Systems and methods for determining wear of a downhole tool

Abstract

A method of detecting wear of a downhole tool implemented in a main wellbore includes receiving deviated wellbore data for one or more deviated wellbores and determining, based on the deviated wellbore data, an expected downhole tool indicator for the downhole tool at one or more measured depths including a dynamic measured depth of the main wellbore. The method further includes receiving main wellbore data and determining, based on the main wellbore data, a main downhole tool indicator for the downhole tool at the dynamic measured depth in real time. The method further includes determining wear of the downhole tool based on comparing the main downhole tool indicator to the expected downhole tool indicator at the dynamic measured depth in real time.

Description

[0001] Cross-reference paragraphs

[0002] This application claims the benefit of U.S. nonprovisional application No. 18 / 505,231, filed November 9, 2023, entitled “SYSTEMS AND METHODS FOR DETERMININGWEAR OF DOWNHOLE TOOLS”, the disclosure of which is hereby incorporated herein by reference. Background Technology

[0003] Wells can be drilled into surface locations or the seabed for various exploration or extraction purposes. For example, wells can be drilled to access fluids stored in underground formations, such as liquid and gaseous hydrocarbons, and to extract the fluids from the formation. Wells used to produce or extract fluids can be formed in soil formations using drilling tools such as drill bits for drilling wells and reamers for enlarging the diameter of the well.

[0004] Tools implemented in downhole systems to form wellbores can wear down due to their interaction with and degradation of the subsurface formation. In many cases, identifying and monitoring the wear condition of downhole tools can be challenging, even extremely difficult. For example, downhole drill bits are often inaccessible because they are implemented thousands of feet below the Earth's surface. Wear on tools can lead to operational inefficiencies in downhole systems and irreparable tool damage. Therefore, systems and methods for determining and monitoring the wear of downhole tools can be beneficial. Summary of the Invention

[0005] In some embodiments, a method for detecting wear of a downhole tool implemented in a subject wellbore includes: receiving offset wellbore data of one or more offset wellbores; and determining, based on the offset wellbore data, an expected downhole tool index at one or more measurement depths including a dynamic measurement depth of the subject wellbore. The method further includes receiving subject wellbore data and, based on the subject wellbore data, determining, in real time, the subject downhole tool index at the dynamic measurement depth. The method further includes determining downhole tool wear by comparing the subject downhole tool index with the expected downhole tool index at the dynamic measurement depth in real time.

[0006] In some embodiments, a method for detecting wear of a downhole tool implemented in a main wellbore includes: receiving offset wellbore data from one or more offset wellbores; and determining, based on the offset wellbore data, expected downhole tool parameters of the downhole tool at a plurality of measurement depths including a dynamic measurement depth of the downhole tool. The method further includes receiving main wellbore data from the main wellbore and determining, based on the main wellbore data, main downhole tool parameters of the downhole tool at the plurality of measurement depths. The method further includes determining a cumulative wear index of the downhole tool at the plurality of measurement depths by comparing the main downhole tool with the expected downhole tool parameters.

[0007] In some embodiments, a method for detecting wear of a downhole tool implemented in a main wellbore includes: receiving offset wellbore data of one or more offset wellbores; and determining, based on the offset wellbore data, the expected formation stiffness at multiple measurement depths of the formation in which the one or more offset wellbores and the main wellbore are located. The method further includes receiving main wellbore data of the main wellbore and, based on the main wellbore data, determining the primary formation stiffness of the downhole tool at the multiple measurement depths. The method further includes comparing the primary formation stiffness with the expected formation stiffness to determine a formation stiffness ratio at each of the multiple measurement depths, and classifying the formation stiffness ratio based on one or more predetermined thresholds. The method further includes determining a cumulative wear index of the downhole tool based on the sum of the products of the downhole tool's revolutions and the normalized formation stiffness ratios at the multiple measurement depths.

[0008] This summary is provided to introduce the selection of concepts further described in the detailed embodiments. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help limit the scope of the claimed subject matter. Additional features and aspects of the embodiments of this disclosure will be set forth herein and will be apparent in part from the description, or may be learned by practicing these embodiments. Attached Figure Description

[0009] To describe how the above and other features of this disclosure can be obtained, a more specific description will be presented by reference to specific embodiments shown in the accompanying drawings. For better understanding, in the various drawings, the same elements are denoted by the same reference numerals. While some drawings may be schematic or exaggerated representations of concepts, at least some drawings are drawn to scale. It should be understood that the drawings depict some exemplary embodiments, and the embodiments will be described and explained with additional features and details using the drawings, in which:

[0010] Figure 1 These are examples of downhole systems according to at least one embodiment of the present disclosure;

[0011] Figure 2 An exemplary environment for implementing a wear detection system according to at least one embodiment of the present disclosure is shown;

[0012] Figure 3 Exemplary implementations of a wear detection system as described herein, according to at least one embodiment of the present disclosure, are shown;

[0013] Figure 4 Exemplary threshold values ​​for formation stiffness ratio according to at least one embodiment of the present disclosure are shown;

[0014] Figure 5 Exemplary thresholds for cumulative wear indicators as described herein are shown according to at least one embodiment of the present disclosure;

[0015] Figure 6 A report generated by a reporting engine according to at least one embodiment of this disclosure is shown;

[0016] Figure 7 A report generated by a reporting engine according to at least one embodiment of this disclosure is shown;

[0017] Figure 8 A report generated by a reporting engine according to at least one embodiment of this disclosure is shown;

[0018] Figure 9 A report generated by a reporting engine according to at least one embodiment of this disclosure is shown;

[0019] Figure 10 A method or series of actions for determining the wear of a downhole tool implemented in the main wellbore as described herein, according to at least one embodiment of the present disclosure, is illustrated.

[0020] Figure 11 A method or series of actions for determining the wear of a downhole tool implemented in the main wellbore as described herein, according to at least one embodiment of the present disclosure, is illustrated.

[0021] Figure 12 The present disclosure illustrates a method or series of actions according to at least one embodiment for determining the wear of a downhole tool performed in the main wellbore as described herein; and

[0022] Figure 13 It shows some components that may be included within a computer system. Detailed Implementation

[0023] This disclosure generally relates to systems and methods for determining wear of downhole tools. A computer-implemented wear detection system can receive data from a main wellbore and one or more offset wellbores that are similar to the main wellbore in one or more respects. Based on the offset wellbore data, the wear detection system can determine various tool parameters associated with the downhole tools implemented to form these offset wellbores. The wear detection system can then determine expected values ​​for the associated tool parameters for use in conjunction with the downhole tools dynamically used to drill the main wellbore. For example, the wear detection system can determine the corresponding main downhole tool parameters in real time and monitor them relative to expected values. Thus, deviations between actual and expected values ​​may indicate wear, dulling, or damage to the downhole tool due to material loss from wear, spalling, fracturing, etc.

[0024] Wear detection systems can also determine cumulative wear indices. Compared to tool indices, which provide comparisons of tool indices for individual time instances, cumulative wear indices correlate the wear level of a downhole tool with the number of revolutions performed by the downhole tool within a measured depth range under a given wear condition. In this way, cumulative wear indices represent the overall wear of the downhole tool based on the degree of deviation of the tool indices from expected values ​​throughout the entire depth of the wellbore.

[0025] Wear detection systems can generate one or more reports to provide a visual representation of any of these defined measures, and these reports can be presented via a graphical user interface on a user device. The reports can be real-time and / or updated to provide a real-time representation of one or more of the wear measures discussed herein.

[0026] As will be discussed in further detail below, this disclosure includes numerous practical applications having the features described herein, which provide benefits and / or solve problems associated with determining wear of downhole tools. This document discusses some example benefits in conjunction with various features and functionalities provided by wear detection systems implemented on one or more computing devices. It should be understood that the benefits explicitly discussed in relation to one or more embodiments described herein are provided by way of example and are not intended to exhaustively list all possible benefits of wear detection systems.

[0027] In many cases, assessing and detecting wear on downhole tools can be challenging. For example, these tools are often located deep within the wellbore, typically thousands of feet below the Earth's surface, making them difficult to access. The downhole environment is also extremely harsh; high temperatures, extreme pressures, and abrasive formations limit the availability of sensors or other electronic equipment specifically designed for detecting drill bit wear. Furthermore, even when these tools can be inspected and assessed, wear characterization is often performed manually by skilled and experienced personnel, introducing elements such as subjectivity, human error, fatigue, and inconsistencies. However, the techniques disclosed herein provide a variety of quantifiable and measurable metrics for assessing tool wear, thus providing an objective and verifiable method for determining whether downhole tools are dull and to what extent. In fact, this technique not only provides wear metrics for comparison with expected values ​​but also allows this information to be determined and presented factually, in real-time, and during drilling. Therefore, the wear detection system described herein facilitates real-time monitoring of downhole tool wear.

[0028] Furthermore, the techniques described herein can be performed without the need for specialized or dedicated tools and sensors for measuring wear metrics of downhole tools. Wear metrics are based on several measurements or data channels that can be easily and / or routinely collected by the downhole system, such as the downhole tool's drilling rate, bit pressure, rotational speed (RPM), torque, etc. Based on this information (for the main borehole and the offset borehole data used to determine expected values), the wear detection system can determine several wear indicators of the downhole tool and can present these indicators in a simple, intuitive, and accessible manner so that wear on the downhole tool can be identified. In practice, the underlying data for this technique can be downhole data measured directly at or near the downhole tool, or even estimated based solely on surface data where downhole data is unavailable. Therefore, wear monitoring systems can be easily and widely implemented in many downhole systems.

[0029] Furthermore, conventional techniques can determine that downhole tools are dull based on a decrease in downhole system productivity (e.g., reduced drilling rate, increased bit pressure, or other observations). However, low drilling rate drilling can be expensive due to inefficiency and prolonged drilling time. Drilling at increased bit pressure levels can lead to premature wear of downhole system components. Moreover, by the time such poor performance of the downhole system is identified, the drill bit may already be severely worn or damaged, wasting resources replacing bits that could otherwise be repaired or refurbished. By characterizing and monitoring drill bit wear in a quantifiable manner, wear detection systems can help prevent inefficient operation of downhole systems due to worn drill bits. In fact, by determining the level of drill bit wear, the drill bit can be removed and / or replaced before damage occurs after the repair point. In this way, wear detection systems can reduce costs and improve downhole system productivity.

[0030] Additional details about the system described herein will now be provided with reference to the illustrative drawings depicting exemplary embodiments. For example, Figure 1 An example of a downhole system 100 for drilling formation 101 to form a wellbore 102 is shown. The downhole system 100 includes a drilling rig 103 for rotating a drilling tool assembly 104 that extends downward into the wellbore 102. The drilling tool assembly 104 may include a drill string 105, a bottom hole assembly (“BHA”) 106, and a drill bit 110 attached to the downhole end of the drill string 105.

[0031] The drill string 105 may include several joints of the drill pipe 108 connected end-to-end via a tool joint 109. The drill string 105 transmits drilling fluid through a central bore and transmits rotational power from the drilling rig 103 to the BHA 106. In some embodiments, the drill string 105 further includes additional downhole drilling tools and / or components, such as subs, short joints, etc. The drill pipe 108 provides a hydraulic passage through which drilling fluid is pumped from the surface 111. The drilling fluid exits through nozzles, orifices, or other orifices of selected size in the drill bit 110 to cool the drill bit 110 and its cutting structures, and to remove drill cuttings from the wellbore 102 during drilling.

[0032] BHA 106 may include drill bit 110, other downhole drilling tools, or other components. Exemplary BHA 106 may include additional or other downhole drilling tools or components (e.g., connected between drill string 105 and drill bit 110). Examples of additional BHA components include drill collars, stabilizers, measurement-while-drilling (“MWD”) tools, logging-while-drilling (“LWD”) tools, downhole motors, reamers, segmented end mills, hydraulic disconnectors, slappers, vibration or damping tools, other components, or combinations thereof.

[0033] Typically, downhole system 100 may include other downhole drilling tools, components, and accessories, such as specialized valves (e.g., kerb plugs, blowout preventers, and safety valves). Additional components included in downhole system 100 may be considered part of drilling tool assembly 104, drill string 105, or BHA 106, depending on their location within downhole system 100.

[0034] Drill bit 110 in BHA 106 can be any type of drill bit suitable for degrading downhole materials. For example, drill bit 110 can be a drill bit suitable for drilling formation 101. Example types of drill bits used for drilling formations are stationary cutter or scraper bits. In other embodiments, drill bit 110 can be a milling cutter for removing downhole metal, composite materials, elastomers, other materials, or combinations thereof. For example, drill bit 110 can be used with a directional drilling tool to grind into the casing 107 that lines the wellbore 102. Drill bit 110 can also be a chip mill for grinding away tools, plugs, cement, other materials, or combinations thereof within the wellbore 102. The chips or other cuttings generated by using the mill can be lifted to the surface 111 or can be allowed to fall downhole. Drill bit 110 may include one or more cutting elements for degrading formation 101.

[0035] BHA 106 may also include a rotary steerable system (RSS). The RSS may include a directional drilling tool that alters the orientation of drill bit 110, thereby changing the trajectory of the wellbore. At least a portion of the RSS may maintain a geostationary position relative to an absolute reference frame (e.g., gravity, magnetic north, or true north, or one or more). Using measurements obtained from the geostationary position, the RSS can position drill bit 110, alter its path, and guide the directional drilling tool along its projected trajectory. The RSS can steer drill bit 110 according to or based on its trajectory. For example, a trajectory may be determined to guide drill bit 110 toward one or more subsurface targets, such as oil or gas reservoirs.

[0036] The downhole system 100 may include or be associated with one or more client devices 112, each client device 112 having a wear detection system 120 implemented thereon (e.g., implemented on one, several, or across multiple client devices 112). The wear detection system 120 facilitates the determination of wear on one or more downhole tools, such as the wear of a drill bit 110.

[0037] Figure 2 An example environment 200 for implementing a wear detection system 120 according to one or more embodiments described herein is shown. Figure 2 As shown, environment 200 includes one or more server devices 114. The server devices 114 may include one or more computing devices (e.g., processing units, data storage devices, etc.) organized in an architecture with various network interfaces for connecting to one or more client systems and providing data management and distribution across the one or more client systems. Figure 2As shown, server device 114 can be connected to one or more client devices 112 via network 116 and can communicate with one or more client devices 112 (directly or indirectly). Network 116 may include one or more networks and may use one or more communication platforms and / or technologies suitable for transmitting data. Network 116 may refer to any data link that enables the transmission of electronic data between devices in environment 200. Network 116 may refer to a hardwired network, a wireless network, or a combination of a hardwired network and a wireless network. In one or more embodiments, network 116 includes the Internet. Network 116 may be configured to facilitate communication between various computing devices via Wellfield Information Transmission Standard Markup Language (WITSML) or similar protocols or any other protocol or form of communication.

[0038] Client device 112 can refer to various types of computing devices. For example, one or more client devices 112 may include mobile devices such as mobile phones, smartphones, personal digital assistants (PDAs), tablet computers, laptop computers, or any other portable devices. Alternatively, client device 112 may include one or more non-mobile devices such as desktop computers, server devices, surface or downhole processors or computers (e.g., associated with sensors, systems, or functions of a downhole system) or other non-portable devices. In one or more embodiments, client device 112 includes a graphical user interface (GUI) (e.g., the screen of a mobile device). Alternatively, one or more of client devices 112 may be communicatively coupled (e.g., wired or wireless) to a display device having a graphical user interface for providing a display of system content. Server device 114 can similarly refer to various types of computing devices. Each device in environment 200 may include the following combinations Figure 13 The described features and / or functions.

[0039] like Figure 2 As shown, environment 200 may include a wear detection system 120 implemented on one or more computing devices. Wear detection system 120 may be implemented on one or more client devices 112, server devices 114, or combinations thereof. Additionally or alternatively, wear detection system 120 may be implemented across client devices 112 and / or server devices 114, such that different portions or components of wear detection system 120 are implemented on different computing devices within environment 200. In this way, environment 200 may be a cloud computing environment, and wear detection system 120 may be implemented across one or more devices within the cloud computing environment to leverage the processing power, memory capacity, connectivity, speed, etc., provided by such cloud computing environment to facilitate the features and functions described herein.

[0040] Figure 3An exemplary embodiment of a wear detection system 120 as described herein, according to at least one embodiment of the present disclosure, is shown.

[0041] The wear detection system 120 may include a data manager 122, a tool index manager 124, and a reporting engine 126. The wear detection system 120 may also include a data storage device 130 having main wellbore data 13, offset wellbore data 134, tool index data 136, and reporting data 138 stored thereon. While one or more embodiments described herein describe features and functions performed by specific components 122-126 of the wear detection system 120, it should be understood that in some examples, a specific feature described in conjunction with one component of the wear detection system 120 may be performed by one or more other components of the wear detection system 120.

