Note : Les descriptions sont présentées dans la langue officielle dans laquelle elles ont été soumises.
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METHOD AND SYSTEM FOR FIELD PLANNING
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. Provisional Patent
Application
61/444,916 filed February 21, 2011 entitled METHOD AND SYSTEM FOR FIELD
PLANNING, the entirety of which is incorporated by reference herein.
FIELD OF THE INVENTION
[0002] Exemplary embodiments of the present techniques relate to a
method and system
for field planning by selecting well site locations and their corresponding
reservoir target
groupings.
BACKGROUND
[0003] This section is intended to introduce various aspects of the art,
which may be
associated with exemplary embodiments of the present techniques. This
discussion is
believed to assist in providing a framework to facilitate a better
understanding of particular
aspects of the present techniques. Accordingly, it should be understood that
this section
should be read in this light, and not necessarily as admissions of prior art.
[0004] Field planning involves the design of a drilling plan for an
oilfield, or other
hydrocarbon resource. One of the objectives of field planning is to maximize
the total field
production by selecting appropriate well sites for accessing a hydrocarbon
reservoir.
Selecting well sites is complicated by numerous considerations, such as
environmental issues,
maintaining safe distances around wells, and cost. Costs may include costs for
facilities and
for drilling over the life cycle of the reservoir.
[0005] Field planning decisions are typically made over a long period of
time, and further
involve complexities arising from land use, planned well site locations, well
trajectory
design, and business considerations. The complexity of field planning
decisions leads to
complex models for which optimal solutions are difficult and tedious to
obtain.
[0006] One research article published on field planning presents a model
of hierarchical
planning and a scheduling decision tool including strategic, tactical and
operational processes
to address an optimal utilization and production of a gas field. See Udoh et
al., "Applications
of Strategic Optimization Techniques to Development and Management of Oil and
Gas
Resources," 27th SPE meeting, (2003).
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[0007] The following paragraphs of this Background section provide
specific examples of
known techniques. U.S. Patent No. 7,460,957 presents a method that
automatically designs a
multi-well development plan given a set of previously interpreted subsurface
targets. The
method focuses on how to calculate well paths from selected platforms or
targets in order to
optimize the drilling planning.
[0008] U.S. Patent No. 7,200,540 also presents a method that selects a
possible set of
well platform locations from automatically generated target locations.
[0009] U.S. Patent Application Publication No. 2009/0119076 discloses a
method for
generating an invertible 3D hydrodynamic earth model. The model is allegedly
suitable for
defining target characteristics of a subsurface area formed by a plurality of
formations and
comprising drilling positions of potential and real wells.
[0010] An initial three-dimensional (3D) earth model may be constructed
by combining
solutions for a set of single one-dimensional (1D) models. Each of the 1D
models correspond
to a real or potential well drilling position.
[0011] Each of the 1D models also covers the entire respective aggregate of
formations
along the wellbore, with solutions for a relevant set of 2D earth models which
are constructed
only for single formations. The method further includes optimizing the
constructed initial 3D
earth model by defining an optimal set of formations and an optimal set of
model parameters
that may be calibrated.
[0012] A method and system for application of the earth model construction
method for
predicting overpressure evolution before and during drilling are also
provided. As the earth
model constructed in accordance with the above method provides efficient
inversion of data,
in particular gathered while drilling, the prediction can be updated in real-
time while drilling.
This method allegedly ensures optimization of the drilling process and
improves its safety.
[0013] International Patent Application Publication No. W02009/032416
discloses
methods and systems to make completion design an integral part of the well
planning process
by enabling the rapid evaluation of completion performance. This integration
may include an
earth model and may specify well-path parameters, completion parameters, and
other
parameters in a simulation of operations using the earth model. The simulation
generates well
performance measures, which may be optimized depending on well performance
technical
limits. The optimization may be used to maximize an objective function. The
system may
include multiple users at the same or different locations (e.g. over a
network) interacting
through graphic user interfaces (GUI's).
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[0014] U.S. Patent Application Publication No. 2009/0056935 discloses a
method to
automatically design a multi-well development plan given a set of previously
interpreted
subsurface targets. This method allegedly identifies an optimal plan by
minimizing the total
cost as a function of existing and required new platforms, the number of
wells, and the
drilling cost of each of the wells. The cost of each well is a function of the
well path and the
overall complexity of the well.
[0015] U.S. Patent No. 6,549,879 discloses a systematic, computationally-
efficient, two-
stage method for determining well locations in a 3D reservoir model while
satisfying various
constraints including: minimum interwell spacing, maximum well length, angular
limits for
deviated completions, and minimum distance from reservoir and fluid
boundaries. In the first
stage, the wells are placed assuming that the wells can only be vertical. In
the second stage,
these vertical wells are examined for optimized horizontal and deviated
completions. This
solution is expedient, yet systematic, and it provides a good first-pass set
of well locations
and configurations.
