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CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    Not applicable. 
       STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
       [0002]    Not applicable. 
       FIELD OF THE INVENTION 
       [0003]    The present invention generally relates to planning well locations (targets) and corresponding wellbores. More particularly, the present invention relates to the use of dynamic production criteria to optimally plan multiple well locations and corresponding wellbores. 
       BACKGROUND OF THE INVENTION 
       [0004]    In the oil and gas industry, current practice in planning a multiple-well package for a field does not include determination of the optimal placement for wells and their target completion zones based on the production from the field and the associated economics. Currently, well planning is limited to evaluating a few scenarios for well plans in a static-geologic model with manual and time-consuming evaluation in a simulator. This conventional well planning method, and its associated technology, is limited to multiple, discrete planning steps. 
         [0005]    In “Optimal Field Development Planning of Well Locations with Reservoir Uncertainty” by Cullick et al. (“SPE 96986”), for example, a part of the well planning process is described as being automated by optimizing movement of perforation zones in a simulator to evaluate field production. Similarly, U.S. Pat. No. 7,096,172 describes automated well target selection based on static properties of the geologic formation. The workflow described in SPE 96986 begins with a static, geologic, base model of the oilfeld, which may include porosity, permeability, and the like. New well locations are planned based upon the static geologic model and the various corresponding properties in a three-dimensional grid, Cartesian grid or corner point grid. The new well locations and associated characteristics are exported as locations in a three-dimensional grid, for example. Perforations are then computed in the i, j, k grid coordinates and exported as well perforation intervals. A model is then compiled by selecting decision variables in a simulator data deck; selecting delta i, delta j, delta k for perforations subject to grid boundary conditions; selecting on off parameters for perforations; and setting up an objective function. The model is then executed by techniques further described in SPE 96986. 
         [0006]    Nevertheless, the techniques and workflows described in SPE 96986 and U.S. Pat. No. 7,096,172, which are incorporated herein by reference, fail to describe a solution for: i) optimizing while simultaneously verifying well drillability and hazards; ii) computing updates to true well geometry/trajectory and tie-back connections to pipelines and delivery systems; and iii) locating optimal formation perforation zones with true production from dynamic flow of oil, gas, and water. In other words, these conventional techniques and workflows merely move perforations from one grid location to another grid location without recomputing the wellbore geometry and honoring drilling constraints. 
         [0007]    There is therefore, a need for automatically planning well locations with dynamic production criteria. 
       SUMMARY OF THE INVENTION 
       [0008]    The present invention therefore, meets the above needs and overcomes one or more deficiencies in the prior art by providing systems and methods for automatically planning well locations with dynamic production criteria. 
         [0009]    In one embodiment, the present invention includes a computer implemented method for planning a well location, comprising: i) defining a decision variable bound for a well target based on movement of the well target from its original location; ii) defining an objective function for the well location; iii) initializing an on/off variable for a perforation interval containing the well target; iv) defining a stopping criteria; v) defining a constraint for the well target and a constraint for a subsurface well plan; vi) defining coordinates for each well target subject to the well target constraint and one or more sets of property filters; vii) computing a subsurface well plan for the well location, which connects each well target that satisfies the subsurface well plan constraint; viii) discarding each well target that does not satisfy the subsurface well plan constraint; ix) computing profile data for the subsurface well plan; x) computing a well perforation based on the one or more sets of property filters; xi) simulating production based on the well perforation, the subsurface well plan and the profile data; xii) computing the objective function based on data from the simulated production; and xiii) determining whether the stopping criteria are met. 
