Predicting telemetry mode of downhole tools

Abstract

A method for predicting a telemetry mode of a downhole tool includes receiving, at a computing system at a surface, a signal from a downhole tool. The method also includes predicting a telemetry mode of the downhole tool based on the signal. The method also includes switching a telemetry mode of the computing system to match the telemetry mode of the downhole tool. The method also includes using the computing system to demodulate the signal after the telemetry mode of the computing system is switched.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. nonprovisional application No. 16 / 655,192, entitled “PREDICTING A TELEMETRY MODE OF ADOWNHOLE TOOL”, filed on October 16, 2019, the disclosure of which is incorporated herein by reference. Background Technology

[0003] Downhole tools can be inserted into a wellbore formed within the formation. Downhole tools may include measurement-while-drilling (MWD) tools, logging-while-drilling (LWD) tools, or both, and are configured to acquire downhole measurements. Downhole tools can communicate with a computing system at the surface via telemetry. For example, downhole tools can use mud pulse telemetry or electromagnetic (EM) telemetry to transmit measurements to the surface.

[0004] Downhole tools can have multiple telemetry modes to transmit measurements to the surface. In an example where a downhole tool uses mud pulse telemetry communication, each mode may correspond to a different duration of a pressure pulse transmitted by the downhole tool. For example, a pressure pulse may have a duration of 0.5 seconds in a first mode, 0.6 seconds in a second mode, 0.8 seconds in a third mode, and so on. The downhole tool can be set to one of these modes, and the surface computing system can be set to the same mode to enable communication between them. However, the downhole tool may unintentionally change its mode, making it different from the computing system's mode. For example, the downhole tool may unintentionally change its mode in response to vibration, which could occur while traveling to the well site, being stuck in the wellbore, or other similar situations. When the downhole tool's mode differs from the surface computing system's mode, the computing system may have difficulty decoding the pulse. Summary of the Invention

[0005] A method for predicting telemetry patterns of downhole tools is disclosed. The method includes receiving signals from the downhole tool at a surface computing system. The method further includes predicting the telemetry patterns of the downhole tool based on the signals. The method also includes switching the telemetry mode of the computing system to match the telemetry pattern of the downhole tool. Finally, the method includes demodulating the signals using the computing system after the telemetry mode of the computing system has been switched.

[0006] In another embodiment, the method includes receiving a signal from a downhole tool at a computing system at a surface. The signal includes encoded measurement data captured by a measurement-while-drilling tool in the downhole tool, a logging-while-drilling tool in the downhole tool, or both. A telemetry mode of the downhole tool is unknown at a time the signal is received. The method also includes identifying a first frequency band of the signal. The first frequency band of the signal includes the encoded measurement data. The method also includes applying a low-pass filter to the signal at a predetermined frequency with a predetermined cutoff to remove one or more second frequency bands of the signal outside of the first frequency band when a modulation type of the signal includes pulse position modulation. The method also includes comparing the first frequency band of the signal to a library of signals. The signals in the library have known telemetry modes. The method also includes predicting the telemetry mode of the downhole tool based on the comparison. The method also includes switching a telemetry mode of the computing system to match the telemetry mode of the downhole tool. The method also includes demodulating the signal using the computing system after the telemetry mode of the computing system is switched.

[0007] A system is also disclosed. The system includes a downhole tool configured to be run into a wellbore, capture measurement data while positioned within the wellbore, encode the measurement data while positioned within the wellbore, and transmit a signal containing the encoded measurement data while positioned within the wellbore. The system also includes a computing system positioned at a surface. The computing system is configured to perform operations. The operations include receiving the signal. The telemetry mode of the downhole tool is unknown to the computing system at a time the signal is received. The operations also include identifying a first frequency band of the signal. The first frequency band of the signal includes the encoded measurement data. The operations also include applying a low-pass filter to the signal at a predetermined frequency with a predetermined cutoff to remove one or more second frequency bands of the signal outside of the first frequency band when a modulation type of the signal includes pulse position modulation. The operations also include comparing the first frequency band of the signal to a library of signals. The signals in the library have known telemetry modes. The operations also include predicting the telemetry mode of the downhole tool based on the comparison. The operations also include switching a telemetry mode of the computing system to match the telemetry mode of the downhole tool. The operations also include demodulating the signal after the telemetry mode of the computing system is switched.

[0008] It should be understood that the summary is only intended to introduce some aspects of the methods, systems and media of the present application, which are described and / or claimed below in more detail. As such, the summary is not intended to narrow the scope of the present application in any way. BRIEF DESCRIPTION OF DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description, explain the principles of the application. In the drawings:

[0010] Figure 1An example of a system according to an embodiment is shown, the system including various management components for managing various aspects of a geological environment.

