AUTOMATED OFFSET SELECTION METHOD FOR DOWHNOLE TOOL PERFORMANCE EVALUATION

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 63/590,018, filed on Oct. 13, 2023, which is incorporated by reference.

BACKGROUND

Evaluating the performance of a drill bit run versus “offset” runs has been done for a long time. This evaluation is performed within a “field run report” (FRR), where the bit/field/product engineer evaluates the test run versus offset runs of their choosing. These offset runs are selected based on multiple criteria-such as the geographic location, depths and formations drilled, BHA design, customer (operator, contractor, rig), and drilling parameters-attempting to find the most similar bit runs to compare against to make the performance evaluation of the bit as fair as possible. However, this process is done manually, and the final offsets selected are thus subjective. People are trained in various ways to select offset runs, and selection rules vary based on who trained the person or the geographic area where the person was trained. This performance evaluation approach may be applied to any drilling system component, such as rotary steerable tools, downhole motors, drilling fluids, etc.

SUMMARY

A method for selecting one or more offset runs to use for a performance evaluation at a wellsite is disclosed. The method includes determining that a drilling run is a valid subject run. The method also includes identifying potential offset runs based upon the subject run. The method also includes determining a score for each of the potential offset runs. Determining the score includes determining parameter scores of a plurality of parameters. The parameter scores are weighted such that some of the parameters have higher potential values than others of the parameters. Determining the score also includes determining a total score based upon the parameter scores. The parameters include a radial geographic distance between the subject run and the potential offset runs; an absolute value difference between depth in of the subject run and the potential offset runs; a difference between a run date of the subject run and the potential offset runs; an operator standard name of the subject run matching the potential offset runs; a rig type of the subject run matching the potential offset runs; a drilling rig of the subject run matching the potential offset runs; a drilling contractor of the subject run matching the potential offset runs; a downhole drive type of the subject run matching the potential offset runs; a run direction of the subject run matching the potential offset runs; a mud type of the subject run matching the potential offset runs; and a well target zone formation of the subject run matching the potential offset runs. The method also includes ranking the potential offset runs based upon the score of each of the potential offset runs. The method also includes identifying a subset of the potential offset runs based upon the ranking. The potential offset runs in the subset are the most similar to the subject drilling run. The method also includes comparing a drilling performance of the subject run against a statistic (e.g., median) of drilling performance of the subset of the potential offset runs. The comparison of the drilling performance includes a first graph showing a distance drilled versus an average rate of penetration (ROP). The method also includes determining an improvement score based upon the comparison of the drilling performance. The improvement score is positive in response to the subject run performing better than the statistic of the drilling performance. The improvement score is negative in response to the subject run performing worse than the statistic of the drilling performance. The method also includes comparing a plurality of groups of bit runs against one another based at least partially upon the improvement score. The comparison of the groups includes a second graph showing median distance drilled and ROP improvement scores for each of the groups of bit runs. The method also includes selecting one of the groups of bit runs based upon the comparison of the groups. The method also includes performing a wellsite action based upon the selected group. Performing the wellsite action includes drilling a wellbore using a drilling system component from the selected group.

A method for selecting potential offset drilling runs to automatically evaluate a drilling performance of a subject drilling run is also disclosed. The method includes identifying the potential offset drilling runs based upon the subject drilling run. The potential offset drilling runs are identified based upon a status of a drilling system component that is used to drill the subject drilling run. The method also includes determining a score for each of the potential offset drilling runs. The method also includes ranking the potential offset drilling runs based upon the score of each of the potential offset drilling runs. The method also includes identifying a subset of the potential offset drilling runs based upon the ranking. The potential offset drilling runs in the subset are most similar to the subject drilling run. The method also includes performing a plurality of first comparisons of the drilling performance of the subject drilling run against drilling performances of the subset. The method also includes selecting one of a plurality of drilling system components used in the subset based at least partially upon the first comparisons.

A computing system is also disclosed. The computing system includes one or more processors and a memory system. The memory system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include identifying potential offset drilling runs based upon a subject drilling run. The potential offset drilling runs are identified based upon a status of a drilling system component that is used to drill the subject drilling run. The operations also include determining a score for each of the potential offset drilling runs. The operations also include ranking the potential offset drilling runs based upon the score of each of the potential offset drilling runs. The operations also include identifying a subset of the potential offset drilling runs based upon the ranking. The potential offset drilling runs in the subset are most similar to the subject drilling run. The operations also include performing a plurality of first comparisons of a drilling performance of the subject drilling run against drilling performances of the subset. The operations also include selecting one of a plurality of drilling system components used in the subset based at least partially upon the first comparisons.

