U.S. patent application number 11/700396 was filed with the patent office on 2008-07-31 for systems for monitoring sensor and actuator health and performance.
This patent application is currently assigned to Halliburton Energy Services Inc.. Invention is credited to Jason D. Dykstra.
Application Number | 20080179056 11/700396 |
Document ID | / |
Family ID | 39666645 |
Filed Date | 2008-07-31 |
United States Patent
Application |
20080179056 |
Kind Code |
A1 |
Dykstra; Jason D. |
July 31, 2008 |
Systems for monitoring sensor and actuator health and
performance
Abstract
Systems for assessing health and performance of actuators and
sensors in process equipment system. In one example, the equipment
system comprises subsystems (preferably physically coupled
subsystems), at least some of which are characterizable by
transmitted signals. Some of these signals are transformed into a
comparable form and compared, so as to identify signals that are
outside of operating bounds.
Inventors: |
Dykstra; Jason D.; (Addison,
TX) |
Correspondence
Address: |
JOHN W. WUSTENBERG
P.O. BOX 1431
DUNCAN
OK
73536
US
|
Assignee: |
Halliburton Energy Services
Inc.
|
Family ID: |
39666645 |
Appl. No.: |
11/700396 |
Filed: |
January 31, 2007 |
Current U.S.
Class: |
166/250.15 |
Current CPC
Class: |
E21B 43/267
20130101 |
Class at
Publication: |
166/250.15 |
International
Class: |
E21B 47/12 20060101
E21B047/12 |
Claims
1. An oilfield equipment monitoring system, comprising: at least
three oilfield equipment subsystems that are physically coupled to
one another; a control system configured to receive signals from at
least some of the oilfield equipment subsystems; and wherein the
control system is configured to: (i) transform one or more of the
oilfield equipment subsystem signals into units associated with the
type of physical coupling among the three or more oilfield
equipment subsystems; (ii) compare at least some of the signals;
and (iii) indicate at least one oilfield equipment subsystem's
signal that does not agree with at least two other oilfield
equipment subsystems' signals.
2. The system of claim 1, wherein the type of physical coupling is
selected from the group consisting of: hydrostatic pressure, flow
rate, and mass transfer.
3. The system of claim 1, wherein the control system is further
configured to modify a control algorithm based on an identified
oilfield equipment subsystem signal.
4. The system of claim 1, wherein the control system is further
configured to send a signal to an operator identifying an oilfield
equipment subsystem, where that subsystem's signal does not agree
with at least two other oilfield equipment subsystems' signals.
5. The system of claim 1, wherein the control system is further
configured to send a signal to indicate a result is outside
acceptable bounds when the step of comparing indicates the result
is outside acceptable bounds.
6. The system of claim 5, wherein the acceptable bounds are
selected from the group consisting of: predetermined bounds,
dynamical bounds, operationally dependent bounds, and bounds
associated with dynamic constraints of a physical system.
7. The system of claim 1, wherein the units are selected from the
group consisting of: physical units, normalized expressions without
physical units, and monotonic transformations of physical
units.
8. The system of claim 1, wherein the control system is further
configured to replace the identified signal's input to the control
algorithm with another signal, without modifying the control
algorithm, when the identified signal is an input to a control
algorithm.
9. An oilfield equipment monitoring system, comprising: at least
three oilfield equipment subsystems that are physically coupled to
one another; a control system configured to receive signals from at
least some of the oilfield equipment subsystems; and wherein the
control system is configured to check the respective readings of
said multiple subsystems against each other to determine whether
any subsystems have readings which are physically inconsistent with
each other; and under at least some conditions, exclude the output
of a respective subsystem which has been determined in said check
of respective readings to be showing inconsistent output.
10. The system of claim 9, wherein the control system is further
configured to include the output of a respective subsystem which
had been excluded if said check of respective readings ceases to
detect inconsistencies.
11. The system of claim 9, wherein the control system is further
configured to send a signal to indicate a result is outside
acceptable bounds when said check of respective readings shows the
result is outside acceptable bounds.
