Note: Descriptions are shown in the official language in which they were submitted.
CA 02864265 2015-04-09
CONTROL PARAMETER DETERMINATION IN STEAM ASSISTED GRAVITY
DRAINAGE OIL DRILLING
BACKGROUND
[0002] Steam assisted gravity drainage (SAGD) may be effective in
reservoirs
containing highly viscous oil, or bitumen. Referring to Fig. 1, in the SAGD
process,
two parallel horizontal wells, one above the other, are utilized where a steam
injection
well 100 injects steam into a top line 102. A producer well 108 is an oil
producer.
When steam is continually injected into top line 102, a steam chamber is
developed
that grows upward to the surroundings and heats the surrounding oil in rock
and
shale. Hot oil and condensed water drain, through the force of gravity, to
bottom line
104 that is below top line 102. The mixture of bitumen and water is pumped to
producer well 108 at the surface using a pump 106. At the surface, the water
is
separated from the bitumen and returned to steam injection well 100 for re-
injection
into top line 102.
SUMMARY
[0003] In an example embodiment, a method of determining an optimal value
for a
control parameter of a production operation is provided. Drilling data
including a
plurality of values measured for each of a plurality of drilling parameters is
received.
An objective function is determined using the received drilling data. The
objective
function maximizes production of a material produced by a production
operation,
minimizes water usage by the production operation, and minimizes a difference
between a boiling point of water and a temperature of the material produced by
the
production operation at a same pressure. An association rule is determined
using the
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received drilling data. The association rule defines a range of values for a
control
parameter of the plurality of drilling parameters that is selected based on a
value of a
first parameter of the plurality of drilling parameters. Measured drilling
data is
received that indicates current control parameter values of the production
operation.
An optimal value for the control parameter of the production operation is
determined
by executing the determined objective function with the received, measured
drilling
data as an input and subject to the determined association rule. The
determined
optimal value is output.
[0004] In another example embodiment, a computer-readable medium is
provided
having stored thereon computer-readable instructions that, when executed by a
computing device, cause the computing device to perform the method of
determining
an optimal value for a control parameter of a production operation.
[0005] In yet another example embodiment, a computing device is provided.
The
system includes, but is not limited to, a processor and a computer-readable
medium
operably coupled to the processor. The computer-readable medium has
instructions
stored thereon that, when executed by the computing device, cause the
computing
device to perform the method of determining an optimal value for a control
parameter
of a production operation.
[0006] Other principal features of the disclosed subject matter will become
apparent to those skilled in the art upon review of the following drawings,
the detailed
description, and the appended claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Illustrative embodiments of the disclosed subject matter will
hereafter be
described referring to the accompanying drawings, wherein like numerals denote
like
elements.
[0008] Fig. 1 depicts a block diagram of a steam assisted gravity drainage
production operation in accordance with an illustrative embodiment.
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[0009] Fig. 2 depicts a block diagram of a data optimization device in
accordance
with an illustrative embodiment.
[0010] Fig. 3 depicts a flow diagram illustrating examples of operations
performed
by the data optimization device of Fig. 2 in accordance with an illustrative
embodiment.
[0011] Fig. 4 depicts a further block diagram of the data optimization
device of Fig.
2 in accordance with an illustrative embodiment.
[0012] Fig. 5 depicts a flow diagram illustrating further examples of
operations
performed by the data optimization device of Fig. 4 in accordance with an
illustrative
embodiment.
[0013] Fig. 6 depicts a block diagram of a distributed processing system in
accordance with an illustrative embodiment.
[0014] Fig. 7 depicts a block diagram of a distributed control device of
the
distributed processing system of Fig. 6 in accordance with an illustrative
embodiment.
[0015] Fig. 8 depicts a block diagram of a data node device of the
distributed
processing system of Fig. 6 in accordance with an illustrative embodiment.
DETAILED DESCRIPTION
[0016] Referring to Fig. 2, a block diagram of a data optimization device
200 is
shown in accordance with an illustrative embodiment. Data optimization device
200
may include an input interface 202, an output interface 204, a communication
interface 206, a computer-readable medium 208, a processor 210, a control
optimization application 222, a data warehouse 224, and control models 226.
Fewer,
different, and/or additional components may be incorporated into data
optimization
device 200.
[0017] Input interface 202 provides an interface for receiving information
from the
user for entry into data optimization device 200 as understood by those
skilled in the
art. Input interface 202 may interface with various input technologies
including, but
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not limited to, a keyboard 212, a mouse 214, a microphone 215, a display 216,
a
track ball, a keypad, one or more buttons, etc. to allow the user to enter
information
into data optimization device 200 or to make selections presented in a user
interface
displayed on the display. The same interface may support both input interface
202
and output interface 204. For example, display 216 comprising a touch screen
provides user input and presents output to the user. Data optimization device
200
may have one or more input interfaces that use the same or a different input
interface
technology. The input interface technology further may be accessible by data
optimization device 200 through communication interface 206.
