Note: Descriptions are shown in the official language in which they were submitted.
METHODS, AND SYSTEMS FOR CONTROLLING OPERATION OF
STEAM STIMULATED WELLS
FIELD
[0001] The present application relates to the field of steam stimulated
hydrocarbon
production technologies, and particularly to methods and systems for steam
assisted gravity
drainage (SAGD) wells.
BACKGROUND
[0002] In steam assisted gravity drainage, the production of a well is
dependent on geological
characteristics of the well as well as the age of the well and amount of steam
supplied to the
well.
[0003] When developing large resources with numerous wells, the performance of
individual
wells are often independently monitored by different engineers.
SUMMARY
[0004] In accordance with one aspect, there is provided a system for
controlling operation of
a plurality of wells. The system includes a plurality of control devices for
adjusting the
operational inputs of the plurality of wells; a plurality of input devices for
measuring well
conditions and production rates; and a controller having at least one
processor. The at least one
processor is configured for: generating models for each of the plurality of
wells based on
historical well data from the plurality of input devices, the models mapping a
production rate
based on at least one operational input; based on one or more defined total
operational
constraints across all of the plurality of wells and the models, determining a
distribution of
operational inputs across the plurality of wells or well portions which
results in an optimal total
production rate; and generating signals for applying, at the plurality of
control devices, the
operational inputs to the wells or portions of the wells in accordance with
the determined
distribution.
[0005] In accordance with one aspect, there is provided a method of
controlling operation of a
plurality of wells. The method includes: generating, with at least one
processor, models for each
of the plurality of wells based on historical well data, the models mapping a
production rate
based on at least one operational input; based on one or more defined total
operational
constraints across all of the plurality of wells and the models, determining a
distribution of
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operational inputs across the plurality of wells or well portions which
results in an optimal total
production rate ; and applying the operational inputs to the wells or portions
of the wells in
accordance with the determined distribution.
[0006] Many further features and combinations thereof concerning embodiments
described
herein will appear to those skilled in the art following a reading of the
present disclosure.
DESCRIPTION OF THE FIGURES
[0007] In the figures,
[0008] Fig. 1A is a cross sectional view of an example geological
formation and SAGD well;
[0009] Fig. 1B is a top view of a geological area illustrating SAGD wells
and infrastructure for
.. an example project;
[0010] Fig. 2 is an example system to which aspects of the present
disclosure may be
applied; and
[0011] Fig. 3 is a flowchart illustrating aspects of an example method of
controlling operations
of a plurality of wells.
DETAILED DESCRIPTION
[0012] Fig. 1A shows an example of a steam assisted gravity drainage
(SAGD) well 155 in a
geological resource 110. In SAGD, production is typically effected by a pair
of wells 155: an
injector well 150 for injecting steam into the geological formation, and a
producer well 160 for
collecting the resulting bitumen.
[0013] Fig. 1B shows a top elevation view of a geological resource 110
having many wells
(pairs) 155. The well(s) may be part of one or more SAGD projects for
extracting the
hydrocarbon resources in the geological formation. As illustrated by the
example project in Fig.
1B, these projects may have any number of wells 155 having any number of
orientations and
locations. The project(s) may include one or more facilities 120 such as well
pads, plants, water
sources, control systems, monitoring systems, steam generators, upgraders and
any other
infrastructure for extracting and/or processing input and output materials.
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[0014] The wells and/or infrastructure can include one or more input
devices 130 for
measuring, detecting or otherwise collecting data regarding the wells and
processes. This data
can, in some examples, include well conditions and output or production rates.
[0015] In some examples, the input devices 130 can include thermocouples
or other
temperature sensors, pressure sensors, and the like for measuring temperature,
pressure
and/or other conditions within the wells, proximate to the wells, and/or at
the surface. In some
examples, multiple input devices can be positioned along the length of the
wells to measuring
well conditions at various points in or around the length of the wells. For
example, pressure
and/or temperature sensors may be positioned at the toe of the well, the heel
of the well, at the
surface and/or elsewhere in the project infrastructure. In some examples,
input sensors from
reference wells, surrounding production wells, or other wells may also provide
well condition
information for a proximate well.
