the model from t=800 which has a very low ramp or
even a step, once the volume data starts increasing at t=1300 (and
similarly at t=1700) that model would be a poor fit and have a very
low weight associated with it. Therefore, at t=1300, the weighted
average calculation would give the segmentation that includes a
ramp beginning at t=1280 a very large weight and that segmentation
would have a high influence on the weighted average calculation and
the final result.
[0121] In FIG. 10B, the probability 225 approaches unity around the
time the kick may be manually identified in the pit volume data 215
in FIG. 10A. As such, the changepoint detector of the present
disclosure may provide for using probabilistic gradient analysis of
data retrieved during a drilling process to determine in real-time
the occurrence of a kick or the like.
[0122] FIG. 10C illustrates flow-in and flow-out data corresponding
to the pit volume data of FIG. 10A for the drilling process. As
illustrated, flow-in data 230 and flow-out-data 233 for the
wellbore drilling operation is plotted against the time axis 220.
The flow-in/flow-out data is not used in the changepoint detection
method illustrated in FIG. 10B. However, it may be seen that there
is a fluctuation in the data at time, t=1700, that corresponds to
the kick that the changepoint detector of FIG. 10B seeks to
detect.
[0123] The changepoint detector of FIG. 10B may have the following
characteristics: [0124] (a) The probability analysis for the
changepoint detector may also approaches unity when a connection of
a drilling pipe is made at time t=1300. [0125] (b) When the
circulation of the system is not at steady-state, the pit volume
may be affected by flowline delays and wellbore ballooning. [0126]
(c) Thresholding of the gradient of pit volumes may be somewhat
arbitrary. To analyze the automated drilling process in real-time,
shallow gradients of the received data over long durations may be
as determinative in the analysis process as steep gradients
received over short durations. As such, since the height of the
ramp is the volume of the influx, it may be used to threshold, base
real-time analysis, upon this statistic. [0127] (d) The kick may
also be seen in the flow data associated with the drilling process,
shown in FIG. 10C. However, the gradient algorithm does not use
this additional data.
[0128] To take the additional information available from drilling
process into account, the output from the changepoint detector may
be fed into additional analysis software for fusing the changepoint
detector output with such additional information. For example, the
changepoint detector output may be one input to a Bayesian Belief
Network used to combine that output with detection of changes in
rig state, i.e., the current state of the drilling rig.
[0129] FIG. 12 is a flow-type illustration of changepoint detector
for analyzing an automated drilling process in which flow-out minus
flow-in, called delta flow, and pit volume are probabilistically
analyzed to identify changepoints, in accordance with an embodiment
of the present disclosure. As depicted in FIG. 12, pit volume data
305 and delta flow data 310 are detected during an automated
drilling process. In an embodiment of the present disclosure,
changepoint detectors 901a and 901b may be applied to both the pit
volume data 305 and the delta flow data 310.
[0130] As described previously, for example in conjunction with
FIGS. 6 and 7, in an embodiment of the present disclosure, the pit
volume data 305 and delta flow data 310 may be broken down into
homogeneous segments in real-time. A first changepoint detector 901
a associated with the pit volume data 305 may analyze the pit
volume data 305 and from comparisons with previous segments may
detect when one of the homogeneous segments of the incoming data
does not have a positive gradient, e.g., the changepoint detector
901a may detect a step model or a ramp with negative gradient.
Similarly, a second changepoint detector 901b associated with the
delta flow data 310 may analyze the pit volume data 305 and from
comparisons with previous segments may detect when one of the
homogeneous segments of the incoming data does not have a positive
gradient, e.g., the detector 901b may detect a step model or a ramp
with negative gradient.
[0131] In accordance with some embodiments of the present
disclosure, each of the plurality of the changepoint detectors 901
may process for the segment(s) with positive gradient the
probability that the influx volume is greater than a threshold
volume T. In FIG. 12 the volume is an area under the delta flow
ramp(s) 323 and a vertical height 326 of the pit volume ramp(s).
Each changepoint detector 901 may calculate the overall probability
p(vol>T) as a weighted sum of the probabilities from all the
segmentation hypotheses it has under consideration.
[0132] The two continuous probabilities p(vol>T) 121a and 121b
may be entered into a BBN 123, specifically into a Pit Gain node
131 and an Excess Flow node 133. In an embodiment of the present
disclosure, a condition Well Flowing node 135 may describe the
conditional probabilities of an existence of more fluid exiting the
wellbore being drilled in the automatic drilling process than
entering the wellbore. Such a condition occurring in the drilling
process may cause PitGain and ExcessFlow signatures in the surface
channels. The Well Flowing node output 135 may be a result of a
change in the drilling process, i.e., a recent change in rig state,
node 137. For example, the circulation of fluid in the wellbore may
not be at a steady-state due, for example to switching pumps on/off
or moving the drilling pipe during the drilling process. Deliberate
changes in the drilling process, such as changing pump rates,
moving the drill pipe, changing drilling speed and/or the like may
be referred as rig states. Detection of change of rig state is
described in U.S. Pat. No 7,128,167, System and Method for Rig
State Detection, to Jonathan Dunlop, et al., issued Oct. 31,
2006.
[0133] In an embodiment of the present disclosure, a rig state
detector 345 may be coupled with the drilling process system. The
rig state detector 345 may receive data from the components of the
drilling system, the wellbore, the surrounding formation and/or the
like and may input a probability of recent change in rig state 137
to the changepoint detectors. In this way, the changepoint
detectors 901 may determine when a detected changepoint results
from the recent change in rig state 137. For example, in FIG. 12,
the changepoint detector may identify when the Well Flowing node
135 may be caused by the recent change in rig state 137.
[0134] As depicted in FIG. 12, another cause of well flowing 135
may be a kick 353. In an embodiment of the present disclosure, the
changepoint detector may analyze the pit volume data 305 and the
delta flow data 310 to determine occurrence of a changepoint to
determine whether the condition of the well flowing 135 has
occurred and may use the probability of a recent change in rig
state 350 to determine an existence of the kick 353.
[0135] In an embodiment of the present disclosure, the online
determination of the kick 353 may cause an output of an alarm for
manual intervention in the drilling process, may cause a control
processor to change the automated drilling process and/or the like,
for example, the detection of a kick 353 may be reported on a
control console connected to the central surface processor 96. In
certain aspects, data concerning the wellbore, the formation
surrounding the wellbore, such as permeable formation in open hole
with pore pressure greater than ECD may be input to the changepoint
detector and may allow for greater accuracy in detection of the
kick 353. In some aspects of the present disclosure, if fluid is
being transferred into the active mud pit 78, data concerning such
a transfer or addition 356 may be provided to the changepoint
detector as it may cause the Pit Gain 330 but not Excess Flow 335.