         [0010]    In another embodiment, the present invention includes a program carrier device for carrying computer executable instructions for planning a well location. The instructions are executable to implement: i) defining a decision variable bound for a well target based on movement of the well target from its original location; ii) defining an objective function for the well location; iii) initializing an on/off variable for a perforation interval containing the well target; iv) defining a stopping criteria; v) defining a constraint for the well target and a constraint for a subsurface well plan; vi) defining coordinates for each well target subject to the well target constraint and one or more sets of property filters; vii) computing a subsurface well plan for the well location, which connects each well target that satisfies the subsurface well plan constraint; viii) discarding each well target that does not satisfy the subsurface well plan constraint; ix) computing profile data for the subsurface well plan; x) computing a well perforation based on the one or more sets of property filters; xi) simulating production based on the well perforation, the subsurface well plan and the profile data; xii) computing the objective function based on data from the simulated production; and xiii) determining whether the stopping criteria are met. 
         [0011]    In yet another embodiment, the present invention includes a program carrier device for carrying a data structure, the data structure comprising a data field, the data field comprising a well plan based on dynamic production criteria and an objective function, the well plan representing multiple wellbore trajectories with perforation locations on a geologic model. 
         [0012]    Additional aspects, advantages and embodiments of the invention will become apparent to those skilled in the art from the following description of the various embodiments and related drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0013]    The present invention is described below with references to the accompanying drawings in which like elements are referenced with like reference numerals, and in which: 
           [0014]      FIG. 1  is a block diagram illustrating a system for implementing the present invention. 
           [0015]      FIG. 2  is a flow diagram illustrating one embodiment of a method for implementing the present invention. 
           [0016]      FIG. 3A  is a flow diagram illustrating another embodiment of a method for implementing the present invention. 
           [0017]      FIG. 3B  is a flow diagram illustrating another embodiment of a method for implementing the present invention. 
           [0018]      FIG. 4  is an image illustrating step  212  in  FIG. 2 . 
           [0019]      FIG. 5  is an image illustrating step  216  in  FIG. 2 . 
           [0020]      FIG. 6  is an image illustrating step  218  in  FIG. 2 . 
           [0021]      FIG. 7  is an image illustrating step  222  in  FIG. 2 . 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0022]    The subject matter of the present invention is described with specificity, however, the description itself is not intended to limit the scope of the invention. The subject matter thus, might also be embodied in other ways, to include different steps or combinations of steps similar to the ones described herein, in conjunction with other present or future technologies. Moreover, although the term “step” may be used herein to describe different elements of methods employed, the term should not be interpreted as implying any particular order among or between various steps herein disclosed unless otherwise expressly limited by the description to a particular order. 
       System Description 
       [0023]    The present invention may be implemented through a computer-executable program of instructions, such as program modules, generally referred to as software applications or application programs executed by a computer. The software may include, for example, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. The software forms an interface to allow a computer to react according to a source of input. AssetPlanner™, Network Planner™, DataStudio™ and NEXUS® or, alternatively, VIP®, which are commercial software applications marketed by Landmark Graphics Corporation, may be used as interface applications to implement the present invention. The software may also cooperate with other code segments to initiate a variety of tasks in response to data received in conjunction with the source of the received data. The software may be stored and/or carried on any variety of memory media such as CD-ROM, magnetic disk, bubble memory and semiconductor memory (e.g., various types of RAM or ROM). Furthermore, the software and its results may be transmitted over a variety of carrier media such as optical fiber, metallic wire, free space and/or through any of a variety of networks such as the Internet. 
         [0024]    Moreover, those skilled in the art will appreciate that the invention may be practiced with a variety of computer-system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable-consumer electronics, minicomputers, mainframe computers, and the like. Any number of computer-systems and computer networks are acceptable for use with the present invention. The invention may be practiced in distributed-computing environments where tasks are performed by remote-processing devices that are linked through a communications network. In a distributed-computing environment, program modules may be located in both local and remote computer-storage media including memory storage devices. The present invention may therefore, be implemented in connection with various hardware, software or a combination thereof in a computer system or other processing system. 
         [0025]    Referring now to  FIG. 1 , a block diagram of a system for implementing the present invention on a computer is illustrated. The system includes a computing unit, sometimes referred to a computing system, which contains memory, application programs, a client interface, and a processing unit. The computing unit is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. 