[0042] As an example, one or more of the data receiving, collection, or storage features of data manager 122 may be delegated to other components of wear detection system 120. As another example, while data may be selected, aligned, filtered, and / or modified by data manager 122, in some cases, some or all of these features may be performed by tool indicator manager 124 (or other components of wear detection system 120). In practice, it will be understood that some or all of certain components may be combined into other components, and certain functions may be performed by one or more components 122-126 of wear detection system 120.

[0043] In addition, although Figure 1 For example, a wear detection system 120 implemented on a client device 112 of a downhole system is depicted; however, it should be understood that some or all of the features and functions of the wear detection system 120 may be implemented on or across multiple client devices 112 and / or server devices 114. For example, data may be input and / or received by a data manager 122 on (e.g., local) client devices, and one or more tool metrics may be determined by a tool metric manager 124 on one or more remote, server, or cloud devices. In practice, it should be understood that some or all of the specific components 122-128 may be implemented on or across multiple client devices 112 and / or server devices 114, including the individual functions of the specific components performed across multiple devices.

[0044] As described above, the wear detection system 120 includes a data manager 122. The data manager 122 can receive various types of data associated with the downhole system and can store the data in the data storage device 130. The data manager 122 can receive data from various sources, such as sensors, measuring tools, downhole tools, other (e.g., client) devices, user input, etc.

[0045] In some embodiments, data manager 122 receives main wellbore data 132. The main wellbore may be the wellbore of a downhole system associated with the wear detection system 120 as described herein. The main wellbore may be a wellbore in which (e.g., a primary) downhole tool is dynamically implemented, or a wellbore that is being dynamically drilled, lengthened, widened, or otherwise formed.

[0046] The main wellbore data 132 may include information associated with the main wellbore. For example, the main wellbore data 132 may indicate one or more of the following: rate of drilling (ROP), weight on bit (WOB), and rotational speed (RPM) of the main downhole tool implemented in the main wellbore. For example, the data manager 122 may receive the main wellbore data 132 from one or more downhole sensors and / or surface sensors. The main wellbore data 132 may indicate one or more measurement depths relative to one or more measurements of the main wellbore. The main wellbore data 132 may include any other data associated with the main wellbore and / or with the downhole system, such as torque, pump pressure, flow rate, etc. The main wellbore data 132 may include formation assessment data, directional drilling data, mud and fluid analysis data, pressure and temperature data, or any other type of data. The data manager 122 may store the main wellbore data 132 to the data storage device 130.

[0047] In some embodiments, some or all of the main wellbore data 132 are directly measured. For example, the data manager 122 may receive one or more of the ROP, WOB, and RPM of the main downhole tool based on a real-time downhole data channel that directly measures associated values. In some embodiments, some or all of the main wellbore data 132 are indirectly measured and / or calculated or estimated based on indirect (e.g., surface) measurements. For example, the RPM of the main downhole tool may be estimated based on motor curves of the mud motors of the downhole system from the data channel (such as flow rate and / or differential pressure of the mud motors measured at the surface). In another example, the downhole WOB may be estimated based on the surface WOB. In this way, the data manager 122 may receive the main wellbore data 132 in various ways. This can help accommodate downhole systems of different costs and / or complexities. For example, low-cost and / or less complex downhole systems may have fewer data channels and / or measuring equipment for downhole measurements and may be limited to surface measurements. As another example, higher-cost and / or more complex downhole systems can have more data channels and / or measuring devices for downhole measurements in addition to surface measurements. In this way, the data manager 122 can collect and / or estimate relevant data in multiple ways to facilitate the implementation of the wear detection system 120 within any downhole system.

[0048] In some embodiments, the data manager 122 receives at least some of the main borehole data 132 in real time. For example, the data manager 122 may communicate with one or more downhole or surface sensors and may receive main borehole data 132, such as ROP, WOB, and / or RPM, in real time during dynamic drilling of the main borehole. These real-time measurements may facilitate one or more of the dynamic and / or real-time functions of the wear detection system 120 as described herein.

[0049] In some embodiments, the data manager receives offset wellbore data 134 for one or more offset wellbores. For example, the offset wellbore data 134 may be associated with a global database for data on all known or available offset wellbores. In another example, the offset wellbore data 134 may be associated with a selection of offset wellbores similar to or related to the main wellbore as described herein.

[0050] Offset wellbore data 134 may include any data described above with respect to main wellbore data 132, but used for the offset wellbore. For example, offset wellbore data 134 may indicate the ROP, WOB, and RPM of the offset downhole tool implemented in the associated offset wellbore. Offset wellbore data 134 may indicate one or more measured depths of the offset wellbore relative to any data included in offset wellbore data 134. Offset wellbore data 134 may include any other data associated with the offset wellbore. Offset wellbore data 134 may be collected, measured, calculated, or otherwise received in any of the manner described above with respect to main wellbore data 132. Data manager 122 may store offset wellbore data 134 in data storage device 130.

[0051] In some embodiments, the data manager 122 separates some or all of the offset wellbore data 134 into specific runs. For example, in some cases, multiple runs of one or more offset downhole tools may be performed at or within a single wellbore or well site, such as drilling different portions of the wellbore, implementing different offset downhole tools within the wellbore, performing different downhole operations within the wellbore, or creating one or more sidetracks outside the wellbore. Therefore, the data manager 122 can separate the offset wellbore data 134 for a specific offset wellbore or well site to represent various runs entered into the offset wellbore by one or more offset downhole tools.

[0052] In some embodiments, the offset wellbore data 134 includes an indication of the type and / or degree of wear of the offset downhole tool (e.g., a drill bit) implemented in the associated offset wellbore. For example, the offset wellbore may be a drilled wellbore (or drilled to or beyond the depth of interest), and the associated offset downhole tool may have been removed or pulled to the surface for inspection. The offset wellbore data 134 may indicate a classification of the bit bluntness grade of the offset downhole tool, such as based on the IADC bluntness grade standard. For example, for an offset downhole tool, the offset wellbore data 134 may indicate one or more of an inner grade, an outer grade, and bluntness characteristics such as annularity or core removal. In this way, the offset wellbore data 134 may indicate the wear condition or status of the offset downhole tool associated with the corresponding offset wellbore.

[0053] In some embodiments, data manager 122 selects one or more offset wellbores (e.g., from a database or global collection of offset wellbores) to receive offset wellbore data 134 for those selected offset wellbores. For example, data manager 122 may identify one or more offset wellbores that have one or more similarities to the main wellbore. For example, similar offset wellbores may be wellbores that are geographically close to the main wellbore within the same basin, oil field, region, location, or otherwise. Similar offset wellbores may be wellbores that extend to the same or similar depth or depth range, drill into the same or similar formations, enter the same or similar reservoirs, follow the same or similar trajectory (or part of a trajectory), have one or more of the same or similar bends or doglegs, or otherwise exhibit the same or similar features or aspects as the main wellbore, and combinations thereof. Data manager 122 may accordingly receive offset wellbore data 134 for these selected similar offset wellbores so that the offset wellbore data 134 can be associated with the main wellbore data 132.

[0054] In some embodiments, the data manager 122 filters offset wellbore data 134 based on one or more offset wellbores. For example, the data manager 122 may separate or exclude some of the offset wellbore data 134 based on the classification of the bit bluntness level associated with the corresponding offset wellbore. For example, the data manager 122 may filter out offset wellbore data 134 associated with offset wellbores having inner and / or outer levels higher than one or more thresholds. The data manager 122 may filter out offset wellbore data 134 having one or more specific bluntness characteristics, such as annular or core-out.

[0055] As an example, in some cases, it may be advantageous to use the offset wellbore data 134 for wellbores not associated with severely passivated or damaged offset downhole tools (e.g., when removed from and inspected from the offset wellbore). Therefore, the data manager 122 can filter out the offset wellbore data 134 of offset wellbores having one or more of the following: (8) an inner grade of 4 or higher, (8) an outer grade of 4 or higher, annularity characteristics, and coreout characteristics, based on the IADC passivation grade criterion. Filtering out severely worn offset wellbore data 134 can be a minimum or worst-case constraint for implementing the wear detection techniques described herein. In some cases, such as when sufficient offset wellbore data 134 is available, the data manager 122 can filter out the offset wellbore data 134 in a more restrictive manner, such as filtering out the offset wellbore data 134 of wellbores associated with offset downhole tools exhibiting anything exceeding a slight wear level. For example, data manager 122 can filter wellbores based on one or more of any associated wear characteristics (e.g., annular or core-out) on an inner grade of 2 or higher, an outer grade of 2 or higher, or an IADC passivation grade criterion. In this way, data manager 122 can filter offset wellbore data 134 with one or more thresholds of the passivation grade criterion based on the availability of the offset wellbore data 134. This not only facilitates the selection of a sufficient amount of offset wellbore data 134 to implement the techniques described herein, but also facilitates the selection of the best possible data, for example, for optimal operation of offset downhole tools exhibiting less wear. This can help to determine with high confidence that the expected tool indices determined by the wear detection system 120 (as described herein) are an accurate representation of the characteristics represented by these indices and are not significantly affected or influenced by the passivation or wear state of the associated offset downhole tool.

[0056] In some embodiments, instead of eliminating some of the offset wellbore data 134 based on a bluntness rating criterion, the data manager 122 separates or classifies the offset wellbore data 134 based on a bluntness rating criterion. For example, the data manager 122 may divide the offset wellbore data 134 into a severely worn group and a non-severely worn group. In another example, the data manager 122 may divide the offset wellbore data 134 into a worn group and a no / slightly worn group. Classifying the offset wellbore data 134 in this way can facilitate one or more of the features described herein.

[0057] In some embodiments, data manager 122 receives formation data. Formation data may include information associated with formations traversed, drilled, or otherwise located within the main wellbore and / or one or more offset wellbores (e.g., main wellbore data 132 and / or offset wellbore data 134 may include formation data). For example, formation data may include information about the geological characteristics of rocks encountered during drilling the main wellbore and / or offset wellbore. Formation data may include data from gamma-ray sensors, resistivity sensors, porosity sensors, density sensors, acoustic sensors, calipers, core samples, or any other formation data. Formation data may indicate the boundaries of different subsurface formations, such as the top, bottom, and / or thickness of one or more formations. Formation data may indicate one or more measurement depths associated with any of the foregoing measurements and / or data. In this way, formation data may, for example, identify one or more formations of interest relative to main wellbore data 132 and / or offset wellbore data 134.

[0058] In some embodiments, the data manager 122 prepares and / or modifies any data it receives and / or accesses. For example, in some cases, wellbore data may be measured and / or recorded in the time domain. The data manager 122 may transform or depth-gated the data to the depth domain. In other words, the data manager 122 may modify the data to express it, for example, relative to a measurement depth rather than relative to time. Transforming data in this way can help conceptualize the data and / or tool metrics described herein, allowing the data to be considered relative to one or more measurement depths or ranges of measurement depths.

[0059] In some embodiments, data manager 122 aligns one or more instances of main borehole data 132 and / or offset borehole data 134. For example, data manager 122 may align some or all of the main borehole data 132 and / or offset borehole data 134 based on the measurement depth or measurement depth range (e.g., of interest). In another example, data manager 122 may align some or all of the main borehole data 132 and / or offset borehole data 134 based on the formation. For example, based on a main borehole and an offset borehole drilled or traversing the same formation, data manager 122 may align or correlate some or all of the main borehole data 132 measured within the formation with the offset borehole data 134 also measured within the same formation (of the corresponding offset borehole). Data manager 122 may align data based on the top of the formation. In some embodiments, this results in some or all of the measurement depths of the main borehole data 132 and / or offset borehole data 134 being misaligned at one or more locations. For example, formations may exhibit slopes or inclinations, allowing two or more wellbores to reach or drill into the formation at different measurement depths (e.g., the top of the formation). As described herein, it may be advantageous to correlate wellbore data of associated wellbores with respect to the formation (e.g., location within the formation) rather than strictly with respect to the measurement depth, and data manager 122 may accordingly align the data based on formation data. In some cases, formations may exhibit different thicknesses at one or more locations, allowing two or more wellbores to drill into or traverse the different thicknesses of the formation. In some embodiments, data manager 122 compresses and / or stretches some or all of the main wellbore data 132 and / or offset wellbore data 134 to account for thickness differences. This adjustment may be in addition to the data manager 122 aligning the data based on formation data (e.g., the depth of the data). In this way, data manager 122 may modify the main wellbore data 132 and / or offset wellbore data 134 to facilitate the correlation of data for any wellbore of interest based on the formation in which the wellbore is located (e.g., in contrast to depth).

[0060] In some embodiments, data manager 122 receives user input. Data manager 122 may receive user input, for example, via either client device 112 and / or server device 114. Any data described herein may be entered or enhanced via user input. For example, in some cases, some or all of the offset wellbore data may be received by data manager 122 as user input. User input may be received in association with one or more functions or features of wear detection system 120, such as selecting one or more offset wellbores for offset wellbore data 134, selecting one or more thresholds for classifying tool indicators, or a portion of any other features described herein.

[0061] As described above, the wear detection system 120 includes a tool index manager 124. The tool index manager 124 can identify and monitor one or more downhole tool indices to characterize and / or quantify the wear of the main downhole tool implemented in the main wellbore. Downhole tool indices can describe or represent one or more characteristics or features of the formation and / or the downhole tool used to degrade the associated wellbore.

[0062] In some embodiments, the tool index manager 124 determines one or more expected downhole tool indices (or expected values ​​of downhole tool indices) based on the offset borehole data 134. For example, as described below, the tool index manager 124 may determine the formation stiffness at one or more (or all) measurement depths for each offset borehole in the offset borehole data 134. The tool index manager 124 may determine the mean, median, percentile, or any other statistical calculation of the determined formation stiffness of the offset boreholes as the expected formation stiffness of the main borehole at one or more measurement depths (e.g., or formation-aligned measurement depths). According to at least one embodiment of this disclosure, the tool index manager 124 determines the expected formation stiffness as the median of the determined formation stiffness of the offset boreholes. In some embodiments, the tool index manager 124 determines a threshold range, such as a maximum and / or minimum formation stiffness, based on the determined formation stiffness of the offset boreholes. For example, the tool index manager 124 may determine the maximum or upper limit threshold as the 75th percentile (P75), the 90th percentile (P90), or any other percentile. In another example, the tool indicator manager 124 can determine a minimum or lower threshold as the 25th percentile (P25), the 10th percentile (P10), or any other percentile. The tool indicator manager can determine the threshold based on geographic location or application. The tool indicator manager 124 can determine the expected value of any one and any number of downhole tool indicators in this way. The tool indicator manager 124 can store any of this information as tool indicator data 136 in the data storage device 130.

[0063] In some embodiments, downhole tool parameters include formation stiffness K. Formation stiffness K can be a measure or estimate of the mechanical stiffness or hardness of a formation or its ability to resist deformation under applied loads. Formation stiffness can be expressed as Young's modulus or bulk modulus. Formation stiffness K can be a useful measure for understanding the interaction between the downhole tool and the formation, and can help assess the wear of the downhole tool as it traverses the formation.

[0064] The formation stiffness K can be determined by the following formula:

[0065]

[0066] WOB = Drilling Pressure

[0067] ROP = Drilling Rate

[0068] RPM = Downhole tool rotation speed or (revolutions per minute)

[0069] Formation stiffness K can be determined in any other way or according to any other formula or principle used to characterize formation stiffness K. Formation stiffness K is typically in the range of 0.1-5 Mlbf / in. The tool index manager 124 can determine the formation stiffness K of one or more wellbores and one or more (or all) depths of interest. WOB and / or RPM can be measured from downhole sensors or inferred from surface measurements. In some embodiments, measurements with higher confidence (e.g., direct or downhole measurements) are preferred.

[0070] In some embodiments, downhole tool metrics include mechanical specific energy (MSE). MSE can be a measure or estimate of the energy required per unit volume of rock required to degrade, break down, or otherwise remove rock units from a formation. MSE can provide insights into drilling efficiency and energy consumption during the drilling operation of downhole tools.

[0071] MSE can be determined using the following formula:

[0072]

[0073] WOB = Drilling Pressure

[0074] ROP = Drilling Rate

[0075] RPM = Downhole tool rotation speed or (revolutions per minute)

[0076] TOR = Drill bit torque

[0077] A bit =Area of ​​the drill bit

[0078] The MSE can be determined in any other way or according to any other formula or principle used to characterize the MSE. The tool index manager 124 can determine the MSE of one or more wellbores and one or more (or all) depths of interest.

[0079] In some embodiments, downhole tool parameters include bit aggressiveness μ. Bit aggressiveness μ can be a measure or estimate of the coefficient of friction between the downhole tool and the formation under given applied weight and torque.

[0080] The drill bit's attack power μ can be determined by the following formula:

[0081]

[0082] WOB = Drilling Pressure

[0083] TOR = Drill bit torque

[0084] D bit =Diameter of the drill bit

[0085] Drill bit aggression can be determined in any other way or according to any other formula or principle used to characterize drill bit aggression μ. The tool index manager 124 can determine the drill bit aggression μ for one or more wellbores and one or more (or all) depths of interest.