In such aspects of the present disclosure, by inputting the
transfer or addition 356 to the changepoint detector(s), mistaken
detection of the Kick 353 may be avoided.
[0136] In FIG. 12, the changepoint detectors 901 are provided raw
data and may use Bayesian probability analysis or the like to model
the data and determine an existence of a changepoint. The
segmenting of the raw data may provide for flexible modeling of the
data within individual segments, e.g., as linear, quadratic, or
other regression functions.
[0137] If a kick is suspected a flow check is performed, whereby
the mud pumps are stopped and any subsequent flow-out can
definitively confirm a kick. To control a kick, the drillstring is
lifted until a tool joint is just above the drill floor and then
valves called blowout preventers are then used to shut-in the well.
The influx is then circulated to the surface safely before drilling
can resume. Small influxes are generally quicker and more simple to
control, so early detection and shut-in is extremely desireable.
Automating the above process should consistently minimize the
non-productive time.
[0138] Other processes the present disclosure may be applied to in
the hydrocarbon industry include: stuck pipe, lost circulation,
drill bit stick-slip, plugged drill bit nozzles, drill bit nozzle
washout, over- or under-sized gauge hole, drill bit wear, mud motor
performance loss, drilling-induced formation fractures, ballooning,
poor hole cleaning, pipe washout, destructive vibration, accidental
sidetracking, twist-off onset, trajectory control of steerable
assemblies, rate-of-penetration optimization, tool failure
diagnostics and/or the like.
[0139] Turning now to a second example use of the changepoint
detector 901, namely the application thereof to optimize the
rate-of-penetration in drilling processes.
[0140] Consider again FIG. 5, which illustrates the changes to the
linear bit response according to the PDC bit model as a drilling
operation advances from one formation having one set of
characteristics to another. As discussed hereinabove, the data
points 509 lie on one line in the three dimensional WOB-bit
torque-depth of cut space. And the three data points 511 lie on
another line in that space. As discussed above, real-time modeling
of this data is challenging around formation boundaries. Therefore,
in an embodiment, a changepoint detector 901 is used to determine
the linear bit response and parameter values that may be derived
therefrom. Using the changepoint detector 901a straight line is
fitted through the first set 509 and a second straight line is
fitted through the second set 511 thereby avoiding polluting
estimates for one formation with data collected from another, for
example.
[0141] Projecting the three dimensional fit onto the WOB-depth of
cut plane gives a linear equation linking WOB, RPM and ROP. This
can be rearranged to give ROP as a function of WOB and RPM, as
shown by the contours in FIG. 13. Thus, for a given WOB-RPM pair a
particular ROP may be expected.
[0142] The coefficients of the bit/rock model allow various
constraints to the drilling process to be expressed as a function
of WOB and RPM and superimposed on the ROP contours as is
illustrated in FIG. 14:
[0143] the ROP at which cuttings are being generated too fast to be
cleaned from the annulus, 141,
[0144] the WOB that will generate excessive torque for the top
drive, 143,
[0145] the WOB that will generate excessive torque for the drill
pipe, 144,
[0146] the WOB that exceeds the drill bit specification for maximum
weight on bit, 145,
[0147] the RPM that causes excessive vibration of the derrick,
147.
[0148] The region 149 below these constraints is the safe operating
envelope. The WOB and RPM that generate the maximum ROP within the
safe operating envelope may be sought and communicated to the
driller. In some embodiments, the WOB and RPM may be passed
automatically to an autodriller or surface control system.
[0149] Examination of the boundaries of the safe operating window
149 reveal that the highest ROP within the safe operating window
may be found at the intersection of the hole cleaning plot 141 and
the top drive torque plot 143, referred to herein as the optimal
parameters 151. For the sake of example, consider the drilling
operation current RPM and WOB being located at 80 rpm and 15 klbf
(153), respectively, with an ROP of approximately 18 ft/hr. The ROP
at the optimal parameter combination 151, on the other hand, is
approximately 90. Thus, a driller increasing the RPM and WOB in the
direction of the optimal parameters would improve the ROP. In an
embodiment, an ROP optimizer suggests an intermediate combination
of RPM and WOB, e.g., the parameter combination approximately 1/2
the distance 155 between the current parameter combination 153 and
the optimal combination 151.
[0150] The data that defines the ROP contours and the parameters
for the safe operating window are continuously reported from
sensors on the drilling apparatus. These sensors may either be
located at the surface or in the drill string. If located at the
surface, some filtering and preprocessing may be used to translate
the measured values to corresponding actual values encountered by
the drillbit and drillstring.
[0151] The continuous stream of data is modeled using the PDC model
of FIG. 5. As new data arrives, the best fit for the data points
may change slightly and require minimal adjustments in the model
used for determining the ROP contours. When encountering new
formations, abrupt changes may be expected. The changepoint
detector 901 is used to segment the incoming data to allow for
changes in the model used to calculate the ROP contours.
[0152] FIG. 15 is a graphics user's interface 157 of an ROP
optimizer using a changepoint detector 901 to determine
segmentation models for the PDC model, the ROP contours that may be
derived therefrom, the safe operating envelope, and recommended WOB
and RPM parameters. Four windows 161 plot WOB, torque, ROP, and
RPM, respectively, against a depth index. In another window 163,
depth-of-cut is plotted against WOB. In yet another window 165,
torque is plotted against WOB. Finally, torque is plotted against
depth-of-cut in yet another window 167.
[0153] The data is segmented using the changepoint detector 901 and
fit to appropriate linear models corresponding to each segment in
the manner discussed hereinabove. The different colors illustrated
in the various graphs 161 through 167 represent different segments,
respectively. By examining the plots against depth index of graphs
161 it will be appreciated that in this example, blue represents
the first segment, red, the second, and green, the current segment.
As will be appreciated from the depth of cut versus WOB graph 163,
the linear relationship expected between these from the PDC model
has changed dramatically in the course of the drilling operation
corresponding to the data points plotted in FIG. 15.
[0154] The safe operating envelope and drilling contours window 169
contains a display of the safe operating envelope 149, the current
parameters 153, the optimal parameters 151 and recommended
parameters 155 corresponding to the current segmentation model.
[0155] The graphic user's interface 157 may be reported on a
control console connected to the central surface processor 96.