A non-transitory computer-readable medium is also disclosed. The medium stores instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include identifying potential offset drilling runs based upon a subject drilling run. The potential offset drilling runs are identified based upon a status of a drilling system component that is used to drill the subject drilling run. The operations also include determining a score for each of the potential offset drilling runs. The operations also include ranking the potential offset drilling runs based upon the score of each of the potential offset drilling runs. The operations also include identifying a subset of the potential offset drilling runs based upon the ranking. The potential offset drilling runs in the subset are most similar to the subject drilling run. The operations also include performing a plurality of first comparisons of a drilling performance of the subject drilling run against drilling performances of the subset. The operations also include selecting one of a plurality of drilling system components used in the subset based at least partially upon the first comparisons.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

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 graph showing weighting points for an offset quality rating score, according to an embodiment.

FIG. 3 illustrates a performance plot of subject run (e.g., the star) versus 10 total offset runs (5 from Company A bits and 5 from Company B bits), according to an embodiment.

FIG. 4 illustrates a graph showing the median rate of penetration (ROP) score percentage, according to an embodiment.

FIG. 5 illustrates a schematic view of an automated offset analyses process, according to an embodiment.

FIG. 6 illustrates a flowchart of a method for selecting one or more offset wells to use for a performance evaluation at a wellsite, according to an embodiment.

FIG. 7 illustrates a schematic view of a computing system for performing at least a portion of the method(s) 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 invention. However, it will be apparent to one of ordinary skill in the art that the invention 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.

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 can 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 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. 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 (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 110 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 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 (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. 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 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. 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 can 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 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. 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 can be accessed and restored using the model simulation layer 180, which can 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.).

Automated Offset Selection Method for Drilling Bit/Tool/Run Performance Evaluation

One of the objectives of the present disclosure, referred to as “auto-offsets” for brevity, is to select offset runs automatically and objectively for drilling performance analysis. This performance analysis may be within the context of the drill bit, the various drilling tools in the BHA (e.g., RSS tool, positive displacement motor, underreamer, etc.), the drilling fluid, or the entire drilling system. In terms of granularity, the performance evaluation can be done at the sub-day level, daily level, run level, section level, or well level. The current implementation of the present disclosure is focused on evaluating the performance of a drill bit at the drilling run level.

A bit run may be considered a test for each bit run. It may be determined if that test was fair and proper. Each bit test may be unique and have its own set of defined criteria which is called the application. The uniqueness is due to the natural variability that exists. The criteria the defines an application is:

• • 1. Specific set of operation parameters and drilling conditions (e.g., WOB, RPM, hydraulics, mud system, etc.)
  • 2. Particular drilling assembly (e.g., BHA, packed, pendulum, etc.)
  • 3. Specific location
  • 4. Specific formation interval (e.g., depth, lithology, properties, etc.)
  • 5. Particular customer (e.g., operation personnel and procedures, rig capabilities, etc.)

No two applications are exactly the same, and therefore, no bit runs or tests will be exactly the same. What a user tries to do when comparisons are made is to get close to an “apples to apples” comparison instead of an “apples to oranges” comparison. As there are no field applications that are exactly the same, the user may make comparisons where the applications are most similar. That is, where the bits are placed with similar bits run in similar and existing applications.

The present disclosure provides an algorithm which, for any bit run, can score, and ultimately select the most relevant offset runs considering many factors and weights which mimics how a skilled person would select offsets. A second algorithm quantifies the normalized performance “improvement score” of the bit run as compared to the most relevant offset runs. Various performance metrics may be considered, but the present implementation evaluates two-(1) total run drilled footage and (2) overall run rate-of-penetration (ROP). This allows high performance and low performing runs to be easily identified, which may be useful for marketing and selling high-performing bit designs. Finally, by aggregating the improvement scores together and evaluating the statistics of these scores, high-level performance evaluation can be quantified—such as the performance of one set of bit runs versus similar runs with other bits—across the globe, per geographic location, or over time.

The offset selection algorithm may include:

• • 1) Determine if a drilling run is a valid subject run.
  • 2) For a subject run, find the potential offset runs.
  • 3) Score and rank the potential offsets runs to find the (up to) 10 most similar offset runs.
  • 4) Evaluate the drilling performance (e.g., drilled feet and/or average ROP) of the subject run against the median drilling performance of the 10 most similar offset runs.

