Patent Description:
Thus, OWA may assist users in identifying potential problem areas in the formation and/or the subject well, so that they can be addressed in the planning phase. OWA may also allow a user to identify past events on similar wells that might influence well design, equipment selection and schedule, identify beneficial practices from similar wells that should be continued, provide the information to conduct a risk analysis, establish a baseline measure performance for benchmarking, identify potential constraints and areas of opportunity, and/or validate new well design assumptions.

One challenge in OWA is identifying the wells that are likely to include helpful information, as the data set can contain vast numbers of wells, many of which are dissimilar from the subject well and thus unlikely to be of much assistance. The initial step for OWA is a selection of relevant offset wells with geometrical and geological similarity. Trajectory similarity analysis (geometrical type) in most of the cases is done considering existing wells from within the vicinity of planned well, through search and basic filtering by trajectory type, maximum inclination and hole depth. Accordingly, OWA often resolves to a time-consuming, manual process by which a user searches through and analyzes drilling reports, logs, downhole data, etc. of geographically close wells.

<CIT> describes an automated system for identifying an optimal re-drilling trajectory to reach a target using a previously drilled well. The system includes an input device for receiving information from a user and a server receives information from the input device. An automated re-drilling software program is provided for identifying an optimal well path to reach a target using a previously drilled well and performs several steps. A plurality of well paths for reaching the target is identified. The software automatically identifies a subset of the plurality of well paths that satisfy selected criteria, and at least one of the subset of well paths is designated as the optimal well path.

The present invention resides in a computer-implemented method for offset well analysis as defined in claim <NUM>, a computing system as defined in claim <NUM> and a non-transitory computer-readable media as defined in claim <NUM>.

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.

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

In the example of <FIG>, the management components <NUM> include a seismic data component <NUM>, an additional information component <NUM> (e.g., well/logging data), a processing component <NUM>, a simulation component <NUM>, an attribute component <NUM>, an analysis/visualization component <NUM> and a workflow component <NUM>. In operation, seismic data and other information provided per the components <NUM> and <NUM> may be input to the simulation component <NUM>.

In an example embodiment, the simulation component <NUM> may rely on entities <NUM>. Entities <NUM> may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system <NUM>, the entities <NUM> can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities <NUM> may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data <NUM> and other information <NUM>). 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 <NUM> 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. NET® framework, an object class encapsulates a module of reusable code and associated data structures. Object classes can be 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>, the simulation component <NUM> may process information to conform to one or more attributes specified by the attribute component <NUM>, which may include a library of attributes. Such processing may occur prior to input to the simulation component <NUM> (e.g., consider the processing component <NUM>). As an example, the simulation component <NUM> may perform operations on input information based on one or more attributes specified by the attribute component <NUM>. In an example embodiment, the simulation component <NUM> may construct one or more models of the geologic environment <NUM>, which may be relied on to simulate behavior of the geologic environment <NUM> (e.g., responsive to one or more acts, whether natural or artificial). In the example of <FIG>, the analysis/visualization component <NUM> may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component <NUM> may be input to one or more other workflows, as indicated by a workflow component <NUM>.

As an example, the simulation component <NUM> may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (Schlumberger Limited, Houston Texas), the INTERSECT™ reservoir simulator (Schlumberger Limited, 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 <NUM> may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (Schlumberger Limited, 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 can 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) can 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 <NUM> 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 (Schlumberger Limited, 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> also shows an example of a framework <NUM> that includes a model simulation layer <NUM> along with a framework services layer <NUM>, a framework core layer <NUM> and a modules layer <NUM>. The framework <NUM> may include the commercially available OCEAN® framework where the model simulation layer <NUM> 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 can 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>, the model simulation layer <NUM> may provide domain objects <NUM>, act as a data source <NUM>, provide for rendering <NUM> and provide for various user interfaces <NUM>. Rendering <NUM> may provide a graphical environment in which applications can display their data while the user interfaces <NUM> may provide a common look and feel for application user interface components.

As an example, the domain objects <NUM> can 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>, 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 <NUM> 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 can be accessed and restored using the model simulation layer <NUM>, which can recreate instances of the relevant domain objects.

In the example of <FIG>, the geologic environment <NUM> may include layers (e.g., stratification) that include a reservoir <NUM> and one or more other features such as the fault <NUM>-<NUM>, the geobody <NUM>-<NUM>, etc. As an example, the geologic environment <NUM> may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment <NUM> may include communication circuitry to receive and to transmit information with respect to one or more networks <NUM>. Such information may include information associated with downhole equipment <NUM>, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment <NUM> 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> shows a satellite in communication with the network <NUM> 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> also shows the geologic environment <NUM> as optionally including equipment <NUM> and <NUM> associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures <NUM>. 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 <NUM> and/or <NUM> 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 <NUM> 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.).