[0018] Output interface 204 provides an interface for outputting
information for
review by a user of data optimization device 200 and/or for use by another
application. For example, output interface 204 may interface with various
output
technologies including, but not limited to, display 216, a speaker 218, a
printer 220,
etc. Data optimization device 200 may have one or more output interfaces that
use
the same or a different output interface technology. The output interface
technology
further may be accessible by data optimization device 200 through
communication
interface 206.
[0019] Communication interface 206 provides an interface for receiving and
transmitting data between devices using various protocols, transmission
technologies, and media as understood by those skilled in the art.
Communication
interface 206 may support communication using various transmission media that
may
be wired and/or wireless. Data optimization device 200 may have one or more
communication interfaces that use the same or a different communication
interface
technology. For example, data optimization device 200 may support
communication
using an Ethernet port, a Bluetooth antenna, a telephone jack, a USB port,
etc. Data
and messages may be transferred between data optimization device 200 and/or a
distributed control device 230, distributed systems 232, and/or a production
operation
system 234 using communication interface 206.
[0020] Computer-readable medium 208 is an electronic holding place or
storage
for information so the information can be accessed by processor 210 as
understood
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by those skilled in the art. Computer-readable medium 208 can include, but is
not
limited to, any type of random access memory (RAM), any type of read only
memory
(ROM), any type of flash memory, etc. such as magnetic storage devices (e.g.,
hard
disk, floppy disk, magnetic strips, ...), optical disks (e.g., compact disc
(CD), digital
versatile disc (DVD), ...), smart cards, flash memory devices, etc. Data
optimization
device 200 may have one or more computer-readable media that use the same or a
different memory media technology. For example, computer-readable medium 208
may include different types of computer-readable media that may be organized
hierarchically to provide efficient access to the data stored therein as
understood by a
person of skill in the art. As an example, a cache may be implemented in a
smaller,
faster memory that stores copies of data from the most frequently/recently
accessed
main memory locations to reduce an access latency. Data optimization device
200
also may have one or more drives that support the loading of a memory media
such
as a CD, DVD, an external hard drive, etc. One or more external hard drives
further
may be connected to data optimization device 200 using communication interface
206.
[0021] Processor 210 executes instructions as understood by those skilled
in the
art. The instructions may be carried out by a special purpose computer, logic
circuits,
or hardware circuits. Processor 210 may be implemented in hardware and/or
firmware. Processor 210 executes an instruction, meaning it performs/controls
the
operations called for by that instruction. The term "execution" is the process
of
running an application or the carrying out of the operation called for by an
instruction.
The instructions may be written using one or more programming language,
scripting
language, assembly language, etc. Processor 210 operably couples with input
interface 202, with output interface 204, with communication interface 206,
and with
computer-readable medium 208 to receive, to send, and to process information.
Processor 210 may retrieve a set of instructions from a permanent memory
device
and copy the instructions in an executable form to a temporary memory device
that is
generally some form of RAM. Data optimization device 200 may include a
plurality of
processors that use the same or a different processing technology.
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[0022] Control optimization application 222 performs operations associated
with
determining an optimization function from data stored in data warehouse 224.
Some
or all of the operations described herein may be embodied in control
optimization
application 222. The operations may be implemented using hardware, firmware,
software, or any combination of these methods. Referring to the example
embodiment of Fig. 2, control optimization application 222 is implemented in
software
(comprised of computer-readable and/or computer-executable instructions)
stored in
computer-readable medium 208 and accessible by processor 210 for execution of
the
instructions that embody the operations of control optimization application
222.
Control optimization application 222 may be written using one or more
programming
languages, assembly languages, scripting languages, etc.
[0023] Control optimization application 222 may be implemented as a Web
application. For example, control optimization application 222 may be
configured to
receive hypertext transport protocol (HTTP) responses and to send HTTP
requests.
The HTTP responses may include web pages such as hypertext markup language
(HTML) documents and linked objects generated in response to the HTTP
requests.
Each web page may be identified by a uniform resource locator (URL) that
includes
the location or address of the computing device that contains the resource to
be
accessed in addition to the location of the resource on that computing device.
The
type of file or resource depends on the Internet application protocol such as
the file
transfer protocol, HTTP, H.323, etc. The file accessed may be a simple text
file, an
image file, an audio file, a video file, an executable, a common gateway
interface
application, a Java applet, an extensible markup language (XML) file, or any
other
type of file supported by HTTP.
[0024] Data warehouse 224 stores drilling data that includes a plurality of
values
measured for each of a plurality of drilling parameters during a production
operation
such as a SAGD production operation. The plurality of values may be measured
for
each of the plurality of drilling parameters at a plurality of time points
during a time
period. The time period may be greater than one-month. For example, the
plurality of
values may be measured for each of the plurality of drilling parameters hourly
for a
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year long time period though other time period lengths and measurement
intervals
may be used.
