METHOD FOR SELECTING AN OFFSET WELL WHEN PLANNING A SECTION OF A WELL WITHIN A DRILLING PLANNING SYSTEM

Description

BACKGROUND

When planning a well section or other wellsite action, a user often looks at historical data to understand what operations were done to drill sections of other wells in the area or under similar circumstances. However, the historical data of these offset wells may include sections that have a different architecture or a different set of objectives when being drilled, or were drilled so long ago that their historical data may no longer be relevant when planning a modern well section. Relying on this type of historical data may lead to errors when planning the well which in turn leads to increased costs and time consumed when drilling the well.

The conventional offset well analysis approach has been to use the location of the planned or proposed well and then choosing the offset wells by using the geographical distance to the planned well. For decades, offset well analysis has been defined by “looking at nearby wells” and hoping that geographical similarity also meant similar wells.

What is needed is a method for the user of a drilling planning system to identify one or more offset wells based on their operational similarity to the planned or proposed well, namely by identifying the various types of sections for a number of offset wells in order for the well engineer to select from the offset wells the data that is the most relevant to his scope of work.

SUMMARY

The present disclosure provides a method for planning a well section. The method includes receiving data representing a plurality of offset wells, wherein the data includes an operational sequence performed therein for each of the offset wells, and wherein the operational sequence includes a sequence of drilling activities.

The method also includes generating a consensus sequence based upon the data, wherein the consensus sequence includes a proposed operational sequence. The operational sequence of each of the plurality of offset wells may be compared and then adding a drilling activity to the consensus sequence when the drilling activity to be added is present within in a predetermined amount of the offset wells.

The method further includes calculating a dissimilarity score between the consensus sequence and the operational sequence for each of the offset wells. A dissimilarity score may also be calculated between each operational sequence for each of the offset wells. The dissimilarity score may be calculated by determining if there are any mismatches between the consensus sequence and each of the operational sequences corresponding to the offset wells, assigning a penalty value for every determined mismatch between the consensus sequence and each of the operational sequences corresponding to the offset wells, and then calculating a total penalty value for each of the offset wells.

The method also includes selecting the operational sequence associated with a lowest dissimilarity score to use in the well section.

The method further includes identifying any offset well as a possible outlier based on the dissimilarity scores and then removing the operational sequence corresponding to any offset well identified as an outlier from the dissimilarity scores.

The method further includes displaying the dissimilarity scores between the consensus sequence and operational sequences of the offset wells. The dissimilarity score of each offset well relative to the consensus well may be displayed within a matrix, specifically on a display that is associated with a user.

The method further includes performing the operational sequence associated with the lowest dissimilarity score in the well section by generating or transmitting a signal that instructs or causes an action to occur, wherein the action comprises a physical action, and wherein the physical action comprises drilling to a depth of the well section, conducting a flow check, performing a pressure test, pulling out a casing to a depth of the well section, running a tubing to a depth of the well section, installing a wellhead, or a combination thereof.

It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and/or claimed below. Accordingly, this summary is not intended to be limiting.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

FIG. 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

FIG. 2 illustrates a flowchart of a drilling planning system implementing a graphical interface of the present disclosure, according to an embodiment.

FIGS. 3A-3D illustrate a graphical interface used to compare offset wells in order to generate a consensus sequence, according to an embodiment.

FIG. 4 illustrates a matrix displaying dissimilarity scores between a consensus sequence and a plurality of offset wells, according to an embodiment.

FIG. 5 illustrates a flowchart of a method for planning a well section, according to an embodiment.

FIG. 6 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Further, as used herein, the term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures, methods, techniques, and workflows disclosed herein may be combined and/or the order of some operations may be changed.

System Overview

FIG. 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

In the example of FIG. 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well/logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis/visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 may include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT®.NET® framework (Redmond, Washington), which provides a set of extensible object classes. In the .NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes maybe used to instantiate object instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

In the example of FIG. 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of FIG. 1, the analysis/visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc.).

In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implemented as add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

FIG. 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software may include a framework for model building and visualization.

As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

In the example of FIG. 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications may display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

As an example, the domain objects 182 may include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

In the example of FIG. 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, a user may store a project. At a later time, the project maybe accessed and restored using the model simulation layer 180, which may recreate instances of the relevant domain objects.

In the example of FIG. 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of a workflow. In such an example, the workflow editor may provide for selection of one or more pre-defined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).

Method for Selecting an Offset Well when Planning a Section of a Well within a Drilling Planning System

The present disclosure provides a method to simplify the activities described within the historical records for a number of offset wells, aligns the operations using a genomics based approach, and then assigns penalties and rewards based on the presence or absence of those operations. The current method changes the definition of an offset well by computing the actual operational similarity between wells and providing a mechanism for the end user to select the offset wells that are the most relevant to their purpose using a similarity matrix.

