Characterizing connection practices

Abstract

A method for determining and characterizing connection practices for a drill string to prevent transient drilling dysfunctions includes receiving first surface data. The first surface data is related to a plurality of first connections that are made to form one or more first drill strings. The method also includes performing surface comparisons based upon the first surface data. The method also includes identifying connection practices used to make the first connections. The method also includes training a machine-learning (ML) model based upon the surface comparisons and connection practices to produce a trained ML model. The method also includes receiving second surface data. The method also includes selecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string. The selection is made using the trained ML model based upon the second surface data.

Description

PATENT Atorney Docket No.: IS24.1488-WO-PCTCHARACTERIZING CONNECTION PRACTICESCross-Reference to Related Applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 719134, filed on November 12, 2024, which is incorporated by reference.Background

[0002] Drilling dysfunction causes premature failure of drill bits, motors, and other botom hole assembly (BHA) components in various formations. Drilling dysfunctions may be influenced by connection practices, bit design, bottom hole assembly (BHA) design, rig control systems, and rotating head use. Sensors that measure weight, torque, and vibration in the bit can offer insights that are not detectable further up the BHA. By understanding whether the connection practice is the root cause of a failure before the next bit run, it is possible to improve performance and prolong BHA life. Therefore, what is needed is an improved system and method for characterizing connection practices to identify and prevent transient drilling dysfunction.Summary

[0003] A method for determining and characterizing connection practices for a drill string to prevent transient drilling dysfunctions is disclosed. The method includes receiving first surface data. The first surface data is related to a plurality of first connections that are made to form one or more first drill strings. The first surface data is measured during a first predetermined time window before the first connections are formed. The method also includes performing surface comparisons based upon the first surface data. The surface comparisons are based upon a value of the first surface data at a beginning of the first predetermined time window and a value of the first surface data at an end of the first predetermined time window. The method also includes identifying connection practices used to make the first connections. The method also includes training a machine-learning (ML) model based upon the surface comparisons and the connection practices to produce a trained ML model. The method also includes receiving second surface data. The second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string. The method also includes selecting one of thePATENT Atorney Docket No.: IS24.1488-WO-PCTconnection practices to minimize transient drilling dysfunctions of the second drill string. The selection is made using the trained ML model based upon the second surface data.

[0004] A computing system is also disclosed. The computing system includes one or more processors and a memory system. The computing system includes one or more non-transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations. The operations include receiving first surface data. The first surface data is related to a plurality of first connections that are made to form one or more first drill strings. The first surface data is measured during a first predetermined time window before the first connections are formed. The operations also include performing surface comparisons based upon the first surface data. The operations also include receiving downhole data related to the first connections. The downhole data is measured during the first predetermined time window. The operations also include performing downhole comparisons based upon the downhole data. The operations also include identifying connection practices used to make the first connections. The operations also include training a machinelearning (ML) model based upon the surface comparisons, the downhole comparisons, and the connection practices to produce a trained ML model. The operations also include receiving second surface data. The second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string. The operations also include selecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string. The selection is made using the trained ML model based upon the second surface data.

[0005] A non-transitory computer-readable medium is also disclosed. The medium includes instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations. The operations include receiving first surface data. The first surface data is related to a plurality of first connections that are made to form one or more first drill strings. The first surface data is measured during a first predetermined time window before the first connections are formed. The operations also include performing surface comparisons based upon the first surface data. The surface comparisons are based upon a value of the first surface data at a beginning of the first predetermined time window and a value of the first surface data at an end of the first predetermined time window. The operations also include receiving downhole data related to the first connections. The downhole data is measured during the first predetermined time window. The operations also include performing downholePATENT Atorney Docket No.: IS24.1488-WO-PCTcomparisons based upon the downhole data. The downhole comparisons are based upon a value of the downhole data at the beginning of the first predetermined time window and a value of the downhole data at the end of the first predetermined time window. The operations also include identifying connection practices used to make the first connections. The operations also include training a machine-learning (ML) model based upon the surface comparisons, the downhole comparisons, and the connection practices to produce a trained ML model. The operations also include receiving second surface data. The second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string. The operations also include selecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string. The selection is made using the trained ML model based upon the second surface data.

[0006] It will be appreciated that this summary is intended merely to introduce some aspects of the present methods, systems, and media, which are more fully described and / or claimed below. Accordingly, this summary is not intended to be limiting.Brief Description of the Drawings

[0007] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the present teachings and together with the description, serve to explain the principles of the present teachings. In the figures:

[0008] Figure 1 illustrates an example of a system that includes various management components to manage various aspects of a geologic environment, according to an embodiment.