[0025] The plurality of values may be measured from the same production
operation, a neighboring production operation, a production operation with
similar
geological characteristics, etc. For example, a production operation in an
environment
with a similar permeability and porosity may be used. The plurality of values
result
from control parameter values chosen by an operator.
[0026] The data stored in data warehouse 224 may include any type of
content
represented in any computer-readable format such as binary, alphanumeric,
numeric,
string, markup language, etc. The content may include textual information,
graphical
information, image information, audio information, numeric information, etc.
that
further may be encoded using various encoding techniques as understood by a
person of skill in the art. Data warehouse 224 may be stored in computer-
readable
medium 208 or on one or more other computing devices (e.g., distributed
control
device 230 and/or distributed systems 232) and accessed using communication
interface 206. Data warehouse 224 may be stored using various formats as known
to
those skilled in the art including a file system, a relational database, a
system of
tables, a structured query language database, etc. For example, data warehouse
224
may be stored in a cube distributed across a grid of computers as understood
by a
person of skill in the art. As another example, data warehouse 224 may be
stored in a
multi-node Hadoop cluster, as understood by a person of skill in the art.
[0027] Some systems may use Hadoop , an open-source framework for storing
and analyzing big data in a distributed computing environment. Some systems
may
use cloud computing, which can enable ubiquitous, convenient, on-demand
network
access to a shared pool of configurable computing resources (e.g., networks,
servers,
storage, applications and services) that can be rapidly provisioned and
released with
minimal management effort or service provider interaction. Some grid systems
may
be implemented as a multi-node Hadoop cluster, as understood by a person of
skill
in the art. Apache TM Hadoop is an open-source software framework for
distributed
computing. Some systems may use the SAS LASRTM Analytic Server developed
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and provided by SAS Institute Inc. of Cary, North Carolina, USA in order to
deliver
statistical modeling and machine learning capabilities in a highly interactive
programming environment, which may enable multiple users to concurrently
manage
data, convert variables, perform exploratory analysis, build and compare
models and
score. Some systems may use SAS In-Memory Statistics for Hadoop developed
and provided by SAS Institute Inc. of Cary, North Carolina, USA to read big
data once
and analyze it several times by persisting it in-memory for the entire
session. Some
systems may be of other types, designs and configurations.
[0028] Referring to Fig. 3, example operations associated with control
optimization
application 222 are described. Control optimization application 222 may be
used to
create control models 226 using the data stored in data warehouse 224. Control
models 226 support a determination of an optimal value for a control parameter
of the
production operation using sensed data measured during the production
operation.
Additional, fewer, or different operations may be performed depending on the
embodiment. The order of presentation of the operations of Fig. 3 is not
intended to
be limiting. Although some of the operational flows are presented in sequence,
the
various operations may be performed in various repetitions, concurrently (in
parallel,
for example, using threads), and/or in other orders than those that are
illustrated. For
example, a user may execute control optimization application 222, which causes
presentation of a first user interface window, which may include a plurality
of menus
and selectors such as drop down menus, buttons, text boxes, hyperlinks, etc.
associated with control optimization application 222 as understood by a person
of skill
in the art. An indicator may indicate one or more user selections from a user
interface, one or more data entries into a data field of the user interface,
one or more
data items read from computer-readable medium 208 or otherwise defined with
one
or more default values, etc. that are received as an input by control
optimization
application 222.
[0029] In an operation 300, a first indicator of data warehouse 224 is
received. For
example, the first indicator indicates a location of data warehouse 224. As an
example, the first indicator may be received by control optimization
application 222
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after selection from a user interface window or after entry by a user into a
user
interface window. In an alternative embodiment, data warehouse 224 may not be
selectable. For example, a most recently created data warehouse may be used
automatically.
[0030] As discussed previously, data warehouse 224 may be stored in a cube
distributed across a grid of computers, may be stored in a multi-node Hadoop
cluster distributed across one or more computers, may be stored in a file
system
distributed across one or more computers, in a relational database, in one or
more
tables, in a structured query language database, etc.
[0031] In an operation 302, a second indicator of parameters to include in
determining control models 226 is received. The parameters are production
operation
parameters measured by sensors of various types during the production
operation.
Example parameters include a pump speed, a header pressure, an injection in a
short string, an injection in a long string, a casing gas, a sub-cool
temperature value,
a material extracted, a porosity, a permeability, an azimuth or angular
measurements,
a produced emulsion (that contains oil and water or other combinations of
fluid), etc.
A sub-cool temperature value is a difference between a boiling point of water
and a
temperature of a material produced by the production operation at a same
pressure.