12. The system of claim 11, wherein the acceptable bounds are
selected from the group consisting of: predetermined bounds,
dynamical bounds, operationally dependent bounds, and bounds
associated with dynamic constraints of a physical system.
13. The system of claim 9, wherein the control system is further
configured to replace a first subsystem's signal with a second
subsystem's signal as input to a control algorithm if said first
subsystem has been determined to be showing inconsistent
output.
14. The system of claim 13, wherein the second subsystem's signal
is transformed into a form comparable to the first subsystem's
signal before being input into the control algorithm.
15. The system of claim 9, wherein at least one of the subsystems
is selected from the group consisting of: a sensor, an actuator, a
mixer, and a pumping system.
16. An oilfield equipment monitoring system, comprising: at least
three oilfield equipment subsystems that are physically coupled to
one another; a control system configured to monitor one or more
signals derived from the oilfield equipment subsystems; and wherein
said signals from different oilfield equipment subsystems are
compared to identify an oilfield equipment subsystem's signal that
does not substantially agree with at least two other oilfield
equipment subsystems' signals.
Description
BACKGROUND AND SUMMARY OF THE INVENTION
[0001] The following applications filed concurrently herewith are
not necessarily related to the present application, but are
incorporated by reference herein in their entirety: "Methods for
Managing Flow Control Valves in Process Systems" (U.S. patent
application Ser. No. ______, filed simultaneously with the
effective filing date of the present application, Attorney Docket
Number HES-2005-IP-019666U3); "Systems for Managing Flow Control
Valves in Process Systems" (U.S. patent application Ser. No.
______, filed simultaneously with the effective filing date of the
present application, Attorney Docket Number HES-2005-IP-019666U4);
and "Methods to Monitor System Sensor and Actuator Health and
Performance", (U.S. patent application Ser. No. ______, filed
simultaneously with the effective filing date of the present
application, Attorney Docket Number HES-2006-IP-019666U1).
[0002] The present application relates to monitoring complex
systems such as automated equipment used in oilfields, and more
particularly to monitoring sensor and actuator health and
performance.
DESCRIPTION OF BACKGROUND ART
[0003] Modern oilfield rigs use automated equipment in many aspects
of an operation. A key element of such complex systems is the
control and monitoring system. These systems include sensors and
other elements that signal a control unit in a feedback loop. The
control unit monitors the system, providing stability and ensuring
the system operates within desired parameters.
[0004] Sensors are often placed at specific locations within a
system to provide information necessary for the control unit to
function. For example, on a drill rig, mud must be provided within
specific parameters. Sensors monitor the flow rate of the mud,
pressure, density, and other measurables, and this information is
fed back to the control unit and/or to an operator who manually
monitors the system for failures.
[0005] Current systems normally rely on operators to take action
when failure occurs. These failures can affect job performance and
lead to job failure. Also, the operators receive minimal feedback
from the control system about its current operating state relative
to its expected state. This means an operator is liable to be
unaware of impending or immediate failures, and requires a higher
degree of knowledge on the part of an operator. The lack of
diagnostic systems to monitor performance and an interface designed
to give an operator assistance means that operators are required to
have a higher level of skill and knowledge to safely and
efficiently monitor and operate these systems.
Systems for Monitoring Sensor and Actuator Health and
Performance
[0006] In one example embodiment, the present innovations provide a
system to monitor for failures in one or more subsystems
(preferably physically coupled subsystems) in a larger system, and
(in some embodiments) update the operator of failures or impending
failures to improve process control. It also can include a system
with process control knowledge to help operation of the equipment
and reduce operator error.
[0007] In one class of preferred embodiments, the innovations
include a plurality of subsystems (such as sensors or actuators, or
combinations of parts) that can signal operation or state
information. This information is used to determine if one or more
subsystems are in or near failure mode.
[0008] For example, in one example implementation, a sensor of
interest is selected, such as a flow rate sensor. Other subsystems
of the total system that are physically coupled to the flow rate
sensor provide information that is transformed into data that is
comparable to the output of the flow rate sensor. This information
is compared, and discrepancies indicate that some sensor of the
system may be failing or outside preferred operating conditions.