[0009] Figure 2 illustrates a schematic view of a wellsite with a drill string extending from a drilling rig into a well, according to an embodiment.

[0010] Figure 3 illustrates a log containing surface data, according to an embodiment.

[0011] Figure 4 illustrates a flowchart of a method for determining and characterizing connection practices to prevent transient drilling dysfunctions, according to an embodiment.

[0012] Figure 5 illustrates a schematic view of the method in Figure 4, according to an embodiment.

[0013] Figure 6 illustrates a chart with a list of channels, according to an embodiment.

[0014] Figures 7A-7E illustrate graphs showing the channels that are determined for each connection at each well, according to an embodiment.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0015] Figure 8 illustrates a schematic view of a computing system for performing at least a portion of the method(s) described herein, according to an embodiment.Detailed Description

[0016] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings and figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0017] It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first object or step could be termed a second object or step, and, similarly, a second object or step could be termed a first object or step, without departing from the scope of the present disclosure. The first object or step, and the second object or step, are both, objects or steps, respectively, but they are not to be considered the same object or step.

[0018] The terminology used in the description herein is for the purpose of describing particular embodiments and is not intended to be limiting. As used in this description and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and encompasses any possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Further, as used herein, the term “if’ may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context.

[0019] Attention is now directed to processing procedures, methods, techniques, and workflows that are in accordance with some embodiments. Some operations in the processing procedures,PATENT Atorney Docket No.: IS24.1488-WO-PCTmethods, techniques, and workflows disclosed herein may be combined and / or the order of some operations may be changed.System Overview

[0020] Figure 1 illustrates an example of a system 100 that includes various management components 110 to manage various aspects of a geologic environment 150 (e.g., an environment that includes a sedimentary basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 may allow for direct or indirect management of sensing, drilling, injecting, extracting, etc., with respect to the geologic environment 150. In turn, further information about the geologic environment 150 may become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0021] In the example of Figure 1, the management components 110 include a seismic data component 112, an additional information component 114 (e.g., well / logging data), a processing component 116, a simulation component 120, an attribute component 130, an analysis / visualization component 142 and a workflow component 144. In operation, seismic data and other information provided per the components 112 and 114 may be input to the simulation component 120.

[0022] In an example embodiment, the simulation component 120 may rely on entities 122. Entities 122 may include earth entities or geological objects such as wells, surfaces, bodies, reservoirs, etc. In the system 100, the entities 122 can include virtual representations of actual physical entities that are reconstructed for purposes of simulation. The entities 122 may include entities based on data acquired via sensing, observation, etc. (e.g., the seismic data 112 and other information 114). An entity may be characterized by one or more properties (e.g., a geometrical pillar grid entity of an earth model may be characterized by a porosity property). Such properties may represent one or more measurements (e.g., acquired data), calculations, etc.

[0023] In an example embodiment, the simulation component 120 may operate in conjunction with a software framework such as an object-based framework. In such a framework, entities may include entities based on pre-defined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the MICROSOFT® .NET® framework (Redmond, Washington), which provides a set of extensible object classes. In thePATENT Atorney Docket No.: IS24.1488-WO-PCT.NET® framework, an object class encapsulates a module of reusable code and associated data structures. Obj ect classes can be used to instantiate obj ect instances for use in by a program, script, etc. For example, borehole classes may define objects for representing boreholes based on well data.

[0024] In the example of Figure 1, the simulation component 120 may process information to conform to one or more attributes specified by the attribute component 130, which may include a library of attributes. Such processing may occur prior to input to the simulation component 120 (e.g., consider the processing component 116). As an example, the simulation component 120 may perform operations on input information based on one or more attributes specified by the attribute component 130. In an example embodiment, the simulation component 120 may construct one or more models of the geologic environment 150, which may be relied on to simulate behavior of the geologic environment 150 (e.g., responsive to one or more acts, whether natural or artificial). In the example of Figure 1, the analysis / visualization component 142 may allow for interaction with a model or model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 may be input to one or more other workflows, as indicated by a workflow component 144.

[0025] As an example, the simulation component 120 may include one or more features of a simulator such as the ECLIPSE™ reservoir simulator (SLB, Houston Texas), the INTERSECT™ reservoir simulator (SLB, Houston Texas), etc. As an example, a simulation component, a simulator, etc. may include features to implement one or more meshless techniques (e.g., to solve one or more equations, etc.). As an example, a reservoir or reservoirs may be simulated with respect to one or more enhanced recovery techniques (e.g., consider a thermal process such as SAGD, etc ).