According to certain embodiments, the current method is used to predict the most likely or most useful sequence of operations to construct a planned or proposed well section. The user may then select the offset wells that are most relevant to their scope of work and the method aligns the offset wells according to their respective operational sequences and proposes a consensus one which most closely resembles or matches the criteria of the planned well section. It should be understood that while a planned or proposed well section will be discussed, the method discussed herein may be used while planning other appropriate wellsite actions.

According to certain embodiments, a system as seen in FIG. 2 is provided which may be a drilling planning system 200. The drilling planning system 200 may be communicated to an information management system 202 in which daily drilling reports and other data associated with wellsite actions are received. According to certain embodiments, the drilling planning system 200 includes an alignment and similarity computation system 204 which is configured to align and compare selected offset wells as described in further detail below. A graphical interface 206 may be included which allows a user to select the data representing the offset well that is most similar to the planned well section as determined by the alignment and similarity computation system 204. Following selection of the offset well by the user, further planning of the well section may take place using a performance computing system 208 and an activity planning system 210 which may be included within the drilling planning system 200.

Generation of Consensus Sequence

According to certain embodiments, the alignment and similarity computation system 204 compares a plurality of offset wells to each other within a graphical interface 300 as seen in FIGS. 3A-3D. The user may select which specific offset wells to include within the graphical interface 300, for example by selecting the desired offset wells from a database of offset wells stored within the information management system 202, FIG. 2. The selected offset wells are then populated into the graphical interface 300. FIGS. 3A-3D illustrate four different offset wells disposed within the graphical interface 300, namely Well A 302a, Well B 302b, Well C 302c, and Well D 302d, however it is to be expressly understood that fewer or additional offset wells may be included, according to certain embodiments.

According to certain embodiments, each of the offset wells 302a-d includes an operational sequence 304a-d that is displayed as a column within the graphical interface 300. Each operational sequence 304a-d in turn may be formed by a sequence of events or drilling activities 306a-d that were performed to drill or otherwise form that particular offset well 302a-d, with the sequence of drilling activities 306a-d being listed vertically in the order that they were performed. Each offset well 302a-d may be disposed side by side or adjacent with one another so that their respective operational sequences 304a-d are also adjacently disposed as seen in FIGS. 3A-3D. In certain embodiments, the drilling activities 306a-d forming each of the operational sequences 304a-d are aligned with each other so that each drilling activity 306a-d is displayed within its own row within its respective operational sequence 304a-d. According to certain embodiments, an activity code or other numerical value may be assigned to each drilling activity 306a-d.

After populating the graphical interface 300 with the selected offset wells 302a-d, the alignment and similarity computation system 204 generates a consensus sequence 308 which includes a sequence of proposed drillings activities 310. According to certain embodiments, the consensus sequence 308 is generated by first determining when a drilling activity 306a-d is common to, or otherwise detected within, a predetermined amount of the offset wells 302a-d, and then adding that common drilling activity 306a-d to the consensus sequence 308 as a newly added proposed drilling activity 310.

For example, as seen in FIGS. 3A-3D, on the 11^th row of each of the operational sequences 304a-d corresponding to each of the offset wells 302a-d, a drilling activity 306a-d denoted as “Conduct flow check” 312 is present. Because the drilling activity “Conduct flow check” 312 is at the same position within the operational sequences 304a-d of each of the offset wells 302a-d, “Conduct flow check” is then added to the consensus sequence 308 as one of the proposed drilling activities 310, specifically at a position 314 within the consensus sequence 308 which matches or is consistent with its position within the operational sequences 304a-d corresponding to the offset wells 302a-d. According to certain embodiments, a drilling activity 306a-d is added to the consensus sequence 308 whenever the drilling activity 306a-d is found within a predetermined amount of the offset wells 302a-d, the predetermined amount being selected to retrieve drilling activities 306a-d that are common to most offset wells 302a-d. In certain embodiments, a drilling activity 306a-d is added to the consensus sequence 308 whenever the drilling activity 306a-d is found within at least 70% of the offset wells 302a-d being compared.

Alternatively, if a drilling activity 306a-d is not present within the operational sequences 304a-d of a predetermined amount of the offset wells 302a-d, the drilling activity 306a-d is not added to the consensus sequence 308. For example, in the 12^th row of the operational sequence 304a for Well A 302a, the drilling activity “Perform pressure test” 316 is present. However at the same position within the operational sequence 304b for Well B 302b and the operational sequence 304c for Well C 302c, the drilling activity “Recover wear bushing” 318 is present, while at that same position for operational sequence 304d for Well 302d, the drilling activity “Charge B.O.P. rams” 320 is listed. Because there is no common drilling activity 306a-d between the offset wells 302a-d at that position within their respective operational sequences 304a-d, no proposed drilling activity is added to the consensus sequence and instead a blank position 322 within the consensus sequence 308 is provided.