Subject Runs

Some of the DRS bit runs are subject runs, and some are not. An example set of subject runs meets the following criteria:

• • PDC Fixed Cutter (FP) and TCI/MT roller cone bits (RT & RM), from any bit manufacturer
  • The subject bit is not a “green” bit
    • A PDC bit is considered “green” when drilling time is less than 2 hours, or when drilling time is less than 7 hours and the bit's rig dull condition tooth grade is less than 2 out of 8 (i.e., 25% worn)
    • A roller cone bit is considered “green” when drilling time is less than 2 hours, or when drilling time is less than 7 hours and the bit's rig dull condition tooth grade less is than 5 out of 8 (i.e., 62.5% worn)
  • Run is a drilling (DRLG) run
  • Run has drilled footage
  • Run Date after Jan. 1, 2018
  • Well location
    • For US and Canada wells, the run's well has latitude and longitude recorded
    • For wells in all other countries, the run's well has country and field name

Potential Offset Runs (“Bit Vs Competitor” Analysis)

In an example, for each subject run, potential offset runs meet the following criteria:

• • Offset run bit is the same family (e.g., PDC, MT, or TCI) and same diameter as subject run bit
  • The offset bit is not a “green” bit
    • A PDC bit is considered “green” when drilling time is less than 2 hours, or when drilling time is less than 7 hours and the bit's rig dull condition tooth grade is less than 2 out of 8 (i.e., 25% worn)
    • A roller cone bit is considered “green” when drilling time is less than 2 hours, or when drilling time is less than 7 hours and the bit's rig dull condition tooth grade less is than 5 out of 8 (i.e., 62.5% worn)
  • Offset run has drilled footage
  • Location
    • Within 20 miles of the subject run (US/Canada) or within 30 miles of subject run (International)
    • If no latitude and longitude is available for the well, distance matches are performed by matching country and the first 6 characters of the field name. This is more common for international wells.
  • Depth in
    • Depth in of offset is within +/−1000 feet of subject run
  • Run date
    • Run date range is 3 years before and 1 year after the subject run

Potential Offset Runs (“Bit Vs Past” Analysis)

Potential offset runs selection criteria is the same as above for “bit vs competitor” analysis, except for the following changes:

• • Offset bit is in the same family (e.g., PDC, MT, or TCI), same size, and same manufacturer as subject bit
  • Run date range is 1.75 years (21 months) to 11 months before the target run

10 Best Offset Runs

FIG. 2 illustrates a graph showing weighting points for an offset quality rating score, according to an embodiment. If more than a predetermined number (e.g., 10) potential offset runs are found using the offset selection criteria, the predetermined number (e.g., 10) best offset runs are selected, based on “offset quality” rating. In an example, this rating may be based on:

• • Regular points (100 points total)
    • Location radial distance between offset run and subject run (60 points)
    • Absolute value of difference between depth in of offset run and subject run (30 points)
    • Difference between run date of offset run and subject run (10 points)
  • Bonus points are awarded when metadata from offset run matches that of subject run (100 points total)
    • Operator standard name (20 points)
    • Rig (20 points)
    • Same drilling contractor: Contractor standard name (10 points)
    • Same exact rig: Contractor name AND rig number (10 points)
    • Downhole drive type (20 points)
    • Run direction (15 points)
    • Mud type (15 points)
    • Well target zone formation (10 points)

FIG. 3 illustrates a performance plot of subject run (e.g., the star) versus 10 total offset runs (5 from Company A bits and 5 from Company B bits), according to an embodiment. Once the most relevant offsets are found, evaluating the performance of the subject run versus the offset runs can be performed by plotting the overall run ROP (x-axis) against the run total drilled footage (y-axis). In this way, the best performing runs are on the top right corner of the plot-meaning they drilled the most footage at the highest overall ROP. Such a chart can be generated within the DRS application by using the “One-Click Performance Tool.”

Performance Evaluation

The drilling performance (e.g., drilled feet and/or average ROP) of the subject run may be evaluated against the median drilling performance of the 10 best offset runs. In an example, “improvement” scores may be determined using the rules below:

• • If the performance of the subject run is better than the median performance of the 10 best offset runs:

Improvement ⁢ score = ( ( Subject ⁢ run ⁢ performance ) / ⁢ 
 ( Median ⁢ performance ⁢ of ⁢ 10 ⁢ best ⁢ offset ⁢ runs ) ) - 1

• • If the performance of the subject run is worse than the median performance of the 10 best offset runs:

Improvement ⁢ score = 1 - ( ( Median ⁢ performance ⁢ of ⁢ 10 ⁢ best ⁢ offset ⁢ runs ) / ( Subject ⁢ run ⁢ performance ) )

FIG. 4 illustrates a graph showing the median rate of penetration (ROP) score percentage, according to an embodiment. Improvement scores may be centered on zero. A positive improvement score means that the subject run performed better than the median of the 10 best offset runs. A negative improvement score means that the subject run performed worse than the median of the 10 best offset runs.