[0026] As an example, the simulation component 120 may include one or more features of a simulator such as SYMMETRY™ software (SLB, Houston, Texas). More particularly, SYMMETRY™ may process workflows in a single integrated environment with accurate thermodynamic fluid representation and consistent modeling across multiple disciplines including process, production, and HSE. The simulator integrates steady-state and transient (e.g., dynamic) analyses that can be tailored for each domain. This approach enables users to optimize processes in upstream, midstream, and downstream sectors while maximizing profits and minimizing capital expenditures. It may also help reduce emissions, energy consumption, and waste.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0027] As an example, the simulation component 120 may include one or more features of a simulator such as PIPESIM™ (SLB, Houston, Texas). More particularly, PIPESIM™ is steadystate multiphase flow simulator that incorporates the three areas of flow modeling: multiphase flow, heat transfer and fluid behavior.

[0028] As an example, the simulation component 120 may include one or more features of a simulator such as OLGA™ (SLB, Houston, Texas). More particularly, OLGA™ is a dynamic multiphase flow simulator that models transient flow (e.g., time-dependent behaviors) to maximize production potential. Transient modeling is a component for feasibility studies and field development design. Dynamic simulation is useful in deep water and is used in both offshore and onshore developments to investigate transient behavior in pipelines and wellbores. Transient simulation with the OLGA™ simulator provides an added dimension to steady-state analysis by predicting system dynamics, such as time-varying changes in flow rates, fluid compositions, temperature, solids deposition, and operational changes.

[0029] In an example embodiment, the management components 110 may include features of a commercially available framework such as the PETREL® seismic to simulation software framework (SLB, Houston, Texas). The PETREL® framework provides components that allow for optimization of exploration and development operations. The PETREL® framework includes seismic to simulation software components that can output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) can develop collaborative workflows and integrate operations to streamline processes. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

[0030] In an example embodiment, various aspects of the management components 110 may include add-ons or plug-ins that operate according to specifications of a framework environment. For example, a commercially available framework environment marketed as the OCEAN® framework environment (SLB, Houston, Texas) allows for integration of add-ons (or plug-ins) into a PETREL® framework workflow. The OCEAN® framework environment leverages .NET® tools (Microsoft Corporation, Redmond, Washington) and offers stable, user-friendly interfaces for efficient development. In an example embodiment, various components may be implementedPATENT Atorney Docket No.: IS24.1488-WO-PCTas add-ons (or plug-ins) that conform to and operate according to specifications of a framework environment (e.g., according to application programming interface (API) specifications, etc.).

[0031] Figure 1 also shows an example of a framework 170 that includes a model simulation layer 180 along with a framework services layer 190, a framework core layer 195 and a modules layer 175. The framework 170 may include the commercially available OCEAN® framework where the model simulation layer 180 is the commercially available PETREL® model-centric software package that hosts OCEAN® framework applications. In an example embodiment, the PETREL® software may be considered a data-driven application. The PETREL® software can include a framework for model building and visualization.

[0032] As an example, a framework may include features for implementing one or more mesh generation techniques. For example, a framework may include an input component for receipt of information from interpretation of seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework may include a mesh generation component that processes input information, optionally in conjunction with other information, to generate a mesh.

[0033] In the example of Figure 1, the model simulation layer 180 may provide domain objects 182, act as a data source 184, provide for rendering 186 and provide for various user interfaces 188. Rendering 186 may provide a graphical environment in which applications can display their data while the user interfaces 188 may provide a common look and feel for application user interface components.

[0034] As an example, the domain objects 182 can include entity objects, property objects and optionally other objects. Entity objects may be used to geometrically represent wells, surfaces, bodies, reservoirs, etc., while property objects may be used to provide property values as well as data versions and display parameters. For example, an entity object may represent a well where a property object provides log information as well as version information and display information (e.g., to display the well as part of a model).

[0035] In the example of Figure 1, data may be stored in one or more data sources (or data stores, generally physical data storage devices), which may be at the same or different physical sites and accessible via one or more networks. The model simulation layer 180 may be configured to model projects. As such, a particular project may be stored where stored project information may include inputs, models, results and cases. Thus, upon completion of a modeling session, aPATENT Atorney Docket No.: IS24.1488-WO-PCTuser may store a project. At a later time, the project can be accessed and restored using the model simulation layer 180, which can recreate instances of the relevant domain objects.