[0016] In some examples, inputs devices 130 may include flow sensors at
the surface, at
positions along the well and/or within any other project infrastructure to
provide flow information
and/or bitumen production rates. In some examples, input devices 130 can
include sensors,
measuring devices, and/or computational devices for determining a well's
production rates of a
desired hydrocarbon after processing and/or removal of water and/or other
materials. In some
examples, the devices may include flow meters for measuring total fluid
extracted from the well.
[0017] The wells and/or infrastructure can include one or more control
devices 140 for
adjusting the operational inputs of the wells. In some examples, these control
devices 140 can
include valves, pumps, mixers, boilers, nozzles, sliding sleeves,
inflow/injection control devices,
drives, motors, relays and/or any other devices which may control or affect
the operational
inputs of the wells. In some examples, these control device(s) 140 may be
configured,
controlled or otherwise adjusted to change operational inputs via signals or
instructions received
from one or more processors in the system. For example, one or more of the
control devices
140 may include controllers, processors, communication devices, electrical
switches and/or
other circuitry, devices or logic which can be configured, instructed or
otherwise triggered to
change operational inputs such as steam injection rates, temperatures,
pressures, steam
injection locations, pump speeds, water consumption rates, fuel consumption
and any other
adjustable or controllable aspect of the system. In some examples, one or more
of the control
devices 140 may be additionally or alternatively controlled by physical
mechanisms.
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[0018] The number and location of the input devices 130 and control
devices 140 in Figs. 1A
and 1B are illustrative examples only as any number, location and/or type of
these devices 130,
140 is possible.
[0019] In some example embodiments, the input devices 130 can include
sensing device
cables/wires which may run the length of an entire well or portion of a well,
and may provide
continuous or spaced measurements along the length of the cable/wire.
[0020] In SAGD, the rate at which bitumen and hydrocarbon materials are
produced from a
well is dependent on various factors including, but not limited to, steam
injection rates, pressure,
temperature, steam chamber size/shape and properties, and physical properties
of the
reservoir. In some instances, too much or too little steam, pressure,
temperature or other
operational inputs/constraints may negatively and/or permanently affect the
current and future
production of a well.
[0021] During production, control device(s) 140 can be adjusted, for
example, to affect a
maximum predicted production rate or a desired production rate which may
maximize the
lifespan and total production for the well.
[0022] Individual wells may be monitored and/or managed by different
individuals to produce
optimal results for each well. Data driven simulations can be performed by
applying initial
conditions and parameters for every 3D cell and adjusting them over time based
on differentials
and thermal properties. These simulations can be computationally intensive,
may take weeks of
an expensive and powerful computing device's time before results are obtained.
[0023] However, in some situations, simulations may not provide timely
enough information
to react to the changing nature of steam chambers and SAGD production factors,
and may not
take operational constraints or trade-offs into consideration.
[0024] In some example embodiments, systems and methods described herein may
provide
models which can, in some instances, provide timely production rates, and may
determine
operational inputs which may provide optimal or improved production rates
based on constraints
on an individual well as well as global constraints for a large number of
wells or system.
[0025] For example, in some instances, resource production at a
site/project/resource/system/number of wells may have limited water supply
and/or steam
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producing capabilities. In another example, there may be a global constraint
on the rate at
which the extracted hydrocarbon/water mixture can be processed, or the amount
of carbon
dioxide which can be generated.
[0026] In some example embodiments, systems and methods described herein may
apportion limited resources/constraints for an entire system/set of wells to
achieve optimal or
improved production results whiles meeting these constraints.
[0027] The embodiments of the devices, systems and methods described herein
may be
implemented in a combination of both hardware and software. These embodiments
may be
implemented on programmable computers, each computer including at least one
processor, a
data storage system (including volatile memory or non-volatile memory or other
data storage
elements or a combination thereof), and at least one communication interface.
[0028] Program code may be applied to input data to perform the functions
described herein
and to generate output information. The output information may be applied to
one or more
output devices. In some embodiments, the communication interface may be a
network
communication interface. In embodiments in which elements may be combined, the
communication interface may be a software communication interface, such as
those for inter-
process communication. In still other embodiments, there may be a combination
of
communication interfaces implemented as hardware, software, and combination
thereof. In
some examples, devices having at least one processor may be configured to
execute software
instructions stored on a computer readable tangible, non-transitory medium.