[0086] In some embodiments, the downhole tool metric includes the drilling rate per revolution (PPR). PPR can represent the distance the downhole tool travels through the formation per revolution.

[0087] PPR can be determined using the following formula:

[0088]

[0089] ROP = Drilling Rate

[0090] RPM = Downhole tool rotation speed or (revolutions per minute)

[0091] The PPR can be determined in any other way or according to any other formula or principle used to characterize the PPR. The tool index manager 124 can determine the PPR of one or more wellbores and one or more (or all) depths of interest.

[0092] In this way, the tool index manager 124 can identify one or more downhole tool indices, any other relevant indices or metrics, to characterize and / or quantify the wear of the relevant downhole tools.

[0093] In some embodiments, the tool index manager 124 determines and monitors one or more downhole tool indices of the main wellbore and / or the main downhole tools. For example, based on the main wellbore data 132, the tool index manager 124 can determine (e.g., dynamically and / or in real-time) the main downhole tool indices of the main wellbore. For example, the tool index manager 124 can determine the dynamic, current, or real-time formation stiffness (or other downhole tool indices) of the main wellbore based on the real-time main wellbore data 132. The tool index manager 124 can store the main downhole tool index information as tool index data 136 in the data storage device 130.

[0094] As described in further detail below, determining the primary downhole tool indices can help assess the wear condition of the downhole tools. Comparing the primary downhole tool indices with associated expected downhole tool indices and / or thresholds for expected downhole tool indices can help conceptualize the extent or degree of wear of the primary downhole tool based on the characteristics, aspects, or properties represented by the associated downhole tool indices. For example, the expected formation stiffness K can be determined based on various offset wellbores that are similar to the primary wellbore in one or more aspects. exp As mentioned above, offset borehole data can be filtered to remove offset borehole data associated with offset downhole tools exhibiting a certain degree of wear. Therefore, the expected formation stiffness K... exp The expected formation stiffness K can be determined with high precision, or in other words, with a high degree of confidence. exp It accurately represents the actual stiffness of the formation, unaffected by offsets in the base data or the blunting conditions of the downhole tools. The tool index manager 124 can determine the primary formation stiffness K in real time based on real-time primary wellbore data 132. subj The main wellbore data 132 and the offset wellbore data 134 can be aligned with the formation to achieve the expected formation stiffness K. exp This can represent the actual formation stiffness at the current location of the main downhole tool within the formation. By comparing the main formation stiffness K... subj and expected formation stiffness K exp This allows for the identification of the wear condition of the main downhole tools. For example, the observed main formation stiffness K... subj Increasing the value above the expected value can signal that the main downhole tool is becoming or has become worn (e.g., contrary to signaling an increase in formation stiffness, based on the expected formation stiffness K). exp With a high level of confidence, we can know that this is not the case.

[0095] A similar approach can be followed for any downhole tool parameters described in this article. For example, the main mechanical energy specific energy (MSE) was observed. subj Increase to the expected mechanical specific energy MSE exp This can indicate that more energy is being used to remove the equivalent unit of rock from the formation, which can signal that the drill bit is becoming dull (e.g., the opposite of signaling that the rock is becoming harder). In another example, a main drill bit aggression µ was observed. subj Reduced to the expected drill bit attack µ exp The following can indicate that the friction between the main downhole tool and the formation has decreased. Therefore, this could signal that the main downhole tool is experiencing less frictional resistance from the formation due to a dull drill bit, for example, opposite to the formation becoming harder. In another example, the observed main drilling rate per revolution (PPR) subj Reduce to the expected drilling rate per revolution (PPR) expThe following can indicate that the main downhole tool penetrates the formation less per revolution. Therefore, this may signal that the main downhole tool is working harder to remove material from the formation due to a dull bit, contrary to, for example, the formation becoming harder. In this way, the downhole tool indices (or any other indices) described herein can help characterize and / or quantify the wear status of the main downhole tool based on the different characteristics represented by the respective downhole tool indices when comparing the primary indices with expected indices.

[0096] In some embodiments, the tool index manager 124 determines the index ratio (IR) of a primary downhole tool index to a corresponding expected downhole tool index. For example, the tool index manager 124 may determine the IR of formation stiffness as the ratio of primary formation stiffness to a corresponding expected formation stiffness (e.g., the formation stiffness ratio FSR referred to herein). The tool index manager 124 may determine the IR of any downhole tool index described herein. IR in this manner facilitates comparison of the primary downhole tool index with the corresponding expected downhole tool index. For example, while observing and comparing the values ​​and / or graphical representations (e.g., side-by-side) of the primary and expected downhole tool indices may be useful, the IR provides a quantitative representation of that comparison.

[0097] In some embodiments, the tool index manager 124 classifies the IR based on one or more predetermined thresholds (and accordingly classifies the wear of the main downhole tool). For example, Figure 4 Example thresholds for the Formation Stiffness Ratio (FSR) are shown. The same or similar classifications can be established and implemented for any IR (Indicator Reduction) of any downhole tool index. As shown, potential FSR values ​​can be divided into several categories or classifications. An FSR from 0 to 2 might be a low classification for FSR. An FSR greater than 3.5 might be a severe classification for FSR. As shown, there can also be one or more intermediate classifications for FSR, such as medium and / or high. The classification of FSR can be determined based on historical data from (e.g., geographically proximate) offset wellbores and / or from selected wellbores of similar applications (e.g., if no proximate offset wellbores are available). The Tool Index Manager 124 can determine and / or classify the FSR at any (or all) measurement depths of the main wellbore, including dynamic measurement depths. For example, the Tool Index Manager 124 can determine and update the FSR and associated classifications in real time during drilling to provide an accurate and dynamic representation of the wear status of the main downhole tool. In this way, the FSR can provide a simple and intuitive indication of the wear level or severity of the main downhole tool.

[0098] In some embodiments, the FSR classification corresponds to a rating metric or rating system. For example, as shown, a first or lower classification may correspond to an FSR rating of 0, the next classification may correspond to a rating of 1, and so on. The tool metric manager 124 may determine and / or associate FSR ratings to facilitate one or more functions of the wear detection system 120 as described herein. The FSR rating scale and associated classifications may include any other ratings and / or may be developed in any other manner.

[0099] Thus far, the wear detection system 120 primarily describes one or more downhole tool indices for the main downhole tool, which can be compared with expected values. For example, the value of the main downhole tool index can be compared with the corresponding value of the expected downhole tool index at the relevant measurement depth and / or time to characterize the wear of the main downhole tool. In this way, the aforementioned downhole tool indices can provide comparisons, for example, with time snapshots (e.g., real-time and / or historical) of the main wellbore data 132 and the offset wellbore data 134.

[0100] In some embodiments, it may be advantageous to cumulatively conceptualize and / or quantify the wear of downhole tools with respect to multiple data points, measurement depths, and / or instantaneous total bit wear. In some embodiments, the tool index manager 124 determines the cumulative wear index (CWI) of the main downhole tool. The CWI can represent the wear of the main downhole tool by relating the determined downhole tool wear level (e.g., based on one or more downhole tool indices) to the downhole tool revolutions at the relevant wear level. For example, the CWI can be represented as the equivalent cumulative bit damage revolutions and can be based on or associated with formation stiffness determined at one or more (or all) previous measurement depths on the well at the dynamic measurement depth. The CWI can include the RPM and ROP of the main downhole tool at each measurement depth. The CWI can include the expected formation stiffness at each measurement depth. The expected formation stiffness can be normalized based on a normalization factor. The CWI can include a rating or classification of FSR, for example, expressed as a value between 0 and 3 (or any other scale). The CWI can be determined by the following formula:

[0101]

[0102] i = summation index

[0103] n = Total number of dynamically measured depths

[0104] ΔMD = Change in measured depth from previously summed indices

[0105] RPM = Downhole tool rotation speed or (revolutions per minute)

[0106] FSR_RI = Formation stiffness ratio rating (in normalized scaling)

[0107] FS = Expected formation stiffness based on migration data

[0108] FS_norm_factor = Normalization factor for calculating relative formation stiffness

[0109] Additional or alternative locations, CWI can be determined by the following formula, with similar parameters defined above:

[0110]

[0111] CWI can be determined in any other way or according to any other formula or principle used to characterize CWI. For example, while CWI is specifically described relative to formation stiffness and FSR, in some embodiments, CWI is determined relative to one or more other downhole tool parameters, such as in addition to or instead of formation stiffness. The tool parameter manager 124 can determine and / or update CWI in real time and during drilling to provide a real-time indication of CWI.

[0112] CWI can represent the cumulative or total wear of the main downhole tools at some or all of the measured depth in the main wellbore. For example, CWI can represent the determined wear level or classification (e.g., as combined with...) Figure 4 The FSR rating described is related to the number of revolutions the downhole tool has completed when it is observed to have the aforementioned wear rating / classification. CWI can be the sum of multiple non-negative values, such that CWI may simply remain constant or increase over time. This may be consistent with the real-world behavior of downhole tool wear, which may be constant and relatively low over a period of time, but may wear down to increasingly severe levels over time. Figure 4 As shown, the rating associated with the lowest (e.g., acceptable) FSR classification can be 0, and the CWI (e.g., due to the FSR_RI term) can also be 0, while observing the main downhole tool at or within the lowest wear classification (e.g., the sum of negligible or zero-value terms). When the determined FSR classification / rating becomes non-zero (e.g., medium, high, or severe) based on the underlying main formation stiffness exceeding the expected formation stiffness, the CWI can take into account the number of revolutions in which the downhole tool is observed to have that non-zero wear classification. Therefore, the CWI calculation can include the sum of one or more non-zero iterations representing these revolutions at non-negligible (e.g., medium, high, or severe) wear levels. In this way, the CWI can increase over time based on instances of increasing FSR, but the level of increase can depend on the associated revolutions of the downhole tool.

[0113] In this way, CWI can provide a more detailed characterization of the wear of the main downhole tool than, for example, the downhole tool indices discussed above. For instance, the IR of the downhole tool indices discussed above can provide a valuable but simple comparison of the master (e.g., actual) value with the expected value at a time snapshot, but CWI can provide a more detailed characterization by considering how long the main downhole tool interacts with the formation at higher than expected index values. In some embodiments, CWI is more reliable and / or stable by showing wear over time. For example, because downhole tool indices are associated with a specific time, data quality issues, depth / formation alignment issues, etc., that deviate from the borehole data 134 and / or the main borehole data 132 can cause spikes or sudden increases in the IR between the actual and expected values ​​(as described below). Figure 8 (As described). It can be difficult to discern whether these spikes are due to such data issues or whether they truly indicate wear on the main downhole tool. However, CWI may be less susceptible to misalignment or data quality problems because even large spikes in IR may only slightly increase the CWI based on a relatively small associated revolutions. Therefore, due to the time element of CWI, and because CWI is the accumulation of all identified wear over the (e.g., large) operating time of the main downhole tool, CWI can more accurately reflect the actual wear of the main downhole tool.

[0114] As described above, CWI can include a normalization factor for normalizing expected formation stiffness. The normalization factor can represent, typically, for example, the typical (e.g., average) formation stiffness observed for a related or similar wellbore at all measurement depths and / or throughout all formations or subsurfaces. For example, the normalization factor can be based on a set of offset wellbores, such as offset wellbore data 134; offset wellbores within a geographic distance from the main wellbore; offset wellbores in the same oilfield, basin, region, formation, or location as the main wellbore; offset wellbores within a global database; or any other set of offset wellbores. The normalization factor can be the mean, median, or percentile of all formation stiffness observed (at all measurement depths) throughout the associated related offset wellbores. In this way, the normalization factor can be a global statistic representing the typical formation stiffness of any wellbore and at any location and / or measurement depth. The normalization factor can be expressed as a single value, a polynomial, an index, or any other suitable expression in order to measure the relative level of expected formation stiffness as described herein.

[0115] A normalization factor can be used to determine how the expected formation stiffness (e.g., at a specific measurement depth) compares to the typical or average stiffness of the formation or earth typically used for a master wellbore at any measurement depth. For example, if the expected formation stiffness is greater than the normalization factor, it can be determined that the expected formation stiffness at a given measurement depth is above normal. Similarly, if the expected formation stiffness is less than the normalization factor, it can be determined that the expected formation stiffness at a given measurement depth is below normal. This comparison can be implemented to weight the expected formation stiffness in the calculation of CWI. For example, as shown in the formula above, the expected formation stiffness can be inversely weighted by the normalization factor (e.g., divided by the normalization factor). This can have the effect of downweighting instances where the expected formation stiffness is below normal, such that when summed, these cases will accumulate wear to a smaller extent. Similarly, instances where the expected formation stiffness is above normal can be upweighted, such that when summed, these cases will accumulate wear to a larger extent. CWI can be determined in this way to reflect the concept that even if wear indicators of the downhole tool are given at a given measurement depth (e.g., elevated FSR), if the expected formation stiffness at that particular measurement depth is less than normal (e.g., the formation is softer than normal), the main downhole tool may wear to a lesser degree in formations that are softer than normal. Therefore, CWI can be determined by giving these cases a lower weight. Similarly, if the expected formation stiffness at a particular measurement depth is higher than normal (e.g., the formation is harder than normal), the main downhole tool may wear to a greater extent in formations that are harder than normal. Therefore, CWI can be determined by giving these cases a higher weight.

[0116] In some embodiments, the tool indicator manager 124 classifies CWIs based on one or more predetermined thresholds. For example, Figure 5 Example thresholds for CWI are shown. As illustrated, observed CWI values ​​can be categorized into several distinct classes or classes. CWI values ​​between 0 and 10,000 revolutions may be classified as the minimum, low, or acceptable class of CWI. CWI values ​​exceeding 30,000 revolutions may be classified as the maximum or severe class of CWI. As illustrated, one or more intermediate classes, such as medium and / or high, may also exist for CWI. The Tool Indicator Manager 124 can classify CWI based on any threshold or class consistent with those described herein. The Tool Indicator Manager 124 can determine and / or classify CWI values ​​at any (or all) measurement depths (including dynamic measurement depths) in the main wellbore. For example, the Tool Indicator Manager 124 can determine and update CWI values ​​and associated classifications in real time and during drilling to provide an accurate and dynamic representation of the wear condition of the main downhole tools. In this way, CWI can provide a comprehensive and intuitive indication of the severity of wear on the main downhole tools.

[0117] In some embodiments, the tool index manager 124 determines one or more general statistics. General statistics can be, for example, values, measures, and / or indications representing general characteristics or aspects of the main wellbore and / or off-center wellbore at a high level. For example, general statistics may include an indication of footage. Based on off-center wellbore data 134, the tool index manager 124 can determine the footage or total drilling distance associated with the downhole tool for each off-center wellbore. The tool index manager 124 can accordingly determine the average, median, or any other statistical calculation of the footage values ​​for all off-center wellbores in the off-center wellbore data 134. In this way, the footage general statistics can provide, for example, a simple, high-level generalization of what footage can be expected from the main downhole tool. The tool index manager 124 can thus determine general statistics for any other relevant aspects, parameters, or characteristics, such as drilling rate, CWI, FSR, IR, ROP, etc.

[0118] As described above, the wear detection system 120 includes a reporting engine 126. The reporting engine 126 can generate one or more reports. In some embodiments, the reporting engine 126 displays one or more of the reports via a graphical user interface of a user device.

[0119] Figure 6 An example report 600 generated by report engine 126 according to at least one embodiment of the present disclosure is shown. In some embodiments, report 600 represents one or more of the downhole tool indices described herein for offset wellbores and / or main wellbores. For example, report 600 shows formation stiffness 641 determined for several offset wellbores 640, and main formation stiffness 643 for main wellbore 642. In addition to or in lieu of formation stiffness, report 600 may include one or more other downhole tool indices. Report 600 may indicate formation stiffness 641 of offset wellbores 640 through a series of measurement depths (e.g., from 8150 feet to approximately 8400 feet as shown in the figure). Report 600 may indicate the main formation stiffness 643 of main wellbore 642 in real time. For example, report 600 may indicate the dynamic measurement depth 644 of main wellbore 642, and may indicate the real-time main formation stiffness 643 at the dynamic measurement depth 644. Report 600 can indicate the primary formation stiffness 643 at one or more other measurement depths prior to dynamic measurement depth 644 or on the well surface. Report engine 126 can continuously and / or periodically update or regenerate report 600 to represent the dynamic or current value of the target formation stiffness 643.