Once the improvement scores are determined, the statistics of these scores can be compared for various groups of bit runs. In the example below, 5 groups of bit runs are considered—bit runs from Companies A through E. The median ROP score of the group is plotted on the x-axis versus the median footage score on the y-axis. Again, the top right corner is the best overall performance. In this example, Company E has the best performance.

Further, the median of one sample set is being compared versus the median of another sample set, a mood's median test can be used to determine if the two medians are actually different. Put another way, is there a statistical significance to the difference in median value that we see from the two samples?

FIG. 5 illustrates a schematic view of an automated offset analyses process, according to an embodiment. The auto-offsets can be used to answer various business and engineering questions. At a high level, the auto-offsets can help support the product development process. For example, evaluating the performance of single run on a newly introduced bit design. Further, evaluating the overall performance of many runs of a new bit design. Thus, the present disclosure can be used to accelerate the product development process and bring new products to market more quickly.

FIG. 6 illustrates a flowchart of a method 600 for performing a performance evaluation at a wellsite, according to an embodiment. More particularly, the method 600 may select potential offset drilling runs to automatically evaluate a performance of a subject drilling run. An illustrative order of the method 600 is provided below; however, one or more portions of the method 600 may be performed in a different order, simultaneously, repeated, or omitted.

The method 600 may include determining that a subject drilling run is valid, as at 605. The subject drilling run may be determined to be valid by determining that the subject drilling run has drilled footage on a bit record from a well with a known geographic location. The subject drilling run may also or instead be determined to be valid by determining a status of a drilling system component that is used to drill the subject drilling run. The drilling system component may be or include a drill bit, a cutter for the drill bit, a drive system, a rotary steerable system (RSS), a motor, a drilling fluid, or a combination thereof. When the drilling system component is the drill bit, determining the status may include determining that the drill bit used to drill the subject drilling run is not a green drill bit. The green drilling bit has been used to drill for less than a predetermined amount of time and has less than a predetermined amount of damage. The predetermined amount of time is about 2 hours if the drill bit comprises a polycrystalline diamond cutter (PDC). The predetermined amount of time is about 7 hours and the predetermined amount of damage includes a bit dull grade that is less than 25% worn.

The method 600 may also include identifying potential offset drilling runs based upon the subject drilling run, as at 610. The potential offset drilling runs may be identified based upon the status of the drilling system component. The potential offset drilling runs may be identified based upon a determination that drilling system components used to drill the potential offset drilling runs are in a same family as the drilling system component used to drill the subject drilling run. In an example, the family may include PDC, mill tooth roller cone, or tungsten carbide insert (TCI) roller cone when the drilling system component is a drill bit. In another example, the family may include a push-the-bit or a point-the-bit when the drilling system component is the RSS. In yet another example, the family may include a water-based fluid or a non-aqueous-based fluid when the drilling system component is the drilling fluid. The potential offset drilling runs may also or instead be identified based upon a determination that the potential offset drilling runs are within a predetermined geographical distance of the subject drilling run. The potential offset drilling runs may be identified based upon a determination that the potential offset drilling runs have a starting depth within a predetermined depth of the subject drilling run.

The method 600 may also include determining a score for each of the potential offset drilling runs, as at 615. An example of this is shown in FIG. 2. Determining the score may include determining parameter scores of a plurality of parameters and/or determining a total score based upon the parameter scores. Determining the total score may include aggregating the parameter scores. The parameter scores may be weighted such that some of the parameters have higher potential values than others of the parameters. The parameters may be or include:

• • The geographic distance between the subject drilling run and the potential offset drilling runs. A value of the geographic distance may be from about 20% to about 40% of a total weight.
  • A difference between the depth of the subject drilling run and the potential offset drilling runs. A value of the difference may be from about 10% to about 20% of the total weight.
  • A difference between a run date of the subject drilling run and the potential offset drilling runs. A value of the difference may be from about 2% to about 8% of the total weight.
  • An operator name of the subject drilling run matching the potential offset drilling runs. A value of the operator name may be from about 5% to about 15% of the total weight.
  • A drilling rig used to drill the subject drilling run matching the potential offset drilling runs. A value of the drilling rig may be from about 5% to about 15% of the total weight.
  • A downhole drive type of the subject drilling run matching the potential offset drilling runs. A value of the downhole drive type may be from about 5% to about 15% of the total weight.
  • A run direction of the subject drilling run matching the potential offset drilling runs. A value of the run direction may be from about 5% to about 10% of the total weight.
  • A mud type of the subject drilling run matching the potential offset drilling runs. A value of the mud type may be from about 5% to about 10% of the total weight.
  • A well target zone formation of the subject drilling run matching the potential offset drilling runs. A value of the well target zone formation may be from about 2% to about 8% of the total weight.