[0036] In the example of Figure 1, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and one or more other features such as the fault 153-1, the geobody 153-2, etc. As an example, the geologic environment 150 may be outfitted with any of a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a well site and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, Figure 1 shows a satellite in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or instead include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

[0037] Figure 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc ). As an example, the equipment 157 and / or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

[0038] As mentioned, the system 100 may be used to perform one or more workflows. A workflow may be a process that includes a number of worksteps. A workstep may operate on data, for example, to create new data, to update existing data, etc. As an example, a may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, a system may include a workflow editor for creation, editing, executing, etc. of aPATENT Atorney Docket No.: IS24.1488-WO-PCTworkflow. In such an example, the workflow editor may provide for selection of one or more predefined worksteps, one or more customized worksteps, etc. As an example, a workflow may be a workflow implementable in the PETREL® software, for example, that operates on seismic data, seismic attribute(s), etc. As an example, a workflow may be a process implementable in the OCEAN® framework. As an example, a workflow may include one or more worksteps that access a module such as a plug-in (e.g., external executable code, etc.).Characterizing Connection Practices

[0039] The present disclosure provides a method for identifying and / or preventing transient drilling dysfunctions that are due to connection practices. More particularly, the method may digitalize each connection, define optimal practices, and ultimately avoid costly transient drilling dysfunctions. Digitalization has opened up the possibility of applying machine learning (ML) techniques that may enable identifying optimal practices that achieve the balance between short connection times on surface and prevention of transient drilling dysfunctions downhole. The method provides a large-scale characterization of connection practices using well contextual data, surface time-based measurements, and high-frequency downhole measurements. The method enables optimization and customization of connection practices for each particular well, rig, client, and / or basin depending on a scale.

[0040] The method described herein seamlessly uses drilling parameters, such as block position, hookload, and block velocity as inputs. The method computes and output measurements (also referred as channels) of drill off, pipe reciprocation, and in-slips distance to bottom in conventional engineering units. The method also processes data from high-frequency downhole measurements that capture the instantaneous torque (or any other energy) release in a transient state. This feature is based on automatic synchronization between downhole and surface data.

[0041] Conventional methods have not systematically and automatically detected, measured, and stored these connection practice attributes for each connection in each well. Such digitalization of these connection practices can now be used in failure investigations and performance optimization strategies.

[0042] Figure 2 illustrates a schematic view of a wellsite 200 with a drill string 210 extending from a drilling rig 220 into a well 230, according to an embodiment. The drill string 210 may include two or more segments 212A, 212B that may be connected together via a connection 214.PATENT Atorney Docket No.: IS24.1488-WO-PCTOne or more surface sensors 240 may be configured to measure surface data before, during, and / or after the connection 214 is formed. One or more downhole sensors 250 may be configured to measure downhole data before, during, and / or after the connection 214 is formed. In an example, the downhole sensor(s) 250 may be part of a downhole tool in the well 230.

[0043] Figure 3 illustrates a log 300 containing surface data, according to an embodiment. For example, the log 300 contains inclination data 305, mud flow data 310, hookload 315, surface torque 320, block position 325, surface rotational speed data 330, and high resolution downhole RPM 335. In another embodiment, the log 300 may also contain more downhole data. The connection 214 (Figure 2) may occur at point 340. As described below, a period 345 before the time 340 of the connection 214 may be identified. In the example, the period is 55 seconds. The surface data may be compared within this period.

[0044] Figure 4 illustrates a flowchart of a method 400 for determining and characterizing connection practices to prevent transient drilling dysfunctions, according to an embodiment. An illustrative order of the method 400 is provided below; however, one or more portions of the method 400 may be performed in a different order, simultaneously, repeated, or omitted. At least a portion of the method 400 may be performed with a computing system (described below).Figure 5 illustrates a schematic view of the method 400 in Figure 4, according to an embodiment.

[0045] The method 400 may include receiving first surface data, as at 405. This is also shown at 505 in Figure 5. The first surface data may be related to one or more first connections that are made to form one or more first drill strings in one or more first wells. The first surface data may be measured at the surface. The first surface data may be measured during a first predetermined time window (e.g., 10 seconds - 1 minute) before the first connections are formed. The first surface data may also or instead be measured during a second predetermined time window (e.g., 10 seconds - 1 minute) after the first connections are formed. The first surface data may include hookload, weight-on-bit (WOB), rotations per minute (RPM), surface torque (STOR), block positions (BPOS), block velocity, depth, flow rate, or a combination thereof. These channels may then be used to compute new channels that characterize the connection process and / or drilling dysfunctions during connection in more analytical way.