Operating conditions or bounds can be chosen or generated in a
number of ways, including static, dynamic, or operationally
dependent bounds. Bounds may be also be reevaluated in real time,
in dependence, for example, on system dynamics.
[0009] In another example implementation, subsystem signals are
aggregated and transformed into comparable form so that
discrepancies can be identified. Thus, for example, multiple
physically coupled subsystems form a redundant check on one another
so as to monitor each individual subsystem's health and
performance.
[0010] In preferred embodiments, actual subsystem (e.g., sensor or
actuator) readings are compared to a model of the system dynamics,
so actual subsystem operation can be compared to expected subsystem
operation.
[0011] By using the available sensor data in conjunction with a
model of the system dynamics, the controller can be designed to
estimate sensor and actuator failures and update the operator
through the interface. The controller can also be designed with
system intelligence which can be used to help the operator perform
the job and reduce operator error.
[0012] The disclosed innovations, in various embodiments, provide
one or more of at least the following advantages:
[0013] detection of individual sensor or actuator failure or
inaccuracy;
[0014] overall system health monitoring;
[0015] reduction of necessary operator skill and chance of operator
error;
[0016] ability to switch control modes depending on sensor or
actuator health.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The disclosed inventions will be described with reference to
the accompanying drawings, which show important sample embodiments
of the invention and which are incorporated in the specification
hereof by reference, wherein:
[0018] FIG. 1 shows one embodiment of the present innovations as
implemented in an exemplary hydrocarbon well drilling rig site.
[0019] FIG. 2 shows an example of actuator slippage.
[0020] FIG. 3 shows a sand and liquid slurry system consistent with
implementing an embodiment of the present innovations.
[0021] FIG. 4 shows a detail of the liquid supply side of the sand
and liquid slurry system consistent with implementing an embodiment
of the present innovations.
[0022] FIG. 5 shows a control diagram of a blender unit consistent
with an embodiment of the present innovations.
[0023] FIG. 6 shows an example implementation of redundant sensor
checking relative to dynamic links of a physical system, consistent
with an embodiment of the present innovations.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0024] The numerous innovative teachings of the present application
will be described with particular reference to the presently
preferred embodiment (by way of example, and not of
limitation).
[0025] FIG. 1 shows an example system in which embodiments of the
present innovations can be implemented. This example shows an
oilfield drilling system 100, including a drill string 102, and
downhole tool 104. Drilling system 100 also includes a pump system
106 which controls insertion of materials downhole, such as
drilling mud for cooling and removal of debris, or other slurries
(such as sand and water combinations) for various tasks.
[0026] In a preferred embodiment, the drilling system 100 includes
sensors such as flow meter 101 that monitor and characterize the
performance of various subsystems. This information is used, often
by an operator, but also by automated systems, to determine when
performance is outside desired bounds or failure occurs or is about
to occur.
[0027] Specifically, FIG. 1 also shows one embodiment of the
present innovations as an oilfield equipment system 100 which can
be comprised of a pump system 106, a rotary flow control valve with
an actuator/position indicator assembly as 103, a flow meter 101, a
drill string 102, a drill bit down hole at 104, and a plurality of
signal operations, computations, and other actions that can be
configured with a general purpose computer (not shown) that is
monitoring system 100. Pump 106 can pump a drilling fluid through
control valve 103 and through flow meter 101, then down drill
string 102 through bit 104 and then can re-circulate the fluid back
to itself. Thus, the pump, the valve, and the meter are physically
coupled by the drilling fluid. Pump 106 can send a pump speed
signal to stage 106A for transformation of the speed signal to a
volumetric fluid flow rate, in say, gallons per minute ("GPM").