[0156] FIG. 16 is a flow-chart illustrating the operation of the
changepoint detector to determine recommended parameters in an ROP
optimizer illustrating the operation as new drilling data is
received in real-time. First, the drilling data is segmented 171
using the changepoint detector 901, in the manner discussed herein
above. The segmentation divides the data into homogenous segments
and associates models to fit to the data in the segment. Thus, at a
given time, there is a best segmentation. That best segmentation
further has a current segment that corresponds to the most recently
arrived drilling data. The data fit is performed in real-time thus
adjusting the models to take the latest arrived data into
account.
[0157] Having determined the best segmentation and the models for
the current segment these models are used to determine the ROP
contours corresponding to the PDC model fit to the data points in
the current segment and the safe operating envelope corresponding
to the drilling constraints corresponding to the current segment,
173.
[0158] The ROP contours and safe operating envelope are used to
determine the optimal ROP contour inside the safe operating
envelope and the WOB and RPM that correspond to that optimal ROP
contour, 175.
[0159] A mud motor or turbine is sometimes added to the bottomhole
assembly 56 that converts hydraulic power from the mud into rotary
mechanical power. With such an assembly, bit RPM is function of
surface RPM and mud flow rate, and consequently, the optimum ROP is
a function of surface RPM, WOB and flow rate; the algorithm
corresponding algorithm therefore suggests these three drilling
parameters to the driller. The relationship between flow rate and
the RPM of the shaft of the motor/turbine is established by
experimentation and published by most vendors. In some embodiments
by measuring rotor speed downhole, this relationship may be
inferred in real-time. Given either of these relationships, the
algorithm above can be extended to give an equation of ROP as a
function of surface RPM, WOB and flow rate. Useful extra
constraints to add are: [0160] the flow rate that causes the
pressure of the mud in the annulus to fall below a given value that
may cause the borehole to collapse or formation fluids to enter the
wellbore and cause a kick [0161] the flow rate that causes the
pressure of the mud in the annulus to exceed a given value that may
cause the borehole to fracture [0162] the mechanical power output
of the motor at which there is a risk of motor stalling (reference
Walter Aldred et al., Optimized Drilling With Positive Displacement
Drilling Motors, U.S. Pat. No. 5,368,108 (Nov. 29, 1994) and
Demosthenis Pafitis, Method For Evaluating The Power Output Of A
Drilling Motor Under Downhole Conditions, U.S. Pat. No. 6,019,180
(Feb. 1, 2000)
[0163] A recommended set of new drilling parameters, e.g., RPM and
WOB, that move the current parameters towards the optimal
parameters is provided, 177, either to a human operator or to an
automated drilling apparatus.
[0164] The above-described technology for optimizing
rate-of-penetration is applicable to other structures and
parameters. In some embodiments the technique is applied to roller
cone bits using appropriate models for modeling the drilling
response of a roller cone bit. In some embodiments, the
above-described mechanisms are applied to drilling processes that
include additional cutting structures to the bit, such as reamers,
under-reamers or hole openers by including a downhole measurement
of WOB and torque behind the drill bit. In some embodiments, a
second set of measurements behind the additional cutting structure
is included.
[0165] In some embodiments, a bit wear model could be added to
allow the bit run to reach the casing point without tripping for a
new bit.
[0166] Turning now to a third example of the use of a changepoint
detector 901 in the realm of industrial automation, namely, in
directional drilling of wells into targeted subterranean
formations. Calculation of wellbore curvature (also known as dogleg
severity ("DLS")) and direction (also known as toolface) are very
useful in the field of Directional Drilling. The directional
driller uses curvature and direction to predict whether or not a
target will be intersected. In an embodiment of the disclosure,
curvature and direction estimates are provided continuously during
a drilling operation on the order of, e.g., every 1/4 foot, every
1/2 foot, every foot, etc., to allow a driller the opportunity to
take corrective action during the drilling operation if the
wellbore is deviating off plan. The directional driller thus is
able to evaluate deflection tool performance using higher
resolution curvature and direction estimates.
[0167] The curvature and direction can be used to determine
formation effects on directional drilling. In particular, if the
changepoint detector indicates a changepoint at a formation bed
boundary, the new formation will have a different directional
tendency from the previous formation. The resultant curvature and
direction can be used to study and evaluate the effects of surface
driving parameters such as weight on bit and rpm on directional
performance. A detailed understanding of how current deflection
tools deviate a well can be used to engineer future tools. Finally,
a continuous curvature and direction of the curvature may be used
in autonomous and semi-autonomous directional drilling control
systems.
[0168] FIG. 17 is a three-dimensional graph illustrating azimuth
and inclination of a wellbore through a three-dimensional space at
two different locations. Azimuth 181a and 181b at a location is the
compass direction of a wellbore 46 as measured by a directional
survey. The azimuth 181a is oftentimes specified in degrees with
respect to the geographic or magnetic north pole. Inclination 183a
and 183b at a location is the deviation from vertical, irrespective
of compass direction, expressed in degrees. Inclination is measured
initially with a pendulum mechanism, and confirmed with
accelerometers or gyroscopes.
[0169] FIG. 18 is a flow-chart illustrating the use of a
changepoint detector in determining real-time estimates for dogleg
severity and toolface from azimuth and inclination data collected
during a drilling operation. The continuous inclination and azimuth
measurements received from these sensors on the drilling equipment
are processed by a changepoint detection system using a general
linear model (changepoint detector). The changepoint detector
segments the data into a plurality of segmentations and associated
segment models as discussed herein above, 184, resulting in a
segmentation, for example, as shown in FIG. 9.
[0170] Segmentation 184 results in a number of different
segmentations of the input azimuth and inclination data. Each is
associated with a particle in a tertiary tree as illustrated in
FIG. 7 and has associated therewith a list of segments and
corresponding models, e.g., ramps and steps. These segment models
are used to estimate the azimuth and inclination at the current
drilling location, 185. Thus, rather than accepting the sensor
values for azimuth and inclination, those sensor values being used
to adjust the models by being considered by segmentation 184, the
azimuth and inclination values used to estimate dogleg severity and
toolface are the estimated values obtained by using the
segmentation models. The azimuth and inclination values are
calculated for each active segmentation.