[0016] The first stage solution formulates the well placement problem as a
binary integer
programming (BIP) problem which uses a "set-packing" approach that exploits
the problem
structure, strengthens the optimization formulation, and reduces the problem
size.
Commercial software packages are readily available for solving BIP problems.
[0017] The second stage sequentially considers the selected vertical
completions to
determine well trajectories that connect maximum reservoir pay values while
honoring
configuration constraints including: completion spacing constraints, angular
deviation
constraints, and maximum length constraints.
[0018] The parameter to be optimized in both stages is a tortuosity-
adjusted reservoir
"quality." The quality is preferably a static measure based on a proxy value
such as porosity,
net pay, permeability, permeability-thickness, or pore volume. These property
volumes are
generated by standard techniques of seismic data analysis and interpretation,
geology and
petrophysical interpretation and mapping, and well testing from existing
wells. An algorithm
is disclosed for calculating the tortuosity-adjusted quality values.
SUMMARY
[0019] A method is presented for field planning. The method includes
obtaining a shared
earth model comprising the hydrocarbon field. The hydrocarbon field comprises
an area of
ground surface and a reservoir disposed beneath the area of ground surface.
The method also
includes obtaining a plurality of targets for the reservoir. Additionally, the
method includes
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specifying one or more field planning parameters for accessing the plurality
of targets from
the surface. The method further includes determining a plurality of well site
locations for an
entirety of the hydrocarbon field using constraint optimization. The number of
well site
locations is minimized. The number of the plurality of targets accessible from
the plurality of
well site locations is maximized.
[0020] In some embodiments, the method includes generating a cost
function that
optimizes for the number of well site locations and a number of accessible
targets of the
plurality of targets. The method also includes generating a plurality of
target groups.
[0021] A target group comprises a plurality of targets corresponding to
a plurality of slots
on a well site. Only one target group comprises each of the plurality of
targets.
[0022] Additionally, the method includes determining a plurality of well
site locations.
The plurality of well site locations are determined based on the cost
function, and correspond
to the plurality of target groups.
[0023] Another exemplary embodiment of the present techniques provides a
system for
field planning. The system may include a plurality of processors, and a
machine readable
medium comprising code configured to direct at least one of the plurality of
processors to
generate a cost function that optimizes for the number of well site locations
and a number of
accessible targets of the plurality of targets. The code is also configured to
direct at least one
of the processors to generate the plurality of target groups. The code is
further configured to
direct at least one of the processors to determine a plurality of well site
locations
corresponding to the plurality of target groups based on the cost function.
[0024] Another exemplary embodiment of the present techniques provides a
method for
producing hydrocarbons from an oil and/or gas field using a field planning
method. The
method for producing hydrocarbons may include obtaining a shared earth model
comprising
the hydrocarbon field.
[0025] The hydrocarbon field comprises an area of ground surface and a
reservoir
disposed beneath the area of ground surface. The method also includes
identifying a plurality
of targets for the reservoir.
[0026] Additionally, the method includes specifying one or more field
planning
parameters for accessing the plurality of targets from the surface. The method
further
includes determining a plurality of well site locations for an entirety of the
hydrocarbon field
using constraint optimization. The number of well site locations is minimized.
The number of
the plurality of targets accessible from the plurality of well site locations
is maximized.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The advantages of the present techniques are better understood by
referring to the
following detailed description and the attached drawings, in which:
[0028] Fig. lA is a schematic view of the reservoir, in accordance with
an exemplary
embodiment of the present techniques;
[0029] Fig. 1B is a view of an exemplary surface of a hydrocarbon field,
in accordance
with an exemplary embodiment of the present techniques;
[0030] Fig. 2 is a process flow diagram of a method for field planning,
in accordance
with an exemplary embodiment of the present techniques;
[0031] Fig. 3 is a block diagram of exemplary well site configurations, in
accordance
with an exemplary embodiment of the present techniques;
[0032] Fig. 4 is a process flow diagram of an exemplary method for
constraint
optimization, in accordance with an exemplary embodiment of the present
techniques;
[0033] Fig. 5A is a disjoint target groupset, in accordance with an
exemplary embodiment
of the present techniques;
[0034] Fig. 5B is a composite diagram of the exemplary surface overlaid
on the target
group assignment, in accordance with an exemplary embodiment of the present
techniques;
[0035] Fig. 6 is a block diagram of an exemplary cluster computing
system that may be
used in exemplary embodiments of the present techniques.