Flow meter 101 can send a flow rate signal to stage 101A for
transformation to a volumetric fluid flow rate in GPM. Valve 103
position indicator can send a signal to stage 103A for
transformation of the "% OPEN" signal of the valve to a volumetric
flow rate in GPM. Stage 107 can compare the three transformed
signals for agreement in stages 107A, 107B, and 107C. If one signal
is found to disagree with the other two signals, an output signal
can be made to notify an operator that the particular component
that is not in agreement needs maintenance or attention. Further,
the output signal can be used to effect an automatic
reconfiguration of the control system operating the overall system
100 to thereby exclude the disagreeing signal from the control
methods being used to operate the system.
[0028] For an example of a rotary-actuated valve, FIG. 2 shows a
top view of an example rotary-actuated valve 206 that is operated
by an actuator attached to the valve shaft 208, which opens and
closes the valve by rotating the valve shaft according to a signal.
In some situations, such as when a valve is stuck, aged, or
otherwise not operating correctly, there can be a difference
between the signaled valve movement 202 and the actual valve
movement 204. In the example of FIG. 2, the actuator was signaled
to move the valve a first amount 202, while the actual valve
movement 204 was less. For example, the difference in movement can
represent a difference in the signaled angle of rotation. In other
instances, a valve can be vertically actuated and the difference
can represent the error in valve stroke. In some situations,
reports of valve movement can depend on signaled movement 202 and
not actual movement 204. Especially in complex systems, failure to
obtain accurate information about actual subsystem performance
(such as the movement of the valve) can harm production and
propagate to other parts of the system.
[0029] In one example embodiment of the present innovations,
subsystems of a larger system (preferably physically coupled
subsystems, or subsystems that can otherwise be characterized in
terms of one another) are redundantly monitored. For example,
subsystems that affect a sensor or actuator (in preferred
embodiments) are compared in order to characterize a given sensor
or actuator's current, actual level of performance in order to
determine if the sensor or actuator is performing within accepted
bounds.
[0030] Inputs and outputs that affect (or are affected by) the
subsystem are, in preferred embodiments, transformed into
comparable sensor or actuator states to monitor sensor or actuator
performance. For example, when a given system includes several
sensors that monitor physically coupled subsystems, some or all the
sensors outputs can be transformed into the same units or data as
one of the sensors, to determine if that sensor is sending accurate
signals of the subsystem which it monitors. By transforming these
signals into a single, comparable set of data, the present
innovations provide a way to redundantly check each individual
sensor of the group of sensors. This redundant checking can be
performed in a number of ways, such as by selecting a sensor of
interest and transforming all other sensor data into data that is
comparable to the sensor of interest, or by transforming all sensor
data into a single form so their signals can be aggregated and
compared, for example, by checking standard deviations between
signals, spread, and other statistical analysis.
[0031] For example, a sensor or actuator of interest can be viewed
as being coupled (such as physically coupled) to other actuators
and sensors if the signal or operation of one is affected by, or
affects, the other actuators or sensors. Transformation of the
various signals is derived from physical system dynamics. The
transformed signals of multiple coupled subsystems effectively
become redundant sensors.
[0032] In preferred embodiments, subsystem performance, as
determined by one or more of the redundant sensors, is compared to
predetermined or dynamic bounds to determine if the subsystem is
performing properly, for example, or close to or in failure. These
bounds can be static or operationally dependent, and/or reevaluated
in real time. Other performance constraints can be created from the
dynamic limits of the physical system. The physical system
operational envelop can be defined, for example, as a state vector
of first order derivatives (i.e., change over time) which can be
used to define acceptable operational ranges of the sensors. Such a
mechanism can be used to detect, for example, when a sensor
registers severe change, which can indicate either a subsystem in
failure, or sensor malfunction. Operational bounds or envelopes can
also be dynamically reset, for example, relative to physical system
dynamics.
[0033] Further embodiments of the present innovations include
interfaces wherein results of one or more of the redundant sensors
are reported to an operator, preferably coupled with information to
help the operator or give assistance in detecting, for example,
when corrective action needs to be taken and reduce operator
error.