[0171] To calculate the azimuth and inclination values at a depth
location MD2, using a segmentation p, the following formula is
used:
DLS.sub.p=A
COS(COS(I2-I1)-SIN(I1)*SIN(I2)*(1.0-COS(A2-A1))/(MD2-MD1)) (1)
y=COS(A2-A1)*SIN(I2)*SIN(I1) (2)
GTF.sub.p=A COS(COS(I1)*y-COS(I2))/(SIN(I1)*SIN(A COS(y))) (3)
where: [0172] I1 and I2 are the inclination values computed at the
changepoint MD1 starting the segment to which the particular depth
location MD2 belongs and at the particular depth location MD2 using
the inclination model associated with the segment to which the
particular depth location MD2 belongs, respectively; [0173] A1 and
A2 are the azimuth values computed at the changepoint MD1 starting
the segment to which the particular depth location MD2 belongs and
at the particular depth location MD2 using the inclination model
associated with the segment to which the particular depth location
MD2 belongs, respectively; [0174] DLS.sub.p is the dogleg severity
at MD2 computed with the segmentation p; and [0175] GTF.sub.p is
the toolface at MD1 computed with the segmentation p.
[0176] Weighted averages are then calculated from the
per-segmentation calculated values for dogleg severity and
toolface, 189, using the following formulas:
DLS = p .di-elect cons. Segmentations DLS p * Weight p ##EQU00001##
TF = ATAN ( p .di-elect cons. Segmentations SIN ( TF p ) * Weight p
p .di-elect cons. Segmentations COS ( TF p ) * Weight p )
##EQU00001.2##
[0177] where Segmentations is the set of all active
segmentations,
[0178] Weight.sub.p is the weight associated with a particular
segmentation p.
[0179] The resulting dogleg severity ("DLS") and toolface ("TF")
values are then reported to a directional driller who may use these
values to assess the effect of surface driven parameters such a
weight-on-bit and RPM on the directional drilling process, 191. The
driller may then adjust these parameters to improve the trajectory
of the wellbore with respect to a desired target. In some
embodiments, the resulting dogleg severity ("DLS") and toolface
("TF") values are input into an automated drilling system that
automatically adjusts the surface driven parameters based on these
values to improve the wellbore trajectory with respect to a desired
target. The resulting dogleg severity ("DLS") and toolface ("TF")
values may be reported on a control console connected to the
central surface processor 96.
[0180] As discussed hereinabove, in some embodiments, the
changepoint detector output may be fed into analysis software, for
example, in the form of a Bayesian Belief Network (BBN). By way of
example, in one embodiment, the combination of a changepoint
detector, a signal processing system, and an expert system are used
to analyze sensor data for artificial lift operations using
electrical submersible pumps (ESPs) or progressive cavity pumps
(PCPs). Such pumps are used in many oil fileds to improve oil
production. Downhole and surface gauges as well as sensor data
provided from control equipment may be used in such a system to
assess pump performance to detect equipment failures or stress
conditions that may lead to equipment failures. The amount and
complexity of the data from artificial lift operations using ESP or
PCP easily overwhelm a human operator who may therefore miss such
failure or potential failure events in the mass of data.
[0181] The minute-by-minute measurements made by these pump systems
provide information about the pump behavior and performance (e.g.,
pump efficiency), as well as indications of impending problems
(e.g., upthrust versus downthrust, gas ingestion, mechanical
imbalance). However, using the available data in an efficient and
effective way to assess pump performance and problems is a
challenge because the volume of high-frequency data overwhelms the
capacity of most direct methods based on human observation of and
response to the data.
[0182] FIG. 19 is a high-level schematic drawing illustrating
examples of sensor data that may be supplied from a downhole ESP
assembly 241 and related surface equipment as well as from a pump
variable frequency controller 243 to a control system 245. The pump
assembly 241 may typically include a motor 247, a protector 249, a
pump intake 251, and the pump 253. In many cases an ESP motor 247
is a variable frequency drive which may be controlled by a variable
frequency controller 243 by varying the frequency of the electrical
current supplied to the motor 247. As such the variable frequency
controller 243 may provide the control system 245 with signals
indicative of drive frequency, current draw (average amps), and
voltage supplied. Typically these measurements are provided as a
time-indexed data stream.
[0183] The pump assembly 241 also contains a downhole monitoring
tool 255 which includes any combination of pressure, temperature
and accelerometers for measuring intake pressure, motor temperature
and motor vibration (in x, y, and z axis), respectively, as well as
a discharge pressure sensor 257 which measures pump discharge
pressure. At the wellhead, surface sensors 259 are provided for
measuring wellhead pressure and wellhead temperature. These
measurements are provided as time-indexed data streams to the
control system 245. The foregoing sensors and physical properties
for which measurements are taken are provided as examples. Other
sensors measuring other physical properties may also be included in
addition to or as alternative to these examples.
[0184] Herein, ESP failures, i.e., equipment breakdown of some
sort, and ESP Stress Conditions, which may ultimately lead to
equipment breakdown, are referred to equipment events.
[0185] ESP Failures. An ESP may suffer from many types of failures,
for example, related to the pump 253, to the motor 247, or to a
sensor 255, 257, 259. Timely failure detection is very desireable
so as to allow an operator to take appropriate actions to correct
the failure and to prevent the failure from causing additional
problems. The main failures are downhole mechanical failures and
gauge faults. [0186] Downhole mechanical failure, i.e., a pump 253
or a motor 247 failure: [0187] A broken shaft can cause pump
failure. The impeller does not rotate, and no fluid can go up. The
shaft is a moveable component of the pump; this piece provides to
the pump the mechanical power given by the motor. In this case, the
motor continues to operate, and the torque decreases. Therefore,
the power needed by the motor decreases, as well as the current
draw. The flow emitted by the pump decreases rapidly to zero, and
intake pressure increases rapidly. However, no flow passes in the
pump, so there is no material to evacuate the heat. Therefore, an
increased motor temperature may be expected to result from a shaft
failure. [0188] Pump wear is another example of a downhole
mechanical failure. In pump wear, as the pump begins to wear out,
less fluid is output from the pump; with deteriorating efficiency,
the pump produces less. Generally, pump wear also results in
vibrations generated by the pump and the vibrations increase as the
pump continues to deteriorate. The pump efficiency declines due to
wear and the current draw also decreases. Less fluid flows to the
surface (thus flow rate drops off) and more fluid stays at the
bottom of the well, which provokes naturally an increasing intake
pressure. [0189] Gauge fault. In this situation, sensors provide
inaccurate measurements; this could be related to an error in a
wire connection or a short circuit. This type of error is not as
detrimental as a downhole mechanical failure; nevertheless, actions
should be taken to address the issue. Based on electrical
parameters the root cause can be highlighted.