[0046] The method 400 may also include receiving downhole data, as at 410. The downhole data may be related to the first connections. The downhole data may be measured in the one or more first wells (e.g., using a measuring-while-drilling and / or logging-while-drilling tool). ThePATENT Atorney Docket No.: IS24.1488-WO-PCTdownhole data may be measured during the first and / or second predetermined time windows. The downhole data may indicate transient drilling dysfunctions. More particularly, the downhole data may include high-frequency measurements captured by a MWD tool, a LWD tool, and / or a rotary steerable system (RSS) that are reflective of the transient drilling dysfunctions. The transient drilling dysfunctions may be or include an energy release of the first drill string in a transient state. The energy release may be or include a torque release or any other potential energy trapped inside the drill string as a result of compression and rotation while drilling.

[0047] The method 400 may also include synchronizing the first surface data and the downhole data to produce synchronized data, as at 415. This is also shown at 515 in Figure 5.

[0048] The method 400 may also include performing comparisons based upon the first surface data, the downhole data, and / or the synchronized data, as at 420. The comparisons may include surface comparisons (i.e., comparing the first surface data at different times), downhole comparisons (i.e., comparing the downhole data at different times), or combination comparisons (i.e., the first surface data being compared with the downhole data).

[0049] The surface comparisons may include first surface comparisons, which may be based upon a value of the first surface data at a beginning of the first predetermined time window (e.g., t=0 seconds) and a value of the first surface data at an end of the first predetermined time window (e.g., t=30 seconds). The first surface comparisons may be determined by subtracting the value of the first surface data at the beginning of the first predetermined time window from the value of the first surface data at the end of the first predetermined time window or vice versa. In another example, the first surface comparisons may be determined by dividing the value of the first surface data at the beginning of the first predetermined time window by the value of the first surface data at the end of the first predetermined time window or vice versa (e.g., to create a ratio). In yet another example, the first surface comparisons may be determined by combining the channels during the first pre-determined window by applying variety of mathematical operations. Such mathematical operations may include integration of a channel overtime during the pre-determined time window, determining a derivative of a channel over time or depth change during the predetermined time window, obtaining an average value of a channel during the pre-determined time window, multiplication and / or / addition of two or more channels during the pre-determined time window, or a combination thereof.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0050] The surface comparisons may also or instead include second surface comparisons, which may be based upon a value of the first surface data at a beginning of the second predetermined time window and a value of the first surface data at an end of the second predetermined time window. The second surface comparisons may be determined by subtracting the value of the first surface data at the beginning of the second predetermined time window from the value of the first surface data at the end of the second predetermined time window. In another example, the second surface comparisons may be determined by dividing the value of the first surface data at the beginning of the second predetermined time window by the value of the first surface data at the end of the second predetermined time window. In yet another example, the comparisons may be determined by combining the channels during the first pre-determined window by applying variety of mathematical operations. Such mathematical operations may include integration of a channel over time during the pre-determined time window, determining a derivative of a channel over time or depth change during the pre-determined time window, obtaining an average value of a channel during the pre-determined time window, multiplication and / or / addition of two or more channels during the pre-determined time window, or a combination thereof.

[0051] The downhole comparisons may include first downhole comparisons, which may be based upon a value of the downhole data at the beginning of the first predetermined time window and a value of the downhole data at the end of the first predetermined time window. The first downhole comparisons may be determined by subtracting the value of the downhole data at the beginning of the first predetermined time window from the value of the downhole data at the end of the first predetermined time window. In another example, the first downhole comparisons may be determined by dividing the value of the downhole data at the beginning of the first predetermined time window by the value of the downhole data at the end of the first predetermined time window. In yet another example, the comparisons may be determined by combining the channels during the first pre-determined window by applying variety of mathematical operations. Such mathematical operations may include integration of a channel over time during the predetermined time window, determining a derivative of a channel over time or depth change during the pre-determined time window, obtaining an average value of a channel during the predetermined time window, multiplication and / or / addition of two or more channels during the predetermined time window, or a combination thereof.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0052] The downhole comparisons may also or instead include second downhole comparisons, which may be based upon a value of the downhole data at the beginning of the second predetermined time window and a value of the downhole data at the end of the second predetermined time window. The second downhole comparisons may be determined by subtracting the value of the downhole data at the beginning of the second predetermined time window from the value of the downhole data at the end of the second predetermined time window. In another example, the second downhole comparisons may be determined by dividing the value of the downhole data at the beginning of the second predetermined time window by the value of the downhole data at the end of the second predetermined time window. In yet another example, the comparisons may be determined by combining the channels during the first pre-determined window by applying variety of mathematical operations. Such mathematical operations may include integration of a channel overtime during the pre-determined time window, determining a derivative of a channel over time or depth change during the pre-determined time window, obtaining an average value of a channel during the pre-determined time window, multiplication and / or / addition of two or more channels during the pre-determined time window, or a combination thereof.