[0029] The following discussion provides many example embodiments. Although
each
embodiment represents a single combination of inventive elements, other
examples may
include all possible combinations of the disclosed elements. Thus if one
embodiment comprises
elements A, B, and C, and a second embodiment comprises elements B and 0,
other remaining
combinations of A, 13, C, or D, may also be used.
[0030] The technical solution of embodiments may be in the form of a software
product. The
software product may be stored in a non-volatile or non-transitory storage
medium, which can
be a compact disk read-only memory (CD-ROM), a USB flash disk, or a removable
hard disk.
The software product includes a number of instructions that enable a computer
device (personal
computer, server, or network device) to execute the methods provided by the
embodiments.
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[0031] The embodiments described herein are implemented by physical computer
hardware, including computing devices, servers, receivers, transmitters,
processors, memory,
displays, and networks. The embodiments described herein provide useful
physical machines
and particularly configured computer hardware arrangements. The embodiments
described
herein are directed to electronic machines and methods implemented by
electronic machines
adapted for processing and transforming electromagnetic signals which
represent various types
of information. The embodiments described herein pervasively and integrally
relate to machines,
and their uses; and the embodiments described herein have no meaning or
practical
applicability outside their use with computer hardware, machines, and various
hardware
components. Substituting the physical hardware particularly configured to
implement various
acts for non-physical hardware, using mental steps for example, may
substantially affect the
way the embodiments work. Such computer hardware limitations are clearly
essential elements
of the embodiments described herein, and they cannot be omitted or substituted
for mental
means without having a material effect on the operation and structure of the
embodiments
described herein. The computer hardware is essential to implement the various
embodiments
described herein and is not merely used to perform steps expeditiously and in
an efficient
manner.
[0032] Fig. 2 shows an example system 200 including one or more devices 205
which may
be used to control operation of multiple wells. In some examples, a device 205
may be a
computational device such as a computer, server, tablet or mobile device, or
other system,
device or any combination thereof suitable for accomplishing the purposes
described herein. In
some examples, the device 205 can include one or more processor(s) 210,
memories 215,
and/or one or more devices/interfaces 220 necessary or desirable for
input/output,
communications, control and the like. The processor(s) 210 and/or other
components of the
device(s) 205 or system 250 may be configured to perform one or more aspects
of the
processes described herein.
[0033] In some examples, the device(s) 205 may be configured to receive or
access data
from one or more volatile or non-volatile memories 215, or external storage
devices 225 directly
coupled to a device 205 or accessible via one or more wired and/or wireless
network(s)/communication link(s) 260. In external storage device(s) 225 can be
a network
storage device or may be part of or connected to a server or other device.
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[0034] In some examples, the device(s) 205 may be configured to receive or
access
data from sensors or input devices 130 in the field or infrastructure. These
sensors or devices
130 may be configured for collecting or measuring well, infrastructure,
operational, and/or other
geological and/or physical data. In some examples, the sensor(s)/device(s) 130
can be
configured to communicate the collected data to the device(s) 205 and/or
storage device(s) 225
via one or more networks/links 260 or otherwise. In some examples, the sensors
or devices 130
may be connected to a local computing device 250 which may be configured to
receive the data
from the sensors/devices 130 for local storage and/or communication to the
device(s) 205
and/or storage device(s) 225. In some examples, data from sensor(s) or
device(s) may be
manually read from a gauge or dial, and inputted into a local computing device
for
communication and/or storage.
[0035] In some examples, the device(s) 205 may be configured to generate
and/or transmit
signals or instructions to one or more control device(s) 140 to apply desired
operational
inputs/conditions to the wells. These signals/instructions may, in some
examples, be
communicated via any single or combination of networks/links 260. In some
examples, the
device(s) 205 may be configured to send signals/instructions via local
computing device(s) 250
connected to or otherwise in communication with the control device(s) 140. In
some examples,
a local computing device 250, display or other device may be configured to
communicate
instructions to a person for manual adjustment/control of the control
device(s) 140.
[0036] In some examples, a client device 260 may connect to or otherwise
communicate with
the device(s) 205 to gain access to the data and/or to instruct or request
that the device(s) 205
perform some or all of the aspects described herein.