[0034] In many complex systems, such as those described below,
sensor information is used in feedback loops to aid in controlling
systems to provide stability and to ensure that a system operates
within acceptable limits or bounds. When data from a plurality of
sensors are used by a control unit in a feedback and control
system, the present innovations allow for more robust control in
several ways. For example, in one example embodiment, if a
plurality of sensors are used to inform a control unit, and if one
of those sensors goes out of operational bounds, that sensor's
signal can be removed from input to the control unit. In preferred
embodiments, the control algorithm used in the control system can
be modified to operate without the data from the sensor that was
removed. In other embodiments, a sensor can experience temporary
periods when its signal is outside of operational bounds,
indicating bad sensor data, for example. In such cases, the sensor
can be temporarily removed from input to the control unit, and
later, when it has resumed operation that is within operational
bounds, its signal can be reintroduced to the control unit.
[0035] The present innovations are discussed with reference to an
example system, such as that depicted in FIG. 3. In this case, a
sand and liquid blending system 300 that includes a sand supply
302, a liquid supply 304, a blender 306, and a pump system 308. In
this example, because of such physical realities as fluid dynamics,
various parts of the system are physically coupled. For example,
the input and output of the blender are dependent on one another,
in that changes in one are affected by, affect, or can otherwise be
detected in changes in the other. For example, measured rate of
flow into the blender would be coupled with measured rate of flow
out of the blender. These two quantities could therefore be
expressed as functions of one another. More detailed examples
follow.
[0036] FIG. 4 shows a detailed view of the liquid supply subsystem
400 of the system shown generally in FIG. 3. Liquid supply tank 304
sends liquid to blender 306 which outputs to a pump system 308.
Output from liquid supply tank 304 is monitored by a flow sensor
402 and is controlled by a valve 404. Downstream of blender 306,
another flow sensor 406 monitors output to the pumping system
308.
[0037] Because, in this example, all these elements are physically
coupled (via the flow stream hydraulics, in this example), they can
be characterized in terms of one another. For example, flow sensor
402 directly measures the liquid flow rate. However, changes in the
height of the liquid supply tank 304 over time and the area of the
tank can provide an expression that also provides a determination
of flow rate that is comparable to, or should agree with, that
directly measured by sensor 402. Likewise, valve 404 can be used to
express rate as a function of the valve flow constant, the
valve-open angle and drive signal applied to the valve 404. The
blender 306 and flow sensor 406 can, together, provide rate as a
function of the height, the change in height over time, the area,
density, and output flow of the blender. Finally, rate can be
expressed at the pumping system 308 in terms of the efficiency,
output curve, and RPMs of the pumping system.
[0038] These multiple functions that result in flow rate
determinations effectively form a system or plurality of redundant
sensor measurements for flow rate measurements (in this example).
In one embodiment of the present innovations, these values are
compared to the sensor 402 to determine if the sensor 402 is
operating correctly. For example, if the subsystems that also
indirectly measure the flow rate yield a relatively consistent flow
rate, and if sensor 402 differs significantly from this rate, then
the accuracy of sensor 402 is called into question. In other
embodiments, all five of these subsystems (including sensor 402)
can be aggregated and statistically analyzed, for example, by
measuring their standard deviation, and/or identifying any
individual subsystem that differs from the other readings beyond a
predetermined threshold or envelope. Other statistical manipulation
or analysis of these data is also possible.
[0039] Thus, the various data of the subsystems can be dynamically
transformed into an interested subsystem's performance.
[0040] The disclosed sensor checking and dynamic characterization
system can be used in other ways as well. For example, in one
embodiment, if a sensor is found to operate outside of
predetermined (or dynamic, or operationally dependent) bounds, that
sensor can be removed. In other embodiments, the sensor can be
temporarily removed, and reintroduced when its operation returns
within desired limits. Changes in the sensor operation over time,
as detected by the present innovations, can also exceed limits as
described above. In other embodiments, a sensor or subsystem might
go out of operational bounds and be removed from input to the
control algorithm that maintains stability in the system. In some
embodiments, the sensor's input is simply removed, and may or may
not be reintroduced when the sensor is once again found to be
operating within desired limits.