[0190] ESP Stress Patterns. The second type of equipment event is
stress pattern or stress condition. Stress patterns are very
interesting events to detect, as they can anticipate downhole
mechanical failures. By removing early stages of stress patterns, a
failure may be prevented, thus increasing the life expectancy of
the pump and avoiding other costly operation delays. Some stress
patterns are low flow, deadhead, and gas ingestion.
[0191] Low flow. An ESP running below the minimum speed induces a
low flow (FIG. 20). A lower flow brings higher intake pressure and
lower discharge pressure. Without enough flow to cool the motor
247, motor temperature increases and may damage the ESP 241.
Average amps goes down due to low load. Furthermore, low flow
causes wellhead pressure to be either constant or dropping
depending on whether the well is normally producing through a
choke. If there is no choke, the wellhead pressure remains steady,
while with a choke the pressure decreases.
[0192] FIG. 20 is a graph illustrating changes in operating
properties during a low flow condition. Zone A 311 illustrates a
normal operating condition. In Zone B 313 illustrates a shut-down
of the ESP 241. In Zone C 315, the In zone C, the ESP cannot
overcome the high wellhead pressure. After a small increase, the
wellhead temperature decreases toward the ambient temperature,
which is an indication of no flow at the surface. In zone D 317,
the drive frequency increases, and the well choke is opened. Then
the wellhead temperature increases at surface due to flow.
[0193] Deadhead. Deadheads are any restrictions above the ESP 241.
Two cases are possible: the restriction can be before or after the
wellhead. The wellhead pressure is a parameter to determine if the
restriction is before or after the wellhead. In those two cases,
motor temperature increases because there is no fluid flow to cool
the motor, resulting in motor damage. Without corrective action,
the ESP would shut down by the constraint threshold on the motor
temperature.
[0194] FIG. 21 is a graph illustrating changes in operating
properties during a deadhead event. In this example, an alert of a
deadhead event was sent to the operator at time 04:55 and that the
remedial action was to check the valve status. Zone A reflects the
deadhead event with a sudden increase in discharge pressure 321 and
intake pressure 323 as well as the difference in pressures (delta
pressure). The framework detected the deadhead event. The ESP was
shut down, and the surface valves were checked. Zone B reflects
that after finding one valve closed, the operator restarted the ESP
normally.
[0195] Gas ingestion. Gas issues occur when fluid level drawdown
approaches the pump intake and intake pressure is lower than the
bubblepoint. It is very difficult for a pump to evacuate gas as
impellers have less effect on gas than on liquid. The volume of
this bubble of gas can change with time. Therefore, the volume for
fluid is changing irregularly and consequently less fluid would
pass through the pump. This induces unstable intake pressure,
unstable average current, reduction of the flow rate, and unstable
motor temperature.
[0196] FIG. 22 is a graph illustrating changes in operating
properties during a gas ingestion event. Motor temperature and
intake pressure have successive peaks around a constant value.
However, temperature is highly dependent on pump shutdowns whereas
pressure peaks account for transition between fluid and gas. Gas
issues occur when fluid level drawdown approaches the pump intake
and intake pressure is lower than the bubblepoint. It is very
difficult for a pump to evacuate gas as impellers have less effect
on gas than on liquid. The volume of this bubble of gas can change
with time. Therefore, the volume for fluid is changing irregularly
and so less fluid can pass through the pump. This induces unstable
intake pressure, unstable average current, reduction of the flow
rate, and unstable motor temperature.
[0197] FIG. 23 is a schematic illustration of a control system 245
connected to various sensors, as described above in conjunction
with FIG. 19. These signals are fed into a signal-processing module
301. The signal-processing module 301 produces segmentations as
described hereinabove, and answers several high-level questions
based on the segmentations, which is described in greater detail
hereinbelow. Probabilities 303 associated with the various answers
to the high-level questions are provided as input to an expert
system 305.
[0198] Typically the control system 245 is a computerized system
having a processor, data stores for storing data and programs, and
user interface devices such as to allow a user to receive
diagnostic information from the control system and to allow the
user to enter input parameters to the control system. Programs such
as the signal processing module 301 and the expert system 305 may
be stored in the control system 245 data stores and provide
instructions to the control system 245 processor to receive and
manipulate data streams from the sensors connected to the control
system.
[0199] FIG. 24 is a high-level flowchart illustrating processes
performed by the signal-processing module 301 and the expert system
305.
[0200] Segmentation. The input streams from the sensors 255, 257,
and 259, as well as input from the variable frequency drive 243 are
input into the signal-processing module 301, 421. The data streams
are segmented (as described herein above), 423.
[0201] In a typical segmentation, linear models are used to model
the input data either as steps or as ramps. In a general sense:
y=G*0 (1)
with: [0202] y: the variable to be analyzed (e.g., pressure,
current, temperature) [0203] G: a matrix of associated regressed
variables or independent variables (typically time, but also
possible are a constant, time square, velocity, position, etc.)
[0204] .theta.: the parameter vector for the current segment
[0205] Based on equation (1), many models can be created simply by
which variables are included in the matrix G. For example, if the
regressed variable is time t, we have: [0206] Step: G=[1] [0207]
Ramp: G=[1 t] [0208] Quadratic Splines: G=[1 t t.sup.2]
[0209] Another possible model is cross-variable regression: G=[1
x], where x is another operations property. For example, a
correlation may be made between intake pressure and average current
draw, i.e., y is intake pressure and x is average amps.
[0210] High-Level Questions. The segmentation may be used to
determine specific information derived from the underlying data:
general trends, noise level, convergence of operations properties,
etc. This high-level information is referred to herein as
high-level questions. The high-level questions are answered from
the segmentation and statistical descriptors of the distribution of
data values for all segmentations of the data stream, e.g.,
variance, mean, standard deviation. Thus, following the
segmentation of the data streams, the high-level questions are
answered, 425. In some embodiments, there are three fundamental
high-level questions: Basic Tendency, Noise Level, and Correlation
Between Properties.
[0211] Basic Tendency. The basic tendency question is a
determination of the direction in which an operations property is
tending. In an embodiment, the tendency is classified into five
categories: decrease strongly, decrease, steady, increase, and
increase strongly. The signal processing module 301 produces
probability estimates for each of these categories.
[0212] FIG. 25 is a flowchart illustrating the basic tendency
question. Based on the segmentation, a section of data from the
past that is considered normal values for the operation property is
selected for reference. From this reference (Ref) it is possible to
define a measure of variation relating to the signal for the
operations property. FIG. 26 is a graph showing distributions of
the value for the operations property in this normal section as
well as for the portion of the data stream being evaluated. The
reference may be changed as normal operating conditions may
change.