For example, the sub-cool temperature value may be measured at a heel of
producer
well 108 as understood by a person of skill in the art. Example sensors
include
pressure sensors, temperature sensors, position sensors, velocity sensors,
acceleration sensors, flow rate sensors, etc. that may be mounted to various
components used as part of the production operation. Production operation
systems
234 include the sensors mounted to various components used as part of the
production operation as well as the various components themselves.
[0032] The second indicator may indicate that only a portion of the
parameters
stored in data warehouse 224 be included when defining control models 226. In
an
alternative embodiment, the second indicator may not be received. For example,
all
or a subset of the parameters may be used automatically.
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[0033] In an operation 304, a third indicator for selecting training data
from data
warehouse 224 is received. The third indicator may be received by control
optimization application 222, for example, after selection from a user
interface
window or after entry by a user into a user interface window. The third
indicator
identifies a first portion of the data stored in data warehouse 224 to use in
training a
model. The third indicator may indicate a number of data points to include, a
percentage of data points of the entire data warehouse 224 to include, etc. A
subset
may be created from data warehouse 224 by sampling. An example sampling
algorithm is uniform sampling. Other random sampling algorithms may be used.
[0034] In an operation 306, a fourth indicator for selecting validation
data from
data warehouse 224 is received. The fourth indicator may be received by
control
optimization application 222, for example, after selection from a user
interface
window or after entry by a user into a user interface window. The fourth
indicator
identifies a second portion of the data stored in data warehouse 224 to use in
validating the model. The fourth indicator may indicate a number of data
points to
include, a percentage of data points of the entire data warehouse 224 to
include, etc.
A subset may be created from data warehouse 224 by sampling. An example
sampling algorithm is uniform sampling. Other random sampling algorithms may
be
used. The data points from data warehouse 224 selected for the validation data
may
be distinct from the data points from data warehouse 224 selected for the
training
data.
[0035] In an operation 308, a fifth indicator of one or more types of
objective
function models and configurations is received. For example, the fifth
indicator
indicates a name of a type of objective function model. For illustration, the
name may
be "Neural Network", "Linear Regression", "Non-linear Regression", Support
Vector
Machine", etc. A configuration identifies one or more initialization values
based on the
type of objective function model. For example, when the type of objective
function
model is indicated as "Neural Network", a number of hidden layers, a number of
nodes per layer, a propagation method, etc. may be identified by the fifth
indicator. A
plurality of configurations may be defined. For example, when the type of
objective
CA 02864265 2014-09-19
function model is neural network, a range of numbers of hidden layers, a range
of
numbers of nodes per layer, etc. also may be identified by the fifth
indicator.
[0036] The fifth indicator may be received by control optimization
application 222
after selection from a user interface window or after entry by a user into a
user
interface window. A default value for the types of objective function models
and
configurations may further be stored, for example, in computer-readable medium
208.
In an alternative embodiment, the types of objective function models and
configurations may not be selectable.
[0037] In an operation 310, an objective function model is selected based
on the
fifth indicator or based on a default model stored in computer-readable medium
208.
In an operation 312, the selected objective function model is initialized. In
an
operation 314, the initialized objective function model is trained using the
training data
as indicated by the second indicator and the third indicator.
[0038] In an operation 316, output data is predicted with the validation
data, as
indicated by the second indicator and the fourth indicator, as an input to the
trained
objective function model. In an operation 318, the predicted output data is
compared
to the actual output data included in the validation data. In an operation
320, a validity
score is determined based on the comparison. In an operation 322, the
determined
validity score is stored, for example, in computer-readable medium 208 in
association
with an indicator of the selected objective function model. As an example, SAS
Enterprise MinerTM developed and provided by SAS Institute Inc. of Cary, North
Carolina, USA includes a neural network procedure PROCNEURAL that can be used
to configure, initialize, train, predict, and score a neural network model.
The input
nodes are the control variables and the output node(s) is oil production,
water usage,
or sub-cool temperature value, or all three. For illustration, the objective
function
model may be implemented as a neural network with two or three hidden nodes, a
feed-forward adaptation, a supervised learning mode, and back-propagation to
perform sensitivity analysis that determines how each input parameter (e.g.,
the
pump speed, header pressure, the short injection string, the long injection
string, the
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casing gas, the produced emulsion) influences the output node(s) (e.g., water
usage,
oil production, sub-cool temperature value).
[0039] In an operation 324, a determination is made concerning whether or
not
there is another objective function model to evaluate. When there is another
objective
function model to evaluate, processing continues in operation 310. When there
is not
another objective function model to evaluate, processing continues in an
operation
326. In operation 310, a next objective function model is selected based on
the fifth
indicator.
[0040] In operation 326, a best objective function model is selected. For
example,
the validity scores stored for each iteration of operation 322 are compared
and the
objective function model associated with the best validity score is selected.
The best
validity score may be a minimum or a maximum value of the validity scores
stored for
each iteration of operation 322. For example, if the validity score is a
misclassification
rate, a minimum validity score indicates the best model; whereas, if the
validity score
is a correct classification rate, a maximum validity score indicates the best
model.