[0037] Fig. 3 shows a flowchart illustrating aspects of an example method
300 for controlling
operation of a number of wells. In some examples, these wells can be part of
the same project
or may be targeting areas of the same resource. The wells may be inter-related
by their sharing
of one or more operational constraints such as a limited input resource shared
between the
wells, or a output limitation on the group of wells as a whole such as a limit
on the processing
capabilities of well outputs or a limit on carbon dioxide or other bi-products
across the group of
wells. In some instances, the wells may be part of different projects or have
different well pads
.. or processing infrastructure, but may still share one or more of the same
global operating
constraints.
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[0038] At 310, one or more processor(s) 210 and/or other aspects of device(s)
205 may
be configured to generate models for each well and/or for portions (segments)
of each well. The
models can, in some examples, map a production rate for the respective well or
well portion
based on an operational input.
[0039] In some embodiments, the processor(s) 210 can be configured to generate
the
production rate models based on historical well data. The historical well data
may include data
collected by input device(s) 130 for each respective well. In some examples,
the historical well
data may include, but is not limited to, well conditions (such as temperature,
pressure and/or
any other conditions as described herein or otherwise) at one or more portions
of a well or at the
surface. In some examples, the historical well data can include input
conditions such as
amounts/rates of steam (e.g. volume or volumetric rate) injection, pump
speeds, water
production/consumption (e.g. volume or rate), amount of carbon dioxide
production which is
attributable to the well's inputs, amounts/rates of solvent injection, etc. In
some examples, the
historical well data can include well outputs such as production rates of
wells or portions of
wells. In some examples, historical well data can include geological or other
physical attributes
of the wells or resource.
[0040] In some embodiments, in addition or as an alternative to historical
well data, the
processor(s) can be configured to generate production rate models based on
well
characteristics. In some embodiments, well characteristics may include one or
more
well/project/resource/formation/reservoir/drainage area identifier(s), well
types, well statuses,
length of time a well is in production/operation, well location (e.g.
latitude, longitude, depth for
surface hole, well heel, well toe, for injector and producer wells, etc.),
geological or other
physical attributes of the wells, etc.
[0041] In some examples, by considering locations, identifiers and/or other
well
characteristics, the processor(s) may generate production rate models which
inherently or
explicitly encompass factors such as similarities in performance between
wells/formations/resources within close proximity to each other.
[0042] In some examples, by considering well characteristics such as the
age or length of
time a well is in production/operation, the processor(s) may generate
production rate models
which compensate for changes in production profiles as a SAGD well ages.
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[0043] In some embodiments, in addition or as an alternative to historical
well data and/or
well characteristics, the processor(s) can be configured to generate
production rate models
based on data which is interpreted, inferred or otherwise derived from
historical well data (e.g.
from input devices) and/or from geological or other physical well attributes.
[0044] The processor(s) 210 can, in some embodiments, be configured to
generate
production rate models by training one or more neural networks, genetic
algorithms, decision
trees, or other supervised learning algorithms. In some examples, the
processor(s) can be
configured to use operational inputs as input variables, and production rates
as outputs for the
training set. In some instances, this may result in a model which inherently
incorporates any well
conditions and in some examples, may result in a less complex model.
[0045] In some examples, the processor(s) can be configured to
additionally use well
conditions and/or other attributes such as physical/geological attributes of
the resource and/or
age/size/shape of a steam chamber as input variables. In some examples, the
processor(s) can
be configured to additionally use well characteristics and/or derived data. In
some instances,
this may result in a more complex model (e.g. more inputs) which may be more
sensitive to
changes to current well conditions.
[0046] In some embodiments, generating models can include selecting inputs
to the model by
evaluating the influence of input variables. In some examples, this can
include generating one
or more models based on multiple input variables, and varying a single input
while keeping all
other inputs constant. By observing the effect on the output of the model as
the single input
changes, the processor(s) can determine a relative influence of that input.
The processor(s) can
be configured to repeat this process for all input variables for the model to
determine which
inputs have the greatest influence on the model output. In some examples, the
processor(s) can
be configured to generate a new, simpler model by eliminating the least
influential inputs (i.e.
the inputs which when varied had the smallest effect on the output) of the
previous model.
[0047] In some embodiments, the processor(s) can be configured to generate
models using
only the 5-10 most influential inputs.