[0213] Threshold levels are set to reflect where a sample would fit
with respect to the reference distribution, 523. The thresholds are
typically set as a percentage of the reference level. To segment
tendency into the five categories, two threshold levels are set
above and below the reference, e.g.,
[0214] Thrld=10%Ref
[0215] ThrldStrg=40%Ref
Thus, if the reference level Ref is changed, the thresholds are
similarly changed. The percentages may be set to reflect the level
of detail that is desired. For example, a broken shaft creates a
substantial variation. Thus, to detect a broken shaft, the
threshold would be set very high (e.g., 40% of Ref for increase
strongly). On the other hand, to highlight pump wear, the threshold
would be set relatively low (e.g., 5% of Ref for the increase
category).
[0216] Next, probabilities for the tendency categories (decrease
strongly, decrease, steady, increase, increase strongly) are
computed, 525, according to the following formulas:
p(decrease strongly)=p(y.sub.n<Ref-ThrsldStrg)
p(decrease)=p(Ref-ThrsldStrg<y.sub.n<Ref-Thrsld)
p(steady)=p(Ref-Thrsld<y.sub.n<Ref+Thrsld)
p(increase)=p(Ref+Thrsld<y.sub.n<Ref+ThrsldStrg)
p(decrease strongly)=p(Ref+ThrsldStrg<y.sub.n)
[0217] An example distribution for y.sub.n is illustrated in FIG.
26. In the example of FIG. 26, it can be seen that there the
probability p(decrease strongly) and p(decrease) are quite high,
whereas the probability p(increase strongly) is very low.
[0218] As discussed above, a reference period is selected and a
probability distribution is determined for that time series 521.
This is oftentimes a stable period of suitable duration. Then the
current statistical model of the signal is compared to the
corresponding reference to determine if the signal is above, below,
correlated to the reference etc. d, 525. Implied in this is that
when any changes are made, a new stable period may be reached in
which the data input is in a "reference state." One example of that
is whenever the frequency is changed for an ESP. After a frequency
change it takes some time to reach a new reference state. During
that period the changepoint detector is limited because it does not
have the knowledge of the reference to compare the signal to. In
one embodiment, to compensate for this, the signal processing
module 301 estimates the new reference based on known physics laws
or mathematical approximations. That allows the control system to
continue to operate until a new reference is determined by the
changepoint detector.
[0219] Noise Level. The noise level question, which measures the
variation in the signal, is determined from comparison of signal
instability against two thresholds, ThrldUns and ThrldUnsHigh. The
thresholds depend on signal features that are expected to remain
stable. Furthermore, sensor resolution should be taken into
consideration when setting noise thresholds. Thus, three levels are
defined for the answers to the noise level question: stable,
unstable, and highly unstable. These are determined as follows:
p(stable)=p(y.sub.n<ThrldUns)
p(unstable)=p(ThrldUns<y.sub.n<ThrldUnsHigh)
p(highly unstable)=p(ThrldUnsHigh<y.sub.n)
[0220] Correlation Between Operations Properties. The correlation
question is an inspection of the correlation between two properties
that are being measured (or derived from measurements) by the
sensors 255, 257, 259, or provided by the variable frequency
controller 243. In an embodiment, the correlation is a linear
relationship between the two quantities. However, other
mathematical relationships are possible as well as the involvement
of multiple operations properties in the calculation.
[0221] For purpose of illustration, consider the linear
relationship between to properties X and Y that are monitored, then
the model is:
Y=ax+b
[0222] The coefficients in the above equation fall within some
distribution, e.g., a normal distribution
N(a.sub.0,.sigma..sub.a.sup.2) with a mean a.sub.o and a standard
deviation .sigma..sub.a.sup.2:
a.about.N(a.sub.0,.sigma..sub.a.sup.2);
b.about.N(a.sub.0,.sigma..sub.a.sup.2)
[0223] The correlation-between-properties question deals with
comparing the coefficients against some thresholds thereby
producing probability values for categories of correlation, e.g.,
negative correlation, positive correlation and no correlation:
p(negative correlation)=p(a<-Threshold)
p(positive correlation)=p(a>Threshold)
p(no correlation)=p(-Threshold<a<Threshold)
[0224] The Threshold may be set separately for each channel. The
foregoing approach allows an analysis of dependencies between
operations properties. If the correlation drops between a cause and
a consequence, that occurrence reveals that the causal link is
broken.
[0225] FIG. 27 is a graph that illustrates determination of
probabilities to answer alternatives for the correlation question.
The red line 421 is the embodiment of the correlation, in this case
a positive correlation between the two operations properties. The
negative-slope blue line 425 is the Threshold between negative
correlation (minus signs in the figure) and no correlation (zeros
in the figure), and the positive-slope blue line 423 is the
Threshold between positive correlation (plus signs in the figure)
and no correlation (zeros in the figure).
[0226] Thus, the output from the correlation question is the
probability values for each of the categories negative correlation,
positive correlation and no correlation.
[0227] To summarize, at the conclusion of the a computation of
probabilities for the high-level questions for each possible
answer, i.e., the categories for the basic tendency, the noise
level, or the correlation between properties questions, has a
probability value associated therewith. These probability values
for each question add to 1.0. For example, for the basic tendency
illustrated in FIG. 26, the probabilities may be:
p(decrease strongly)=0.28
p(decrease)=0.46
p(steady)=0.13
p(increase)=0.11
p(decrease strongly)=0.02
[0228] Not all of the high-level questions have to be used in a
control system for detecting EFS failures of stress conditions.
[0229] The probability values for the various answers to the
high-level questions are input (FIG. 24, 427) into an expert system
305 to detect EFS failures or stress conditions by computing
probabilities of the occurrence of an equipment event, 429. As
discussed hereinabove, several EFS equipment events may be
predicted by observing the operational properties. For example,
deadhead may be detected from a sudden increase in discharge and
intake pressure taken together with a difference in the pressures
(delta pressure). In an embodiment, the expert system 305 is based
on a Bayesian Belief Network (BBN). A Bayesian Belief Network is a
mathematical tool to model conditional dependencies of random
variables. One Bayesian Belief Network engine is Netica from Norsys
Software Corp., Vancouver, Canada.