[0041] In an operation 328, a sixth indicator of an association rule model
type and
configuration is received. For example, the sixth indicator indicates a name
of the
association rule model type. For illustration, the name may be "Decision
Tree",
"Associate", etc. As an example, SAS Enterprise MinerTm includes a procedure
PROCASSOC that can be used as an association rule model type. A configuration
identifies one or more initialization values based on the association rule
model type.
For example, when the association rule model type is decision tree, a stop
criterion
may be identified by the sixth indicator. As an example, SAS Enterprise
MinerTM
includes a decision tree procedure that can be used to build one or more
decision
tree models.
[0042] In an operation 330, an association rule or rules are determined
using the
data in the data warehouse indicated in operation 302. The association rule
defines a
range of values for a control parameter ("Then" in Table I below) of a
plurality of
production parameters that is selected based on a value of a first parameter
("When"
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in Table I below) of the plurality of production parameters. As an example,
SAS
Enterprise MinerTM includes a procedure HPFOREST that can be used to determine
an association rule(s). For example, Table I below identifies a plurality of
associations
rules:
Association When Then
Confidence
Rule #
1 Oil production volume is Steam to long tubing flow 93%
between 6.6 and 6.9 rate is between -00 and 191.8
meters (m)3/hour (h) kilopascal (kPa)
2 Oil production volume is Steam to long tubing flow 80%
between 5.4 and 5.8 m3/h rate is between 1342 and
1534 kPa
3 Oil production volume is Steam to long tubing flow 76%
between 2.7 and 3.1 m3/h rate is between 3644 and
3835 kPa
4 Oil production volume is Sub-cool temperature is 67%
between 6.4 and 6.9 m3/h between 24.1 and 25.1 C
Sub-cool temperature is Oil production volume is 54%
between 18.4 and 19.4 C between 3.4 and 4.1 m3/h
Table I
[0043] As another example, Table II below identifies a plurality of
associations
rules:
Association When Then
Confidence
Rule #
1 Oil production volume is Steam to long tubing flow 87%
between 8.9 and 9.9 m3/h rate is between 4076 and
4524 kPa
2 Steam to long tubing flow Oil production volume is 75%
rate is between 4524 and between 4.9 and 5.9 m3/h
4972 kPa
3 Steam to long tubing flow Oil production volume is 60%
rate is between 3627 and between 3.9 and 4.9 m3/h
4076 kPa
4 Sub-cool temperature is Oil production volume is 88%
between 25.8 and 28.5 C between 2.9 and 3.9 m3/h
5 Sub-cool temperature is Oil production volume is 65%
between 20.6 and 23.2 C between 8.9 and 9.9 m3/h
Table II
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[0044] A confidence value may be determined for each association rule and
only
those rules having a confidence value above a predetermined threshold may be
determined as strong association rules or association rules that show a
consistent
pattern in the data. For illustration, the confidence value may be determined
as a ratio
of the number of occurrences of the "When" to the number of occurrences of the
"Then" in the data.
[0045] In an operation 332, the selected objective function model and the
association rule(s) are stored, for example, in computer-readable medium 208.
The
selected objective function model and the association rule(s) may be stored in
association with a specific drilling location, a specific drilling field, a
specific type of
drilling environment, etc. The selected objective function model and the
association
rule(s) are control models 226. The association rule(s) may be stored as
constraints.
[0046] As an example, the best objective function model may have the form
0i/max = k11/1/p ¨ k2147c + k3Ps ¨k4S¨ k5S/ subject to T1 < Wp T2; T3 < v vc
T4; T5 < Ps T6; T7 í Ss Tg; T9 < Tim, where Wp is a well production to
header
pressure, v vc is a well casing gas to header pressure, Ps is a pump speed
reference in
hertz, Ss is a steam to short tubing, Si is a steam to long tubing, kl, k2,
k3, k4, ks are
determined from the training of the objective function model, and
T1, T2, T3, T4, T5, T6,T7,T8,T9,T10 are association rule constraint values.
[0047] In an operation 334, a seventh indicator is received to update the
selected
objective function model and the association rule(s). For example, the
selected
objective function model and the association rule(s) may be updated
periodically such
as every day, every week, every month, every year, etc. A timer may trigger
receipt of
the seventh indicator. A user may trigger receipt of the seventh indicator.
For
example, a user may monitor production parameters and determine that an update
of
the selected objective function model and the association rule(s) be
performed.
When the seventh indicator is received, processing continues in operation 310.
Processing may continue in operation 310 using the data used in a previous
iteration
of operation 332 in addition to data measured and stored subsequent to the
last
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iteration of operation 332. As another option, processing may continue in
operation
310 using the data measured and stored subsequent to the last iteration of
operation
332. Data from the same or a different production operation may be used. In an
alternative embodiment, the seventh indicator may not be received and
processing is
complete.