[0011] Figure 2 A cross-sectional view of a wellsite according to an embodiment is shown, the wellsite including a downhole tool in a wellbore.

[0012] Figure 3 A flowchart of a method according to an embodiment is shown, the method for predicting a mode of a downhole tool.

[0013] Figures 4A-4D An example of a graph according to an embodiment is shown, the graph including different unprocessed signals transmitted from a downhole tool. More particularly, Figure 4A A graph of pressure versus time is shown with respect to a signal having a pulse length of 0.6 seconds. Figure 4B A graph of pressure versus time is shown with respect to a signal having a pulse length of 0.8 seconds. Figure 4C A graph of pressure versus time is shown with respect to a signal having a pulse length of 1.0 seconds. Figure 4D A graph of pressure versus time is shown with respect to a signal having a pulse length of 1.5 seconds.

[0014] Figures 5A-5D An example of a graph according to an embodiment is shown, the graph including the signals after processing, respectively. Figures 4A-4D More particularly, Figure 5A A graph of the signals after processing in Figure 4A with a pulse length of 0.6 seconds is shown. Figure 5B A graph of the signals after processing in Figure 4B with a pulse length of 0.8 seconds is shown. Figure 5C A graph of the signals after processing in Figure 4C with a pulse length of 1.0 seconds is shown. Figure 5D A graph of the signals after processing in Figure 4D with a pulse length of 1.5 seconds is shown 540.

[0015] Figure 6 An example of a computing system according to an embodiment is shown, the computing system for performing at least a portion of the methods disclosed herein. DETAILED DESCRIPTION

[0016] Reference will now be made in detail to embodiments illustrated in the accompanying drawings. In the following detailed description of embodiments, numerous specific details are set forth in order to provide a thorough understanding of the embodiments. However, it will be apparent to one skilled in the art that the present application can 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 also should be understood that, although the terms first, second, etc. can 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. So, the first object or step and the second object or step are both either objects or steps, 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 example embodiments only 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 “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. Additionally, as used herein, the term “if’ can 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 programs, methods, techniques, and workflows in accordance with some embodiments. Some operations in the processing programs, methods, techniques, and workflows disclosed herein can be combined and / or the order of some operations can be changed.

[0020] Figure 1 An instance of a system 100 is shown that includes various management components 110 for managing various aspects of a geological environment 150 (e.g., an environment including a depositional basin, a reservoir 151, one or more faults 153-1, one or more geobodies 153-2, etc.). For example, the management components 110 can allow for the management of sensing, drilling, injection, extraction, etc., directly or indirectly with respect to the geological environment 150. In turn, further information about the geological environment 150 can become available as feedback 160 (e.g., optionally as input to one or more of the management components 110).

[0021] In Figure 1 Examples, the management component 110 includes a seismic data component 112, an additional information component 114 (e.g., well / log 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 by each component 112 and 114 can be input to the simulation component 120.

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

[0023] In example embodiments, the simulation component 120 can operate in conjunction with a software framework, such as an object-based framework. In such a framework, entities can include entities based on predefined classes to facilitate modeling and simulation. A commercially available example of an object-based framework is the Microsoft®.NET® Framework (Redmond, Washington), which provides a set of extensible object classes. In the.NET® Framework, object classes encapsulate modules of reusable code and related data structures. Object classes can be used to instantiate object instances for use by programs, scripts, etc. For example, a wellbore class can define an object for representing a wellbore based on well data. In example embodiments, the simulation component 120 can process information to conform to one or more attributes specified by the attribute component 130, which can include an attribute library. Such processing can occur prior to input to the simulation component 120 (e.g., in view of the processing component 116). As an example, the simulation component 120 can perform operations on input information based on one or more attributes specified by the attribute component 130. In example embodiments, the simulation component 120 can construct one or more models of the geological environment 150, which can rely on model simulations to model behavior of the geological environment 150 (e.g., in response to one or more actions, whether natural or perceived). In

[0024] In example embodiments, the analysis / visualization component 142 can allow for interaction with model / model-based results (e.g., simulation results, etc.). As an example, output from the simulation component 120 can be input to one or more other workflows, as indicated by the workflow component 144. Figure 1 Figure 1 In example embodiments, the simulation component 120 can process information to conform to one or more attributes specified by the attribute component 130, which can include an attribute library. Such processing can occur prior to input to the simulation component 120 (e.g., in view of the processing component 116). As an example, the simulation component 120 can perform operations on input information based on one or more attributes specified by the attribute component 130. In example embodiments, the simulation component 120 can construct one or more models of the geological environment 150, which can rely on model simulations to model behavior of the geological environment 150 (e.g., in response to one or more actions, whether natural or perceived). In​​