[0120] Report 600 can help compare values ​​of one or more downhole tool parameters in the main wellbore with values ​​calculated or observed in one or more offset wellbores. This side-by-side comparison can help determine when the main downhole tool wears out. For example, as shown in the figure, the determined formation stiffness 641 of each offset wellbore 640 consistently appears between approximately 1 Mlbf / in and 3 Mlbf / in throughout the measurement depth. Furthermore, the determined formation stiffness 641 of each offset wellbore 640 appears relatively continuous throughout the measurement depth shown. However, from approximately 8290 ft and further, the primary formation stiffness 643 of the main wellbore 642 increases to approximately 2–8 Mlbf / in, exceeding the primary formation stiffness of the offset wellbore 640. Additionally, the primary formation stiffness 643 is observed to be significantly more discontinuous and fragmented than the primary formation stiffness of the offset wellbore 640. Therefore, these data characteristics of the main formation stiffness 643 of the main wellbore 642, which become apparent by comparison with the formation stiffness 641 of the offset wellbore 640, can indicate that the main downhole tool has worn, damaged, or both. In this way, report 600 can help identify the wear condition of the main downhole tool.

[0121] The report engine 126 can store the report 600 to a data storage device as report data 138. In some embodiments, the report engine 126 presents the report 600 via a graphical user interface of a user device.

[0122] Figure 7 An example report 700 generated by a report engine 126 according to at least one embodiment of the present disclosure is shown. Report 700 may indicate or represent a anticipated downhole tool indicator 746 as described herein. For example, the anticipated downhole tool indicator 746 may be the anticipated formation stiffness. Report 700 may indicate or represent a corresponding primary downhole tool indicator 743, such as primary formation stiffness. Report 700 may indicate one or more thresholds 750 or boundaries for the anticipated downhole tool indicator 746. Thresholds 750 may be maximum and / or minimum values, percentile ranges, standard deviations, or any other thresholds or boundaries used for or based on the anticipated downhole tool indicator 746. Report 700 may show the primary downhole tool indicator 743 at a dynamic measurement depth 744. Report engine 126 may update and / or regenerate report 700 to represent the primary downhole tool indicator 746 in real time and during drilling as the dynamic measurement depth 744 advances down through the formation. In addition to or in lieu of formation stiffness, Report 700 may include one or more other downhole tool parameters and associated thresholds.

[0123] Thus, report 700 can show the status of the primary downhole tool indicator 743 relative to the expected downhole tool indicator 746 and / or the threshold 750, providing a useful comparison to measure the observed value of the primary downhole tool indicator 743. For example, as shown in the figure, from approximately 8290 feet and beyond, the primary downhole tool indicator 743 was observed to exceed both the expected downhole tool indicator 746 and the threshold 750 one or more times. Additionally, the primary downhole tool indicator 743 was observed to become fragmented and discontinuous. In this way, report 700 can indicate that the primary downhole tool has become dull and / or damaged.

[0124] The report engine 126 can store the report 700 to a data storage device as report data 138. In some embodiments, the report engine 126 presents the report 700 via a graphical user interface of a user device.

[0125] Figure 8 An example report 800 generated by report engine 126 according to at least one embodiment of this disclosure is shown. Similar to the combination... Figure 7 As discussed herein, Report 800 may indicate or represent one or more primary downhole tool indicators 843, expected downhole tool indicators 841, and / or thresholds 850. Report 800 may indicate these measures of any number of downhole tool indicators as discussed herein, such as formation stiffness, bit attack, MSE, or PPR, or any other indicator. Report 800 may indicate one or more measurement depths of the primary wellbore, including at dynamic measurement depth 844. In this way, Report 800 may facilitate, for example, the evaluation of one or more of the primary downhole tool indicators 843 against expected downhole tool indicators 841 and / or thresholds 850.

[0126] In some embodiments, report 800 indicates one or more drilling parameters 852. For example, for a measurement depth range, report 800 may indicate ROP, WOB, RPM, torque (TOR), or any other parameter associated with the main downhole tool and / or the main wellbore. In some embodiments, report 800 indicates one or more statistics and / or ranges associated with drilling parameters 852. For example, report 800 may indicate the mean, median, etc., of the drilling parameter based on offset wellbore data 134. Report 800 may indicate one or more boundaries of drilling parameters 852, such as maximum and / or minimum values, quartile ranges, standard deviation ranges, percentile ranges, or any other boundaries. These statistics may be determined by tool indicator manager 124 based on offset wellbore data 134. In this way, report 800 may facilitate comparison of one or more drilling parameters of the main wellbore with those implemented by the offset wellbore.

[0127] In some embodiments, Report 800 indicates the Inverse Reduction (IR) of one or more of the primary downhole tool indicators (PSPIs) and their associated expected downhole tool indicators. For example, Report 800 may plot the primary wellbore's FSR 854 across the entire measurement depth. A graph of the FSR provides a visual representation of both dynamic (e.g., at dynamic measurement depth 844) and historical values ​​of the FSR. In this way, the FSR can be monitored to facilitate the determination and conceptualization of the wear level of the primary downhole tool. In some embodiments, Report 800 indicates a classification or rating of the FSR as described herein. For example, color codes or scales (or any other suitable technique) may indicate the classification of the FSR, such as from low to severe. As shown, the FSR may exhibit one or more increases corresponding to the deviation of the underlying primary formation stiffness from the expected formation stiffness. The increase may be manifested as a spike or peak, or it may be a smaller or more subtle increase. Report 800 may indicate the increase in FSR using an associated color (or other indicator) for the classification, thereby indicating the degree of the increase. In this way, Report 800 can facilitate the identification of primary formation stiffness conditions that can indicate wear of primary downhole tools (e.g., via FSR).

[0128] In some embodiments, report 800 indicates the CWI 856 of the main downhole tool as described herein. For example, report 800 may plot the CWI of the main wellbore across the entire measurement depth range. A graph of the CWI can provide a visual representation of the dynamic (e.g., at dynamic measurement depth 844) and historical values ​​of the CWI. Report 800 may indicate a classification or rating of the CWI as described herein. For example, color codes or scales (or any other technique) may indicate the classification of the CWI, such as from low to severe. The classification of wear levels may be determined based on historical data from similar and / or geographically proximate offset wellbores. For example, CWI may be calculated for relevant offset wellbores with and without severe wear levels to determine a reference level for the classification. As shown, CWI may grow or increase over time, consistent with the increase in wear of the main downhole tool over time. As the CWI progresses to an increased wear level of the classification, report 800 may indicate the classification of the CWI by incorporating the relevant color (or other indication) of the classification into the graph of the CWI.

[0129] As mentioned above, CWI may not be as susceptible to misalignment or data quality issues as FSR, for example. Example data from Report 800 illustrates this. For instance, at approximately 7900 feet, the FSR exhibits a significant spike of about 100 feet. Report 800 indicates this spike is classified as severe. Based solely on the FSR, this spike could indicate damage or wear to the main downhole tool and its potential removal from the main wellbore. However, the corresponding CWI value at approximately 7900 feet indicates that the cumulative wear of the main downhole tool remains relatively low and is classified as low. As mentioned above, the spike and / or high level of the FSR can be factored into the CWI calculation, but the relatively short span of the spike (e.g., approximately 100 feet out of 8000 total drill feet) and therefore the relatively low rotational speed of the downhole tool only result in a small increase in CWI, as shown in the figure. Additionally, the expected formation stiffness is observed to be relatively low at 7900 feet, which further reduces the impact of the spike on CWI, as mentioned above. Therefore, CWI can be a more accurate measure of the wear of the main downhole tool, because based solely on FSR, it might appear that the main downhole tool is severely worn at 7900 feet, when in reality it may not have reached that level of wear in another 400 feet or more, as shown by CWI. Spikes can accordingly indicate, for example, data alignment issues between main borehole data 132 and offset borehole data 134, rather than the main downhole tool becoming worn.

[0130] In some embodiments, report 800 indicates one or more summary statistics 858 for the main downhole tool as described herein. Summary statistics 858 may indicate one or more top-level or advanced characteristics or values ​​used to compare the performance of the main downhole tool with that of an off-pivot downhole tool in an off-pivot wellbore. For example, summary statistics 858 in this manner can provide a simple and accessible assessment of one or more aspects of the main downhole tool compared to more detailed information included in other sections of report 800.

[0131] The report engine 126 can store the report 800 to a data storage device as report data 138. In some embodiments, the report engine 126 presents the report 800 via a graphical user interface of a user device.

[0132] Figure 9 An example report 900 generated by report engine 126 is shown. Report 900 may include the above combinations. Figure 8 The report discusses any of the features mentioned in Report 800, but is based on different example datasets, for example.

[0133] In some embodiments, the reporting engine 126 helps identify that the main downhole tool has worn or damaged. For example, based on or in conjunction with any reports discussed herein, the reporting engine 126 can monitor one or more values, metrics, indicators, etc., and can generate flags or alerts. For example, the reporting engine 126 can monitor the main downhole tool indicator against associated expected downhole tool indicators and / or one or more associated thresholds to identify that the main downhole tool indicator has exceeded or surpassed one or more of these values. In another example, the reporting engine 126 can monitor IR (such as FSR) to identify when it exceeds a certain value. In yet another example, the reporting engine 126 can monitor CWI against one or more predetermined categories or classifications to identify when the CWI changes classification or reaches a certain classification. The reporting engine 126 can monitor any value, metric, or indicator to make any relevant determination consistent with those described herein. The reporting engine 126 can monitor one or more metrics in this manner and can generate alerts based on one or more criteria. For example, an alert can be based on a metric that exceeds (e.g., expected) a value or threshold (or both). In another example, an alert can be based on a metric that exceeds a value to a certain extent, or over a certain amount of time (or distance), or a combination of both. In another example, an alarm could be based on a metric classified into a given category or category, or on a metric that changes the classification. In yet another example, the reporting engine 126 could generate an alarm based on considerations of how much of the main wellbore remains to be drilled or how far the main wellbore is from the target. For example, an alarm could signal to the downhole system operator that the main downhole tool is worn and should be removed and / or replaced. However, in some cases, if the main wellbore is nearing completion, it may be advantageous to complete the wellbore with the main downhole tool despite the wear condition and potential damage to the main downhole tool. Therefore, the reporting engine 126 could incorporate considerations of the remaining drilling distance into the determination of alarm generation.

[0134] The reporting engine 126 can alert the user of the wear detection system 120. For example, the reporting engine 126 may present an alarm or indicator to the user via a graphical user interface on the user equipment, or may otherwise alert the user. In some embodiments, the reporting engine 126 facilitates changes to the operation of the downhole system. For example, the reporting engine 126 may alert the user to the wear status of the main downhole tool so that one or more drilling parameters can be adjusted. In some embodiments, the reporting engine 126 assists in adjusting one or more drilling parameters based on identified indicators or alarms. For example, the reporting engine 126 may suggest adjustments to the user, provide information about the wear status of the main downhole tool to one or more auxiliary systems, automatically adjust one or more drilling parameters, stop the operation of the downhole system, or any other action or combination thereof for adjusting drilling parameters.

[0135] Figure 10A flowchart illustrating a method 1000 or a series of actions for detecting wear of downhole tools implemented in a main wellbore, as described herein, according to at least one embodiment of this disclosure. Although Figure 10 Actions according to one embodiment are shown, but alternative embodiments may include additions, omissions, reordering, or modifications. Figure 10 Any action by [them].

[0136] In some embodiments, method 1000 includes the action 1010 of receiving offset wellbore data for one or more offset wellbores. For example, the offset wellbore data may include drilling rate, bit pressure, and rotational speed associated with each offset wellbore. In some embodiments, wear detection system 120 filters out one or more offset wellbores based on the wear condition of the downhole tools associated with the offset wellbore. In some embodiments, the offset wellbore data is based on offset wellbores associated with the main wellbore in the same formation and / or at the same depth.

[0137] In some embodiments, method 1000 includes action 1020 of determining expected downhole tool indices at one or more measurement depths, including the main wellbore, based on offset wellbore data. For example, the expected downhole tool indices could be the expected formation stiffness of the main wellbore at one or more measurement depths based on offset wellbore data. The expected downhole tool indices could also be the median downhole tool indices based on offset wellbore data. In another example, based on offset wellbore data, the expected downhole tool indices could be the expected mechanical energy of the formation, the expected bit attack capability of the downhole tool, or the expected rate of revolution per revolution of the downhole tool.

[0138] In some embodiments, method 1000 includes the action 1030 of receiving main wellbore data.

[0139] In some embodiments, method 1000 includes action 1040 of determining, in real time, the main downhole tool index at the dynamic measurement depth based on the main wellbore data.

[0140] In some embodiments, method 1000 includes the action 1050 of determining downhole tool wear based on comparing a primary downhole tool index with a expected downhole tool index at a dynamically measured depth. In some embodiments, the wear detection system 120 aligns primary wellbore data with offset wellbore data based on formation depth. In some embodiments, the wear detection system 120 classifies the determined downhole tool wear based on one or more predetermined thresholds for the primary downhole tool index. In some embodiments, the wear detection system determines a downhole tool index ratio of the primary downhole tool index to the expected downhole tool index.

[0141] In some embodiments, method 1000 includes generating a graph representing expected downhole tool parameters and primary downhole tool parameters. In some embodiments, the graph represents a downhole tool parameter ratio. In some embodiments, method 1000 includes adjusting one or more drilling parameters based on determined downhole tool wear.

[0142] Figure 11 A flowchart illustrating a method 1100 or series of actions for detecting wear of downhole tools implemented in a main wellbore, as described herein, according to at least one embodiment of this disclosure. Although Figure 11 Actions according to one embodiment are shown, but alternative embodiments may include additions, omissions, reordering, or modifications. Figure 11 Any action by [them].

[0143] In some embodiments, method 1100 includes the action 1110 of receiving offset wellbore data of one or more offset wellbores.

[0144] In some embodiments, method 1100 includes action 1120 of determining expected downhole tool indicators at each of a plurality of measurement depths including the dynamic measurement depth of the downhole tool based on offset wellbore data.

[0145] In some embodiments, method 1100 includes the action 1130 of receiving main wellbore data. For example, the main wellbore data may include rotation data of downhole tools.

[0146] In some embodiments, method 1100 includes action 1140 of determining main downhole tool indices at each of a plurality of measurement depths based on main wellbore data.

[0147] In some embodiments, method 1100 includes action 1150 of determining a cumulative wear index of the downhole tool at multiple measurement depths based on a comparison of the primary downhole tool index with a expected downhole tool index. For example, the cumulative wear index can correlate the comparison of the primary downhole tool index with the expected downhole tool index with the rotation of the downhole tool based on rotation data. For example, the cumulative wear index can identify the number of rotations of the downhole tool relative to the primary downhole tool index, such as compared to one or more threshold ranges of the expected downhole tool index. In some embodiments, wear detection system 120 classifies the determined cumulative wear index based on one or more predetermined thresholds. In some embodiments, wear detection system 120 determines one or more formation normalization factors for the offset wellbore and the primary wellbore. The cumulative wear index can be determined based on normalization of the expected downhole tool index based on the normalization factor.

[0148] Figure 12A flowchart illustrating a method 1200 or series of actions for detecting wear of downhole tools implemented in a main wellbore, as described herein, according to at least one embodiment of this disclosure. Although Figure 12 Actions according to one embodiment are shown, but alternative embodiments may include additions, omissions, reordering, or modifications. Figure 12 Any action by [them].

[0149] In some embodiments, method 1200 includes action 1210 of receiving offset wellbore data of one or more offset wellbores.

[0150] In some embodiments, method 1200 includes action 1220 of determining expected downhole tool indicators at each of a plurality of measurement depths in which one or more offset wellbores are located, based on offset wellbore data.

[0151] In some embodiments, method 1200 includes the action 1230 of receiving main wellbore data from the main wellbore.

[0152] In some embodiments, method 1200 includes action 1240 of determining the primary formation stiffness of the downhole tool at each of a plurality of measurement depths based on primary wellbore data.

[0153] In some embodiments, method 1200 includes actions 1240 of comparing a primary formation stiffness with a expected formation stiffness to determine a formation stiffness ratio at each of a plurality of measurement depths, and classifying the formation stiffness ratio based on one or more predetermined thresholds of the formation stiffness ratio.

[0154] In some embodiments, method 1200 includes the action 1250 of determining a cumulative wear index for the downhole tool based on correlating the classification of formation stiffness ratio with the number of revolutions of the downhole tool at multiple measurement depths. In some embodiments, method 1200 includes determining a normalization factor for formation stiffness based on offset wellbore data. The cumulative wear index may be determined based on normalizing a expected formation stiffness according to the normalization factor. In some embodiments, method 1200 includes adjusting one or more drilling parameters based on the determined cumulative wear index.

[0155] Now go to Figure 13 This figure illustrates certain components that may be included within computer system 1300. One or more computer systems 1300 may be used to implement the various devices, components, and systems described herein.

[0156] Computer system 1300 includes processor 1301. Processor 1301 can be a general-purpose single-chip or multi-chip microprocessor (e.g., an advanced RISC (Reduced Instruction Set Computer) machine (ARM)), a special-purpose microprocessor (e.g., a digital signal processor (DSP)), a microcontroller, a programmable gate array, etc. Processor 1301 can be referred to as a central processing unit (CPU). Although in Figure 13 The computer system 1300 shows only a single processor 1301, but in alternative configurations, a combination of processors (e.g., ARM and DSP) can be used.