         [0026]    The memory primarily stores the application programs, which may also be described as program modules containing computer-executable instructions, executed by the computing unit for implementing the methods described herein and illustrated in  FIGS. 2-7 . The memory therefore, includes a Well Planning Module, which enables the methods illustrated and described in reference to  FIGS. 2-7 . A Base Model includes a static, geologic model of the oilfield, which may include porosity, permeability, and the like. The Base Model is then used by AssetPlanner™ to compute new well targets and well plans based upon the static geologic model and the various corresponding properties in a three-dimensional grid, Cartesian grid or corner point grid. The new well targets and well plans are exported to Network Planner™ as locations in a three-dimensional grid, for example. Network Planner™ then computes well characteristics associated with the well plan using assigned values. DataStudio™ then processes the well plan and the well characteristics to compute perforations in the i, j, k grid space using assigned values, which are exported as well perforation intervals to the DMS Model. 
         [0027]    The Well Planning Module includes the DMS Model, which may be executed according to the methods illustrated and described in reference to  FIGS. 2-7 . The Well Planning Module also may interact with the Base Model, AssetPlanner™, Network Planner™ and DataStudio™ during the DMS™ Execution as further described in reference to  FIGS. 2-7 . 
         [0028]    Although the computing unit is shown as having a generalized memory, the computing unit typically includes a variety of computer readable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. The computing system memory may include computer storage media in the form of volatile and/or nonvolatile memory such as a read only memory (ROM) and random access memory (RAM). A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within the computing unit, such as during start-up, is typically stored in ROM. The RAM typically contains data and/or program modules that are immediately accessible to, and/or presently being operated on by, the processing unit. By way of example, and not limitation, the computing unit includes an operating system, application programs, other program modules, and program data. 
         [0029]    The components shown in the memory may also be included in other removable/nonremovable, volatile/nonvolatile computer storage media. For example only, a hard disk drive may read from or write to nonremovable, nonvolatile magnetic media, a magnetic disk drive may read from or write to a removable, non-volatile magnetic disk, and an optical disk drive may read from or write to a removable, nonvolatile optical disk such as a CD ROM or other optical media. Other removable/non-removable, volatile/non-volatile computer storage media that can be used in the exemplary operating environment may include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The drives and their associated computer storage media discussed above therefore, store and/or carry computer readable instructions, data structures, program modules and other data for the computing unit. 
         [0030]    A client may enter commands and information into the computing unit through the client interface, which may be input devices such as a keyboard and pointing device, commonly referred to as a mouse, trackball or touch pad. Input devices may include a microphone, joystick, satellite dish, scanner, or the like. 
         [0031]    These and other input devices are often connected to the processing unit through the client interface that is coupled to a system bus, but may be connected by other interface and bus structures, such as a parallel port or a universal serial bus (USB). A monitor or other type of display device may be connected to the system bus via an interface, such as a video interface. In addition to the monitor, computers may also include other peripheral output devices such as speakers and printer, which may be connected through an output peripheral interface. 
         [0032]    Although many other internal components of the computing unit are not shown, those of ordinary skill in the art will appreciate that such components and their interconnection are well known. 
       Method Description 
       [0033]    Referring now to  FIG. 2 , a flow diagram illustrates one embodiment of a method  200  for implementing the present invention. Steps  202 - 208  are associated with the DMS™ Model and steps  210 - 232  are associated with the DMS™ Execution. The DMS™ Model and the DMS™ Execution (steps  202 - 232 ) may therefore, be processed in a computer-implemented method by the Well Planning Module illustrated in  FIG. 1 . Steps  202 - 212  may be implemented as input for the Well Planning Module using the client interface illustrated in  FIG. 1 . 
         [0034]    In step  202 , a decision variable bound is defined for each well target as movement in a grid defined by i, j, k coordinates from the well target&#39;s original location. In other words, the decision variable bound is defined for each well target based on movement of the well target from its original location. The decision variable bound for each well target represents an acceptable range for movement of the well target within the grid. The same decision variable bound may be used for each well target or each well target may have its own. The well target generally represents a proposed well location that meets predefined constraints and property filters. 