[0041] In other embodiments, the sensor's input is removed
(temporarily or permanently) and, additionally, the control
algorithm is modified to account for the reduced input information.
For example, some cement mixing systems can be designed to switch
from being controlled using density information (i.e., information
from density sensors/calculations) to being controlled using volume
information i.e., information from volume sensors/calculations). In
such an example system, if the density sensor is determined to be
in a failing mode and is removed from the input to the control
algorithm, then the system can switch from density mode to
volumetric mode, and thereafter the control algorithm would be
modified to accept and use information gathered from the sensors
associated with the volumetric mode. Other examples also apply,
such, such as when a height sensor fails, the innovative system can
switch to density mode and use the changed input in its control
algorithm. In these example cases, in preferred embodiments, an
operator would be informed and may have to take necessary actions,
such as controlling some levels manually.
[0042] FIG. 5 shows a further detail of the blending system 306
shown in FIG. 3, showing the control loops that maintain stability
in the respective systems. A density sensor 502, a height sensor
504, a water sensor 506, and a sand sensor 508 are shown in context
of a control system diagram. Each control loop includes a control
unit or algorithm, represented by PID (proportional, derivative,
integral) controller (shown variously as units 502A-508A) that is
associated with elements in the forward path, between the error
signal and the control signal. (Other types of control models can
of course be implemented, and the present example is illustrative
only.) The depicted system includes signals that represent the
error between the dynamic models (502B-508B) and the outputs of
their respective sensors. Each sensor measures some property that
is also being dynamically modeled. The input to the dynamic models
from the PIDs (in this example) are the amounts needed to correct
the dynamic models so they match their respective sensor readings.
Each control loop also has a dynamic model (502B-508B) of the
system or subsystem on which the control unit imposes
stability.
[0043] As mentioned above, the other inputs and outputs can be
dynamically transformed into an interested system's performance. In
this example, there are three ways to determine expected sand rate.
The mass rate error signal can be dynamically transformed (in the
same way that readings were transformed into liquid flow rates,
above) to achieve an expected sand rate 502C. Likewise, the
volumetric rate error signal can be transformed into an expected
sand rate 504C. And the sand screw dynamic model gives a measure of
the sand rate by taking into account the drive signal, the speed of
the screw, and other known dynamics.
[0044] It should also be noted that this system contains an
adaptive parametric control (APC) to map nonlinearities. This
concept can be applied in several ways, such as examining actuator,
valve, or other system performance and identifying problems.
[0045] For example, in one embodiment, the APC is used in examining
actuator performance and looking for problems.
[0046] There are several ways this innovative concept can be
implemented, and some examples follow. These examples are intended
to describe embodiments, and not to limit the application of the
innovative concepts.
[0047] In general terms, these innovative concepts include, in a
first embodiment, modeling of the dynamics of a system as expected
in normal operation; modeling the dynamics of the system in real
time; and comparing the two models to determine if a failure has
occurred. In another embodiment, the present innovations include
embodiments that use a learning algorithm to determine a parameter
in a model of the dynamics; and using that parameter to detect
system failure, such as by monitoring that parameter (or systems
from which that parameter can be derived) during operation.
[0048] In a first example, a model of failure behavior is
generated. The model of system failure is compared to the system as
the system is running. This comparison can provide additional
information, about both the failure model and the system dynamics.
For example, the dynamics of valve slop (or mismatch between a
valve control signal and actual valve performance) may be well
known. The model of valve slop can be compared to the system
dynamics while the system is running. For example, the deadband of
the valve and the valve coefficient (or an aspect of the control
signal) can be mapped so as to increase the accuracy of the valve
slop model. This will provide information about the wear that is
occurring and the flow characteristics through the valve.