[0157] Computer system 1300 further includes memory 1303 in electronic communication with processor 1301. Memory 1303 may include computer-readable storage media and may be any available medium accessible by a general-purpose or special-purpose computer system. Computer-readable media storing computer-executable instructions are non-transitory computer-readable media (devices). Computer-readable media carrying computer-executable instructions are transmission media. Therefore, by way of example and not limitation, embodiments of this disclosure may include at least two distinct types of computer-readable media: non-transitory computer-readable media (devices) and transmission media.

[0158] Non-transitory computer-readable media (devices) and transmission media can both be temporarily used to store or carry software instructions in the form of computer-readable program code that allows the execution of embodiments of this disclosure. Non-transitory computer-readable media can further be used to persistently or permanently store such software instructions. Examples of non-transitory computer-readable storage media include physical memory (e.g., RAM, ROM, EPROM, EEPROM, etc.), optical disc storage devices (e.g., CD, DVD, HDDVD, Blu-ray, etc.), storage devices (e.g., disk storage devices, magnetic tape storage devices, floppy disks, etc.), flash memory or other solid-state storage devices or memories, or any other non-transmission medium that can be used to store program code in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, whether such program code is stored in software, hardware, firmware, or a combination thereof.

[0159] Instruction 1305 and data 1307 may be stored in memory 1303. Instruction 1305 may be executed by processor 1301 to implement some or all of the functionality disclosed herein. Execution of instruction 1305 may involve using data 1307 stored in memory 1303. Any of the various examples of modules and components described herein may be implemented, in part or in whole, as instruction 1305 stored in memory 1303 and executed by processor 1301. Any of the various examples of data described herein may be data 1307 stored in memory 1303 and used during the execution of instruction 1305 by processor 1301.

[0160] The computer system 1300 may also include one or more communication interfaces 1309 for communicating with other electronic devices. The communication interface 1309 may be based on wired communication technology, wireless communication technology, or both. Some examples of the communication interface 1309 include Universal Serial Bus (USB), Ethernet adapters, wireless adapters operating according to the Institute of Electrical and Electronics Engineers (IEEE) 802.11 wireless communication protocol, Bluetooth® wireless communication adapters, and infrared (IR) communication ports.

[0161] Communication interface 1309 can connect computer system 1300 to a network. A “network” or “communication network” can generally be defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules, engines, or other electronic devices, or combinations thereof. When information is transmitted or provided to a computing device via a communication network or another communication connection (hardwired, wireless, or a combination of hardwired and wireless), the computing device appropriately considers the connection as a transmission medium. The transmission medium may include communication networks and / or data links, carrier waves, wireless signals, etc., which can be used to carry desired program or template code devices or instructions in the form of computer-executable instructions or data structures, and can be accessed by a general-purpose or special-purpose computer.

[0162] Computer system 1300 may also include one or more input devices 1311 and one or more output devices 1313. Some examples of input devices 1311 include a keyboard, mouse, microphone, remote control device, button, joystick, trackball, touchpad, and light pen. Some examples of output devices 1313 include speakers and printers. A particular type of output device typically included in computer system 1300 is a display device 1315. Display device 1315 used with the embodiments disclosed herein may utilize any suitable image projection technology, such as liquid crystal display (LCD), light-emitting diode (LED), gas plasma, electroluminescence, etc. A display controller 1317 may also be provided for converting data 1307 stored in memory 1303 into one or more of text, graphics, or moving images (as applicable) displayed on display device 1315.

[0163] Various components of the computer system 1300 can be interconnected via one or more buses, which may include one or more of a power bus, control signal bus, status signal bus, data bus, other similar components, or combinations thereof. For clarity, the various buses are... Figure 13 It is shown as bus system 1319.

[0164] Unless specifically described as being implemented in a particular manner, the techniques described herein can be implemented in hardware, software, firmware, or any combination thereof. Any features described as modules, components, etc., may also be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be implemented at least in part by a non-transitory processor-readable storage medium comprising instructions that, when executed by at least one processor, perform one or more of the methods described herein. Instructions may be organized into routines, programs, objects, components, data structures, etc., which may perform specific tasks and / or implement specific data types, and may be combined or distributed as needed in various embodiments.

[0165] Furthermore, upon arrival at various computer system components, program code in the form of computer-executable instructions or data structures can be automatically or manually transferred from the transmission medium to a non-transitory computer-readable storage medium (and vice versa). For example, computer-executable instructions or data structures received via a network or data link can be buffered in memory (e.g., RAM) within a network interface module (NIC) and then ultimately transferred to the computer system RAM and / or a less volatile, non-transitory computer-readable storage medium at the computer system location. Therefore, it should be understood that non-transitory computer-readable storage media can be included in computer system components that also (or even primarily) utilize the transmission medium.

[0166] Industrial application

[0167] In some embodiments, a downhole system for drilling formations to form a wellbore is disclosed. The downhole system includes a drilling rig for rotating a drilling tool assembly that extends downward into the wellbore. The drilling tool assembly may include a drill string, a bottom hole assembly (“BHA”), and a drill bit attached to the downhole end of the drill string.

[0168] The drill string may include several drill pipe joints connected end-to-end via tool fittings. The drill string transmits drilling fluid through a central bore and transmits rotational power from the drilling rig to the BHA. In some embodiments, the drill string further includes additional downhole drilling tools and / or components, such as subs, short joints, etc. The drill pipe provides a hydraulic passage through which drilling fluid is pumped from the surface. The drilling fluid exits through nozzles, orifices, or other orifices of selected size in the drill bit to cool the drill bit and its cutting structures, and to remove drill cuttings from the wellbore during drilling.

[0169] A BHA may include a drill bit, other downhole drilling tools, or other components. An exemplary BHA may include additional or other downhole drilling tools or components (e.g., connected between the drill string and the drill bit). Examples of additional BHA components include drill collars, stabilizers, measurement-while-drilling (“MWD”) tools, logging-while-drilling (“LWD”) tools, downhole motors, reamers, segmented end mills, hydraulic disconnectors, slappers, vibration or damping tools, other components, or combinations thereof.

[0170] Typically, downhole systems may include other downhole drilling tools, components, and accessories, such as special valves (e.g., jib pipe, blowout preventers, and safety valves). Additional components included in a downhole system can be considered part of the drilling tool assembly, drill string, or BHA, depending on their location within the downhole system.

[0171] The drill bit in a BHA can be any type of drill bit suitable for degrading downhole materials. For example, the drill bit can be a drill bit suitable for drilling formations. Example types of drill bits used for drilling formations are stationary cutter or scraper bits. In other embodiments, the drill bit can be a milling cutter for removing metal, composite materials, elastomers, other downhole materials, or combinations thereof. For example, the drill bit can be used with a directional drilling tool to grind into casing that lines the wellbore. The drill bit can also be a shredder for grinding away tools, plugs, cement, other materials within the wellbore, or combinations thereof. The chips or other cuttings generated by using the mill can be lifted to the surface or allowed to fall downhole. The drill bit may include one or more cutting elements for degrading the formation.

[0172] BHA may also include a rotary steerable system (RSS). The RSS can include a directional drilling tool that changes the orientation of the drill bit, thereby altering the wellbore trajectory. At least a portion of the RSS can maintain a geostationary position relative to an absolute reference frame (e.g., gravity, magnetic north, or true north, or one or more). Using measurements obtained from the geostationary position, the RSS can position the drill bit, change its path, and guide the directional drilling tool along its projected trajectory. The RSS can guide the drill bit based on or according to its trajectory. For example, a trajectory can be determined to direct the drill bit toward one or more subsurface targets (e.g., oil or gas reservoirs).

[0173] A downhole system may include or be associated with one or more client devices, which have a wear detection system implemented thereon (e.g., implemented on one, several, or across multiple client devices). The wear detection system facilitates the determination of wear on one or more downhole tools, such as drill bit wear.

[0174] In some embodiments, the wear detection system is implemented in an example environment according to one or more embodiments described herein. The environment includes one or more server devices. The server devices may include one or more computing devices (e.g., including processing units, data storage devices, etc.) organized in an architecture with various network interfaces for connecting to one or more client systems and providing data management and distribution across the one or more client systems. The server devices may be connected to one or more client devices via a network and may communicate with one or more client devices (directly or indirectly). The network may include one or more networks and may use one or more communication platforms and / or technologies suitable for transmitting data. A network may refer to any data link capable of transmitting electronic data between devices in the environment. A network may refer to a hardwired network, a wireless network, or a combination of hardwired and wireless networks. In one or more embodiments, the network includes the Internet. The network may be configured to facilitate communication between various computing devices via Well Site Information Transmission Standard Markup Language (WITSML) or similar protocols or any other protocol or form of communication.

[0175] Client devices can refer to various types of computing devices. For example, one or more client devices may include mobile devices such as mobile phones, smartphones, personal digital assistants (PDAs), tablet computers, laptop computers, or any other portable devices. Alternatively, client devices may include one or more non-mobile devices, such as desktop computers, server devices, surface or downhole processors or computers (e.g., associated with sensors, systems, or functions of a downhole system), or other non-portable devices. In one or more embodiments, a client device includes a graphical user interface (GUI) (e.g., the screen of a mobile device). Additionally, or alternatively, one or more client devices may be communicatively coupled (e.g., wired or wirelessly) to a display device having a graphical user interface for providing the display of system content. Server devices can similarly refer to various types of computing devices. Each device in the environment may include the features and / or functions described herein.

[0176] In some embodiments, the environment includes a wear detection system implemented on one or more computing devices. The wear detection system may be implemented on one or more client devices, server devices, or combinations thereof. Additionally or alternatively, the wear detection system may be implemented across client devices and / or server devices, such that different portions or components of the wear detection system are implemented on different computing devices within the environment. In this way, the environment may be a cloud computing environment, and the wear detection system may be implemented across one or more devices within the cloud computing environment to leverage the processing power, memory capacity, connectivity, speed, etc., provided by such a cloud computing environment to facilitate the features and functions described herein.

[0177] In some embodiments, the wear detection system includes a data manager, a tool index manager, and a reporting engine. The wear detection system may also include a data storage device having main wellbore data, offset wellbore data, tool index data, and reporting data stored thereon. While one or more embodiments described herein describe features and functions performed by specific components of the wear detection system, it should be understood that in some examples, a particular feature described in conjunction with one component of the wear detection system may be performed by one or more other components of the wear detection system.

[0178] As an example, one or more of the data receiving, collection, or storage features of the data manager can be delegated to other components of the wear detection system. As another example, while data can be selected, aligned, filtered, and / or modified by the data manager, in some cases, some or all of these features can be performed by the tool indicator manager (or other components of the wear detection system). In practice, it should be understood that some or all of a particular component can be combined into other components, and a particular function can be performed by one component of the wear detection system or across multiple components.

[0179] Furthermore, although the wear detection system has been described as being implemented on client devices of a downhole system, it should be understood that some or all of the features and functions of the wear detection system may be implemented on multiple client devices and / or server devices or across multiple client devices and / or server devices. For example, data may be input and / or received by a data manager on (e.g., local) client devices, and one or more tool metrics may be determined by a tool metric manager on one or more remote, server, or cloud devices. In practice, it should be understood that some or all of a particular component may be implemented on multiple client devices and / or server devices or across multiple client devices and / or server devices, including the individual functions of a particular component performed across multiple devices.

[0180] As described above, the wear detection system includes a data manager. The data manager can receive various types of data associated with the downhole system and can store the data in a data storage device. The data manager can receive data from various sources, such as sensors, measuring tools, downhole tools, other (e.g., client) devices, user input, etc.

[0181] In some embodiments, the data manager receives master wellbore data. The master wellbore can be the wellbore of a downhole system associated with a wear detection system as described herein. The master wellbore can be a wellbore in which (e.g., a master) downhole tool is dynamically implemented, or a wellbore that is being dynamically drilled, lengthened, widened, or otherwise formed.

[0182] Master borehole data can include information associated with the master borehole. For example, master borehole data can indicate one or more of the following: rate of penetration (ROP), weight on bit (WOB), and rotational speed (RPM) of the master downhole tools implemented in the master borehole. For example, the data manager can receive master borehole data from one or more downhole sensors and / or surface sensors. Master borehole data can indicate one or more measurement depths relative to one or more measurements of the master borehole. Master borehole data can include any other data associated with the master borehole and / or downhole systems, such as torque, pump pressure, flow rate, etc. Master borehole data can include formation assessment data, directional drilling data, mud and fluid analysis data, pressure and temperature data, or any other type of data. The data manager can store master borehole data in a data storage device.

[0183] In some embodiments, some or all of the main wellbore data are directly measured. For example, the data manager may receive one or more of the ROP, WOB, and RPM of the main downhole tool based on a real-time downhole data channel with directly measured associated values. In some embodiments, some or all of the main wellbore data are calculated or estimated indirectly and / or based on indirect (e.g., surface) measurements. For example, the RPM of the main downhole tool may be estimated based on the motor curve of the top drive of the downhole system from the data channel, such as the flow rate and / or differential pressure of the top drive measured at the surface. In another example, the downhole WOB may be estimated based on the surface WOB. In this way, the data manager can receive main wellbore data in various ways. This can help accommodate downhole systems of different costs and / or complexities. For example, a low-cost and / or less complex downhole system may have fewer data channels and / or measuring devices for downhole measurements and may be limited to surface measurements. As another example, a higher-cost and / or more complex downhole system may have more data channels and / or measuring devices for downhole measurements in addition to surface measurements. In this way, the data manager can collect and / or estimate relevant data in a variety of ways to facilitate the implementation of wear detection systems in any downhole system.

[0184] In some embodiments, the data manager receives at least some master borehole data in real time. For example, the data manager may communicate with one or more downhole or surface sensors and may receive master borehole data, such as ROP, WOB, and / or RPM, in real time during dynamic drilling of the master borehole. These real-time measurements can facilitate one or more of the dynamic and / or real-time functions of the wear detection system described herein.

[0185] In some embodiments, the data manager receives offset wellbore data for one or more offset wellbores. For example, the offset wellbore data may be associated with a global database for data on all known or available offset wellbores. In another example, the offset wellbore data may be associated with a selection of offset wellbores similar to or related to the main wellbore as described herein.

[0186] Offset wellbore data may include any data described above regarding main wellbore data, but used for the offset wellbore. For example, offset wellbore data may indicate the ROP, WOB, and RPM of the offset downhole tool implemented in the associated offset wellbore. Offset wellbore data may indicate one or more measured depths of the offset wellbore relative to any data included in the offset wellbore data. Offset wellbore data may include any other data associated with the offset wellbore. Offset wellbore data may be collected, measured, calculated, or otherwise received in any of the ways described above regarding main wellbore data. The data manager may store the offset wellbore data in a data storage device.

[0187] In some embodiments, the data manager separates some or all of the offset wellbore data 134 into specific runs. For example, in some cases, multiple runs of one or more offset downhole tools may be performed at or within a single wellbore or well site to drill different portions of the wellbore, implement different offset downhole tools within the wellbore, perform different downhole operations within the wellbore, or create one or more sidetracks outside the wellbore. Therefore, the data manager can separate the offset wellbore data for a specific offset wellbore or well site to represent various runs of one or more offset downhole tools entering the offset wellbore.

[0188] In some embodiments, offset wellbore data includes an indication of the type and / or extent of wear of the offset downhole tool (e.g., drill bit) implemented in the associated offset wellbore. For example, the offset wellbore may be a drilled wellbore (or drilled to or beyond the depth of interest), and the associated offset downhole tool may have been removed or pulled to the surface for inspection. The offset wellbore data may indicate the bit bluntness classification of the offset downhole tool, for example, based on the IADC bluntness classification standard. For example, for offset downhole tools, the offset wellbore data may indicate one or more of inner class, outer class, and bluntness characteristics such as annular or core-out. In this way, the offset wellbore data can indicate the wear condition or status of the offset downhole tool associated with the corresponding offset wellbore.

[0189] In some embodiments, the data manager selects one or more offset wellbores (e.g., from a database or global collection of offset wellbores) to receive offset wellbore data for those selected offset wellbores. For example, the data manager may identify one or more offset wellbores that have one or more similarities to the main wellbore. For example, similar offset wellbores may be wellbores that are geographically close to the main wellbore within the same basin, field, region, location, or otherwise. Similar offset wellbores may extend to the same or similar depth or depth range, penetrate the same or similar formations, enter the same or similar reservoirs, follow the same or similar trajectory (or part of a trajectory), have one or more of the same or similar bends or doglegs, or otherwise exhibit the same or similar characteristics or aspects as the main wellbore, and combinations thereof. The data manager may accordingly receive offset wellbore data for these selected similar offset wellbores so that the offset wellbore data can be correlated with the main wellbore data.

[0190] In some embodiments, the data manager filters offset wellbore data based on one or more offset wellbores. For example, the data manager may separate or exclude some of the offset wellbore data based on the classification of the bit bluntness level associated with the corresponding offset wellbore. For example, the data manager may filter out offset wellbore data associated with offset wellbores having inner and / or outer levels higher than one or more thresholds. The data manager may filter out offset wellbore data with one or more specific bluntness characteristics, such as annular or core exits.