         [0035]    In step  204 , an objective function is defined for the well location. The objective function, for example, may include an objective representing an optimal position of the well location based on an economic metric or a production metric. Exemplary economic and production metrics may include maximum net present value (NPV), minimum water production, maximum oil recovery, minimum capital cost, minimum risk, and maximum rate of return, for example. 
         [0036]    In step  206 , an on/off variable for each perforation interval previously computed is initialized. The on/off variable is simply a decision variable representing whether the perforation interval, which may contain a well target, is on or off based upon the results of step  227 . The on/off variable is preferably on for the initialization. 
         [0037]    In step  208 , stopping criteria are defined. Stopping criteria, for example, may include factors or events such as: i) maximum iterations of the method  200 ; ii) target NPV or oil recovery achieved; iii) global optimality determined; and iv) exhaustion of all combinations of discrete variables. Preferably, the stopping criteria include a maximum number of iterations for the method  200 . 
         [0038]    In step  210 , a constraint for each well target is defined and a constraint for a subsurface well plan is defined. The subsurface well plan constraint may include a well geometry constraint, a well type constraint or a drilling cost constraint. The well geometry constraint represents one of maximum well reach, maximum turn rate or dogleg severity. The well type constraint represents one of horizontal, slanted, multilateral, multi target, single target, producer or injector. The well target constraint may include, for example, a minimum or maximum spacing for each well target and the maximum number of well targets. 
         [0039]    In step  212 , i, j, k coordinates for each well target are defined using the constraints defined in step  210  and one or more sets of property filters. In other words, the coordinates for each well target are defined subject to the well target constraint and the one or more sets of property filters. The one or more sets of property filters may include, for example, a pore volume as illustrated by the image in  FIG. 4 . In  FIG. 4 , the image includes a display  400  illustrating a three-dimensional grid comprising multiple grid elements. Each grid element includes coordinates. For example, grid element  402  includes coordinates 36(i), 28(j), and 1(k) according to the plot  404 . The plot  404  may also be used to display a pore-volume property-filter value for the grid element  402 . The property filter therefore, includes property values, which are assigned to each grid element in  FIG. 4 . Different property values are distinguished in the three-dimensional grid by different shades of gray. Faults  406 ,  408 ,  410 ,  412  and  414 , for example, are identified on the three-dimensional grid. Each property filter therefore, limits the possible well target position or location as illustrated by the image in  FIG. 5 . In  FIG. 5 , the image includes a display  500  illustrating the same three-dimensional grid, property filter(s) and faults illustrated in  FIG. 4 . In addition, well targets  502 - 546  are illustrated in positions and locations limited by the property filter(s) described in reference to  FIG. 4 . 
         [0040]    In step  214 , the method  200  determines whether there is an initial iteration. If the method  200  is in an initial iteration, then the method  200  proceeds to step  218 . If the method  200  is not in an initial iteration, then the method  200  proceeds to step  216 . 
         [0041]    In step  216 , delta_i, delta_j and delta_k coordinates for each well target are added to the original i, j, k coordinates for each well target using techniques well known in the art. In other words, the updated coordinates for each well target in step  230  are added to the original coordinates for each respective well target. In this manner, each well target may be repositioned based upon its updated coordinates. 