[0053] The method 400 may also include identifying (e.g., characterizing) connection practices used to make the first connections, as at 425. This is also shown at 525 in Figure 5. The connection practices may include a plurality of different connection practices. The different connection practices may include weight to slips, drill-off weight, drill-off diffP, downhole RPM (e g., max / avg), cleaning distance, cleaning volume, flow ramp down, RPM ramp down, weight to rotate, weight to pump, in-slips time, or a combination thereof.

[0054] The method 400 may also include storing the different connection practices, as at 430. This is also shown at 530 in Figure 5. The different connection practices may be organized based upon the first connections, the one or more first wells, a pad including the one or more first wells, a drilling rig used to drill the one or more first wells, a basin including the one or more first wells, a time to drill the one or more first wells, a client, or a combination thereof. The different connection practices may be stored in digital format and aligned with a variety of drilling meta data. The drilling meta data may include the time, a depth of the one or more first wells, a name of the one or more first wells, a geological location of the one or more first wells, a well profile of the one or more first wells, the client, or a combination thereof.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0055] The method 400 may also include training a machine-learning (ML) model based upon the first surface data, the downhole data, and the connection practices to produce a trained ML model, as at 435. This is also shown at 535 in Figure 5. The ML model may also or instead be trained based upon the synchronized data, the surface comparisons, the downhole comparisons, or a combination thereof. The ML model may be trained to determine causation between the different connection practices and the transient drilling dysfunctions.

[0056] The method 400 may also include receiving second surface data, as at 440. The second surface data may be measured during the first predetermined time window (e.g., 10 seconds - 1 minute) before a second connection is made to form the first drill string or a second drill string in one of the one or more first wells or in a second well. The second surface data may include the hookload, the RPM, the STOR, the BPOS, the block velocity, the depth, the flow rate, or a combination thereof.

[0057] The method 400 may also include selecting one of the different connection practices to minimize the transient drilling dysfunctions of the second drill string, as at 445. This is also shown at 545 in Figure 5. The selection may be made using the trained ML model. The selection is made based at least partially upon the second surface data.

[0058] The method 400 may also include displaying one or more outputs, as at 450. The output(s) may include the comparisons, the second surface data, the selected connection practice, a connection summary (Figures 7A-7E), or a combination thereof.

[0059] The method 400 may also include forming the second connection of the second drill string using the selected connection practice, as at 455. Forming may include generating and / or transmitting a signal (e.g., using a computing system) that recommends, instructs, or causes the second connection to be formed using selected connection practice. In another embodiment, the second connection may be physically formed (e.g., by a user and / or equipment).

[0060] Figure 6 illustrates a chart with a list of channels (e.g., connection practices), according to an embodiment. The channels shown are merely exemplary. In other words, the method 400 may use one or more of the channels shown in the chart, and other channels (not shown in the chart) may also or instead be used. The channels may be completed and stored in the cloud.

[0061] Figures 7A-7E illustrate graphs showing the channels that are determined for each connection at each well, according to an embodiment. This allows systematic recording of these channels and subsequent application of data science tools on current and future jobs. MorePATENT Atorney Docket No.: IS24.1488-WO-PCTparticularly, Figure 7A shows the distance between the bit and bottom of the hole during each connection (e.g., subtracting one channel from another). Figure 7B shows how long drill off procedure takes at each connection. Figure 7C shows the amount of weight that is drilled off during each connection. Figure 7D shows how quickly the bit is moved back to bottom after each connection. Figure 7E shows the ratio between max and minimum downhole RPM during the post-connection period. These channels are not computed and stored during normal drilling operations. The method introduces the idea to compute these channels automatically as each of them or a combination of them can cause transient dysfunction.Exemplary Computing System

[0062] In some embodiments, the methods of the present disclosure may be executed by a computing system. Figure 8 illustrates an example of such a computing system 800, in accordance with some embodiments. The computing system 800 may include a computer or computer system 801 A, which may be an individual computer system 801 A or an arrangement of distributed computer systems. The computer system 801 A includes one or more analysis modules 802 that are configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis module 802 executes independently, or in coordination with, one or more processors 804, which is (or are) connected to one or more storage media 806. The processor(s) 804 is (or are) also connected to a network interface 807 to allow the computer system 801 A to communicate over a data network 809 with one or more additional computer systems and / or computing systems, such as 801B, 801C, and / or 80 ID (note that computer systems 80 IB, 801C and / or 80 ID may or may not share the same architecture as computer system 801 A, and may be located in different physical locations, e.g., computer systems 801 A and 801B may be located in a processing facility, while in communication with one or more computer systems such as 801 C and / or 80 ID that are located in one or more data centers, and / or located in varying countries on different continents).