[0191] As an example, in some cases, it may be advantageous to utilize offset borehole data that is not associated with severely passivated or damaged offset downhole tools (e.g., when removed from and inspected from an offset borehole). Therefore, the data manager can filter offset borehole data based on IADC passivation rating criteria that have one or more of the following: an inner rating of 4 or higher (out of 8), an outer rating of 4 or higher (out of 8), annularity characteristics, and coreout characteristics. Filtering out severely worn offset borehole data can be a minimum or worst-case constraint for implementing the wear detection techniques described herein. In some cases, such as when sufficient offset borehole data is available, the data manager can filter offset borehole data in a more stringent manner, such as filtering out offset borehole data associated with offset downhole tools exhibiting anything exceeding a slight wear level. For example, the data manager can filter based on boreholes having one or more of the following: an inner rating of 2 or higher, an outer rating of 2 or higher, or any associated wear characteristics (e.g., annularity or coreout) on the IADC passivation rating criteria. In this way, the data manager can filter the offset borehole data using one or more thresholds based on the passivation grading criteria, based on the availability of the offset borehole data. This not only facilitates the selection of a sufficient amount of offset borehole data to implement the techniques described herein, but also facilitates the selection of the best possible data, for example, for optimal operation of offset downhole tools exhibiting less wear. This can help to determine with high confidence that the expected tool indices determined by the wear detection system (as described herein) are an accurate representation of the characteristics represented by these indices and are not significantly affected or influenced by the passivation or wear condition of the associated offset downhole tool.

[0192] In some embodiments, instead of eliminating some off-bore data based on a bluntness grade criterion, the data manager separates or categorizes the off-bore data based on a bluntness grade criterion. For example, the data manager may divide the off-bore data into a severely worn group and a non-severely worn group. In another instance, the data manager may divide the off-bore data into a worn group and a no / slightly worn group. Categorizing the off-bore data in this way can facilitate one or more of the features described herein.

[0193] In some embodiments, the data manager receives formation data. Formation data may include information associated with formations traversed, drilled, or otherwise located within the main wellbore and / or one or more offset wellbores (e.g., main wellbore data and / or offset wellbore data may include formation data). For example, formation data may include information about the geological characteristics of rocks encountered during drilling the main wellbore and / or offset wellbore. Formation data may include data from gamma-ray sensors, resistivity sensors, porosity sensors, density sensors, acoustic sensors, calipers, core samples, or any other formation data. Formation data may indicate the boundaries of different subsurface formations, such as the top, bottom, and / or thickness of one or more formations. Formation data may indicate one or more measurement depths associated with any of the above measurements and / or data. In this way, formation data can identify one or more formations of interest, for example, relative to the main wellbore data and / or offset wellbore data.

[0194] In some embodiments, the data manager prepares and / or modifies any data it receives and / or has access to. For example, in some cases, wellbore data may be measured and / or recorded in the time domain. The data manager may transform or depth-gated the data to the depth domain. In other words, the data manager may modify the data to express it, for example, relative to the measurement depth rather than relative to time. Transforming data in this way can help conceptualize the data and / or tool metrics described herein, allowing the data to be considered relative to one or more measurement depths or ranges of measurement depths.

[0195] In some embodiments, the data manager aligns one or more instances of main borehole data and / or offset borehole data. For example, the data manager may align some or all of the main borehole data and / or offset borehole data based on the measurement depth or measurement depth range (e.g., of interest). In another example, the data manager may align some or all of the main borehole data and / or offset borehole data based on the formation. For example, based on main boreholes and offset boreholes drilled or traversing the same formation, the data manager may align or correlate some or all of the main borehole data measured within the formation with the offset borehole data also measured within the same formation (of the corresponding offset borehole). The data manager may align data based on the top of the formation. In some embodiments, this results in some or all of the measurement depths in the main borehole data and / or offset borehole data being misaligned at one or more locations. For example, the formation may exhibit a slope or inclination, such that two or more boreholes may reach or drill into the formation at different measurement depths (e.g., the top of the formation). As described herein, it may be advantageous to correlate wellbore data of associated wells with respect to the formation (e.g., location within the formation) rather than strictly with respect to the measurement depth, and the data manager can accordingly base it on formation alignment data. In some cases, formations may exhibit different thicknesses at one or more locations, allowing two or more wells to be drilled into or through formations of varying thicknesses. In some embodiments, the data manager compresses and / or stretches some or all of the master wellbore data and / or offset wellbore data to account for the thickness differences. This adjustment may be based on factors other than formation alignment data (e.g., the depth of the data). In this way, the data manager can modify the master wellbore data and / or offset wellbore data to correlate data of any well of interest based on the formation to which the well is located (e.g., in contrast to depth).

[0196] In some embodiments, the data manager receives user input. The data manager may receive user input, for example, via either a client device and / or a server device. Any data described herein may be input or enhanced via user input. For example, in some cases, some or all of the offset borehole data may be received by the data manager as user input. User input may be received in association with one or more functions or features of the wear detection system, such as selecting one or more offset boreholes for the offset borehole data, selecting one or more thresholds for classifying tool indicators, or a subset of any other features described herein.

[0197] As described above, the wear detection system includes a tool index manager. The tool index manager can identify and monitor one or more downhole tool indices to characterize and / or quantify the wear of the main downhole tools implemented in the main wellbore. Downhole tool indices can describe or represent one or more properties or characteristics of the formation and / or the downhole tools used to degrade the associated wellbore.

[0198] In some embodiments, the tool index manager determines one or more expected downhole tool indices (or expected values ​​of downhole tool indices) based on offset wellbore data. For example, as described below, the tool index manager may determine formation stiffness at one or more (or all) measurement depths for each offset wellbore of the offset wellbore data. The tool index manager may determine the mean, median, percentile, or any other statistical calculation of the determined formation stiffness of the offset wellbore as the expected formation stiffness of the main wellbore at one or more measurement depths (e.g., or formation-aligned measurement depths). According to at least one embodiment of this disclosure, the tool index manager determines the expected formation stiffness as the median of the determined formation stiffness of the offset wellbore. In some embodiments, the tool index manager determines a threshold range, such as a maximum and / or minimum formation stiffness, based on the determined formation stiffness of the offset wellbore. For example, the tool index manager may determine a maximum or upper limit threshold as the 75th percentile (P75), the 90th percentile (P90), or any other percentile. In another example, the tool indicator manager can determine a minimum or lower threshold as the 25th percentile (P25), the 10th percentile (P10), or any other percentile. The tool indicator manager can determine the threshold based on geographic location or application. The tool indicator manager can determine the expected value of any one and any number of downhole tool indicators in this way. The tool indicator manager can store any of this information as tool indicator data in a data storage device.

[0199] In some embodiments, downhole tool parameters include formation stiffness K. Formation stiffness K can be a measure or estimate of the mechanical stiffness or hardness of a formation or its ability to resist deformation under applied loads. Formation stiffness can be expressed as Young's modulus or bulk modulus. Formation stiffness K can be a useful measure for understanding the interaction between the downhole tool and the formation, and can help assess the wear of the downhole tool as it traverses the formation.

[0200] The formation stiffness K can be determined by the following formula:

[0201]

[0202] WOB = Drilling Pressure

[0203] ROP = Drilling Rate

[0204] RPM = Downhole tool rotation speed or (revolutions per minute)

[0205] Formation stiffness K can be determined in any other way or according to any other formula or principle used to characterize formation stiffness K. Formation stiffness K is typically in the range of 0.1-5 Mlbf / in. The tool index manager can determine the formation stiffness K of one or more wellbores and one or more (or all) depths of interest. WOB and / or RPM can be measured from downhole sensors or inferred from surface measurements. In some embodiments, measurements with higher confidence (e.g., direct or downhole measurements) are preferred.

[0206] In some embodiments, downhole tool metrics include mechanical specific energy (MSE). MSE can be a measure or estimate of the energy required per unit volume of rock required to degrade, break down, or otherwise remove rock units from a formation. MSE can provide insights into drilling efficiency and energy consumption during the drilling operation of downhole tools.

[0207] MSE can be determined using the following formula:

[0208]

[0209] WOB = Drilling Pressure

[0210] ROP = Drilling Rate

[0211] RPM = Downhole tool rotation speed or (revolutions per minute)

[0212] TOR = Drill bit torque

[0213] A bit =Area of ​​the drill bit

[0214] The MSE can be determined in any other way or according to any other formula or principle used to characterize the MSE. The tool index manager can determine the MSE of one or more wellbores and one or more (or all) depths of interest.

[0215] In some embodiments, downhole tool parameters include bit aggressiveness μ. Bit aggressiveness μ can be a measure or estimate of the coefficient of friction between the downhole tool and the formation under given applied weight and torque.

[0216] The drill bit's attack power μ can be determined by the following formula:

[0217]

[0218] WOB = Drilling Pressure

[0219] TOR = Drill bit torque

[0220] Dbit =Diameter of the drill bit

[0221] Drill bit aggression can be determined in any other way or according to any other formula or principle used to characterize drill bit aggression μ. The tool index manager can determine the drill bit aggression μ for one or more wellbores and one or more (or all) depths of interest.

[0222] In some embodiments, the downhole tool metric includes the drilling rate per revolution (PPR). PPR can represent the distance the downhole tool travels through the formation per revolution.

[0223] PPR can be determined using the following formula:

[0224]

[0225] ROP = Drilling Rate

[0226] RPM = Downhole tool rotation speed or (revolutions per minute)

[0227] The PPR can be determined in any other way or according to any other formula or principle used to characterize the PPR. The Tool Index Manager can determine the PPR for one or more wellbores and one or more (or all) depths of interest.

[0228] In this way, the tool index manager can identify one or more, or any other, relevant indices or metrics among these downhole tool indices to characterize and / or quantify the wear of the associated downhole tools.

[0229] In some embodiments, the tool index manager determines and monitors one or more of these downhole tool indices for the main wellbore and / or the main downhole tools. For example, based on main wellbore data, the tool index manager can determine (e.g., dynamically and / or in real-time) the main downhole tool indices for the main wellbore. For example, the tool index manager can determine the dynamic, current, or real-time formation stiffness (or other downhole tool indices) of the main wellbore based on real-time main wellbore data. The tool index manager can store the main downhole tool index information as tool index data in a data storage device.

[0230] As described in further detail below, determining the primary downhole tool indices can help assess the wear condition of downhole tools. Comparing the primary downhole tool indices with associated expected downhole tool indices and / or thresholds of expected downhole tool indices can help conceptualize the extent or degree of wear of the primary downhole tool based on the characteristics, aspects, or properties represented by the associated downhole tool indices. For example, the expected formation stiffness K can be determined based on various offset wellbores that are similar to the primary wellbore in one or more aspects. expAs mentioned above, offset borehole data can be filtered to remove offset borehole data associated with offset downhole tools that exhibit wear to some extent. Therefore, the expected formation stiffness K... exp The expected formation stiffness K can be determined with high precision, or in other words, with high confidence. exp It accurately represents the actual stiffness of the formation without being affected by offsets in the bottom layer data or the blunting conditions of the downhole tools. The tool index manager can determine the primary formation stiffness K in real time based on real-time primary wellbore data. subj Main wellbore data and offset wellbore data can be aligned based on the formation to achieve the expected formation stiffness K. exp This can represent the actual formation stiffness at the current location of the main wellhead tool within the formation. The main formation stiffness K is then expressed as... subj With expected formation stiffness K exp By comparing these parameters, the wear condition of the main downhole tools can be identified. For example, observing the main formation stiffness K... subj Increasing the value above the expected value can signal that the main downhole tool is wearing out or has already worn out (e.g., contrary to signaling an increase in formation stiffness based on the expected formation stiffness K). exp A high level of confidence indicates that this is not the case.

[0231] A similar approach can be followed for any of the downhole tool parameters described in this article. For example, the main mechanical energy specific energy (MSE) was observed. sub Increase to the expected mechanical specific energy MSE exp The above can indicate that more energy is being used to remove an equivalent unit of rock from the formation, which can signal that the drill bit is becoming dull (e.g., the opposite of a signal indicating that the rock is hardening). In another example, the aggressiveness μ of the main drill bit was observed. subj Reduced to below expected drill bit aggression μ exp This can indicate that the friction between the main downhole tool and the formation has decreased. Therefore, this can signal that the main downhole tool is encountering less frictional resistance from the formation due to a blunt drill bit, rather than, for example, that the formation has become softer. In another example, the main downhole progress per revolution (PPR) was observed. subj Reduced to below the expected PPR per revolution exp This indicates that the main downhole tool penetrates less formation per revolution. Therefore, this can signal that the main downhole tool is struggling more to remove material from the formation due to a dull bit, rather than, for example, that the formation is becoming harder. In this way, when comparing the main indicator with expected indicators, the downhole tool indicators (or any other indicators) described herein can facilitate the characterization and / or quantification of the main downhole tool's wear condition based on the different characteristics represented by the respective downhole tool indicators.

[0232] In some embodiments, the tool index manager determines the index ratio (IR) of a primary downhole tool index to its corresponding expected downhole tool index. For example, the tool index manager may determine the IR of formation stiffness as the ratio of primary formation stiffness to its corresponding expected formation stiffness (e.g., the formation stiffness ratio FSR as mentioned herein). The tool index manager may determine the IR of any of the downhole tool indices described herein. In this way, the IR facilitates comparison of the primary downhole tool index with its corresponding expected downhole tool index. For example, while viewing and comparing the values ​​and / or plotted representations (e.g., side-by-side) of the primary and expected downhole tool indices may be useful, the IR provides a quantitative representation of that comparison.

[0233] In some embodiments, the Tool Indicator Manager categorizes the IR (and correspondingly categorizes the wear of the main downhole tool) based on one or more predetermined thresholds for the IR. Example thresholds can be established and implemented for any IR of any downhole tool indicator. Potential FSR values ​​can be divided into several categories or classifications. An FSR from 0 to 2 can be the minimum, low, or acceptable classification of FSR. An FSR exceeding 3.5 can be the maximum or severe classification of FSR. One or more intermediate classifications, such as medium and / or high, may also exist for FSR. The classification of FSR can be determined based on historical data from (e.g., geographically proximate) offset wellbores and / or from selected wellbores of similar applications (e.g., if no proximate offset wellbores are available). The Tool Indicator Manager can determine and / or classify the FSR for any (or all) measurement depths (including dynamic measurement depths) of the main wellbore. For example, the Tool Indicator Manager can determine and update the FSR and associated classifications in real time and during drilling to provide an accurate and dynamic representation of the wear status of the main downhole tool. In this way, the FSR can provide a simple and intuitive indication of the wear level or severity of the main downhole tool.

[0234] In some embodiments, the FSR classification corresponds to a rating metric or rating system. For example, the first or lowest classification may correspond to an FSR rating of 0, the next lowest classification may correspond to a rating of 1, and so on. The tool metric manager can determine and / or associate FSR rating values ​​to facilitate one or more functions of the wear detection system as described herein. The FSR rating scale and associated classifications may include any other ratings and / or may be developed in any other manner.

[0235] To date, wear detection systems have been primarily described in terms of one or more downhole tool indices that can be used for comparison with expected values ​​for the main downhole tool. For example, the value of a main downhole tool index can be compared to a corresponding value of an expected downhole tool index at a relevant measurement depth and / or time to characterize the wear of the main downhole tool. In this way, the aforementioned downhole tool indices can be compared, for example, at time snapshots (e.g., real-time and / or historical) of the main wellbore data and the offset wellbore data.

[0236] In some embodiments, it may be advantageous to conceptualize and / or quantify the wear of the downhole tool relative to the total wear of the drill bit cumulatively at numerous data points, measurement depths, and / or moments. In some embodiments, the tool index manager determines the cumulative wear index (CWI) of the main downhole tool. The CWI can represent the wear of the main downhole tool based on relating the determined wear level of the downhole tool (e.g., based on one or more downhole tool indices) to the number of revolutions of the downhole tool at the relevant wear level. For example, the CWI can be expressed as equivalent cumulative drill bit damage revolutions and can be based on or associated with formation stiffness determined at one or more (or all) previous measurement depths on the well at the dynamic measurement depth. The CWI can incorporate the RPM and ROP of the main downhole tool at each measurement depth. The CWI can incorporate the expected formation stiffness at each measurement depth. The expected formation stiffness can be normalized based on a normalization factor. The CWI can incorporate the rating or classification of the FSR, for example, expressed as a value between 0 and 3 (or any other scale). The CWI can be determined by the following formula:

[0237]

[0238] i = summation index

[0239] n = Total number of dynamically measured depths

[0240] ΔMD = Change in measured depth from the previously summed index

[0241] RPM = Downhole tool rotation speed or (revolutions per minute)

[0242] FSR_RI = Formation stiffness ratio rating (in normalized scaling)

[0243] FS = Expected formation stiffness

[0244] FS_norm_factor = Normalization factor used to determine the relative level of formation stiffness

[0245] For additional or alternative locations, CWI can be determined by the following formula, with parameter definitions similar to those above:

[0246]

[0247] CWI can be determined in any other way or according to any other formula or principle used to characterize CWI. For example, while CWI is specifically described relative to formation stiffness and FSR, in some embodiments, CWI is determined relative to one or more other downhole tool parameters, such as in addition to or instead of formation stiffness. The tool parameter manager can determine and / or update CWI in real time and during drilling to provide a real-time indication of CWI.