[0048] In some embodiments, the processor(s) can be configured to select
the inputs used in
generating a model based on the cost associated with collecting the input
data. For example,
repeated collection of seismic data may be costly compared to repeatedly
sampling a
temperature sensor positioned within a well. In some examples, the
processor(s) may be
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configured to assign a higher weight to input parameters which are less costly
to collect data.
However, in some examples, when an expensive input parameter has a large
influence score,
the processor(s) may be configured to include the expensive input when
generating a model.
[0049] In some examples, the training data set can include all historical
well data. In some
examples, the training data set can include a selection of the historical well
data such as data
collected within the last X days from when the model is generated. In some
examples, the
model(s) can be generated/updated on a rolling basis to reflect a model based
on data from the
most recent last X days. For example, in some examples, the model(s) can be
generated/updated based on data from the last 30, days, 60 days, 90 days, 60
months, 1 year,
etc.
[0050] In some embodiments, the processor(s) can be configured to use the
number of days
X of historical well data as a variable for generating a production rate. In
some examples, when
predicting near time behaviour (i.e. predicting a production rate for
tomorrow), it may be more
efficient and/or accurate to base the prediction on recent historical data.
Conversely, in some
examples, when predicting behaviour farther in the future, it may be more
efficient and/or
accurate to base the prediction on a larger history of well data. By
incorporating X as a variable,
the processor(s) can, in some examples, capture this flexibility when
generating a model.
[0051] In some examples, the model may be generated based on ranges which are
dependent on the age of the wells. For example, for an earlier period of a
well's production
cycle, the model may be based on data from a shorter period of historical well
data, while for a
later period of a well's production cycle, the model may be based on a larger
period of historical
well data. In some instances, this may accommodate a potential greater
variance of a well's
production at an early stage of production, and a relatively more stable
production a well's
production cycle.
[0052] In some examples, the range of historical data used to generate the
model(s) may
differ for one or more variables. For example, historical temperature data for
large period of time
may be used, while historical oil production data for a smaller period of time
may be used.
Suitable periods may be dependent on the type of variable being used to
generate the models.
In some examples, the periods may depend on the variance of the variable
and/or its effect on
outputs as the well ages. In some examples, the periods may depend on whether
the variable
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measurements are independent (e.g.
temperature at any given time) or somehow
related (e.g. cumulative values are based on previous measurements).
[0053] In
some examples, a subset of a selection of or all of the historical data set
can be
used to train the model(s), while a second different subset of the historical
data (not including
any of the training data) can be used to verify the models. In some examples,
these subsets
may be randomly generated/selected from the available or selected historical
data. In some
embodiments, the processor(s) can be configured to generate new model(s) using
different
training sets if the verification of the previous model(s) does not fall
within defined error
tolerances.
[0054] At 315, with the generated model(s), the processor(s) can be
configured to assign
units of operational inputs across individual wells or well portions while
staying within total or
global operational constraints. In some examples, the global operational
constraints can be
determined from data defining attributes or characteristics of a
system/project's allocated
resources, limitations or physical restrictions. For example, infrastructure
for a group of wells
may only have processing capabilities to provide a certain amount or rate of
water (for creating
steam) or to produce a certain amount/rate of steam. In another example,
infrastructure for a
group of wells may only have capabilities to process a certain rate or volume
of bitumen or
extracted (e.g. oil and water) mixtures. In another example, regulations or
technical
requirements may dictate that a group of wells only produce a certain
amount/rate of carbon
dioxide or other biproduct(s). In another example, there may be constraints on
the amount of
solvent to inject into the wells. Any of these or other factors, alone or in
combination, may be
used by the processor(s) as global/total operational constraints.
[0055] In
some examples, the global/total operational constraints may be received or
accessed from storage device(s) 225, 215 and/or memory(les) 215 or may be
received as
inputs to a device 205 or system 200.
[0056] In
some examples, when multiple operational constraints are defined, the
processor(s)
may apply weightings to determine which operational constraint may take
priority over another
constraint. These weightings may be received or access from a storage ,device
or memory, or
may be received as input(s) to the device 205 or system 200.
[0057] The processor(s), at 315, can, in some examples, be configured to
determine a
distribution of operational inputs across all of the wells / well portions in
the system or group of
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wells which provides optimal production rate(s) for the system/group of wells
as a whole
based on the generated models and the applied distribution method. In some
embodiments, the
optimal distribution of operational inputs and/or optimal resulting production
rate(s) may be the
best solution available based on the distribution method and the computational
and/or time
constraints applied to the distribution method.