[0048] Referring to Fig. 4, data optimization device 200 further may
include
sensed data 402 and control data 404. Control models 226 support a
determination of
an optimal value for a control parameter (control data 404) of the production
operation using sensed data 402 measured during the production operation. Some
or
all of the operations described herein may be embodied in control models 226.
Control optimization application 222 and control models 226 may be integrated
into a
single application, may be separate applications, or control optimization
application
222 and control models 226 may be integrated at other levels than those
illustrated.
[0049] The operations of control models 226 may be implemented using
hardware, firmware, software, or any combination of these methods. Referring
to the
example embodiment of Fig. 4, control models 226 are implemented in software
(comprised of computer-readable and/or computer-executable instructions)
stored in
computer-readable medium 208 and accessible by processor 210 for execution of
the
instructions that embody the operations of control models 226. Control models
226
may be written using one or more programming languages, assembly languages,
scripting languages, etc. Control models 226 may be implemented as a Web
application.
[0050] Referring to Fig. 5, example operations associated with control
models 226
are described. Additional, fewer, or different operations may be performed
depending
on the embodiment. The order of presentation of the operations of Fig. 5 is
not
intended to be limiting. Although some of the operational flows are presented
in
sequence, the various operations may be performed in various repetitions,
concurrently (in parallel, for example, using threads), and/or in other orders
than
those that are illustrated.
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[0051] In an operation 500, measured data 402 is received. For example,
sensed
data associated with the parameters indicated in operation 302 are received in
near
real-time from the sensors measuring the current control parameter values of
the
production operation.
[0052] In an operation 502, an optimal value for a control parameter of the
production operation is determined by executing the selected objective
function
model with the received, measured drilling data as an input and subject to the
determined association rule(s). The selected objective function maximizes
production
of the material produced by the production operation, minimizes water usage by
the
production operation, and minimizes the sub-cool temperature value. As an
example,
SASOOR developed and provided by SAS Institute Inc. of Cary, North Carolina,
USA
includes an OPTMODEL procedure that provides a framework for specifying and
solving a nonlinear optimization problem using range constraints as defined by
the
association rule(s). Example control parameter include a pump speed, a header
pressure, a short injection string, a long injection string, a casing gas, a
produced
emulsion, etc.
[0053] In an operation 504, the determined optimal value for the control
parameter
of the production operation is output. For example, the determined optimal
value is
output to display 216, speaker 218, and/or printer 220 for review by a user.
As
another example, the determined optimal value is output to a prediction model
to
predict the effect of the determined optimal value on the production
operation. As yet
another example, the determined optimal value is output to a control device
associated with adjusting a set point of the control parameter.
[0054] When the determined optimal value for the control parameter of the
production operation is greater than a current value of the control parameter,
the
value of the control parameter may be reduced; whereas, when the determined
optimal value for the control parameter of the production operation is less
than a
current value of the control parameter, the value of the control parameter may
be
increased.
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CA 02864265 2014-09-19
[0055] Predicting the effect of the determined optimal value on the
production
operation provides a method for determining whether or not a global or a local
optimality for the control parameter of the production operation is
determined. For
example, increasing/decreasing the control parameter greater/less than the
determined optimal value and predicting the effect provides a method to test
the
sensitivity to changes in the control parameter and to evaluate if the optimal
value is
a global or a local value.
[0056] If data optimization device 200 is multi-threaded, one or more of
the
operations of Figs. 3 and 5 may be performed in parallel. If data warehouse
224
and/or control models 226 are distributed across one or more other devices, a
distributed processing system can be used. For example, referring to Fig. 6, a
block
diagram of a distributed processing system 600 is shown in accordance with an
illustrative embodiment. In an illustrative embodiment, distributed processing
system
600 may include distributed control device 230, distributed systems 232, data
optimization systems 602, and a network 601. For illustration, distributed
control
device 230 and distributed systems 232 may be implemented as a multi-node
Hadoop cluster, as understood by a person of skill in the art. As another
illustration,
distributed systems 232 may store a cube of data. Data optimization systems
602
access data stored in the cube of data or in the Hadoope cluster distributed
to the
distributed systems 232. Distributed control device 230 coordinates and
controls
access by data optimization systems 602 to the data stored by the distributed
systems 232. One or more components of distributed processing system 600 may
support multithreading, as understood by a person of skill in the art.
[0057] The components of distributed processing system 600 may be located
in a
single room or adjacent rooms, in a single facility, and/or may be distributed
geographically from one another. Each of distributed systems 232, data
optimization
systems 602, and distributed control device 230 may be composed of one or more
discrete devices.
[0058] Network 601 may include one or more networks of the same or
different
types. Network 601 can be any type of wired and/or wireless public or private
network
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CA 02864265 2014-09-19
including a cellular network, a local area network, a wide area network such
as the
Internet, etc. Network 601 further may comprise sub-networks and consist of
any
number of devices.