DETAILED DESCRIPTION
[0036] In the following detailed description section, the specific
embodiments of the
present techniques are described in connection with preferred embodiments.
However, to the
extent that the following description is specific to a particular embodiment
or a particular use
of the present techniques, this is intended to be for exemplary purposes only
and simply
provides a description of the exemplary embodiments. Accordingly, the present
techniques
are not limited to the specific embodiments described below, but rather, such
techniques
include all alternatives, modifications, and equivalents falling within the
true spirit and scope
of the appended claims.
[0037] At the outset, and for ease of reference, certain terms used in this
application and
their meanings as used in this context are set forth. To the extent a term
used herein is not
defined below, it should be given the broadest definition persons in the
pertinent art have
given that term as reflected in at least one printed publication or issued
patent. Further, the
present techniques are not limited by the usage of the terms shown below, as
all equivalents,
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synonyms, new developments, and terms or techniques that serve the same or a
similar
purpose are considered to be within the scope of the present claims.
[0038] "Computer-readable medium", "tangible, computer-readable medium",
"tangible,
non-transitory computer-readable medium" or the like as used herein refer to
any tangible
storage and/or transmission medium that participates in providing instructions
to a processor
for execution. Such a medium may include, but is not limited to, non-volatile
media and
volatile media. Non-volatile media includes, for example, NVRAM, or magnetic
or optical
disks. Volatile media includes dynamic memory, such as main memory. Common
forms of
computer-readable media include, for example, a floppy disk, a flexible disk,
a hard disk, an
array of hard disks, a magnetic tape, or any other magnetic medium, magneto-
optical
medium, a CD-ROM, a holographic medium, any other optical medium, a RAM, a
PROM,
and EPROM, a FLASH-EPROM, a solid state medium like a memory card, any other
memory chip or cartridge, or any other tangible medium from which a computer
can read
data or instructions. When the computer-readable media is configured as a
database, it is to
be understood that the database may be any type of database, such as
relational, hierarchical,
object-oriented, and/or the like.
[0039] The display device may include any device suitable for displaying
the reference
image, such as without limitation a CRT monitor, a LCD monitor, a plasma
device, a flat
panel device, or printer. The display device may include a device which has
been calibrated
through the use of any conventional software intended to be used in
evaluating, correcting,
and/or improving display results (for example, a color monitor that has been
adjusted using
monitor calibration software).
[0040] Rather than (or in addition to) displaying the reference image on
a display device,
a method, consistent with the present techniques, may include providing a
reference image to
a subject.
[0041] "Earth model" or "shared earth model" refer to a
geometrical/volumetric model of
a portion of the earth that may also contain material properties. The model is
shared in the
sense that it integrates the work of several specialists involved in the
model's development
(non-limiting examples may include such disciplines as geologists,
geophysicists,
petrophysicists, well log analysts, drilling engineers and reservoir
engineers) who interact
with the model through one or more application programs.
[0042] "Exemplary" is used exclusively herein to mean "serving as an
example, instance,
or illustration." Any embodiment described herein as "exemplary" is not to be
construed as
preferred or advantageous over other embodiments.
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[0043] "Reservoir" or "reservoir formations" are typically pay zones
(for example,
hydrocarbon producing zones) that include sandstone, limestone, chalk, coal
and some types
of shale. Pay zones can vary in thickness from less than one foot (0.3048 m)
to hundreds of
feet (hundreds of m). The permeability of the reservoir formation provides the
potential for
production.
[0044] "Reservoir properties" and "reservoir property values" are
defined as quantities
representing physical attributes of rocks containing reservoir fluids. The
term "reservoir
properties" as used in this application includes both measurable and
descriptive attributes.
Examples of measurable reservoir property values include porosity,
permeability, water
saturation, and fracture density. Examples of descriptive reservoir property
values include
facies, lithology (for example, sandstone or carbonate), and environment-of-
deposition
(EOD). Reservoir properties may be populated into a reservoir framework to
generate a
reservoir model.
[0045] "Well" or "wellbore" includes cased, cased and cemented, or open-
hole
wellbores, and may be any type of well, including, but not limited to, a
producing well, an
experimental well, an exploratory well, and the like. Wellbores may be
vertical, horizontal,
any angle between vertical and horizontal, deviated or non-deviated, and
combinations
thereof, for example a vertical well with a non-vertical component.
[0046] Wellbores are typically drilled and then completed by positioning
a casing string
within the wellbore. Conventionally, the casing string is cemented to the well
face by
circulating cement into the annulus defined between the outer surface of the
casing string and
the wellbore face. The casing string, once embedded in cement within the well,
is then
perforated to allow fluid communication between the inside and outside of the
tubulars across
intervals of interest.