<FIG> illustrates a flowchart of a method <NUM> for offset well analysis, according to an embodiment. The method <NUM> employs the concept of "distance" between two wells. As the term is used herein, "distance" means the difference in shape and/or orientation of two wells, e.g., if they were considered to start at the same location (or shared another point in common), and not the physical, geographical distance between the drilling locations of two wells. The distance calculation is thus a measurement that represents a similarity between two wells.

The distance calculation allows for an automatic comparison of the offset wells to a subject well, thereby allowing for an automatic selection of the offset wells with the highest quantitative similarity (e.g., least distance) to be employed in an offset well analysis. As such, the identification of the wells, which was previously a manual process, is done automatically by the application of rules that define the similarity of the offset wells to the subject well. This may lead to the subjective, human-based approach being partially replaced with a more objective, repeatable process, completed at least in part by a computer. For example, the vast number of offset wells may be reduced based on the similarity value, allowing a human user to select from a manageable number of wells for further analysis. This may have various practical applications, including providing a display of the most-similar wells (e.g., shortest distance) and/or leading to changes in a well drilling plan. Further, the selection of the most-appropriate wells may increase the accuracy of the offset well analysis, and thus may lead to refinements in the subject well (e.g., trajectory, drilling parameters, etc.) that may avoid certain drilling risks, increase the rate of penetration, increase efficiency, or otherwise assist in the drilling process that might otherwise not have been realized.

Turning to the specific, illustrated embodiment of <FIG>, the method <NUM> includes receiving offset well data, as at <NUM>, and subject well data, as at <NUM>, as input. The offset well data may be data collected while drilling previous wells, whether geographically nearby or not. The offset well data may include various drilling parameters, wellbore trajectory, and may include observations, e.g., in the form of drilling logs, which may be linked to the depth of the offset well. The offset well data thus includes experiential data about wells that were previously drilled, e.g., what worked, what led to hazardous conditions, etc. In contrast, the subject well data may be a well plan for a well that has not yet been drilled or is partially drilled. The subject well data may specify similar characteristics as the offset well data, such as trajectory, drilling parameters, etc..

The method <NUM> may then include automatically determining a distance representing the similarity between the trajectory of the offset wells and the subject well, as at <NUM>. The automatic determination at <NUM> may be done by a computer processor, according to a rules-based algorithm for determining distance. To begin, the surface location (or another location) of the offset well and the subject well may be considered to coincide. The calculated distance may be Euclidian. In other embodiments, the distance may be a modified Hausdorff distance, as will be described below. Further, in some embodiments, two or more distances may be calculated, e.g., along all, a portion, or one or more segments of the offset wells and the subject well, and combined to define a composite distance measurement, which may be a straight combination/superposition, an average, a weighted average, or any other type of combination.

The method <NUM> may then proceed to selecting one or more of the offset wells based in part on the distance, as at <NUM>. For example, a threshold distance may be established, either predetermined, entered by a user, or otherwise determined, and any offset wells with a calculated distance that is lower than the threshold may be selected. In another embodiment, a number of wells with the lowest distance (highest similarity) may be determined, and then that number selected, e.g., from a ranked list of the offset wells. The selection of offset wells based on the distance may serve to reduce the number of offset wells that a user may choose from to a number that is more manageable to a human, for example, a dozen wells, rather than a thousand. The user may then further select from the wells, e.g., based on other factors and/or subjectively.

In some embodiments, the method <NUM> may include displaying a digital model of the selected offset wells and the subject well that visually depicts the similarity/distance, as at <NUM>. Such a digital display may assist in the offset well analysis by allowing for a manual selection of the similar wells, e.g., allowing for a user to discount wells with a similarity that becomes too attenuated. Further, the display may provide the user the ability to make a more subjective comparison of the well trajectories or a comparison of attributes not considered so far in the similarity metric used at the time. For example, some curvatures for a well that have multiple targets may not be considered in the metric but may remain relevant to some users (but not to other users). Thus, the visual display may provide an additional tool to allow a user to make a custom, potentially subjective/qualitative determination, while factoring in the similarity metric.

The method <NUM> may then proceed to conducting an offset well analysis using the subject well and the selected offset wells, as at <NUM>. The offset well analysis may be conducted in any suitable manner but may be based on the wells identified as having sufficient similarity at <NUM>. Accordingly, the result of the offset well analysis may inform the well/drilling plan of the subject well. As such, in some embodiments, one or more parameters or characteristics of the subject well may be adjusted, as at <NUM>, as a result of and according to the offset well analysis. For example, drilling parameters (e.g., weight on bit, rotation speed, mud weight, etc.), or geometric parameters (e.g., dog leg severity) may be adjusted based on risks identified in the offset wells, among various other changes that may be made.