         [0042]    In step  218 , a subsurface well plan is computed for each well location using techniques well known in the art, which connects each well target that satisfies the subsurface well plan constraint as illustrated by the image in  FIG. 6 . Each well target that does not satisfy the subsurface well plan constraint is discarded. In  FIG. 6 , the image includes a display  600  illustrating two subsurface well plans as exemplary slanted wells. One well plan includes well bores  602 ,  604 ,  606 ,  608 ,  610 ,  612 ,  614 ,  616 ,  618 ,  620 ,  624  and  646 , which correspond with respective well targets. Another well plan includes well bores  626 ,  628 ,  630 ,  632 ,  634 ,  636 ,  638 ,  640 ,  642  and  644 , which also correspond with respective well targets. The well targets, three-dimensional grid, faults and property filter(s) illustrated in  FIG. 6  are the same as the well targets, three-dimensional grid, faults and property filter(s) illustrated in  FIG. 5 . AssetPlanner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0043]    In step  220 , profile data for each subsurface well plan are computed using techniques well known in the art. The profile data may include, for example, data representing pipe and tubing connections and trajectories from subsurface locations (e.g. illustrated in  FIG. 6 ) to surface connections. Network Planner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0044]    In step  222 , each well perforation is computed using techniques well known in the art as illustrated by the image in  FIG. 7 . A well perforation is computed for each wellbore associated with a well target, based on the one or more sets of property filters. In  FIG. 7 , the image includes a display  700  illustrating the same well plans, well targets, three-dimensional grid, faults and property filter(s) illustrated in  FIG. 6 . In addition, one well plan includes well perforations  710 ,  712  and  714 , which are positioned on each corresponding wellbore  610 ,  612  and  614  based on the one or more sets of property filters. Likewise, the other well plan includes well perforations  720 ,  726 ,  728  and  734 , which are positioned on each corresponding wellbore  620 ,  626 ,  628  and  634  based on the one more sets of property filters. Thus, each property filter limits the possible position or location of each well perforation. DataStudio™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0045]    In step  224 , production is simulated using techniques well known in the art, which is based on the well perforation(s), each subsurface well plan and the corresponding profile data. In this manner, dynamic production criteria are simulated, which represent simulated production data. Nexus®, which is illustrated in  FIG. 1 , or VIP® may be used to execute this step in a computer implemented method. 
         [0046]    In step  226 , the objective function is computed using techniques well known in the art, which is based on data from the simulated production. An excel spreadsheet or any other well known economics calculator may be used to execute this step in a computer implemented method. 
         [0047]    In step  227 , the last computed objective function is compared with each previously computed objective function using techniques well known in the art to determine the best computed objective function. If the method  200  is in an initial iteration, then the best computed objective function is the last computed objective function. Any well known optimizer algorithm may be used to execute this step in a computer implemented method. 
         [0048]    In step  228 , the method  200  determines whether the stopping criteria are met. If the stopping criteria are met, then the method  200  proceeds to step  232 . If the stopping criteria are not met, then the method  200  proceeds to step  230 . 
         [0049]    In step  230 , delta_i, delta_j and delta_k are updated for each well target, subject to the decision variable bound(s), by using techniques well known in the art and the best computed objective function from step  227 . In addition, the on/off variable is updated in the same manner using techniques well known in the art and the best computed objective function from step  227 . Any well known optimizer may be used to execute this step in a computer implemented method. After completion of step  230 , the method  200  returns to step  214  and the method  200  iteratively proceeds through steps  216 - 228  until the stopping criteria are met. 
         [0050]    In step  232 , each well plan is displayed in the form generally illustrated in  FIG. 7 . The well plan displayed in step  232  therefore, may include the subsurface well plan and corresponding profile data. 
         [0051]    Referring now to  FIG. 3A , a flow diagram illustrates another embodiment of a method  300 A for implementing the present invention. Steps  302 A- 308 A are associated with the DMS™ Model and steps  310 A- 332 A are associated with the DMS™ Execution. The DMS™ Model and the DMS™ Execution (steps  302 A- 332 A) may therefore, be processed in a computer-implemented method by the Well Planning Module illustrated in  FIG. 1 . Steps  302 A- 312 A may be implemented as input for the Well Planning Module using the client interface illustrated in  FIG. 1 . 