[0047] Exemplary embodiments of the present techniques relate to methods
and systems
for field planning. The techniques may determine multiple well site locations
for accessing a
hydrocarbon reservoir, while maximizing the number of reservoir targets
accessible from the
well site locations.
[0048] Fig. lA is a schematic view 100A of a hydrocarbon field, in
accordance with an
exemplary embodiment of the present techniques. The schematic view 100A
includes a
reservoir 102, surface 110, wells 104, targets 120, and well sites 130. The
reservoir 102, such
as an oil or natural gas reservoir, can be a subsurface formation that may be
accessed by
drilling wells 104 from the surface 110 to reach one or more targets 120. The
wells 104 may
be deviated, such as being directionally drilled to follow the subsurface of
the reservoir 102.
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[0049] The reservoir targets 120 may be pre-defined locations or regions
within a gas or
oil reservoir that a planned well trajectory will penetrate. The determination
of the locations
and the size of the targets 120 or target areas are typically performed based
on some
understanding and analysis of certain reservoir properties. These reservoir
properties may
include composition, quality, and connectivity to other areas of the reservoir
102. The
number of targets 120, the spacing between targets 120, may be determined
based on an
analysis of potential development strategies which may be facilitated by an
exemplary
embodiment of the present techniques. The targets 120 are also referred to
herein as targeted
areas.
[0050] As shown, each well site 130 may include a number of wells 104. The
wells 104
may be drilled from slots on the well site 130. Each well site 130 may include
numerous
slots, with each slot corresponding to one well 104. Each target area 120 may
be penetrated
by a well path starting from the slot location on the well site 130. The
number of slots on the
well site 130 is typically limited by drilling constraints.
[0051] Field planning includes selecting the locations of well sites 130.
Well site
selection involves several input considerations. These considerations include,
in part, the cost
of well-site construction, environmental impacts, the number of wells 104 to
adequately drain
the reservoir 102, as well as selection of reservoir targets 120 to correctly
position the well
sites 130.
[0052] One environmental consideration may be the avoidance of surface
obstacles. Fig.
1B is a view of an exemplary surface 110 of the hydrocarbon field, in
accordance with an
exemplary embodiment of the present techniques.
[0053] The surface 110 includes a number of exemplary obstacles,
including a residential
area 122, a river 124, a road 126, and a pipeline 128. It should be understood
that these
exemplary obstacles are not an exhaustive list. The surface 110 may also
include other
obstacles, both man-made and natural. The well sites 130 may be selected to
maintain a
predetermined distance from the surface obstacles.
[0054] In field planning, there are numerous trade-offs between
considerations for a
single well site 130 (location, well design, well drilling costs, well
trajectory design, etc.) and
the economic considerations of producing and developing a hydrocarbon field
over its full
life cycle. One goal of field planning is to place the well site 130 as close
as possible to the
reservoir targets 120 in order to reduce the cost of drilling. Another goal is
to minimize the
number of reservoir targets 120 that are not accessible due to the surface
and/or drilling
constraints.
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[0055] In a typical scenario, field planning is done on an ad-hoc basis,
where each well
site 130 is selected, planned, and built as resources, e.g., surface space,
become available.
However, this approach typically leads to unexpected costs and missed
opportunities.
[0056] For example, a set of reservoir targets may be selected based on
available surface
locations for a well site. The well site 130 may be then chosen in an
appropriate surface
location so that the horizontal reach to each reservoir target 120 does not
exceed a predefined
distance.
[0057] A set of well trajectories starting from the slots of the well
site 130 can then be
designed according to well path algorithms and other engineering constraints.
In addition to
maintaining safe distances from obstacles on the surface 110, field planning
also takes into
account maintaining minimum distances between the paths of the wells 104 and
geological
features of the overburden.
[0058] As the development of the hydrocarbon field progresses, the same
process may be
repeated for a new subset of reservoir targets 120 and a new well site 130.
However,
proceeding in this way over the life cycle of the hydrocarbon field may lead
to reservoir
targets 120 becoming isolated. Such a scenario may result in increased
construction costs.
[0059] For example, the reservoir 102 may include twenty reservoir
targets 120. Each
well site 130 may include five slots, which optimally, may be accessed by four
well sites 130.
The first three well sites 130 may be located as described above, accessing
fifteen of the
reservoir targets 120. However, the final five reservoir targets 120 may be
isolated such that a
single well site 130 cannot access all five targets 120. In such a case, two
or more additional
well sites 130 may be constructed, but the cost may be prohibitively
expensive.
[0060] Further, surface location constraints may complicate field
planning in this manner.