[0058] In some examples, the method for determining the distribution of
operational inputs
may include applying genetic algorithm(s), goal seeking algorithm(s),
deterministic algorithm(s),
greedy algorithm(s), stochastic method(s), Newton-Raphson technique(s), Monte-
Carlo
method(s), and the like. In some examples, the method for determining the
distribution may be
iterative (as illustrated in the example in Fig. 3 and described below) and/or
may be repetitive
(e.g. by repeatedly assigning different inputs and determining the resulting
outcomes, and
selecting the best or optimal outcomes from the resulting set).
[0059] For example, the processor(s) can, in some embodiments, be configured
to combine
the models for the wells / well portions and the global input constraints to
create one or more
equations and/or models for optimization. In some examples, the processor(s)
can be
configured to apply Newton-Raphson method to the equation(s)/model(s) to
determine the input
distribution which results in a peak production rate by iteratively solving
for the roots of the
equation(s)/model(s).
[0060] In some examples, the processor(s) can be configured to create a
genetic algorithm
optimization problem using the models for the wells / well portions and the
global input
constraints with the fitness or objective function being the global production
rate for all wells /
well portions in the group.
[0061] In some examples, the processor(s) can be configured to apply a
greedy algorithm
wherein the processor(s) can be configured to iteratively select the current
best well / well
.. portion to assign operational inputs until a distribution is found.
[0062] In some embodiments, the above optimizations may be repeated any number
of times
or as many times as possible within a defined time/process limitation to try
to ensure that the
optimum distribution found is closer to a global peak production rate rather
than a local
maximum.
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[0063] In other embodiments, any other suitable optimization method or
combination of
methods may be used.
[0064] In some embodiments, at 330, the processor(s) can be configured to
assign a unit of
an operational input to a well or portion of a well which would result in the
greatest incremental
production rate for the system or group of wells as a whole.
[0065] In some examples, one or more of the operational inputs may be the
same as one or
more of the global operational constraints. For example, a global constraint
may include a limit
on the amount of steam available to a group of wells, while an operational
input may include the
amount of steam to provide to a particular well or portion of a well.
[0066] In some examples, one or more operational inputs may be different
than one or more
of the global operational constraints. For example, a global constraint may
include a limit on the
amount of carbon dioxide which can be produced across a group of wells, while
an operational
input may include the amount of steam to provide to a particular well or well
portion. However,
the assigning of a unit of the operational input (i.e. unit rate of steam)
will create carbon dioxide
.. when producing the steam, delivering the steam and/or pumping at least a
portion of the water
from the producer well after the steam condenses. In some embodiments, the
processor(s) are
configured to determine the incremental effect of assigning the unit of
operational input on the
global operational constraint.
[0067] In some examples, the processor(s) can be configured to determine
the well or well
portion which would result in the greatest incremental production by applying
the unit of
operational input to each well/well portion model and comparing the resulting
incremental
production rates.
[0068] After assigning the unit of operational input, the processor(s) can
be configured to
determine the incremental effect of the operational input on the operational
constraints.
[0069] At 340, this process of assigning units of operational inputs to the
next well / well
potion having the greatest incremental production rate and determining the
effect on the
operational constraints is repeated until one or more of the global
operational constraints are
reached.
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[0070] In some situations, data associated with wells or well portions may
indicate that
minimum or maximum operational inputs are required to maintain a well or well
portion. For
example, if the temperature or pressure in a well or well portion gets too
low, a steam chamber
or other physical aspects associated with a well may be permanently damaged
which may affect
all future production from the well. In another example, regulatory
requirements and/or the
location of a well may dictate for example maximum pressure limits to avoid
breaches, etc.
Therefore, minimum and/or maximum operational inputs can be associated with a
well as
defined by a database or input in order to maintain a well's attributes (such
as temperature or
pressure).
[0071] Optionally, at 320, before incrementally assigning units of
operational inputs at 330,
the processor(s) may be configured to assign units of operational inputs to
meet minimum or
maximum operational conditions.