[0230] Most physical phenomena can be observed using a certain
number of variables and some specific conditions associated to it
categorized with corresponding signatures. For instance in the
particular world of ESPs, operating in deadhead condition will
decrease the electrical current drawn by its motor, increase the
pressure at the outlet of the pump as well as the differential
pressure across it (among other things, this is just given here to
simplify as an example). This can be programmed in a Bayesian
network to map the statistical dependencies between a stress
pattern (output node; e.g., deadhead) and measured variables (input
nodes; e.g., for deadhead: current, discharge pressure and pump
pressure differential). Such a network outputs a strong probability
of deadhead when the probabilities for current to decrease and
pressures to increase are high, but that deadhead probability would
drop if any of the given operating properties behaves differently
(drops or remains steady for instance).
[0231] In Bayesian inference, a prior probability distribution,
often called simply the prior, is a probability distribution
representing knowledge or belief about an unknown quantity a
priori, that is, before any data have been observed P(A). Thus,
certain probabilities may be associated with the physical
properties of an EFS operation and the likelihood that certain
equipment events are occurring. However, if a particular condition
is observed or a combination of particular conditions are observed,
those conditions impact the probabilities of particular equipment
events.
[0232] As discussed above, a number of operating properties are
monitored and modeled using the segmentation algorithms and the
high-level questions. The probabilities for each high-level
question and their combined impact on probabilities for equipment
events are modeled in the BBN.
[0233] A Bayesian belief network reproduces different states of a
structure and explains how those states are connected by
probabilities. In an embodiment, this kind of network is used to
model an uncertain reality and to take intelligent decisions that
maximize the chances of a desirable outcome.
[0234] Many physical phenomena interact, and for many, there is
extensive domain knowledge about their possible behavior. The
Bayesian theory is a suitable framework to take advantage of these
priors, i.e., domain knowledge in regard to interaction between
operating properties and equipment events. As provided in An
Introduction to Bayesian Networks, Jensen, F. V., (1996),
Springer-Verlag, ISBN 978-0387682815, incorporated herein by
reference, "[c]ontrary to most other expert system techniques, a
good deal of theoretical insight as well as practical experience is
required in order to exploit the opportunities provided by Bayesian
Network." In the case of ESPs, for example, adding physical priors
inside the Bayesian network relies on domain knowledge in the field
of ESPs.
[0235] A Bayesian network is: [0236] A set of variables and a set
of directed edges between variables [0237] Each variable has a
finite set of states [0238] The variables together with the
directed edges form a directed acyclic graph [0239] For each
variable P(a) with parents B.sub.1 . . . B.sub.n there is attached
a conditional probability table.
[0240] FIG. 28 illustrates a two-layer Bayesian belief network.
Each of the three parameters 1, 2, and 3, has a bearing on the
probability for the Scenario being true or false. For example, one
could envision the Scenario being rainy weather. A prior
probability for rainy weather could be 50:50 in a location with
fifty percent rainy days. Parameter 1 could be grass_is_wet. If the
grass is wet, there is a larger probability of rainy weather had
occurred in that the two are usually coupled. Thus, a True value
indicating the observation of wet grass would increase the
probability of the Scenario being true. On the other hand, if
Parameter 2 is umbrellas, which indicates the distribution of
persons carrying umbrellas, the probability for rainy weather would
decrease if few persons are found to be carrying umbrellas.
[0241] Furthermore, nodes are linked. For example, a parameter may
be sprinkler_has_been_on. That would also cause wet grass, thereby
causing the impact of wet grass on the conclusion that rainy
weather has occurred to decrease. Conversely, if rainy weather has
been observed, that would decrease the probability of
sprinkler_has_been_on as sprinklers are often not deployed during
rainy days.
[0242] Because some situations can be complicated, in some
embodiments intermediary nodes are used to manage different levels
of abstraction. FIG. 29 illustrates one example of a Bayesian
belief network in which two intermediate nodes--Energy Consumption
601 and Impeller Inactive 603--are included. The probability values
for these nodes are derived from the answer to the high-level
questions of Delta Pressure Variation 605 and Current Variation
607. The ultimate equipment event probabilities Pump Seized 609 and
Gas Ingestion 611 are determined from the probability values for
Energy Consumption 601 and Impeller Inactive 603 as well as, in the
case of Gas Ingestion 611, the high-level question Current
Instability 613.
[0243] Each of the nodes in the BBN are linked through conditional
probability tables. The conditional probability tables may be
manually populated when domain knowledge provides sufficient
information for doing so. For instance someone could record
continuously the temperature, pressure etc. in a specific place and
records whenever it rains. Processing this measured data with a
changepoint detector may provide the input channels for a Bayesian
network. Once the signals are segmented, it is possible to compare
the signals to what is defined as a standard condition (for example
the average statistical distribution of each parameter during a
day, which can by the way be determined in parallel in a
changepoint detector). The comparison is performed between the
modeled value distribution (and not the raw value) and the standard
value distribution to give a statistical input (for instance the
measured value is statistically 10% higher than the standard
value). Then a Bayesian network may be built to draw the
dependencies between different input nodes (here the measured
parameters) and output nodes (here the probability for rain event
to happen which will be deterministic in that example). It is then
possible to train the designed network using these inputs/output.
Once the process is complete, the input of new data will allow the
probability of oncoming rain to be inferred.
[0244] In some embodiments, conditional probability tables may be
constructed mathematically. Consider the example of FIG. 30, which
is a portion of a Bayesian belief network linking PumpWorn and
Impellerslnactive events to a DischargePressureVariations
high-level question. In the network depicted in FIG. 30,
quantitative links between nodes are explained using three
additional parameters, PumpWorn 621 and Impellerslnactive 623, as
well as an additional paramenter W.sub.0 which is indicative of the
accuracy of the equations. The mathematical relationship between
these parameters and DischargePressureVariations 625 is determined
by the following equation:
P ( DP | PPW , ImpellerInactive ) .varies. { [ DP = steady ] + W 0
if PW _ , ImpellerInactive _ W imp ' [ DP = .dwnarw. or .dwnarw.
.dwnarw. ] + W 0 if PW _ , ImpellerInactive W PW ' [ DP = .dwnarw.