For example, instead of the remaining targets 120 being isolated as described
above, the
remaining targets may be accessible from a single well site location. However,
the surface
area above the targets 120 may be in the residential area 122, or too close to
the river 124. As
a result, a suitable site location may not be easily found without
compromising other
engineering or drilling constraints. In such a scenario, the opportunity for
exploiting the
remaining targets 120 may be lost.
[0061] Accordingly, typical approaches to field planning may result in
higher costs and
missed opportunities. However, in an exemplary embodiment of the present
techniques, the
well sites 130 for the entire field may be identified so as to maximize the
number of
accessible reservoir targets 120. In such an embodiment, clustering and
optimization
processes may be used to plan well site locations for the entire hydrocarbon
field.
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Advantageously, such a method may maximize the number of accessible reservoir
targets
120, attenuate overall costs of field development, and limit environmental
impact.
[0062] In one embodiment, an interactive environment may be used to
rapidly evaluate
current field development and well path planning on the basis of
environmental, geological,
and engineering constraints. In such an embodiment, many alterative scenarios
for field
development may be quickly evaluated. Further, the method may be repeated
throughout the
life cycle of the hydrocarbon field.
[0063] Additionally, the method may allow a user to obtain optimal field
configurations
in which constraints can be set while minimizing total cost and maximizing
reservoir
productivity. The constraints may include the number of available targets,
number of slots per
well site 130, and minimum avoidance distance to ground and geological
features.
[0064] Fig. 2 is a process flow diagram of a method 200 for field
planning, in accordance
with an exemplary embodiment of the present techniques. The method 200 may
begin at
block 202, where a three dimensional (3D) shared earth model may be obtained.
In some
embodiments, the shared earth model may be generated. The shared earth model
may include
one or more hydrocarbon fields with potential reservoirs 102, and geographic
maps for
ground surface of the fields.
[0065] The maps may indicate man-made and natural objects such as
residential areas
122, rivers 124, and roads 126. The maps may also include near-ground objects
such as
pipelines 128, or other hazard regions. Additionally, geological features
(e.g. salt bodies and
faults), existing well site platforms, and well paths may also be included.
[0066] At block 204, a set of reservoir targets 120 may be obtained. The
reservoir targets
120 may include target areas in the reservoir 102, which are reachable from a
surface location
with planned drillable well trajectories identified.
[0067] At block 206, field planning parameters may be specified. The field
planning
parameters may include well site configuration, maximum horizontal reach, well
trajectory
constraints, anti-collision constraints, and quality of penetration of the
reservoir 102. Other
parameters, such as environmental constraints, minimal stand-off distance to
surface or
subsurface objects may also be specified. In one embodiment of the present
techniques, a
user, such as a geoscientist or drilling engineer, may define field planning
parameters as part
of an optimization process.
[0068] Further examples of field planning parameters include Dogleg
Severity, which
indicates the degree of well path curvature. Dogleg Severity is typically used
by drilling
engineers to ensure a viable well trajectory can be achieved. Other parameters
may be used
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for controlling a well trajectory, such as Hold and Curve to Target and
Specify Angle to
Target. The optimization process is described below with reference to block
208.
[0069] The well site configuration parameters may specify the number of
slots, spacing
between slots, orientation of the well site 130, etc. The orientation of the
well site is
described with reference to Fig. 3, which is a block diagram of two exemplary
well site
configurations 302, 304, in accordance with an exemplary embodiment of the
present
techniques. The well site configuration 302 includes a 9-slot well site 130,
accommodating a
maximum of 9 well bores starting from the given slot locations. Each of the
black dots
represents one well slot 320. The spacing of slots 320 on each row may be 20
feet apart. The
spacing between rows may be 45 feet apart.
[0070] For example, the well site configuration 304 includes 12 slots
320, arranged in a
three by four matrix, with equal spacing for rows and columns. The well site
configuration
304 is also rotated 45 degrees from north.
[0071] Referring back to Fig. 2, the maximum horizontal reach may
specify a constraint
on distance between the well site 130 and the reservoir targets 120. The
maximum horizontal
reach may specify a range within which potential targets 120 may be selected
for a particular
well site 130. The horizontal reach typically correlates to drilling costs. As
such, limiting the
horizontal reach from the well site 130 limits the drilling cost.
[0072] Well trajectory constraints may specify basic trajectory
parameters such as dog-
leg severity, kick-off depth, hold distances and trajectory type. Anti-
collision or inter-well
constraints may also be imposed through well-to-well distance functions.
[0073] Finally, constraints around quality of penetration of the
reservoir as defined by
properties of the targets 120 may also be imposed. Such quality constraints
may include
minimum net sand or net pay penetrated by the well path. The quality
constraints may also
include path segments within selected reservoir target regions.