[0059] Data optimization systems 602 can include any number and type of
computing devices that may be organized into subnets. Data optimization device
200
is an example computing device of data optimization systems 602. The computing
devices of data optimization systems 602 send and receive communications
through
network 601 to/from another of the one or more computing devices of data
optimization systems 602, to/from distributed systems 232, and/or to/from
distributed
control device 230. The one or more computing devices of data optimization
systems
602 may include computers of any form factor such as a server 604, a desktop
606, a
laptop 608, a personal digital assistant, an integrated messaging device, a
tablet
computer, etc. The one or more computing devices of data optimization systems
602
may communicate using various transmission media that may be wired and/or
wireless as understood by those skilled in the art.
[0060] For illustration, Fig. 6 represents distributed systems 232 with a
first server
computer 610, a second server computer 612, a third server computer 614, and a
fourth server computer 616. Distributed systems 232 can include any number and
form factor of computing devices that may be organized into subnets. The
computing
devices of distributed systems 232 send and receive communications through
network 601 to/from another of the one or more computing devices of
distributed
systems 232, to/from distributed control device 230, and/or to/from data
optimization
systems 602. The one or more computing devices of distributed systems 232 may
communicate using various transmission media that may be wired and/or wireless
as
understood by those skilled in the art.
[0061] In the illustrative embodiment, distributed control device 230 is
represented
as a server computing device though distributed control device 230 may include
one
or more computing devices of any form factor that may be organized into
subnets.
Distributed control device 230 sends and receives communications through
network
601 to/from distributed systems 232 and/or to/from data optimization systems
602.
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CA 02864265 2014-09-19
Distributed control device 230 may communicate using various transmission
media
that may be wired and/or wireless as understood by those skilled in the art.
[0062] Distributed processing system 600 may use cloud computing
technologies,
which support on-demand network access to a shared pool of configurable
computing
resources (e.g., networks, servers, storage, applications and services) that
can be
rapidly provisioned and released with minimal management effort or service
provider
interaction. Distributed processing system 600 may use the SAS LASRTM
Analytic
Server to deliver statistical modeling and machine learning capabilities in a
highly
interactive programming environment, which may enable multiple users to
concurrently manage data, transform variables, perform exploratory analysis,
and
build and compare models. Distributed processing system 600 may use SAS In-
Memory Statistics for Hadoop to read big data once and analyze it several
times by
persisting it in-memory. Some systems may be of other types and
configurations.
[0063] Referring to Fig. 7, a block diagram of distributed control device
230 is
shown in accordance with an example embodiment. Distributed control device 230
may include a second input interface 702, a second output interface 704, a
second
communication interface 706, a second computer-readable medium 708, a second
processor 710, a distributed control application 712, and second data 714.
Fewer,
different, and additional components may be incorporated into distributed
control
device 230.
[0064] Second input interface 702 provides the same or similar
functionality as
that described with reference to input interface 102 of data optimization
device 200
though referring to distributed control device 230. Second output interface
704
provides the same or similar functionality as that described with reference to
output
interface 204 of data optimization device 200 though referring to distributed
control
device 230. Second communication interface 706 provides the same or similar
functionality as that described with reference to communication interface 106
of data
optimization device 200 though referring to distributed control device 230.
Data and
messages may be transferred between distributed control device 230 and
distributed
systems 232 and/or data optimization systems 602 using second communication
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CA 02864265 2014-09-19
interface 706. Second computer-readable medium 708 provides the same or
similar
functionality as that described with reference to computer-readable medium 108
of
data optimization device 200 though referring to distributed control device
230.
Second processor 710 provides the same or similar functionality as that
described
with reference to processor 110 of data optimization device 200 though
referring to
distributed control device 230.
[0065] Distributed control application 712 performs operations associated
with
controlling access to the distributed data, with performing one or more
operations
described with reference to Figs. 3 and 5, and/or with instructing distributed
systems
232 to perform one or more operations described with reference to Figs. 3 and
5.
[0066] Some or all of the operations described herein may be embodied in
distributed control application 712. The operations may be implemented using
hardware, firmware, software, or any combination of these methods. Referring
to the
example embodiment of Fig. 7, distributed control application 712 is
implemented in
software (comprised of computer-readable and/or computer-executable
instructions)
stored in second computer-readable medium 708 and accessible by second
processor 710 for execution of the instructions that embody the operations of
distributed control application 712. Distributed control application 712 may
be written
using one or more programming languages, assembly languages, scripting
languages, etc. Distributed control application 712 may be implemented as a
Web
application.
[0067] Second data 714 may include data used by distributed control
application
712 in support of creating control models 226 and/or in determining an optimal
value
for a control parameter of the production operation using measured data sensed
during the production operation.