[0248] CWI can represent the cumulative or total wear of the main downhole tool at some or all of the measurement depth in the main wellbore. For example, CWI can correlate a determined wear level or classification (e.g., an FSR rating as described herein) with the number of revolutions the downhole tool had completed when observed to have that wear rating / classification. CWI can be the sum of multiple non-negative values, such that CWI may remain constant or increase over time. This may be consistent with the real-world behavior of downhole tool wear, which may be constant and relatively low over a period of time, but may wear to increasingly greater levels over time. The rating associated with the lowest (e.g., acceptable) FSR classification can be 0, and CWI (e.g., due to the FSR_RI term) can also be 0, while the main downhole tool is observed at or within the lowest wear classification (e.g., the sum of negligible or zero-value terms). CWI can take into account the number of revolutions in which the downhole tool is observed to have that non-zero wear classification when the determined FSR classification / class becomes non-zero (e.g., medium, high, or severe) based on the underlying primary formation stiffness exceeding the expected formation stiffness. Therefore, CWI calculations can include the sum of one or more non-zero iterations representing these rotations at non-negligible (e.g., medium, high, or severe) wear levels. In this way, CWI can increase over time based on instances of increased FSR, but the level of increase can depend on the associated rotations of the downhole tool.

[0249] In this way, CWI can provide a more detailed characterization of the wear of the main downhole tool than, for example, the downhole tool indices discussed above. For instance, the IR of the downhole tool indices discussed above can provide a valuable but simple comparison of the master (e.g., actual) value with the expected value at a time snapshot, but CWI can provide a more detailed characterization by considering how long the main downhole tool interacts with the formation at higher than expected index values. In some embodiments, CWI is more reliable and / or stable by showing wear over time. For example, because downhole tool indices are associated with a specific time, data quality issues such as offset wellbore data and / or master wellbore data, depth / formation alignment issues, etc., can cause spikes or sudden increases in the IR between actual and expected values ​​(as described below). It can be difficult to discern whether these spikes are due to such data issues or whether they truly indicate wear of the main downhole tool. However, CWI may be less susceptible to misalignment or data quality issues because even large spikes in the IR may only slightly increase the CWI based on a relatively small associated revolutions. Therefore, due to the time element of CWI, and based on the fact that CWI is the accumulation of all identified wear over the (e.g., large) operating time of the main downhole tool, CWI can more accurately reflect the actual wear of the main downhole tool.

[0250] As described above, CWI can include a normalization factor for normalizing expected formation stiffness. The normalization factor can represent typical (e.g., average) formation stiffness typically observed for related or similar wellbores, such as across all measurement depths and / or throughout all formations or subsurfaces. For example, the normalization factor can be based on a set of offset wellbores, such as offset wellbore data; offset wellbores within a geographic distance from the main wellbore; offset wellbores in the same field, basin, region, formation, or location as the main wellbore; offset wellbores within a global database; or any other set of offset wellbores. The normalization factor can be the mean, median, or percentile of all formation stiffness observed (at all measurement depths) across all associated related offset wellbores. In this way, the normalization factor can be a global statistic representing typical formation stiffness typically found in any wellbore and at any location and / or measurement depth. The normalization factor can be expressed as a single value, a polynomial, an index, or any other suitable expression to measure the relative level of expected formation stiffness as described herein.

[0251] A normalization factor can be used to determine how the expected formation stiffness (e.g., at a specific measurement depth) compares to the typical or average stiffness of the formation or earth typically used in a master wellbore at any measurement depth. For example, if the expected formation stiffness is greater than the normalization factor, it can be determined that the expected formation stiffness at a given measurement depth is above normal. Similarly, if the expected formation stiffness is less than the normalization factor, it can be determined that the expected formation stiffness at a given measurement depth is below normal. This comparison can be implemented to weight the expected formation stiffness in the calculation of CWI. For example, as shown in the formula above, the expected formation stiffness can be inversely weighted by the normalization factor (e.g., divided by the normalization factor). This can have the effect of reducing the weight of instances where the expected formation stiffness is below normal, such that when summed, these instances will accumulate wear to a smaller extent. Similarly, cases where the expected formation stiffness is above normal can be weighted such that when summed, these cases will accumulate wear to a larger extent. CWI can be determined in this way to reflect the concept that even if wear indicators of the downhole tool are given at a given measurement depth (e.g., elevated FSR), if the expected formation stiffness at that particular measurement depth is less than normal (e.g., the formation is softer than normal), the main downhole tool may wear to a lesser degree in formations that are softer than normal. Therefore, CWI can be determined by applying a lower weight to these instances. Similarly, if the expected formation stiffness at a particular measurement depth is higher than normal (e.g., the formation is harder than normal), the main downhole tool may wear to a greater extent in formations that are harder than normal. Therefore, CWI can be determined by applying a higher weight to these instances.

[0252] In some embodiments, the tool index manager classifies CWI based on one or more predetermined thresholds. For example, observed CWI values ​​can be classified into several different categories or classifications. CWI between 0 and 10,000 revolutions may be the minimum, low, or acceptable classification of CWI. CWI exceeding 30,000 revolutions may be the maximum or severe classification of CWI. One or more intermediate classifications, such as medium and / or high, may also exist for CWI. The tool index manager can classify CWI based on any threshold or category consistent with those described herein. The tool index manager can determine and / or classify CWI at any (or all) measurement depths (including dynamic measurement depths) of the main wellbore. For example, the tool index manager can determine and update CWI and associated classifications in real time and during drilling to provide an accurate and dynamic representation of the wear status of the main downhole tools. In this way, CWI can provide a comprehensive and intuitive indication of the severity of wear on the main downhole tools.

[0253] In some embodiments, the tool index manager determines one or more general statistics. General statistics can be, for example, values, measures, and / or indications that represent general characteristics or aspects of the main wellbore and / or off-bore at a high level. For example, general statistics may include an indication of footage. Based on off-bore data, the tool index manager can determine the footage or total drilling distance associated with the downhole tool for each off-bore. The tool index manager can accordingly determine the average, median, or any other statistical calculation of the footage values ​​for all off-bore data. In this way, footage general statistics can provide a simple, high-level overview, for example, what footage can be expected from the main downhole tool. The tool index manager can in this way determine general statistics for any other relevant aspects, parameters, or characteristics, such as drilling rate, CWI, FSR, IR, ROP, etc.

[0254] As described above, the wear monitoring system includes a reporting engine. The reporting engine can generate one or more reports. In some embodiments, the reporting engine displays one or more reports via a graphical user interface on a user device.

[0255] In some embodiments, the report represents one or more of the downhole tool indices described herein for the offset wellbore and / or the main wellbore. For example, the report may show formation stiffness determined for several offset wellbores, and the primary formation stiffness of the main wellbore. In addition to or in lieu of formation stiffness, the report may include one or more other downhole tool indices. The report may indicate the formation stiffness of the offset wellbore through a series of measurement depths. The report may indicate the primary formation stiffness of the main wellbore in real time. For example, the report may indicate the dynamic measurement depth of the main wellbore and may indicate the real-time primary formation stiffness at the dynamic measurement depth. The report may indicate the primary formation stiffness before the dynamic measurement depth or at one or more other measurement depths on the wellbore. The reporting engine may continuously and / or periodically update or regenerate the report to represent the dynamic or current value of the target formation stiffness.

[0256] This report facilitates the comparison of values ​​for one or more downhole tool parameters in the main wellbore with values ​​calculated or observed in one or more offset wellbores. This side-by-side comparison can help determine when the main downhole tool has worn out. For example, the determined formation stiffness of each offset wellbore consistently appears between approximately 1 and 3 Mlbf / in across the entire measurement depth. Furthermore, the determined formation stiffness of each offset wellbore appears relatively continuous across the entire measurement depth. However, at some point, the primary formation stiffness of the main wellbore may begin to rise above that of the offset wellbore, for example, rising to approximately 2–8 Mlbf / in. Additionally, the primary formation stiffness is observed to be significantly more discontinuous and fragmented than that of the offset wellbore. Therefore, these data characteristics of the primary formation stiffness of the main wellbore, which become apparent through comparison with the formation stiffness of the offset wellbore, can indicate that the main downhole tool has worn out, is damaged, or both. In this way, the report can help identify the wear condition of the main downhole tool.

[0257] The reporting engine can store reports as report data in a data store. In some embodiments, the reporting engine presents reports via a graphical user interface on the user's device.

[0258] In some embodiments, the report indicates or represents a anticipated downhole tool metric as described herein. For example, the anticipated downhole tool metric may be the anticipated formation stiffness. The report may indicate or represent a corresponding primary downhole tool metric, such as primary formation stiffness. The report may indicate one or more thresholds or boundaries for the anticipated downhole tool metric. Thresholds may be maximum and / or minimum values, percentile ranges, standard deviations, or any other thresholds or boundaries used for or based on the anticipated downhole tool metric. The report may show the primary downhole tool metric at the dynamic measurement depth. The reporting engine may update and / or regenerate the report to represent the primary downhole tool metric in real time and during drilling as the dynamic measurement depth advances down through the formation. In addition to or in lieu of formation stiffness, the report may include one or more other downhole tool metrics and associated thresholds.

[0259] In this way, the report can show the status of the main downhole tool indicators relative to the expected downhole tool indicators and / or thresholds, providing a useful comparison to measure the observed values ​​of the main downhole tool indicators. For example, at a certain measurement depth, it may be observed that the main downhole tool indicators exceed both the expected downhole tool indicators and the thresholds once or multiple times. Additionally, it may be observed that the main downhole tool indicators become fragmented and discontinuous. In this manner, the report can indicate that the main downhole tool has become dull and / or damaged.

[0260] The reporting engine can store reports as report data in a data store. In some embodiments, the reporting engine presents reports via a graphical user interface on the user's device.

[0261] In some embodiments, the report indicates or represents one or more primary downhole tool parameters, expected downhole tool parameters, and / or thresholds. The report may indicate these measures of any number of downhole tool parameters as discussed herein, such as formation stiffness, bit attack, MSE, or PPR, or any other parameter. The report may indicate one or more measurement depths in the primary wellbore, including at dynamic measurement depths. In this way, the report can help, for example, evaluate one or more of the primary downhole tool parameters against expected downhole tool parameters and / or thresholds.

[0262] In some embodiments, the report indicates one or more drilling parameters. For example, for a range of measured depths, the report may indicate ROP, WOB, RPM, torque (TOR), or any other parameter associated with the main downhole tool and / or the main wellbore. In some embodiments, the report indicates one or more statistical values ​​and / or ranges associated with the drilling parameters. For example, the report may indicate the mean, median, etc., of the drilling parameters based on offset wellbore data. The report may indicate one or more boundaries of the drilling parameters, such as maximum and / or minimum values, quartile ranges, standard deviation ranges, percentile ranges, or any other boundaries. These statistics may be determined by the tool index manager based on the offset wellbore data. In this way, the report can facilitate comparison of one or more drilling parameters of the main wellbore with those implemented by the offset wellbore.

[0263] In some embodiments, the report indicates the Inverse Reduction (IR) of one or more of the primary downhole tool indicators (PMRs) and their associated expected PMRs. For example, the report may plot the primary wellbore's Free Surface Stiffness (FSR) across the entire measurement depth. A graph of the FSR can provide a visual representation of both dynamic (e.g., at dynamic measurement depth) and historical values ​​of the FSR. In this way, the FSR can be monitored to facilitate the determination and conceptualization of the wear level of the primary downhole tool. In some embodiments, the report indicates a classification or rating of the FSR as described herein. For example, color codes or scales (or any other suitable techniques) can indicate the classification of the FSR, such as from low to severe. The FSR may exhibit one or more increases corresponding to the deviation of the primary formation stiffness from the expected formation stiffness. The increase may be manifested as a spike or peak, or it may be a smaller or more subtle increase. The report 800 may indicate the increase in FSR using an associated color (or other indicator) for the classification, thereby indicating the degree of increase. In this way, the report can facilitate the identification of instances of primary formation stiffness (e.g., via the FSR) that can indicate wear of the primary downhole tool.

[0264] In some embodiments, the report indicates the CWI of the main downhole tool as described herein. For example, the report may plot the CWI of the main wellbore across the entire measurement depth range. A graph of the CWI can provide a visual representation of the dynamic (e.g., at dynamic measurement depth) and historical values ​​of the CWI. The report may indicate a classification or rating of the CWI as described herein. For example, color codes or scales (or any other techniques) may indicate the classification of the CWI, such as from low to severe. The classification of wear levels may be determined based on historical data from similar and / or geographically proximate offset wellbores. For example, the CWI may be calculated for relevant offset wellbores with and without severe wear levels to determine a reference level for the classification. The CWI may grow or increase over time, consistent with the increase in wear of the main downhole tool over time. When the CWI progresses to a wear level that increases the classification, the report may indicate the CWI classification by incorporating the relevant color (or other indication) of the classification into the CWI graph.

[0265] As mentioned above, CWI may not be as susceptible to misalignment or data quality issues as FSR, for example. For instance, FSR may exhibit significant spikes at one or more measurement depths. This report could indicate that the spike is classified as severe. Based solely on FSR, this spike could indicate that the main downhole tool is damaged or worn and may need to be removed from the main wellbore. However, the corresponding CWI value could indicate that the cumulative wear of the main downhole tool is still relatively low and is classified as low-level. As mentioned above, the spike and / or high level of FSR can be factored into the CWI calculation, but the relatively short span of the spike and therefore the relatively low revolutions of the downhole tool only result in a small increase in CWI. Additionally, the expected relatively low formation stiffness can be observed, which can further reduce the impact of the spike on CWI, as mentioned above. Therefore, CWI can be a more accurate measure of the wear of the main downhole tool, because based solely on FSR, it may appear that the main downhole tool is severely worn, when in reality the main downhole tool may not reach this severe wear state until a later measurement depth, as can be indicated by CWI. Spikes can indicate, for example, data alignment issues between main borehole data and offset borehole data, rather than wear and tear on the main downhole tool.

[0266] In some embodiments, the report indicates one or more summary statistics for the main downhole tool as described herein. Summary statistics may indicate one or more top-level or advanced characteristics or values ​​used to compare the performance of the main downhole tool with that of the offset downhole tool in the offset wellbore. For example, summary statistics in this manner can provide a simple and accessible assessment of one or more aspects of the main wellbore compared to more detailed information included in other sections of the report.

[0267] The report engine can store reports as report data in a data storage device. In some embodiments, the report engine presents reports via a graphical user interface on a user device.

[0268] In some embodiments, the reporting engine helps identify that the main downhole tool has worn or damaged. For example, based on or in conjunction with any reports discussed herein, the reporting engine can monitor one or more values, metrics, indicators, etc., and can generate flags or alerts. For example, the reporting engine can monitor the main downhole tool indicator against associated expected downhole tool indicators and / or one or more associated thresholds to identify that the main downhole tool indicator has exceeded or surpassed one or more of these values. In another example, the reporting engine can monitor IR (such as FSR) to identify when it exceeds a certain value. In yet another example, the reporting engine can monitor CWI against one or more predetermined categories or classifications to identify when the CWI changes classification or reaches a certain classification. The reporting engine can monitor any value, metric, or indicator to make any relevant determination consistent with those described herein. The reporting engine can monitor one or more metrics in this manner and can generate alerts based on one or more criteria. For example, an alert can be based on a metric that exceeds (e.g., expected) a value or threshold (or both). In another example, an alert can be based on a metric that exceeds a value to a certain extent, or over a certain amount of time (or distance), or a combination of both. In another example, an alarm could be based on a metric classified into a given category or category, or on a metric that changes the classification. In yet another example, the reporting engine could generate an alarm based on considerations of how much of the main wellbore remains to be drilled or how far the main wellbore is from its target. For example, an alarm could signal to the downhole system operator that the main downhole tool is worn and should be removed and / or replaced. However, in some cases, if the main wellbore is nearing completion, it may be advantageous to complete the wellbore with the main downhole tool despite the wear condition and potential damage to it. Therefore, the reporting engine could incorporate considerations of the remaining drilling distance into the determination of alarm generation.

[0269] The reporting engine can alert users of the wear monitoring system. For example, the reporting engine can present alarms or flags to the user via a graphical user interface on the user's device, or it can otherwise alert the user. In some embodiments, the reporting engine facilitates changes to the operation of the downhole system. For example, the reporting engine can alert the user to the wear status of the main downhole tool so that one or more drilling parameters can be adjusted. In some embodiments, the reporting engine helps adjust one or more drilling parameters based on identified flags or alarms. For example, the reporting engine can suggest adjustments to the user, provide information about the wear status of the main downhole tool to one or more auxiliary systems, automatically adjust one or more drilling parameters, stop the operation of the downhole system, or any other action or combination thereof for adjusting drilling parameters.