         [0052]    In step  302 A, a decision variable bound is defined for each well target as movement in x, y, z space from the well target&#39;s original location. In other words, the decision variable bound is defined for each well target based on movement of the well target from its original location. The decision variable bound for each well target represents an acceptable range for movement of the well target within the grid. The same decision variable bound may be used for each well target or each well target may have its own. The well target generally represents a proposed well location that meets predefined constraints and property filters. 
         [0053]    In step  304 A, an objective function is defined for the well location. The objective function, for example, may include an objective representing an optimal position of the well location based on an economic metric or a production metric. Exemplary economic and production metrics may include maximum net present value (NPV), minimum water production, maximum oil recovery, minimum capital cost, minimum risk, and maximum rate of return, for example. 
         [0054]    In step  306 A, an on/off variable for each perforation interval previously computed is initialized. The on/off variable is simply a decision variable representing whether the perforation interval, which may contain a well target, is on or off based upon the results of step  327 A. The on/off variable is preferably on for the initialization. 
         [0055]    In step  308 A, stopping criteria are defined. Stopping criteria, for example, may include factors or events such as: i) maximum iterations of the method  300 A; ii) target NPV or oil recovery achieved; iii) global optimality determined; and iv) exhaustion of all combinations of discrete variables. Preferably, the stopping criteria include a maximum number of iterations for the method  300 A. 
         [0056]    In step  310 A, a constraint for each well target is defined and a constraint for a subsurface well plan is defined. The subsurface well plan constraint may include a well geometry constraint, a well type constraint or a drilling cost constraint. The well geometry constraint represents one of maximum well reach, maximum turn rate or dogleg severity. The well type constraint represents one of horizontal, slanted, multilateral, multi target, single target, producer or injector. The well target constraint may include, for example, a minimum or maximum spacing for each well target and the maximum number of well targets. 
         [0057]    In step  312 A, x, y, z coordinates for each well target are defined using the constraints defined in step  310 A and one or more sets of property filters. In other words, the coordinates for each well target are defined subject to the well target constraint and the one or more sets of property filters. The one or more sets of property filters may include, for example, a pore volume. 
         [0058]    In step  314 A, the method  300 A determines whether there is an initial iteration. If the method  300 A is in an initial iteration, then the method  300 A proceeds to step  318 A. If the method  300 A is not in an initial iteration, then the method  300 A proceeds to step  316 A. 
         [0059]    In step  316 A, delta_x, delta_y and delta_z coordinates for each well target are added to the original x, y, z coordinates for each well target using techniques well known in the art. In other words, the updated coordinates for each well target in step  330 A are added to the original coordinates for each respective well target. In this manner, each well target may be repositioned based upon its updated coordinates. 
         [0060]    In step  318 A, a subsurface well plan is computed for each well location using techniques well known in the art, which connects each well target that satisfies the subsurface well plan constraint. Each well target that does not satisfy the subsurface well plan constraint is discarded. AssetPlanner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0061]    In step  320 A, profile data for each subsurface well plan are computed using techniques well known in the art. The profile data may include, for example, data representing pipe and tubing connections and trajectories from subsurface locations to surface connections. Network Planner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0062]    In step  322 A, each well perforation is computed using techniques well known in the art. A well perforation is computed for each wellbore associated with a well target, based on the one or more sets of property filters. Thus, each property filter limits the possible position or location of each well perforation. DataStudio™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0063]    In step  324 A, production is simulated using techniques well known in the art, which is based on the well perforation(s), each subsurface well plan and the corresponding profile data. In this manner, dynamic production criteria are simulated, which represent simulated production data. Nexus®, which is illustrated in  FIG. 1 , or VIP® may be used to execute this step in a computer implemented method. 
         [0064]    In step  326 A, the objective function is computed using techniques well known in the art, which is based on data from the simulated production. An excel spreadsheet or any other well known economics calculator may be used to execute this step in a computer implemented method. 
         [0065]    In step  327 A, the last computed objective function is compared with each previously computed objective function using techniques well known in the art to determine the best computed objective function. If the method  300 A is in an initial iteration, then the best computed objective function is the last computed objective function. Any well known optimizer algorithm may be used to execute this step in a computer implemented method. 