[0049] In another example, the dynamics of the system are mapped
while the system is running, but without a model of how the system
fails or misbehaves. In this case, the mapped dynamics are compared
to a threshold value, such as one or more dynamic performance
specifications, to see if the mapped system dynamics are within
bounds. For example, a pump's performance can be modeled under
normal operating conditions. The parameters of that model can be
dynamically compared to actual performance while the system is
running. The system under normal operating conditions should
produce a torque feedback doe to damping that is a function of
speed. If the mapped damping coefficient becomes large, and outside
the specs, a problem may have occurred, such as the pump
experiencing environmental loading. This could be, for example, a
sign that the piston chamber is filled with sand. The number of
sensors and observable states would determine how many properties
could be mapped to the dynamic model or thresholds.
[0050] In another example, a learning algorithm (such as a neural
network) determines normal operating behavior. The model created by
the learning algorithm can be compared to sensor data to determine
how well the system is tracking "normal" behavior, and to thereby
detect failures.
[0051] These subsystems effectively serve as virtual sensors, and
their outputs are input to a sensor analysis program 510, such as a
computer program product on a computer readable medium that
analyzes the readings, as described above. For example, the sensor
readings can be monitored for behavior so as to indicate (for
example, by a signal to an operator or by automated alarm or
controls) when a given sensor is operating outside predetermined
bounds (whether dynamic or static).
[0052] FIG. 6 shows sensor checking relative to dynamic limits to
the physical system. Here, the known operational envelop, shown as
lower bound (LB) and upper bound (UB) are used to check the sensor
and actuator performance relative to the current operating position
and derivative of that position. The current will determine the
allowable sensor envelope. As an example, if a mixing tub is being
filled with gel and sand, and that mixture is leaving the mixing
tub at some rate, then the rate of change of the tub level sensor
should output a signal value that is close to what would be
expected for that rate of change of volume.
[0053] FIG. 6 includes a plurality of levels of checking. For
example, the water rate includes three separate levels of
performance checks. In a first case, the water rate is directly
measured, for example, by a flow meter or other means of checking
movement of the water. Lower bounds and upper bounds are set for
the water rate, and if the water rate exceeds these bounds, a
signal indicating unacceptable behavior or performance can be sent.
A second condition for bounding the water rate is based on the
commands sent to the actuator that controls the water rate. Known
changes in the actuator correspond to known changes in the water
rate. If a given command is sent, and yet the water rate does not
respond as expected (within bounds), then a signal indicating this
behavior can be sent. Finally, the change in the water rate can be
used to set bounds on the water rate. In this case, the dynamic
behavior of the water rate can, for example, have known bounds
outside which unacceptable behavior is indicated. For example, if
it is known that the change in water rate should not exceed d(water
rate)/dt, and if checks on the water rate indicate that the dynamic
behavior of the water rate exceeds preset bounds, then a signal
indicating such condition can be sent.
[0054] All these bounds or indications of the water rate can be
used, for example, as checks on the water rate. In some cases, the
water rate, or the water actuator command, or the dynamic changes
in the water rate, may be inferred from data from other (coupled)
systems. In such cases, the data from the coupled systems is
preferably transformed into one of the three example measures for
acceptable water rate behavior, and compared to the predetermined
bounds.
[0055] As seen from the examples, the present innovations include,
in at least one embodiment, a multi-layered solution in which all
the sensors and actuators are combined with system intelligence to
determine failure, or likelihood of failure. (For example, bounds
can indicate failure, or conditions that are known or suspected to
lead to failure.) This provides an improved view of system health
and performance, and also permits signaling to operators so that
failures are prevented or caught more quickly, reducing operator
error.
[0056] According to a disclosed class of innovative embodiments,
there is provided at least three oilfield equipment subsystems that
are physically coupled to one another, a control system configured
to receive signals from at least some of the oilfield equipment
subsystems, and wherein the control system is configured to:
transform one or more of the oilfield equipment subsystem signals
into units associated with the type of physical coupling among the
three or more oilfield equipment subsystems, compare at least some
of the signals and indicate at least one oilfield equipment
subsystem's signal that does not agree with at least two other
oilfield equipment subsystems' signals.