Calculation of Dissimilarity Score

The current method also provides a mechanism to view the operational similarity amongst of a number of offset wells 302. According to certain embodiments, the similarity between, for example, at least two operational sequences 304 corresponding to offset wells 302 is computed from the aligned sequence of drilling activities 306, by applying a +5 penalty whenever a difference is identified. For example, as seen in Table 1 below, a dissimilarity score between two offset wells, Well A and Well B, is calculated according to certain embodiments.

From Table 1 above, it may be seen that Well A has an operational sequence including Activity A at the first and fifth positions, Activity C at the third and sixth position, and no activities at either the second or fourth positions. Table 1 also illustrates that Well B has an operational sequence including Activity A at the first and fifth positions, Activity B at the second position, Activity C at the third position, and no activities at either the fourth or sixth positions.

Next the alignment and similarity computational system 204, FIG. 2 distributes or assigns a penalty score for each activity mismatch between the respective operational sequences of the offset wells. In the example seen in Table 1 above, activity mismatches are present at the second position where Well A does not have any activity listed while Well B lists Activity B, and at the sixth position where Well A lists Activity C but where Well B does not list any activity. The remaining positions along the respective operational sequences match and are therefore given no penalty. A cumulative penalty or dissimilarity score is then calculated by adding all the individual penalties. In the example of Table 1 above, because there are two mismatches between the operational sequences of Wells A and B, and each mismatch is given a penalty score of +5, the cumulative penalty or dissimilarity score between Well A and Well B is 10. It should be noted that the specific values given for penalty scores in Table 1 are meant to be for illustrative purposes only and that different or alternative scoring configurations or values may be used according to certain embodiments.

According to certain embodiments, the process of calculating a dissimilarity score is repeated amongst each of the selected offset wells 302a-d, FIGS. 3A-3D, and between each of the offset wells 302a-d and the consensus sequence 308, FIGS. 3A-3D. The calculated dissimilarity scores may then be displayed to the user within a matrix 400 as seen in FIG. 4 so that the user may see how similar each of the offset wells are to each other as well as to the generated consensus sequence in a single glance. For example, a consensus sequence and Wells A-G are listed on the x-axis while every selected offset well, namely Wells A-H, are listed on the y-axis. To see the dissimilarity score between two selected items, the user looks for the first selected item along the x-axis, and then follows the matrix 400 vertically until reaching the second selected item along the y-axis. For example, if the user wishes to see how similar the consensus sequence is to Well D, the user moves horizontally until reaching a first column representing the consensus sequence, and then vertically until reaching a fifth row representing Well D. Alternatively, if the user wishes to see how similar Well B is to Well F, the user moves horizontally until reaching a third column representing Well B, and then vertically until reaching a third row representing Well F.

Upon reaching the second selected item, that specific portion of the matrix 400 displays the relevant dissimilarity score. According to certain embodiments, the dissimilarity score is represented or illustrated by a color code 402, with a darker shade representing a low dissimilarity score and a lighter shade representing a high dissimilarity score. For example, when comparing the offset wells to the consensus sequence, the darkest segment corresponds to the comparison to Well A, thereby signifying that Well A is the most similar to the consensus sequence followed closely by Well C, while the lightest segment corresponds to Well H, thereby signifying that Well H is the most dissimilar or unlike the consensus sequence. According to certain embodiments, the user may view a specific dissimilarity score by selecting the desired segment of the matrix 400 which produces an information window 404 that in turn displays the two selected items and the dissimilarity score that the information window 404 corresponds to. For example, as seen in FIG. 4, the information window 404 displays that the consensus sequence and Well A have a dissimilarity score of “90”. It should be noted that the color code 402 seen FIG. 4 is meant to be for illustrative purposes only and that alternative or additional shades, colors, symbols, numerical values, or other indicators may be used to denote the relative dissimilarity scores between the consensus sequence and the offset wells, according to certain embodiments.

In a further embodiment, the user viewing the matrix 400 may easily identify any offset wells which may be potential outliers, allowing them to remove them from the matrix 400 and from any further consideration for the planned well section. For example, as seen in FIG. 4, the dissimilarity scores between Well H and the consensus sequence and remaining Wells A-G are uniformly high. More specifically, according to the color code 402, all the segments corresponding to Well H are very light in color relative to the remaining portions of the matrix 400, thereby suggesting that the operational sequence corresponding to Well H may be an outlier and therefore is likely not useful when planning a well section. The user may have the ability to then remove or delete any outlier offset wells from the matrix 400.