[0270] In some embodiments, according to at least one embodiment of this disclosure, a method or series of actions for detecting wear of downhole tools implemented in a main wellbore is described herein. The method may include the actions described below, and alternative embodiments may add, omit, reorder, or modify any actions.

[0271] In some embodiments, the method includes receiving offset wellbore data for one or more offset wellbores. For example, the offset wellbore data may include drilling rate, bit pressure, and rotational speed associated with each offset wellbore. In some embodiments, a wear detection system filters out one or more offset wellbores based on the wear condition of the downhole tools associated with the offset wellbore. In some embodiments, the offset wellbore data is based on offset wellbores associated with the main wellbore in the same formation and / or at the same depth.

[0272] In some embodiments, the method includes the action of determining, based on the offset borehole data, expected downhole tool parameters at one or more measurement depths including the main borehole dynamic measurement depth. For example, the expected downhole tool parameters could be the expected formation stiffness of the main borehole at one or more measurement depths based on the offset borehole data. The expected downhole tool parameters could be the median downhole tool parameters based on the offset borehole data. In another example, based on the offset borehole data, the expected downhole tool parameters could be the expected mechanical energy of the formation, the expected bit attack capability of the downhole tool, or the expected rate of revolution per revolution of the downhole tool.

[0273] In some embodiments, the method includes receiving main wellbore data.

[0274] In some embodiments, the method includes the following action: determining, in real time, the main downhole tool parameters at the dynamic measurement depth based on the main wellbore data.

[0275] In some embodiments, the method includes determining downhole tool wear based on comparing the primary downhole tool index with a expected downhole tool index at the dynamically measured depth. In some embodiments, the wear detection system aligns primary wellbore data with offset wellbore data based on formation depth. In some embodiments, the wear detection system classifies the determined downhole tool wear based on one or more predetermined thresholds for the primary downhole tool index. In some embodiments, the wear detection system determines a downhole tool index ratio of the primary downhole tool index to the expected downhole tool index.

[0276] In some embodiments, the method includes generating a graph representing the expected downhole tool metric and the primary downhole tool metric. In some embodiments, the graph represents a downhole tool metric ratio. In some embodiments, the method includes adjusting one or more drilling parameters based on a determined wear of the downhole tool.

[0277] In some embodiments, according to at least one embodiment of this disclosure, a method or series of actions for detecting wear of downhole tools implemented in a main wellbore is described herein. The method may include the actions described below, and alternative embodiments may add, omit, reorder, or modify any actions.

[0278] In some embodiments, the method includes receiving offset wellbore data of one or more offset wellbores.

[0279] In some embodiments, the method includes determining the expected downhole tool indices at each of a plurality of measurement depths, including the dynamic measurement depth of the downhole tool, based on offset wellbore data.

[0280] In some embodiments, the method includes receiving main borehole data. For example, the main borehole data may include rotation data of downhole tools.

[0281] In some embodiments, the method includes the following action: determining the main downhole tool index at each of the plurality of measurement depths based on the main wellbore data.

[0282] In some embodiments, the method includes determining a cumulative wear index of the downhole tool at a plurality of measurement depths based on a comparison of the primary downhole tool index with the expected downhole tool index. For example, the cumulative wear index may correlate the comparison of the primary downhole tool index with the expected downhole tool index with the rotation of the downhole tool based on measurement data. For example, the cumulative wear index may identify the number of rotations of the downhole tool relative to the primary downhole tool index as compared to one or more threshold ranges of the expected downhole tool index. In some embodiments, the wear detection system classifies the determined cumulative wear index based on one or more predetermined thresholds. In some embodiments, the wear detection system determines one or more formation normalization factors for the offset wellbore and the primary wellbore. The cumulative wear index may be determined based on normalization of the expected downhole tool index according to the normalization factor.

[0283] In some embodiments, according to at least one embodiment of this disclosure, a method or series of actions for detecting wear of downhole tools implemented in a main wellbore is described herein. The method may include the actions described below, and alternative embodiments may add, omit, reorder, or modify any actions.

[0284] In some embodiments, the method includes receiving offset wellbore data of one or more offset wellbores.

[0285] In some embodiments, the method includes the following action: determining, based on the offset borehole data, the expected downhole tool parameters at each of a plurality of measurement depths in which the downhole tool is located at one or more offset boreholes.

[0286] In some embodiments, the method includes receiving main wellbore data from the main wellbore.

[0287] In some embodiments, the method includes the following action: determining the primary formation stiffness of the downhole tool at each of the plurality of measurement depths based on the primary wellbore data.

[0288] In some embodiments, the method includes comparing a primary formation stiffness with a expected formation stiffness to determine a formation stiffness ratio at each of a plurality of measurement depths, and classifying the formation stiffness ratio based on one or more predetermined thresholds for the formation stiffness ratio.

[0289] In some embodiments, the method includes determining a cumulative wear index for the downhole tool based on correlating a classification of formation stiffness ratios with the number of revolutions of the downhole tool at multiple measurement depths. In some embodiments, the method includes determining a normalization factor for the formation stiffness based on the offset wellbore data. The cumulative wear index may be determined based on normalizing a desired formation stiffness according to the normalization factor. In some embodiments, the method includes adjusting one or more drilling parameters based on the determined cumulative wear index.

[0290] In some embodiments, certain components may be included within a computer system. One or more computer systems may be used to implement the various devices, components, and systems described herein.

[0291] A computer system includes a processor. The processor can be a general-purpose single-chip or multi-chip microprocessor (e.g., an advanced RISC (Reduced Instruction Set Computer) machine (ARM)), a special-purpose microprocessor (e.g., a digital signal processor (DSP)), a microcontroller, a programmable gate array, etc. The processor can be referred to as a central processing unit (CPU). Although only a single processor is shown in the computer system, in alternative configurations, a combination of processors (e.g., ARM and DSP) can be used.

[0292] The computer system further includes memory in electronic communication with the processor. The memory may include a computer-readable storage medium and may be any available medium accessible by a general-purpose or special-purpose computer system. A computer-readable medium storing computer-executable instructions is a non-transitory computer-readable medium (device). A computer-readable medium carrying computer-executable instructions is a transmission medium. Therefore, by way of example and not limitation, embodiments of this disclosure may include at least two distinct types of computer-readable media: a non-transitory computer-readable medium (device) and a transmission medium.

[0293] Non-transitory computer-readable media (devices) and transmission media can both be temporarily used to store or carry software instructions in the form of computer-readable program code that allows the execution of embodiments of this disclosure. Non-transitory computer-readable media can further be used to persistently or permanently store such software instructions. Examples of non-transitory computer-readable storage media include physical memory (e.g., RAM, ROM, EPROM, EEPROM, etc.), optical disc storage devices (e.g., CD, DVD, HDDVD, Blu-ray, etc.), storage devices (e.g., disk storage devices, magnetic tape storage devices, floppy disks, etc.), flash memory or other solid-state storage devices or memories, or any other non-transmission medium that can be used to store program code in the form of computer-executable instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, whether such program code is stored in software, hardware, firmware, or a combination thereof.

[0294] Instructions and data may be stored in memory. Instructions may be executed by a processor to implement some or all of the functionality disclosed herein. Executing instructions may involve using data stored in memory. Any of the various examples of modules and components described herein may be implemented, in part or in whole, as instructions stored in memory and executed by a processor. Any of the various examples of data described herein may be data stored in memory and used during processor execution of instructions.

[0295] Computer systems may also include one or more communication interfaces for communicating with other electronic devices. Communication interfaces may be based on wired communication technologies, wireless communication technologies, or both. Some examples of communication interfaces include Universal Serial Bus (USB), Ethernet adapters, wireless adapters operating according to the Institute of Electrical and Electronics Engineers (IEEE) 802.11 wireless communication protocol, Bluetooth® wireless communication adapters, and infrared (IR) communication ports.

[0296] A communication interface connects a computer system to a network. A “network” or “communication network” can generally be defined as one or more data links that enable the transmission of electronic data between computer systems and / or modules, engines, or other electronic devices, or combinations thereof. When information is transmitted or provided to a computing device via a communication network or another communication connection (hardwired, wireless, or a combination of hardwired and wireless), the computing device appropriately considers the connection as a transmission medium. The transmission medium may include communication networks and / or data links, carrier waves, wireless signals, etc., which can be used to carry desired program or template code devices or instructions in the form of computer-executable instructions or data structures, and can be accessed by a general-purpose or special-purpose computer.

[0297] Computer systems may also include one or more input devices and one or more output devices. Some examples of input devices include keyboards, mice, microphones, remote control devices, buttons, joysticks, trackballs, touchpads, and light pens. Some examples of output devices include speakers and printers. A particular type of output device typically included in a computer system is a display device. Display devices used with the embodiments disclosed herein can utilize any suitable image projection technology, such as liquid crystal displays (LCDs), light-emitting diodes (LEDs), gas plasma, electroluminescence, etc. A display controller may also be provided for converting data stored in memory into one or more of text, graphics, or moving images displayed on the display device, as appropriate.

[0298] Various components of a computer system can be interconnected via one or more buses, which may include one or more of power buses, control signal buses, status signal buses, data buses, other similar components, or combinations thereof. For clarity, the various buses are described as bus systems.

[0299] Unless specifically described as being implemented in a particular manner, the techniques described herein can be implemented in hardware, software, firmware, or any combination thereof. Any features described as modules, components, etc., may also be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be implemented at least in part by a non-transitory processor-readable storage medium comprising instructions that, when executed by at least one processor, perform one or more of the methods described herein. Instructions may be organized into routines, programs, objects, components, data structures, etc., which may perform specific tasks and / or implement specific data types, and may be combined or distributed as needed in various embodiments.

[0300] Furthermore, upon arrival at various computer system components, program code in the form of computer-executable instructions or data structures can be automatically or manually transferred from the transmission medium to a non-transitory computer-readable storage medium (and vice versa). For example, computer-executable instructions or data structures received via a network or data link can be buffered in memory (e.g., RAM) within a network interface module (NIC) and then ultimately transferred to the computer system RAM and / or a less volatile, non-transitory computer-readable storage medium at the computer system location. Therefore, it should be understood that non-transitory computer-readable storage media can be included in computer system components that also (or even primarily) utilize the transmission medium.

[0301] Embodiments of the wear detection system have been described primarily with reference to wellbore drilling operations; the wear monitoring system described herein can be used in applications other than drilling. In other embodiments, the wear detection system according to this disclosure can be used outside of wellbore or other downhole environments used for the exploration or production of natural resources. For example, the wear monitoring system of this disclosure can be used in boreholes for the placement of utility pipelines. Therefore, the terms "wellbore," "borehole," etc., should not be construed as limiting the tools, systems, components, or methods of this disclosure to any particular industry, field, or environment.

[0302] This document describes one or more specific embodiments of the present disclosure. These described embodiments are examples of the technology currently disclosed. Additionally, to provide a concise description of these embodiments, not all features of actual embodiments may be described in the specification. It should be understood that in the development of any such actual implementation, as in any engineering or design project, many embodiment-specific decisions will be made to achieve the developer's specific objectives, such as complying with system-related and business-related constraints, which may vary from embodiment to embodiment. Furthermore, it should be understood that such development work can be complex and time-consuming, but it remains a routine task of design, fabrication, and manufacturing for those skilled in the art who benefit from this disclosure.

[0303] Furthermore, it should be understood that references to "one embodiment" or "an embodiment" in this disclosure are not intended to be construed as excluding the existence of additional embodiments that also include the described features. For example, any element described with respect to embodiments herein may be combined with any element of any other embodiment described herein. Numbers, percentages, ratios, or other values ​​used herein are intended to include that value, as well as other values ​​that are "about" or "approximate" said values, as will be understood by one of ordinary skill in the art as covered by embodiments of this disclosure. Therefore, the values ​​should be interpreted broadly enough to cover values ​​that are at least sufficiently close to the value to perform the desired function or achieve the desired result. The values ​​include at least the variations expected in a suitable manufacturing or production process and may include values ​​within 5%, 1%, 0.1%, or 0.01% of the stated value.

[0304] In view of this disclosure, those skilled in the art should recognize that equivalent constructions do not depart from the spirit and scope of this disclosure, and various changes, substitutions, and modifications can be made to the embodiments disclosed herein without departing from the spirit and scope of this disclosure. Equivalent constructions including functional "device plus function" clauses are intended to cover structures described herein as performing the stated functions, including structural equivalents operating in the same manner and equivalent structures providing the same functionality. The applicant expressly states that no claims refer to device plus function or other functional claims except those claims that use the term "for... apparatus" in conjunction with the relevant function. Every addition, deletion, and modification to the embodiments falling within the meaning and scope of the claims will be covered by the claims.

[0305] As used herein, the terms “approximately,” “about,” and “substantially” mean quantities close to the stated amount, which are within standard manufacturing or process tolerances, or which still perform the desired function or achieve the desired result. For example, the terms “approximately,” “about,” and “substantially” can refer to quantities less than 5%, less than 1%, less than 0.1%, and less than 0.01% of the stated amount. Furthermore, it should be understood that any directions or frames of reference described above are only relative directions or movements. For example, any references to “up” and “down,” or “above” or “below”, describe only the relative position or movement of the relevant element.

[0306] This disclosure may be practiced in other specific forms without departing from the spirit or characteristics thereof. The described embodiments are to be considered illustrative rather than restrictive. Therefore, the scope of this disclosure is indicated by the appended claims rather than the foregoing description. Variations within the meaning and scope of equivalents of the claims will be included within their scope.

Claims

1. A method for detecting wear of downhole tools implemented in a main wellbore, comprising: Receive offset wellbore data 134 for one or more offset wellbores; Based on the offset wellbore data 134, the expected downhole tool parameters 841 are determined at one or more measurement depths, including the main wellbore dynamic measurement depth 844. Received main wellbore data 132; Based on the main wellbore data 132, the main downhole tool index 843 at the dynamic measurement depth 844 is determined in real time; and The wear of the downhole tool is determined by comparing the main downhole tool index 843 at the dynamic measurement depth 844 with the expected downhole tool index 841 in real time.

2. The method of claim 1, further comprising generating a graph representing the expected downhole tool index and the main downhole tool index.

3. The method according to claim 1 or 2, wherein the expected downhole tool index is the expected formation stiffness of the main borehole at one or more measurement depths based on the offset borehole data.

4. The method according to any one of claims 1 to 3, wherein the offset wellbore data includes drilling rate, drilling pressure and rotation speed associated with each offset wellbore.

5. The method according to any one of claims 1 to 4, wherein receiving the offset wellbore data includes filtering out the one or more offset wellbores based on the bluntness grade of the downhole tools of the one or more offset wellbores.

6. The method according to any one of claims 1 to 5, wherein receiving the offset wellbore data includes selecting a set of offset wellbores, the set of offset wellbores being one or more of the following: in the same formation as the main wellbore, at the same depth as the main wellbore, and performing operations similar to those of the main wellbore.

7. The method according to any one of claims 1 to 6, further comprising adjusting one or more drilling parameters based on a determined wear of the downhole tool.

8. The method according to any one of claims 1 to 7, further comprising classifying determined wear of the downhole tool based on one or more predetermined thresholds of the main downhole tool index.

9. The method according to any one of claims 1 to 8, wherein one or more offset wellbores of the offset wellbore data are in the same formation as the main wellbore, and wherein comparing the main downhole tool index with the expected downhole tool index includes aligning the main wellbore data with the offset wellbore data based on the depth of the formation.

10. The method according to any one of claims 1 to 9, further comprising determining a downhole tool index ratio of the primary downhole tool index to the expected downhole tool index, and generating a graph representing the downhole tool index ratio at the one or more measurement depths.

11. The method according to any one of claims 1 to 10, further comprising determining a cumulative wear index of the downhole tool at one or more measurement depths based on comparing the primary downhole tool index with the expected downhole tool index.

12. The method of any one of claims 1 to 11, wherein the main wellbore data includes rotational speed data of the downhole tool, and wherein determining the cumulative wear index includes relating a comparison of the main downhole tool index with the expected downhole tool index to the rotation of the downhole tool based on the rotational speed data.

13. The method of claim 12, wherein determining the cumulative wear index is based on identifying the number of revolutions of the downhole tool relative to a primary downhole tool index as described in one or more threshold ranges of the expected downhole tool index.

14. The method according to any one of claims 11 to 13, further comprising classifying the determined cumulative wear index based on one or more predetermined thresholds.

15. The method of any one of claims 11 to 14, further comprising determining a formation normalization factor for the one or more offset wellbores and the main wellbore based on the offset wellbore data, and wherein determining the cumulative wear index comprises normalizing the expected downhole tool index based on the normalization factor.

Images