<FIG> illustrates a flowchart of the process for determining the distance representing the similarity at <NUM> (hereinafter, "the process <NUM>"), according to an embodiment. The process <NUM> may include portioning a subject well into a plurality of subject well depth segments based on depth, as at <NUM>. Further, one of the offset wells from the offset well data may be selected, as at <NUM>.

In some embodiments, the selected offset well may be partitioned into a plurality of segments based on depth, whether in the sense of the physical length of the well from the surface or true vertical depth from the surface, as at <NUM>. These segments may then be compared to determine the distance between the wells. In some embodiments, all segments may be compared. In other embodiments, a depth of interest may be selected, and segments that are contained in that depth of interest may be used, and the others ignored.

<FIG> illustrates a plot of a subject well <NUM> and an offset well <NUM>, illustrating the portioning discussed above. In particular, as shown, the subject well <NUM> and the offset well <NUM> are considered to originate at the surface (depth value, represented on the vertical axis, is <NUM>) at a common point <NUM>, as the subject and offset wells <NUM>, <NUM> are considered to start at the same point on the surface. The trajectories of the wells <NUM>, <NUM> are divergent as extending downward and along different azimuths (rotated apart, as indicated) and different inclinations. For example, the offset well <NUM> may turn toward the negative x-axis, as will be described in greater detail below. Further, lines <NUM> (four are shown) conceptually demark segments (e.g., segments <NUM> and <NUM> are indicated) of the wells <NUM>, <NUM>. Segments <NUM>, <NUM> representing the same depth interval (e.g., between two of the same lines <NUM>) may be considered to correspond to one another.

Referring again to <FIG>, the process <NUM> may proceed to selecting a segment of the offset well and a corresponding segment of the subject well, as at <NUM>. For example, in <FIG>, the segments <NUM> and <NUM>, which are "corresponding" as defined above may be selected. The process <NUM> may then proceed to calculating one or more distances between the corresponding segments of the subject well and the offset well, as at <NUM>.

The distance calculation may proceed by calculating the Euclidian distance between the segments (again, either in the depth interval of interest, or along the entire well), which may yield an inclination and azimuth turn rate similarity. Calculating the Euclidian distance may proceed according to the basic distance formula: <MAT> where d is the distance, x<NUM> is the inclination of the subject well, x<NUM> is the inclination of the offset well, y<NUM> is the azimuth turn rate of the offset well, and y<NUM> is the azimuth turn rate of the subject well. It will be appreciated that weighting coefficients could be used to change the relative weight of the azimuth turn rate difference and the inclination difference.

The distance calculation may instead or additionally proceed using a modified Hausdorff distance. For example, this may allow for inclination and azimuth similarity and/or shape similarity to be quantified. In either case, three different distance measures are calculated, and then aggregated to arrive at the distance, which provides the similarity value. Further, polar coordinates and measured depth may be used for this calculation. In the case of similarity analysis for a defined interval of interest (rather than the entire well), polar coordinates of the start point of the analysis may be set to zero and the coordinates below may be shifted for the actual value of the starting point.

In addition, for calculating the shape similarity, the trajectory (or segment) is applied to evaluate the direction and calculate a distance measure to find a minimum value. Thus, shape similarity provides a search of offset wells with directional similarity, without taking into account exact values of azimuth.

<FIG> illustrates a basic example of calculating the modified Hausdorff distance, in this case, between two line segments. This calculation may be applied to wellbore trajectories in any one of several ways, e.g., on a segment-by-segment basis, or considering the wellbores as a whole, or in any other manner. Referring to the specific example of <FIG>, a first line segment Li may be defined between the points sj and ej, and may proceed at an angle θ, in relation to a second line segment Lj, which may extend between points si and ei.

The modified Hausdorff distance may be calculated as follows. First, a vertical distance d⊥ may be calculated, as follows: <MAT> <MAT> <MAT>.

Further, a horizontal distance d may be calculated as: <MAT> <MAT> <MAT>.

An angular distance dθ may also be calculated: <MAT> <MAT> where <MAT> <MAT> and <MAT> which yields <MAT>.

These three distances, vertical distance d⊥, horizontal distance d, and angular distance dθ, may then be combined into an aggregated distance measure which may represent a similarity value between the two segments, e.g., two corresponding segments of the wellbore. The aggregation may proceed using any desired operator, e.g., average, minimum, maximum, etc..