[0074] At block 208 well site locations for accessing the reservoir
targets 120 may be
determined. The well site locations may be determined in a manner that
minimizes the
drilling cost and maximizes production of the hydrocarbon field. A modeling
process may be
used to determine the well site locations such that all the reservoir targets
120 are fully
utilized. The well site locations may be determined in a manner meets the
specified
parameters, and limits the total cost of field development.
[0075] The modeling process may use an optimization process to
accomplish the
following objectives: a) divide the targets 120 into one or more disjoint
groups so that all
targets in the same group would be reached by the same well site 130; and b)
for each group,
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locate the well site 130 such that the environmental impact is limited, and
drilling can be
performed within the given geological and engineering constraints.
[0076] At block 210, well drilling activities may be performed. The
wells 104 may be
drilled at one or more of the determined well site locations. Well site
locations may be
selected for conducting detailed well drilling activities according to each
development stage
of the field. For each well 104 at the selected locations, the potential
production, bore
stability, torque, drag, and the like, may be evaluated. Drilling completion
and performance
processes, such as described in patent application W02009/032416 titled "Well
performance
Modeling in a collaboration well planning environment" by T. Benish, et al.,
may also be
performed.
[0077] If the condition of the ground and reservoir changes over the
life cycle of the field,
a new field plan may be generated. For example, the acquisition of new
acreage, or
identification of new targets 120 field may affect the original field
planning. Once those
changes may be identified, the method 200 may repeat again from block 206,
where new
parameters may be specified for a new field planning.
[0078] Referring back to block 208, the constraint optimization is a
process where the
value of a given function f: Rn ¨> R is to be maximized or minimized over a
given set D in
R. The function f is called the objective function, and the set, D, the
constraint set. The
objective is to maximize (or minimize) f(x) subject to x in D. The constraint
optimization
method typically is defined and formulated such that constraints are expressed
as a number of
weighted cost functions. The aim of constraint optimization is to find a
solution where total
cost is maximized (or minimized) such that imposed constraints are satisfied.
[0079] In an exemplary embodiment of the present techniques, constraints
may be
imposed as cost functions. Constraints for the design and construction of well
sites 130 and
well trajectories can be assigned cost functions such that a minimum cost is
assigned to
preferred values. The preferred construction, implementation, or design cost
may be
determined independent from all other considerations and constraints.
[0080] In contrast, a maximum or unacceptably high cost may be assigned
to a design, for
example, that violates a constraint. A well path exceeding a specified dog leg
severity, or
placing the well site 130 in an environmentally restricted area may have
unacceptably high
costs. When the cost functions are collectively analyzed, the objective
function of the
optimization is to find the set of well sites 130 and well trajectory that
reduce the cost while
still fulfilling the reservoir penetration requirements, i.e. hitting all
targets or target areas in
acceptable locations.
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[0081] The result from the optimization process may provide a field
planning solution.
The solution may include, but is not limited to: identifying a set of well
sites 130 on the
ground to enable well planning that reaches the selected targets 120, and
minimizes the total
field development cost, while solving the problem of target-slot assignments
and well path
trajectory constraints. This solution can be used as a blueprint for current
field planning and
field development. During the long period of field development, drilling
activity may be
conducted according to the availability of resources. As the field development
progresses, the
ground surface or reservoir condition may change. The optimization process
described in
block 208 may be repeated to find a field planning solution based on the newly
acquired
ground surface information as well as reservoir conditions. One embodiment of
this
optimization process is described with reference to Fig. 4.
[0082] One task of an exemplary method may be to divide the available
targets 120 into
distinct groups based on constraints such as number of slots per drill center,
maximum reach
per well, etc. To lower cost, the objective may be formulated to minimize the
number of well
sites 130 since each well site 130 can only accommodate a fixed number of
targets 120.
[0083] Such a method may also cluster reservoir targets 120 into a set
of disjoint target
groups such that each target group would correspond to a well site on the
ground surface 110.
Constraint optimization algorithms and/or clustering optimization algorithms
may be applied
to determine preferred locations of well sites for the targets 120 to be
drilled. Additionally,
the total field development cost may be lowered while solving the problem of
target-slot
assignments and well path trajectory constraints.
[0084] Fig. 4 is a process flow diagram of an exemplary method 400 for
constraint
optimization, in accordance with an exemplary embodiment of the present
techniques. The
method 400 may begin at block 402, where a cost function for the well site
construction is
created.
[0085] The cost function may be represented as a data grid, denoted
herein as DG, on the
surface map. Each cell in the data grid may represent a potential well site
location.