[0063] A processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

[0064] The storage media 806 may be implemented as one or more computer-readable or machine-readable storage media. Note that while in the example embodiment of Figure 8 storagePATENT Atorney Docket No.: IS24.1488-WO-PCTmedia 806 is depicted as within computer system 801 A, in some embodiments, storage media 806 may be distributed within and / or across multiple internal and / or external enclosures of computing system 801A and / or additional computing systems. Storage media 806 may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLURAY® disks, or other types of optical storage, or other types of storage devices. Note that the instructions discussed above may be provided on one computer-readable or machine-readable storage medium, or may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media is (are) considered to be part of an article (or article of manufacture). An article or article of manufacture may refer to any manufactured single component or multiple components. The storage medium or media may be located either in the machine running the machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

[0065] In some embodiments, computing system 800 contains one or more method execution module(s) 808. In the example of computing system 800, computer system 801A includes the method execution module 808. In some embodiments, a single method execution module may be used to perform some aspects of one or more embodiments of the methods disclosed herein. In other embodiments, a plurality of method execution modules may be used to perform some aspects of methods herein.

[0066] It should be appreciated that computing system 800 is merely one example of a computing system, and that computing system 800 may have more or fewer components than shown, may combine additional components not depicted in the example embodiment of Figure 8, and / or computing system 800 may have a different configuration or arrangement of the components depicted in Figure 8. The various components shown in Figure 8 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.PATENT Atorney Docket No.: IS24.1488-WO-PCT

[0067] Further, the steps in the processing methods described herein may be implemented by running one or more functional modules in information processing apparatus such as general purpose processors or application specific chips, such as ASICs, FPGAs, PLDs, or other appropriate devices. These modules, combinations of these modules, and / or their combination with general hardware are included within the scope of the present disclosure.

[0068] Computational interpretations, models, and / or other interpretation aids may be refined in an iterative fashion; this concept is applicable to the methods discussed herein. This may include use of feedback loops executed on an algorithmic basis, such as at a computing device (e.g., computing system 800, Figure 8), and / or through manual control by a user who may make determinations regarding whether a given step, action, template, model, or set of curves has become sufficiently accurate for the evaluation of the subsurface three-dimensional geologic formation under consideration.

[0069] The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or limiting to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and / or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications as are suited to the particular use contemplated.

Claims

PATENT Atorney Docket No.: IS24.1488-WO-PCTCLAIMSWhat is claimed is:

1. A method for determining and characterizing connection practices for a drill string to prevent transient drilling dysfunctions, the method comprising:receiving first surface data, wherein the first surface data is related to a plurality of first connections that are made to form one or more first drill strings, and wherein the first surface data is measured during a first predetermined time window before the first connections are formed; performing surface comparisons based upon the first surface data, wherein the surface comparisons are based upon a value of the first surface data at a beginning of the first predetermined time window and a value of the first surface data at an end of the first predetermined time window;identifying connection practices used to make the first connections;training a machine-learning (ML) model based upon the surface comparisons and the connection practices to produce a trained ML model;receiving second surface data, wherein the second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string; and selecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string, wherein the selection is made using the trained ML model based upon the second surface data.

2. The method of claim 1, wherein the first surface data comprises hookload, weight-on-bit (WOB), rotations per minute (RPM), surface torque (STOR), block positions (BPOS), block velocity, depth, flow rate, or a combination thereof.

3. The method of claim 1, wherein the surface comparisons are determined by:subtracting the value of the first surface data at the beginning of the first predetermined time window from the value of the first surface data at the end of the first predetermined time window; and / ordividing the value of the first surface data at the beginning of the first predetermined time window by the value of the first surface data at the end of the first predetermined time window.PATENT Atorney Docket No.: IS24.1488-WO-PCT4. The method of claim 1, wherein the surface comparisons are determined by combining channels in the first surface data during the first predetermined time window by applying one or more mathematical operations, and wherein the mathematical operations comprise integration of one of the channels overtime during the first predetermined time window, determining a derivative of one of the channels with respect to time or depth change during the first predetermined time window, determining an average value of one of the channels during the first predetermined time window, and / or multiplying or adding two or more of the channels during the first predetermined time window.

5. The method of claim 1, wherein the first surface data is also measured during a second predetermined time window after the first connections are formed, wherein the surface comparisons are also based upon a value of the first surface data at a beginning of the second predetermined time window and a value of the first surface data at an end of the second predetermined time window.