Returning to <FIG>, calculating distances between corresponding segments at <NUM> may be repeated until, as determined at <NUM>, no more segments are available (e.g., at all, or within the depth of interest), or the process <NUM> otherwise determines that no more distance calculations between segments of the offset well and the subject well are called for (e.g., if the distances exceed a certain threshold and it is apparent that the offset well is not sufficiently similar to the subject well so as to warrant continued consideration). In some embodiments, distance between segments may repeat until reaching a distal terminus of the subject well, e.g., in cases where the offset well goes deeper.

At this point, the process <NUM> may include determining a similarity value for the offset well based at least in part on the calculated distances between the corresponding segments, as at <NUM>. Because there are multiple segments and potentially multiple different ways to calculate the distance, the similarity value may be a composite of multiple distance values. These values may be combined in any suitable way to arrive at such a composite value, e.g., by total distance, average distance, weighted average, etc..

The process <NUM> may then determine whether to consider another offset well from the offset well data set, as at <NUM>. If no further wells are to be considered, the process <NUM> may end, and the method <NUM> may proceed to selecting the offset wells for well analysis at <NUM> (<FIG>). Otherwise, the process <NUM> may loop back to selecting another offset well at <NUM> and iterate through again.

As mentioned with reference to box <NUM> of <FIG>, the method <NUM> may include displaying a digital model of one or more of the offset wells (e.g., those selected based on relatively high similarity) and the subject well. <FIG> illustrates an example of a plot of such a visualization <NUM>. In the visualization <NUM>, three offset wells <NUM>, <NUM>, <NUM> are shown, facilitating a comparison between the three offset wells <NUM>, <NUM>, <NUM> and a subject well <NUM>. As mentioned above, such visualizations may enable a user to incorporate additional factors into the comparison of the offset wells.

<FIG> illustrates a comparison of inclination and measured depth. Here again, this visualization may allow a user to apply a more subjective approach to finding wellbore similarities. For example, the calculated similarity metric may be employed to winnow down the number of possible, similar wellbores, e.g., from thousands to dozens or fewer. Next, the wells or metrics thereof, may be displayed, e.g., as shown in <FIG> and <FIG>, and may allow a user to factor in other relevant considerations, as discussed above.

In some embodiments, the methods of the present disclosure may be executed by a computing system. <FIG> illustrates an example of such a computing system <NUM>, in accordance with some embodiments. The computing system <NUM> may include a computer or computer system 801A, which may be an individual computer system 801A or an arrangement of distributed computer systems. The computer system 801A includes one or more analysis modules <NUM> 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 <NUM> executes independently, or in coordination with, one or more processors <NUM>, which is (or are) connected to one or more storage media <NUM>. The processor(s) <NUM> is (or are) also connected to a network interface <NUM> to allow the computer system 801A to communicate over a data network <NUM> with one or more additional computer systems and/or computing systems, such as 801B, 801C, and/or 801D (note that computer systems 801B, 801C and/or 801D may or may not share the same architecture as computer system 801A, and may be located in different physical locations, e.g., computer systems 801A and 801B may be located in a processing facility, while in communication with one or more computer systems such as 801C and/or 801D 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 <NUM> may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of <FIG> storage media <NUM> is depicted as within computer system 801A, in some embodiments, storage media <NUM> may be distributed within and/or across multiple internal and/or external enclosures of computing system 801A and/or additional computing systems. Storage media <NUM> 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 <NUM> contains one or more offset well selection module(s) <NUM>. In the example of computing system <NUM>, computer system 801A includes the offset well selection module <NUM>. In some embodiments, a single offset well selection module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of offset well selection modules may be used to perform some aspects of methods herein.

It should be appreciated that computing system <NUM> is merely one example of a computing system, and that computing system <NUM> may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of <FIG>, and/or computing system <NUM> may have a different configuration or arrangement of the components depicted in <FIG>. The various components shown in <FIG> 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.

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 <NUM>, <FIG>), 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.

Claim 1:
A computer-implemented method for offset well analysis, comprising:
receiving (<NUM>) offset well data collected from an offset well (<NUM>), wherein the offset well data comprises data representing a trajectory of an offset well (<NUM>);
receiving (<NUM>) subject well data comprising a trajectory of at least a portion of a subject well (<NUM>);
partitioning (<NUM>) the trajectory of the offset well into a plurality of offset well segments;
partitioning (<NUM>) the trajectory of the subject well into a plurality of subject well segments;
determining (<NUM>; <NUM>) a distance between at least some of the plurality of offset well segments and at least some of the plurality of subject well segments;
selecting (<NUM>) the offset well based in part on the distance;
performing (<NUM>) an offset well analysis using the offset well and the subject well; and
outputting the results of the offset well analysis for use in refinements to drilling operations of the subject well.