METHOD AND SYSTEM FOR FIELD PLANNING

Abstract

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 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.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application 61/444,916 filed Feb. 21, 2011 entitled METHOD AND SYSTEM FOR FIELD PLANNING, the entirety of which is incorporated by reference herein.

FIELD OF THE INVENTION

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

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.

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.

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.

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).

The following paragraphs of this Background section provide specific examples of known techniques. U.S. Pat. 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.

U.S. Pat. No. 7,200,540 also presents a method that selects a possible set of well platform locations from automatically generated target locations.

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.

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.

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.

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.

International Patent Application Publication No. WO2009/032416 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).

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.

U.S. Pat. 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.

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.

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.

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

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 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.

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.

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.

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.

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.

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.

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.

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.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the present techniques are better understood by referring to the following detailed description and the attached drawings, in which:

FIG. 1A is a schematic view of the reservoir, in accordance with an exemplary embodiment of the present techniques;

FIG. 1B is a view of an exemplary surface of a hydrocarbon field, in accordance with an exemplary embodiment of the present techniques;

FIG. 2 is a process flow diagram of a method for field planning, in accordance with an exemplary embodiment of the present techniques;

FIG. 3 is a block diagram of exemplary well site configurations, in accordance with an exemplary embodiment of the present techniques;

FIG. 4 is a process flow diagram of an exemplary method for constraint optimization, in accordance with an exemplary embodiment of the present techniques;

FIG. 5A is a disjoint target groupset, in accordance with an exemplary embodiment of the present techniques;

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;

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

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.

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, 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.

“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.

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).

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.

“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.

“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.

“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.

“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.

“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.

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.

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.

FIG. 1A 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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. Advantageously, such a method may maximize the number of accessible reservoir targets 120, attenuate overall costs of field development, and limit environmental impact.

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 alternative scenarios for field development may be quickly evaluated. Further, the method may be repeated throughout the life cycle of the hydrocarbon field.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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, locate the well site 130 such that the environmental impact is limited, and drilling can be performed within the given geological and engineering constraints.

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 WO2009/032416 titled “Well performance Modeling in a collaboration well planning environment” by T. Benish, et al., may also be performed.

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.

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 Rⁿ. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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 group 540. Re-assigning targets 520 as such may result in a new well site location, that also honors the other field planning parameters.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Claims

1. A method for field planning, comprising:

obtaining a shared earth model comprising a hydrocarbon field, the hydrocarbon field comprising an area of ground surface and a reservoir disposed beneath the area of ground surface;

obtaining a plurality of targets for the reservoir;

specifying one or more field planning parameters for accessing the plurality of targets from the well sites; and

determining a plurality of well site locations for an entirety of the hydrocarbon field, wherein a number of well sites is minimized, and wherein a number of the plurality of targets accessible from the plurality of well sites is maximized.

2. The method of claim 1, comprising:

generating a cost function that optimizes for a number of the well site locations and a number of accessible targets of the plurality of targets;

generating a plurality of target groups, wherein a target group comprises a plurality of targets corresponding to a plurality of slots on a well site, and wherein only one target group comprises each of the plurality of targets; and

determining a plurality of well site locations corresponding to the plurality of target groups based on ground surface parameters and field planning parameters using optimization methodologies.

3. The method of claim 1, wherein the field planning parameters comprise one or more ground surface parameters, wherein the ground surface parameters comprise a constraint corresponding to a ground surface obstacle.

4. The method of claim 3, wherein the ground surface parameters comprise a constraint corresponding to a specified distance between the well site locations and the ground surface obstacle.

5. The method of claim 2, comprising determining a well trajectory for each of the plurality of slots to one of the plurality of targets.

6. The method of claim 5, wherein the cost function comprises a constraint corresponding to a specified distance between a well trajectory and a subsurface geo-hazard.

7. The method of claim 5, wherein the cost function comprises a constraint corresponding to a specified distance between trajectories to the plurality of targets.

8. The method of claim 1, wherein the field planning parameters comprise one or more of:

a number of slots on the well sites;

a spacing between the slots;

a maximum horizontal reach;

kick-off depth;

hold distances;

trajectory type;

an azimuth orientation of the well sites;

a hold and curve to target parameter; and

a dogleg severity.

9. A system for field planning, comprising:

a plurality of processors;

a machine readable medium comprising code configured to direct at least one of the plurality of processors to:

obtain a shared earth model comprising a hydrocarbon field, the hydrocarbon field comprising an area of ground surface and a reservoir disposed beneath the area of ground surface;

identify a plurality of targets for the reservoir;

specify one or more field planning parameters for accessing the plurality of targets from the surface; and

determine a plurality of well site locations for an entirety of the hydrocarbon field using constraint optimization, wherein a number of well site locations is minimized, and wherein a number of the plurality of targets accessible from the plurality of well sites is maximized.

10. The system of claim 9, 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;

generate a plurality of target groups, wherein a target group comprises a plurality of targets corresponding to a plurality of slots on a well site, and wherein only one target group comprises each of the plurality of targets; and

determine a plurality of well site locations corresponding to the plurality of target groups based on the cost function.

11. The system of claim 9, wherein the field planning parameters comprise one or more ground surface parameters, wherein the ground surface parameters comprise a constraint corresponding to a ground surface obstacle.

12. The system of claim 11, wherein the ground surface parameters comprise a constraint corresponding to a specified distance between the well site locations and the ground surface obstacle.

13. The system of claim 10, comprising code configured to direct at least one of the plurality of processors to determine a well trajectory for each of the plurality of slots to one of the plurality of targets.

14. The system of claim 13, wherein the cost function comprises a constraint corresponding to a specified distance between the well trajectory and a subsurface geo-hazard.

15. The system of claim 10, wherein the cost function comprises a constraint corresponding to a specified distance between trajectories to the plurality of targets.

16. A method for producing hydrocarbons from an oil and/or gas field using a field planning method relating to a hydrocarbon field, the method for producing hydrocarbons comprising:

obtaining a shared earth model comprising the hydrocarbon field, the hydrocarbon field comprising a surface, and a reservoir disposed beneath the surface;

obtaining a plurality of targets for the reservoir;

specifying one or more field planning parameters for accessing the plurality of targets from the surface; and

determining a plurality of well site locations for an entirety of the hydrocarbon field using constraint optimization, wherein a number of well site locations is minimized, and wherein a number of the plurality of targets accessible from the plurality of well site locations is maximized.

17. The method of claim 16, comprising:

generating a cost function that optimizes for a number of the well site locations and a number of accessible targets of the plurality of targets;

generating a plurality of target groups, wherein a target group comprises a plurality of targets corresponding to a plurality of slots on a well site, and wherein only one target group comprises each of the plurality of targets; and

determining a plurality of well site locations corresponding to the plurality of target groups based on ground surface parameters and field planning parameters using optimization methodologies.

18. The method of claim 16, wherein the field planning parameters comprise one or more ground surface parameters, wherein the ground surface parameters comprise a constraint corresponding to a ground surface obstacle.

19. The method of claim 18, wherein the ground surface parameters comprise a constraint corresponding to a specified distance between the well site locations and the ground surface obstacle.

20. The method of claim 16, comprising determining a well trajectory for each of the plurality of slots to one of the plurality of targets.