[0057] According to a disclosed class of innovative embodiments,
there is provided a An oilfield equipment monitoring system,
comprising at least three oilfield equipment subsystems that are
physically coupled to one another, a control system configured to
receive signals from at least some of the oilfield equipment
subsystems, and wherein the control system is configured to check
the respective readings of said multiple subsystems against each
other to determine whether any subsystems have readings which are
physically inconsistent with each other; and under at least some
conditions, exclude the output of a respective subsystem which has
been determined in said check of respective readings to be showing
inconsistent output.
[0058] According to a disclosed class of innovative embodiments,
there is provided an oilfield equipment monitoring system,
comprising at least three oilfield equipment subsystems that are
physically coupled to one another, a control system is configured
to monitor one or more signals derived from the oilfield equipment
subsystems and wherein signals from different oilfield equipment
subsystems are compared to identify an oilfield equipment
subsystem's signal that does not substantially agree with at least
two other oilfield equipment subsystems' signals.
[0059] Modifications and Variations
[0060] As will be recognized by those skilled in the art, the
innovative concepts described in the present application can be
modified and varied over a tremendous range of applications, and
accordingly the scope of patented subject matter is not limited by
any of the specific exemplary teachings given.
[0061] For example, the disclosed innovations can be applied in a
number of areas outside the oil industry, though the preferred
context is the oil industry.
[0062] For another example, though many of the examples used to
describe the present innovations use specific components, such as
sensors and/or actuators, the present innovations can be applied
using other components as well. For example, any detection and
signaling apparatus that receives information about a system and
that can in any way convey that information could be implemented
into the present innovations. The parameters that are monitored can
also vary widely, including density, flow, volume, various
derivatives, mass transfer, temperature, pressure, and any other
characterizable parameter.
[0063] For another example, though the present innovations are
described in the context of a sand and liquid slurry, this is only
an example context. Other contexts would also benefit from the
present innovations, where preferably physically coupled subsystems
can be characterized in a common way.
[0064] In another example, the present innovations are only one
part of a multi-level filtering system, that can include other
checks on system behavior.
[0065] In other examples, the systems being monitored are
characterized as being "physically coupled," or "coupled." Any
transfer of information, matter and/or energy between two systems
is included in the definition of "coupled" as that term is used in
this application. Further, any two systems that can be
characterized in terms of one another, are also considered to be
"coupled" within the context of this application.
[0066] In another example, the current innovations are
characterized in the context of oilfield equipment. Such equipment
includes a variety of oilfield supply systems, downhole tools,
above-ground equipment, such as valves, screws, pumps, agitators,
and other tools associated with oilfield operations.
[0067] In another example, the signals associated with the oilfield
equipment subsystems are described as being transformed into
"units" associated with the physical coupling that exists among the
subsystems. These units are understood to include not only physical
units (such as mass, volume, rates, or other physical quantities or
one or more derivatives or quantities thereof), but also "unitless"
mathematical quantities or expressions which are consistent with or
associated with the physical coupling (i.e., are derivable from the
type of physical coupling) in any way. For example, the units or
expressions into which signals are transformed for comparison could
include normalized quantities where "physical" units have been
divided out of the expression. These units can also be monotonic
expressions of one another, or another quantity. The units or form
of the compared quantities are intended to be transformed such that
they can be compared with one another, regardless of the form of
the expression.
[0068] In another description of the exemplary embodiments, signals
associated with the various subsystems can refer to, for example, a
sensor reading, a control signal sent to a subsystem, a meter or
other device that is affected by the physical coupling of the
subsystem that can be monitored, or any other quantity associated
with that subsystem that can be monitored in some way, and which
can be expressed in terms that are comparable to at least one other
subsystem that is physically coupled with the first subsystem.
[0069] None of the description in the present application should be
read as implying that any particular element, step, or function is
an essential element which must be included in the claim scope: THE
SCOPE OF PATENTED SUBJECT MATTER IS DEFINED ONLY BY THE ALLOWED
CLAIMS. Moreover, none of these claims are intended to invoke
paragraph six of 35 USC section 112 unless the exact words "means
for" are followed by a participle.
[0070] The claims as filed are intended to be as comprehensive as
possible, and NO subject matter is intentionally relinquished,
dedicated, or abandoned.
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