Accordingly, each cell may be assigned the properties and conditional
constraints for a
particular location.
[0086] A cell can be classified according to its cost as a criterion to
determine a well site
location. Some cells could have extremely high cost because the area may be
restricted for
use. The cost of construction at other cells may depend on the geographic
locations, as well
as the related cost of drilling activities.
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[0087] At block 404, disjoint target group sets may be generated. A
disjoint target group
set may include a set of reservoir targets 120 organized into groups. The
target group set is
referred to as disjoint because each target 120 may be included in only one
group. Different
approaches may be used to generate the disjoint target group sets.
[0088] In one embodiment, a clustering of disjoint target groups may depend
upon the
mathematical functions of the constraint optimization algorithms: a stochastic
method, such
as 'genetic algorithm', may randomly generate a new set of target groups based
on previous
iterations by the permutation of certain parameters. Other deterministic
algorithms may
define new target groups based on the calculated converging trend to the
optimal solutions.
[0089] Disjoint target group sets are described with reference to Fig. 5A,
which is a
disjoint target group set 500A, in accordance with an exemplary embodiment of
the present
techniques. The target group 540 may be a collection of reservoir targets 520
that are
reachable by well trajectories from the same well site 130. Each reservoir
target 520 may be
limited to one target group 540.
[0090] The reservoir targets 520 in each group may also satisfy other field
planning
constraints. For example, each target group may only include reservoir targets
520 that satisfy
the maximum horizontal reach constraint. Additionally, if there are nine slots
on each well
site 130, nine targets 520 may be assigned to each target group 540.
[0091] Fig. 5B is a composite diagram 500B of the exemplary surface 110
overlaid on the
disjoint target group set 500A, in accordance with an exemplary embodiment of
the present
techniques. Well site platforms 550 are shown in viable locations for the
associated target
groups 540.
[0092] In some cases, it may not be possible to fully utilize all slots
on all the well sites
130. In such cases, the number of targets 520 in each group 540 may be as
close as possible
to, but may not exceed the maximum number of slots available on the well sites
130. As such,
the total number of target groups may be minimized.
[0093] As the optimization process continues, a disjoint target group
set may change,
along with the number of target groups in the set. In an exemplary embodiment
of the present
techniques, the target groups 540 may be generated using a clustering
algorithm. If a well site
location cannot be found for the target group 540, an extra cost may be added,
for example,
as a penalty. Similarly, an extra cost may be added for each missing target-
slot assignment if
a selected well site location cannot reach all of the targets 520 in the same
target group 540.
[0094] Additionally, the well site locations may be determined such that
drillable well
trajectories from the well site 130 to each target 520 can be achieved. For
example, for each
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target 520 a viable well trajectory may be planned based on drilling physics
from one of the
slots of the well site 130. All field planning parameters, described above,
may be imposed.
[0095] Additionally, in planning the well trajectories from potential
well site locations,
potential subsurface geo-hazards such as faults, salt formations, over
pressured zones or
unstable intervals may be avoided. As stated previously, the trajectories may
also be planned
to maintain safe distances from other planned or existing well paths.
[0096] The well planning process is typically is performed in an
iterative process wherein
the field planning parameters are modified on successive iterations. For
example, parameters
that relate to drilling difficulty and cost may be modified. In some cases,
even the well site
location may be moved to accommodate a successful planning. In an exemplary
embodiment
of the disclosed techniques, this iterative process may be performed by
visualizing the 3D
shared earth model on a computer with visualization capabilities.
[0097] At block 410, it may be determined whether well site locations
may be determined
for all the target groups 540. If not, blocks 406-410 may be repeated for
another disjoint
target group set.
[0098] If so, at block 412, a cost for the target group set may be
determined, based on the
constraint function described above. At block 414, it may be determined
whether the cost, for
example, meets a specified threshold.
If the specified threshold is not met, blocks 406-412 may be repeated for
another disjoint
target group set. If the threshold is met, the method 400 may stop.
[0099] The method 400 may stop once a first successful disjoint target
group set is found.
In other embodiments of the present techniques, multiple successful sets may
be considered.
From these multiple sets, a solution may be selected based on a total cost or
other criterion
optimized to a preferred value. Additionally, there may be several well sites
130 and well
trajectory configurations which satisfy all given constraints. As such, other
criteria may be
used for further evaluation.
[00100] As stated previously, it may not be possible to locate a well site 130
such that all
of the targets 520 in a target group 540 are penetrated by wells. In such a
scenario, a
threshold may be specified for the number of target groups 540 with unfilled
slots. If a target
group set exceeds this threshold, it may not be further considered. The method
may iterate
back to blocks 406 for another target group set.