[0072] Once one or more of the global operational constraints are reached, or
once an
optimal distribution has been determined, at 350, the processor(s) can be
configured to apply
the total operational inputs to each well or well portion. In some examples,
applying the
operational inputs can include generating signals or instructions for
controlling or otherwise
modifying or maintaining the operation of one or more control devices 140. In
some examples,
the signals or instructions can be communication via the network(s)/link(s)
260. In some
examples, the signals or instructions can be audibly or visually communicated
(e.g. on a display,
printer output, speaker, etc.) for manual configuration or adjustment of the
control devices 140.
[0073] In some embodiments, before applying the operational inputs, the
processor(s) can be
configured to perform a sanity check on the determined optimal distribution.
In some examples,
the processor(s) may be configured to compare the determined predicted optimal
production
rate with a previous distribution's predicted or actual measured production
rate. If the predicted
rate is greater than or less than the previous production rate by a defined
warning threshold
(e.g. extremely large or extremely small different), the processor(s) may be
configured to
generate a warning message and/or to regenerate the model(s) and/or
distribution.
[0074] In some embodiments, before applying the operational inputs, the
processor(s) can be
configured to verify that the operational inputs for the determined optimal
distribution do not
violate the volumetrics or other constraints on the wells and/or
infrastructure.
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[0075] At 360, the processor(s) can be configured to receive well
production and/or well
condition data from the input device(s) 130 associated with the application of
the operational
inputs.
[0076] In some examples, the new well production data, the well condition
data and/or the
operational inputs from 360 can be incorporated to regenerate or update the
model(s) for the
wells / well portions.
[0077] In some examples, the processor(s) can be configured to verify the
expected
production results based on the models and the applied operational inputs
against the actual
received production data. In some examples, the regeneration or updating of
the model(s) is
.. only triggered when the expected production results vary from the actual
production data by a
defined error threshold.
[0078] In some examples, the regeneration or updating of the model(s) is
automatically
triggered periodically (e.g. every X days or weeks).
[0079] In any of the above examples, or otherwise, in some embodiments,
any time the
.. models are regenerated or global constraints change, the iterative
assignment of operational
inputs and their application to the wells/well portions is repeated.
[0080] In some examples, the influence of different variables may change
as the age of the
well (e.g. time a well has been in operation/production). For example, in some
instances, a
young well's production rate may be highly sensitive to the distance between
the injector and
producer wells, and to the rate of steam injection. However, in some
instances, the well's
production rate may become less sensitive to these parameters as the well
ages. For example,
in some instances, an older well's production rate may more greatly influenced
by the geological
characteristics of the formation or by the historic production rates of
surrounding wells. In some
examples, to account for this or otherwise, the processor(s) may be configured
to automatically
trigger the regeneration of model(s) after a well reaches defined age
threshold(s).
[0081] In some examples, the processor(s) can be configured to receive
input(s) from one or
more input devices 130 or user input devices indicating that a malfunction,
shutdown or
maintenance event for one or more of the wells has or is scheduled to occur.
Upon receipt of an
input, the processor(s) can be configured to regenerate the model(s) for the
affected well(s) /
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well portion(s) and reassign and apply the operational inputs based on the
regenerated
model(s).
[0082] In some examples, for future scheduled events, the processor(s) may
be configured to
regenerate model(s) in incremental stages in order to gradually slow down or
turn off a well. In
some scenarios, this may help prevent permanent damage to a well cause, for
example, by a
rapid change in temperature or pressure.
[0083] Although the embodiments have been described in detail, it should
be understood that
various changes, substitutions and alterations can be made herein without
departing from the
scope as defined by the appended claims.
[0084] Moreover, the scope of the present application is not intended to be
limited to the
particular embodiments of the process, machine, manufacture, composition of
matter, means,
methods and steps described in the specification. As one of ordinary skill in
the art will readily
appreciate from the disclosure of the present invention, processes, machines,
manufacture,
compositions of matter, means, methods, or steps, presently existing or later
to be developed,
that perform substantially the same function or achieve substantially the same
result as the
corresponding embodiments described herein may be utilized. Accordingly, the
appended
claims are intended to include within their scope such processes, machines,
manufacture,
compositions of matter, means, methods, or steps
[0085] As can be understood, the examples described above and illustrated are
intended to
be exemplary only. The scope is indicated by the appended claims.
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