[0068] Referring to Fig. 8, a block diagram of a data node device 800 is
shown in
accordance with an illustrative embodiment. Data node device 800 is an example
computing device of distributed systems 232. Data node device 800 may include
a
third input interface 802, a third output interface 804, a third communication
interface
CA 02864265 2014-09-19
806, a third computer-readable medium 808, a third processor 810, a local
control
application 812, and a data subset 814. Fewer, different, and additional
components
may be incorporated into data node device 800.
[0069] Third input interface 802 provides the same or similar functionality
as that
described with reference to input interface 102 of data optimization device
200 though
referring to data node device 800. Third output interface 804 provides the
same or
similar functionality as that described with reference to output interface 104
of data
optimization device 200 though referring to data node device 800. Third
communication interface 806 provides the same or similar functionality as that
described with reference to communication interface 206 of data optimization
device
200 though referring to data node device 800. Data and messages may be
transferred between data node device 800 and distributed control device 230
and/or
data optimization systems 602 using third communication interface 806. Third
computer-readable medium 808 provides the same or similar functionality as
that
described with reference to computer-readable medium 208 of data optimization
device 200 though referring to data node device 800. Third processor 810
provides
the same or similar functionality as that described with reference to
processor 210 of
data optimization device 200 though referring to data node device 800.
[0070] Local control application 812 performs operations associated with
controlling access to the data stored in data subset 2014 and/or with
executing one or
more operations described with reference to Figs. 3 and 5. Some or all of the
operations described herein may be embodied in local control application 812.
The
operations may be implemented using hardware, firmware, software, or any
combination of these methods. Referring to the example embodiment of Fig. 8,
local
control application 812 is implemented in software (comprised of computer-
readable
and/or computer-executable instructions) stored in third computer-readable
medium
808 and accessible by third processor 810 for execution of the instructions
that
embody the operations of local control application 812. Local control
application 812
may be written using one or more programming languages, assembly languages,
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CA 02864265 2014-09-19
scripting languages, etc. Local control application 812 may be implemented as
a Web
application.
[0071] Data subset 814 stores a portion of data warehouse 224 distributed
across
distributed systems 232 with each computing device of the distributed systems
232
storing a different portion of data warehouse 224. Distributed control device
230
further may store a portion of data warehouse 224.
[0072] A user may execute data optimization application 222 that interacts
with
distributed control application 712 by requesting that distributed control
device 230
perform one or more operations described with reference to Figs. 3 and 5.
Distributed
control application 712 triggers processing by local control application 812
executing
at each node device of the distributed systems 232 to perform one or more
operations described with reference to Figs. 3 and 5. Any number of different
users
may be accessing the data at any given time.
[0073] Various levels of integration between the components of distributed
processing system 600 may be implemented without limitation as understood by a
person of skill in the art. For example, local control application 812 and
distributed
control application 712 may be the same or different applications or part of
an
integrated, distributed application supporting some or all of the same or
additional
types of functionality as described herein. As another example, data
optimization
application 222 and distributed control application 712 may be the same or
different
applications or part of an integrated, distributed application supporting some
or all of
the same or additional types of functionality as described herein.
[0074] The various operations described with reference to Figs. 3 and 5
provide a
process for automatically generating optimal control parameter values for use
by a
production operation operator or by a control device to control operational
settings for
various drilling components. The optimal control parameter values may result
in an
increase in material production, while minimizing water and subcool
temperatures
simultaneously.
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[0075] Using similarity measures to systematically choose a best neural
network
model can be an effective way to reduce time in a model building stage.
Automation
of what-if analysis can also be an effective way to determine if the results
from the
optimization routines provide global or local optima.
[0076] The word "illustrative" is used herein to mean serving as an
example,
instance, or illustration. Any aspect or design described herein as
"illustrative" is not
necessarily to be construed as preferred or advantageous over other aspects or
designs. Further, for the purposes of this disclosure and unless otherwise
specified,
"a" or "an" means "one or more". Still further, using "and" or "or" in the
detailed
description is intended to include "and/or" unless specifically indicated
otherwise. The
illustrative embodiments may be implemented as a method, apparatus, or article
of
manufacture using standard programming and/or engineering techniques to
produce
software, firmware, hardware, or any combination thereof to control a computer
to
implement the disclosed embodiments.
[0077] The foregoing description of illustrative embodiments of the
disclosed
subject matter has been presented for purposes of illustration and of
description. It is
not intended to be exhaustive or to limit the disclosed subject matter to the
precise
form disclosed, and modifications and variations are possible in light of the
above
teachings or may be acquired from practice of the disclosed subject matter.
The
embodiments were chosen and described in order to explain the principles of
the
disclosed subject matter and as practical applications of the disclosed
subject matter
to enable one skilled in the art to utilize the disclosed subject matter in
various
embodiments and with various modifications as suited to the particular use
contemplated.
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