[00101] In an exemplary embodiment of the disclosed techniques, successive
iterations
may use results from prior iterations to determine new target group sets. For
example,
unassigned targets 520 from a previous iteration may be re-grouped to a
neighboring target
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group 540. Re-assigning targets 520 as such may result in a new well site
location, that also
honors the other field planning parameters.
[00102] The method of selecting a new clustering of target groups 540 may
depend upon
the mathematical functions of the constraint optimization algorithms: a
stochastic method,
such as 'genetic algorithm', may randomly generate a new set of target groups
540 based on
previous iterations by the permutation of certain parameters. Other
deterministic algorithms
may define new target groups 540 based on the calculated converging trend to
the optimal
solutions.
[00103] The techniques discussed herein may be implemented on a computing
device,
such as that shown in Fig. 6. Fig. 6 shows an exemplary computer system 600 on
which
software for performing processing operations of embodiments of the present
techniques may
be implemented. A central processing unit (CPU) 601 is coupled to a system bus
602. The
CPU 601 may be any general-purpose CPU. The present techniques are not
restricted by the
architecture of CPU 601 (or other components of exemplary system 600) as long
as the CPU
601 (and other components of system 600) supports operations according to the
techniques
described herein.
[00104] The CPU 601 may execute the various logical instructions according to
the
disclosed techniques. For example, the CPU 601 may execute machine-level
instructions for
performing processing according to the exemplary operational flow described
above in
conjunction with Figs. 2 and 4. As a specific example, the CPU 601 may execute
machine-
level instructions for performing the methods of Figs. 2 and 4.
[00105] The computer system 600 may also include random access memory (RAM)
603,
which may be SRAM, DRAM, SDRAM, or the like. The computer system 600 may
include
read-only memory (ROM) 604 which may be PROM, EPROM, EEPROM, or the like. The
RAM 603 and the ROM 604 hold user and system data and programs, as is well
known in the
art. The programs may include code stored on the RAM 604 that may be used for
modeling
geologic properties with homogenized mixed finite elements, in accordance with
embodiments of the present techniques.
[00106] The computer system 600 may also include an input/output (I/O) adapter
605, a
communications adapter 614, a user interface adapter 608, and a display
adapter 609. The I/O
adapter 605, user interface adapter 608, and/or communications adapter 611
may, in certain
embodiments, enable a user to interact with computer system 600 in order to
input
information.
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[00107] The I/O adapter 605 may connect the bus 602 to storage device(s) 606,
such as
one or more of hard drive, compact disc (CD) drive, floppy disk drive, tape
drive, flash
drives, USB connected storage, etc. to computer system 600. The storage
devices may be
used when RAM 603 is insufficient for the memory requirements associated with
storing data
for operations of embodiments of the present techniques. For example, the
storage device 606
of computer system 600 may be used for storing such information as
computational meshes,
intermediate results and combined data sets, and/or other data used or
generated in
accordance with embodiments of the present techniques.
[00108] The communications adapter 611 is adapted to couple the computer
system 600 to
a network 612, which may enable information to be input to and/or output from
the system
600 via the network 612, for example, the Internet or other wide-area network,
a local-area
network, a public or private switched telephone network, a wireless network,
or any
combination of the foregoing. The user interface adapter 608 couples user
input devices, such
as a keyboard 613, a pointing device 607, and a microphone 614 and/or output
devices, such
as speaker(s) 615 to computer system 600. The display adapter 609 is driven by
the CPU 601
to control the display on the display device 610, for example, to display
information
pertaining to a target area under analysis, such as displaying a generated
representation of the
computational mesh, the reservoir, or the target area, according to certain
embodiments.
[00109] The present techniques are not limited to the architecture of the
computer system
600 shown in Fig. 6. For example, any suitable processor-based device may be
utilized for
implementing all or a portion of embodiments of the present techniques,
including without
limitation personal computers, laptop computers, computer workstations, and
multi-processor
servers. Moreover, embodiments may be implemented on application specific
integrated
circuits (ASICs) or very large scale integrated (VLSI) circuits. In fact,
persons of ordinary
skill in the art may utilize any number of suitable structures capable of
executing logical
operations according to the embodiments. In one embodiment of the present
techniques, the
computer system may be a networked multi-processor system.
[00110] While the present techniques may be susceptible to various
modifications and
alternative forms, the exemplary embodiments discussed above have been shown
only by
way of example. However, it should again be understood that the present
techniques are not
intended to be limited to the particular embodiments disclosed herein. Indeed,
the present
techniques include all alternatives, modifications, and equivalents falling
within the true spirit
and scope of the appended claims.
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