] + W 0 if PW , ImpellerInactive _ W exp ' [ DP = .dwnarw. or
.dwnarw. .dwnarw. ] + W pw ' [ DP = .dwnarw. ] + W 0 if PW ,
ImpellerInactive ##EQU00002##
with
[0245] DP for Discharge Pressure [0246] .varies.: proportional
[0247] .dwnarw.: decrease slowly [0248] .dwnarw..dwnarw.: decrease
rapidly [0249] PW: Pump-worn event is true [0250] PW: Pump-worn
event is false [0251] PW, Impellerinactive: Pump-worn event does
not occur and Impellerinactive event occurs [0252] W.sub.imp=2:
weight of impeller inactive event [0253] W.sub.pw=1: weight of pump
worn event [0254] W0=0.1: accuracy
[0254] Assuming that : [ DP = state 0 ] .revreaction. DP .fwdarw. {
1 , if DP == state 0 0 , otherwise ##EQU00003##
[0255] The foregoing equation that pump worn (PW) and .quadrature.
ImpellerInactive are two aggravating factors for the decrese in
discharge pressure. Furthermore, is a more serious factor than PW
since it has twice the weight and makes the discharge pressure
decrease normally and strongly. Moreover, W.sub.0 is an additional
parameter measuring the accuracy of the equations; for example, in
this case, W.sub.imp/W.sub.0 measures the influence of the
impellers-inactive event on discharge pressure.
[0256] FIG. 31 is an illustration of an example BBN 805 that may be
used in an embodiment to detect three types of deadhead conditions
(DeadHead1, DeadHead2, and DeadHead3). DeadHead1 (DH1) is a
restriction above the ESP (up-stream of flowline pressure indicator
where wellhead pressure (WHP) data is available). Deadheads are any
restriction above the ESP. Where tubing wellhead pressure is
available, one can differentiate between a restriction in the
tubing and at the wellhead if WHP data is available. Where WHP is
not available, then the alarm is classified as DeadHead1 (DH1).
DeadHead2 (DH2) is a restriction above the ESP (down-stream of
flowline pressure indicator). One difference between DH1 and DH2 is
wellhead pressure, which is the parameter that allows monitoring to
differentiate between a restriction in the tubing (e.g., closed
subsurface safety valve or deposits in tubing) and at the wellhead,
hence the value of having wellhead pressure. Deadhead3 is a partial
restriction above the ESP. The symptoms are qualitatively the same
as DH2, however it is the speed of change and magnitude that
differ. There are cases where a surveillance center may confuse the
two. The notable operating physical properties that allow
differentiation are the discharge pressure (P.sub.d) and current
draw (Amps) where the change is slower. On some wells where
reservoir pressure and PVT are well understood, it is possible to
calculate the absolute P.sub.d. for deadhead and thereby know when
ESP is in DH3 rather than DH2/DH1.
[0257] The following table illustrates the combination of answers
of high-level questions based on the raw data that would indicate
DeadHead1 (DH1), DeadHead2 (DH2), and DeadHead3 (DH3),
respectively:
TABLE-US-00001 PROBABLE SYMPTOM CAUSE Amps Volts Hertz WHT WHP-T Pi
Pd Pd-Pi Ti Tm WHP-A CODE DH1 DH2 DH3
[0258] At any given time during the operation of an ESP 241, the
signal processing system 301 accepts input from the sensors 255-250
and the variable frequency controller 243 and answers the
high-level questions, thereby producing probability values for
various conditions 809a-809i. For example, in the example of FIG.
28, which corresponds to the table above, the Well_Head_Temperature
809d is decreasing (P(Well_Head_Temperature)=100.00) while the
Delta_Pressure 809f is increasing rapidly
(P(Delta_Pressure)=100.00). That condition--combined with the
probabilities of other conditions, e.g., Average_Amps
Intake_Pressure 809i, Discharge_Pressure 809h and Intake_Pressure
809i--may be used to infer a deadhead condition
(P(Deadhead2)=99.7).
[0259] The underlying effort is to train the Bayesian network to
fulfill the posteriori probabilities tables. It can be done either
manually or automatically if sufficient training data is available.
For example, the Netica program from Norsys Software Corporation
allows for probability tables to be determined based on collected
data in the form of tab-delimited data files.
[0260] In the example above, one can see that the probability for a
DH2 is 99.7% while the other stress patterns probabilities are
close to 0. The case is presented in a deterministic way (100%
probabilities as an input) but it works similarly with an actual
probability from the distribution determined with the changepoint
detector.
[0261] From the foregoing it will be apparent that a technology has
been presented herein that provides for a mechanism for real-time
or near real-time determination of changes in industrial processes
in a manner that allows operators of such processes, which
operators may be human controllers, processors, drivers, control
systems and/or the like to make note of/detect events in the
operation of a hydrocarbon associated procedure, take corrective
action if necessary, change operation of the procedure if desired
and/or optimally operate the processes in light of the changes in
the operating environment, status of the system performing the
procedure and/or the like. The technology presented provides for a
mechanism that is noise tolerant, that may be readily applied to a
variety of hydrocarbon associated processes, and that is
computationally inexpensive.
[0262] The solutions presented may either be used to recommend
courses of action to operators of industrial processes or as input
in process automation systems. While the techniques herein are
described primarily in the context of exploration for subterranean
hydrocarbon resources through drilling, the techniques are
applicable to other hydrocarbon related processes, for example, the
exploration for water, transport of hydrocarbons, modeling of
production data from hydrocarbon wells and/or the like.
[0263] In the foregoing description, for the purposes of
illustration, various methods and/or procedures were described in a
particular order. It should be appreciated that in alternate
embodiments, the methods and/or procedures may be performed in an
order different than that described.
[0264] It should also be appreciated that the methods described
above may be performed by hardware components and/or may be
embodied in sequences of machine-executable instructions, which may
be used to cause a machine, such as a general-purpose or
special-purpose processor or logic circuits programmed with the
instructions, to perform the methods. These machine-executable
instructions may be stored on one or more machine readable media,
such as CD-ROMs or other type of optical disks, floppy diskettes,
ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash
memory, or other types of machine-readable media suitable for
storing electronic instructions. Merely by way of example, some
embodiments of the disclosure provide software programs, which may
be executed on one or more computers, for performing the methods
and/or procedures described above. In particular embodiments, for
example, there may be a plurality of software components configured
to execute on various hardware devices. In some embodiments, the
methods may be performed by a combination of hardware and
software.
[0265] Hence, while detailed descriptions of one or more
embodiments of the disclosure have been given above, various
alternatives, modifications, and equivalents will be apparent to
those skilled in the art without varying from the spirit of the
disclosure. Moreover, except where clearly inappropriate or
otherwise expressly noted, it should be assumed that the features,
devices and/or components of different embodiments can be
substituted and/or combined. Thus, the above description should not
be taken as limiting the scope of the disclosure, which is defined
by the appended claims.
* * * * *