         [0066]    In step  328 A, the method  300 A determines whether the stopping criteria are met. If the stopping criteria are met, then the method  300 A proceeds to step  332 A. If the stopping criteria are not met, then the method  300 A proceeds to step  330 A. 
         [0067]    In step  330 A, delta_x, delta_y and delta_z are updated for each well target, subject to the decision variable bound(s), by using techniques well known in the art and the best computed objective function from step  327 A. In addition, the on/off variable is updated in the same manner using techniques well known in the art and the best computed objective function from step  327 A. Any well known optimizer may be used to execute this step in a computer implemented method. After completion of step  330 A, the method  300 A returns to step  314 A and the method  300 A iteratively proceeds through steps  316 A- 328 A until the stopping criteria are met. 
         [0068]    In step  332 A, each well plan is displayed. The well plan displayed in step  332 A therefore, may include the subsurface well plan and corresponding profile data. 
         [0069]    Referring now to  FIG. 3B , a flow diagram illustrates another embodiment of a method  300 B for implementing the present invention. Steps  302 B- 308 B are associated with the DMS™ Model and steps  310 B- 332 B are associated with the DMS™ Execution. The DMS™ Model and the DMS™ Execution (steps  302 B- 332 B) may therefore, be processed in a computer-implemented method by the Well Planning Module illustrated in  FIG. 1 . Steps  302 B- 312 B may be implemented as input for the Well Planning Module using the client interface illustrated in  FIG. 1 . 
         [0070]    In step  302 B, a decision variable bound is defined for each well target as movement in x, y, z space from the well target&#39;s original location. In other words, the decision variable bound is defined for each well target based on movement of the well target from its original location. The decision variable bound for each well target represents an acceptable range for movement of the well target within the grid. The same decision variable bound may be used for each well target or each well target may have its own. The well target generally represents a proposed well location that meets predefined constraints and property filters. 
         [0071]    In step  304 B, an objective function is defined for the well location. The objective function, for example, may include an objective representing an optimal position of the well location based on an economic metric or a production metric. Exemplary economic and production metrics may include maximum net present value (NPV), minimum water production, maximum oil recovery, minimum capital cost, minimum risk, and maximum rate of return, for example. 
         [0072]    In step  306 B, an on/off variable for each perforation interval previously computed is initialized. The on/off variable is simply a decision variable representing whether the perforation interval, which may contain a well target, is on or off based upon the results of step  327 B. The on/off variable is preferably on for the initialization. 
         [0073]    In step  308 B, stopping criteria are defined. Stopping criteria, for example, may include factors or events such as: i) maximum iterations of the method  300 B; ii) target NPV or oil recovery achieved; iii) global optimality determined; and iv) exhaustion of all combinations of discrete variables. Preferably, the stopping criteria include a maximum number of iterations for the method  300 B. 
         [0074]    In step  310 B, a constraint for each well target is defined and a constraint for a subsurface well plan is defined. The subsurface well plan constraint may include a well geometry constraint, a well type constraint or a drilling cost constraint. The well geometry constraint represents one of maximum well reach, maximum turn rate or dogleg severity. The well type constraint represents one of horizontal, slanted, multilateral multi target, single target, producer or injector. The well target constraint may include, for example, a minimum or maximum spacing for each well target and the maximum number of well targets. 
         [0075]    In step  312 B, x, y, z coordinates for each well target are defined using the constraints defined in step  310 B and one or more sets of property filters. In other words, the coordinates for each well target are defined subject to the well target constraint and the one or more sets of property filters. The one or more sets of property filters may include, for example, a pore volume. 
         [0076]    In step  314 B, the method  300 B determines whether there is an initial iteration. If the method  300 B is in an initial iteration, then the method  300 B proceeds to step  318 B. If the method  300 B is not in an initial iteration, then the method  300 B proceeds to step  316 B. 