According to certain embodiments, after all the dissimilarity scores between the offset wells and the consensus sequence have been displayed and any potential outliers have been removed, the user will have a clear indication of which one of the displayed offset wells is the most similar to the generated consensus sequence and select it for future use. For example, the user may incorporate or implement the operational sequence corresponding to the selected offset well as a template or blueprint when planning a new well section, according to certain embodiments.

FIG. 5 illustrates a flowchart of a method 500 for planning a well section. According to certain embodiments, the method includes providing a plurality of offset wells, as at 502. The offset wells may be selected by a user, according to certain embodiments,

According to certain embodiments, the method includes generating a consensus sequence from the plurality of offset wells, as at 504. Each offset well may include a corresponding operational sequence, the operational sequence itself including a sequence of drilling activities. In certain embodiments, generating the consensus sequence includes comparing the operational sequence of each of the plurality of offset wells and then adding a drilling activity to the consensus sequence when the drilling activity to be added is present within in a predetermined amount of the offset wells.

Next, the method includes calculating a dissimilarity score between the consensus sequence and each operational sequence corresponding to the offset wells, as at 506. According to certain embodiments, calculating the dissimilarity score includes determining if there are any mismatches between the consensus sequence and each of the operational sequences corresponding to the offset wells, assigning a penalty value for every determined mismatch between the consensus sequence and each of the operational sequences corresponding to the offset wells, and then calculating a total penalty value for each of the offset wells.

The method further includes displaying the dissimilarity scores between the consensus sequence and operational sequences of the offset wells, as at 508. In certain embodiments, displaying the dissimilarity scores includes displaying the dissimilarity score of each offset well relative to the consensus well within a matrix and displaying the matrix on a display that is associated with a user.

In certain embodiments, the method includes identifying any offset well as a possible outlier based on the displayed dissimilarity scores, as at 510. In further embodiments, identifying any outliers also includes removing the operational sequence corresponding to any offset well from the displayed dissimilarity scores once it has been identified as an outlier.

In further embodiments, the method includes selecting an offset well with the lowest dissimilarity score to use as a proposed operational sequence in the well section, as at 512. According to certain embodiments, the selected offset well is displayed on the display, as at 514.

The method further includes performing a wellsite action in response to the selected offset well, as at 516. According to certain embodiments, performing a wellsite action includes generating or transmitting a signal that instructs or causes an action to occur, wherein the action comprises a physical action, and wherein the physical action comprises selecting where to drill a wellbore in the subsurface formation, drilling the wellbore, varying a trajectory of the wellbore, varying a weight or torque on a drill bit that is drilling the wellbore, varying a rate or concentration of a fluid being pumped into the wellbore, or a combination thereof.

Exemplary Computing System

In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 6 illustrates an example of such a computing system 600, in accordance with some embodiments. The computing system 600 may include a computer or computer system 601A, which may be an individual computer system 601A or an arrangement of distributed computer systems. The computer system 601A includes one or more analysis modules 602 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 602 executes independently, or in coordination with, one or more processors 604, which is (or are) connected to one or more storage media 606. The processor(s) 604 is (or are) also connected to a network interface 607 to allow the computer system 601A to communicate over a data network 609 with one or more additional computer systems and/or computing systems, such as 601B, 601C, and/or 601D (note that computer systems 601B, 601C and/or 601D may or may not share the same architecture as computer system 601A, and may be located in different physical locations, e.g., computer systems 601A and 601B may be located in a processing facility, while in communication with one or more computer systems such as 601C and/or 601D that are located in one or more data centers, and/or located in varying countries on different continents).

A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

The storage media 606 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 6 storage media 606 is depicted as within computer system 601A, in some embodiments, storage media 606 may be distributed within and/or across multiple internal and/or external enclosures of computing system 601A and/or additional computing systems. Storage media 606 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In some embodiments, computing system 600 contains one or more method execution module(s) 608. In the example of computing system 600, the computer system 601A includes the method execution module 608. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

It should be appreciated that computing system 600 is merely one example of a computing system, and that computing system 600 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of FIG. 6, and/or computing system 600 may have a different configuration or arrangement of the components depicted in FIG. 6. The various components shown in FIG. 6 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits.

Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAS, PLDs, or other appropriate devices. These modules, combinations of these modules, and/or their combination with general hardware are included within the scope of the present disclosure.

Computational interpretations, models, and/or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 600, FIG. 6), and/or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.