6. The method of claim 5, wherein the surface comparisons are determined by subtracting the value of the first surface data at the beginning of the second predetermined time window from the value of the first surface data at the end of the second predetermined time window.

7. The method of claim 5, wherein the surface comparisons are determined by dividing the value of the first surface data at the beginning of the second predetermined time window by the value of the first surface data at the end of the second predetermined time window.

8. The method of claim 1, wherein the connection practices comprise weight to slips, drill-off weight, drill-off, downhole rotations per minute (RPM), cleaning distance, cleaning volume, flow ramp down, RPM ramp down, weight to rotate, weight to pump, in-slips time, or a combination thereof.

9. The method of claim 1, wherein the ML model is trained to determine causation between the connection practices and the transient drilling dysfunctions.PATENT Atorney Docket No.: IS24.1488-WO-PCT10. The method of claim 1, further comprising forming the second connection using the selected connection practice.

11. A computing system, comprising:one or more processors; anda memory system comprising one or more non -transitory computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:receiving first surface data, wherein the first surface data is related to a plurality of first connections that are made to form one or more first drill strings, and wherein the first surface data is measured during a first predetermined time window before the first connections are formed;performing surface comparisons based upon the first surface data;receiving downhole data related to the first connections, wherein the downhole data is measured during the first predetermined time window;performing downhole comparisons based upon the downhole data; identifying connection practices used to make the first connections; training a machine-learning (ML) model based upon the surface comparisons, the downhole comparisons, and the connection practices to produce a trained ML model; receiving second surface data, wherein the second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string; andselecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string, wherein the selection is made using the trained ML model based upon the second surface data.

12. The computing system of claim 11, wherein the downhole data comprises high-frequency measurements captured by a measurement-while-drilling (MWD) tool, a logging-while-drilling (LWD) tool, and / or a rotary steerable system (RSS) that indicate the transient drilling dysfunctions, wherein the transient drilling dysfunctions comprise an energy release of the one or more first drillPATENT Atorney Docket No.: IS24.1488-WO-PCTstrings or the second drill string in a transient state, and wherein the energy release comprises a torque release or other potential energy trapped inside the one or more first drill strings or the second drill string as a result of compression and rotation while drilling.

13. The computing system of claim 12, wherein the downhole comparisons are based upon a value of the downhole data at a beginning of the first predetermined time window and a value of the downhole data at an end of the first predetermined time window.

14. The computing system of claim 13, wherein the downhole comparisons are determined by subtracting the value of the downhole data at the beginning of the first predetermined time window from the value of the downhole data at the end of the first predetermined time window.

15. The computing system of claim 13, wherein the downhole comparisons are determined by dividing the value of the downhole data at the beginning of the first predetermined time window by the value of the downhole data at the end of the first predetermined time window.

16. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations, the operations comprising:receiving first surface data, wherein the first surface data is related to a plurality of first connections that are made to form one or more first drill strings, and wherein the first surface data is measured during a first predetermined time window before the first connections are formed; performing surface comparisons based upon the first surface data, wherein the surface comparisons are based upon a value of the first surface data at a beginning of the first predetermined time window and a value of the first surface data at an end of the first predetermined time window;receiving downhole data related to the first connections, wherein the downhole data is measured during the first predetermined time window;performing downhole comparisons based upon the downhole data, wherein the downhole comparisons are based upon a value of the downhole data at the beginning of the firstPATENT Atorney Docket No.: IS24.1488-WO-PCTpredetermined time window and a value of the downhole data at the end of the first predetermined time window;identifying connection practices used to make the first connections;training a machine-learning (ML) model based upon the surface comparisons, the downhole comparisons, and the connection practices to produce a trained ML model;receiving second surface data, wherein the second surface data is measured during the first predetermined time window before a second connection is made to form a second drill string; and selecting one of the connection practices to minimize transient drilling dysfunctions of the second drill string, wherein the selection is made using the trained ML model based upon the second surface data.

17. The non-transitory computer-readable medium of claim 16, wherein the downhole data is also measured during a second predetermined time window after the first connections are formed.

18. The non-transitory computer-readable medium of claim 17, wherein the downhole comparisons are also based upon a value of the downhole data at a beginning of the second predetermined time window and a value of the downhole data at an end of the second predetermined time window.

19. The non-transitory computer-readable medium of claim 18, wherein the downhole comparisons are determined by subtracting the value of the downhole data at the beginning of the second predetermined time window from the value of the downhole data at the end of the second predetermined time window.

20. The non-transitory computer-readable medium of claim 18, wherein the downhole comparisons are determined by dividing the value of the downhole data at the beginning of the second predetermined time window by the value of the downhole data at the end of the second predetermined time window.

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