The method 600 may also include ranking the potential offset drilling runs based upon the total score of each of the potential offset drilling runs, as at 620.

The method 600 may also include identifying a subset of the potential offset drilling runs based upon the ranking, as at 625. The potential offset drilling runs in the subset may be most similar to the subject drilling run. The potential offset drilling runs in the subset may also or instead be most similar to one another.

The method 600 may also include performing a plurality of first comparisons of a drilling performance of the subject drilling run against drilling performances of the subset, as at 630. The first comparisons may be of the drilling performance of the drilling system component used in the subject drilling run against the drilling performances of drilling system components used in the subset. The first comparisons may include a distance drilled versus an average rate of penetration (ROP), the bit dull grade (e.g., if the drilling system component is a drill bit), a percentage of the drilled footage spent steering the drill bit, non-productive time (NPT), or a combination thereof.

The method 600 may also include generating a first graph showing results of the first comparisons, as at 635. An example of this is shown in FIG. 3. More particularly, FIG. 3 is a graph of the results of one of the first comparisons, not all of the first comparisons. This is how a user may look at the performance of the subject run versus the performances of the automatically selected offset runs. The improvement scores may summarize/quantify this performance comparison.

The method 600 may also include determining a plurality of improvement scores, as at 640. Each of the improvement scores corresponds to one of the first comparisons. The improvement scores may be positive in response to statistics (e.g., medians) of the subject drilling run performing better than statistics (e.g., medians) of the drilling performances of the subset. The improvement scores may be negative in response to the statistics of the subject drilling run performing worse than the statistics of the drilling performances of the subset.

The method 600 may also include displaying the results of the first comparisons and/or the improvement scores, as at 645.

The method 600 may also include performing a second comparison of results of the first comparisons based upon the improvement scores, as at 650. The second comparison may include statistics (e.g., medians) of the improvement scores.

The method 600 may also include generating a second graph showing results of the second comparison, as at 655. An example of this is shown in FIG. 4.

The method 600 may also include selecting one of the drilling system components used in the subset based upon the second comparison, as at 660.

The method 600 may also include performing a wellsite action using the selected drilling system component, as at 665. The wellsite action may be or include generating and/or transmitting a signal (e.g., using a computing system) that instructs or causes a physical action to occur at a wellsite. The wellsite action may also or instead include performing the physical action at the wellsite. The physical action may be or include drilling a next drilling run using the selected drilling system component. In another example, the physical action may include selecting where to drill a wellbore, drilling the wellbore, varying a weight and/or torque on a drill bit that is drilling the wellbore, varying a drilling trajectory of the wellbore, varying a concentration and/or flow rate of a fluid pumped into the wellbore, or the like.

Exemplary Computing System

In some embodiments, the methods of the present disclosure may be executed by a computing system. FIG. 7 illustrates an example of such a computing system 700, in accordance with some embodiments. The computing system 700 may include a computer or computer system 701A, which may be an individual computer system 701A or an arrangement of distributed computer systems. The computer system 701A includes one or more analysis modules 702 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 702 executes independently, or in coordination with, one or more processors 704, which is (or are) connected to one or more storage media 706. The processor(s) 704 is (or are) also connected to a network interface 707 to allow the computer system 701A to communicate over a data network 709 with one or more additional computer systems and/or computing systems, such as 701B, 701C, and/or 701D (note that computer systems 701B, 701C and/or 701D may or may not share the same architecture as computer system 701A, and may be located in different physical locations, e.g., computer systems 701A and 701B may be located in a processing facility, while in communication with one or more computer systems such as 701C and/or 701D 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 706 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of FIG. 7 storage media 706 is depicted as within computer system 701A, in some embodiments, storage media 706 may be distributed within and/or across multiple internal and/or external enclosures of computing system 701A and/or additional computing systems. Storage media 706 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 700 contains one or more performance evaluation module(s) 708. It should be appreciated that computing system 700 is merely one example of a computing system, and that computing system 700 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of FIG. 7, and/or computing system 700 may have a different configuration or arrangement of the components depicted in FIG. 7. The various components shown in FIG. 7 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 700, FIG. 7), 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.