         [0077]    In step  316 B, each well target is moved by a respective updated distance and direction to updated coordinates for each well target using techniques well known in the art. In other words, the updated distance and direction for each well target in step  330 B are used to move each well target to new coordinates. In this manner, each well target may be repositioned based upon its updated coordinates. The direction may be measured using angles ∝ and β. 
         [0078]    In step  318 B, a subsurface well plan is computed for each well location using techniques well known in the art, which connects each well target that satisfies the subsurface well plan constraint. Each well target that does not satisfy the subsurface well plan constraint is discarded. AssetPlanner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0079]    In step  320 B, profile data for each subsurface well plan are computed using techniques well known in the art. The profile data may include, for example, data representing pipe and tubing connections and trajectories from subsurface locations to surface connections. Network Planner™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0080]    In step  322 B, each well perforation is computed using techniques well known in the art. A well perforation is computed for each wellbore associated with a well target, based on the one or more sets of property filters. Thus, each property filter limits the possible position or location of each well perforation. DataStudio™, which is illustrated in  FIG. 1 , may be used to execute this step in a computer implemented method. 
         [0081]    In step  324 B, production is simulated using techniques well known in the art, which is based on the well perforation(s), each subsurface well plan and the corresponding profile data. In this manner, dynamic production criteria are simulated, which represent simulated production data. Nexus®, which is illustrated in  FIG. 1 , or VIP® may be used to execute this step in a computer implemented method. 
         [0082]    In step  326 B, the objective function is computed using techniques well known in the art, which is based on data from the simulated production. An excel spreadsheet or any other well known economics calculator may be used to execute this step in a computer implemented method. 
         [0083]    In step  327 B, the last computed objective function is compared with each previously computed objective function using techniques well known in the art to determine the best computed objective function. If the method  300 B is in an initial iteration, then the best computed objective function is the last computed objective function. Any well known optimizer algorithm may be used to execute this step in a computer implemented method. 
         [0084]    In step  328 B, the method  300 B determines whether the stopping criteria are met. If the stopping criteria are met, then the method  300 B proceeds to step  332 B. If the stopping criteria are not met, then the method  300 B proceeds to step  330 B. 
         [0085]    In step  330 B, the coordinates for each well target are updated using a respective distance and direction for each well target, subject to the decision variable bound(s). The coordinates for each well target are updated using techniques well known in the art and the best computed objective function from step  327 B. In addition, the on/off variable is updated in the same manner using techniques well known in the art and the best computed objective function from step  327 B. Any well known optimizer may be used to execute this step in a computer implemented method. After completion of step  330 B, the method  300 B returns to step  314 B and the method  300 B iteratively proceeds through steps  316 B- 328 B until the stopping criteria are met. 
         [0086]    In step  332 B, each well plan is displayed. The well plan displayed in step  332 B therefore, may include the subsurface well plan and corresponding profile data. 
         [0087]    The present invention therefore: i) optimizes planning and positioning of well locations while simultaneously verifying well drillability and hazards; ii) computes updates to true well geometry/trajectory and tie-back connections to pipelines and delivery systems; and iii) locates optimal formation perforation zones with true production from the dynamic flow of oil, gas and water. The present invention overcomes the deficiencies of the conventional methods described herein by recomputing the wellbore geometry and honoring drilling constraints while planning each well location. The well plan therefore, is based on dynamic production criteria. 
         [0088]    While the present invention has been described in connection with presently preferred embodiments, it will be understood by those skilled in the art that it is not intended to limit the invention to those embodiments. It is therefore, contemplated that various alternative embodiments and modifications may be made to the disclosed embodiments without departing from the spirit and scope of the invention defined by the appended claims and equivalents thereof.

Summary:
Systems and methods for automatically and optimally planning multiple well locations within a reservoir simulator. The systems and methods use dynamic production criteria to create and optimize well target completion intervals and the associated well geometries for new wells dynamically, and directly within a reservoir simulator.