[0025] As an example, the simulation component 120 can include one or more features of a simulator, such as ECLIPSE® TM RESERVOIR SIMULATOR (Schlumberger Limited, Houston Texas), INTERSECT® TM RESERVOIR SIMULATOR (Schlumberger Limited, Houston Texas), and the like. As an example, the simulation component, simulator, and the like can include features to implement one or more meshless techniques (e.g., to solve one or more equations, and the like). As an example, one or more reservoirs can be simulated with respect to one or more enhanced recovery techniques (e.g., considering thermal processes, such as SAGD, and the like).

[0026] In example embodiments, the management component 110 can include features of a commercially available framework, such as SEISMIC SIMULATION SOFTWARE FRAMEWORK (Schlumberger Limited, Houston, Texas). The framework provides components that can optimize exploration and development operations. The framework includes seismic simulation software components that can output information for use in improving reservoir performance, for example, by improving asset team productivity. By using 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 can be considered an application and can be considered a data-driven application (e.g., in which data is input for modeling, simulation, and the like purposes).

[0027] In example embodiments, various aspects of the management component 110 can include add-ons or plug-ins that operate according to the specifications of the framework environment. For example, as a commercially available framework environment marketed by the SEISMIC SIMULATION SOFTWARE FRAMEWORK (Schlumberger Limited, Houston, Texas) allows add-ons (or plug-ins) to be integrated into the framework workflow. The framework environment takes advantage of TOOLBAR (Microsoft Corporation, Redmond, Washington) and provides a stable, humanized interface for efficient development. In example embodiments, various components can be implemented as add-ons (or plug-ins) that conform to the specifications of the framework environment (e.g., according to application program interface (API) specifications, and the like) and operate according to the same.

[0028] Figure 1An instance of framework 160 is also shown that includes model simulation layer 180 along with framework services layer 190, framework core layer 195, and module layer 175. Framework 160 can include commercially available framework, where model simulation layer 180 is commercially available to model-centric software package that hosts framework applications. In example embodiments, software can be considered a data-driven application. software can include a framework for model construction and visualization.

[0029] As an example, a framework can include features for implementing one or more grid generation techniques. For example, a framework can include an input component for receiving information from an interpretation regarding seismic data, one or more attributes based at least in part on seismic data, log data, image data, etc. Such a framework can include a grid generation component that processes the input information, optionally in conjunction with other information, to generate a grid.

[0030] In the example of Figure 1 model simulation layer 180 can provide domain objects 182, act as a data source 184, provide rendering 186, and provide various user interfaces 188. Rendering 186 can provide a graphical environment in which applications can display their data, while user interfaces 188 can provide a common look and feel for application user interface components.

[0031] As an example, domain objects 182 can include entity objects, attribute objects, and optionally other objects. Entity objects can be used to geometrically represent wells, surfaces, objects, reservoirs, etc., while attribute objects can be used to provide attribute values as well as data versions and display parameters. For example, an entity object can represent a well, with attribute objects providing log information as well as version information and display information (e.g., to display the well as part of a model).

[0032] In the example of Figure 1 data can be stored in one or more data sources (or data stores, typically physical data storage devices) that can be located at the same or different physical sites and can be accessed through one or more networks. Model simulation layer 180 can be configured to model projects. Thus, a particular project can be stored, where the stored project information can include inputs, models, results, and cases. Thus, after a modeling phase is complete, a user can store a project. Later, the project can be accessed and restored using model simulation layer 180, which can recreate instances of the relevant domain objects.

[0033] In the example of Figure 1In the example of FIG. 1, the geological environment 150 can include a layer (e.g., a stratigraphic layer) that includes a reservoir 151 and one or more other features, such as a fault 153-1, a geobody 153-2, etc. As an example, the geological environment 150 can be equipped with any of a variety of sensors, detectors, actuators, etc. For example, the equipment 152 can include communication circuitry for receiving and transmitting information regarding one or more networks 155. Such information can include information associated with downhole equipment 154, which can be equipment for acquiring information, assisting resource recovery, etc. Other equipment 156 can be located away from the wellsite and include sensing, detecting, emitting, or other circuitry. Such equipment can include storage and communication circuitry for storing and communicating data, instructions, etc. As an example, one or more satellites can be provided for communication, data acquisition, etc. purposes. For example, Figure 1 A satellite is shown in communication with the network 155, which can be configured for communication, noting that the satellite can additionally or alternatively include circuitry for imaging (e.g., spatial, spectral, temporal, radiometric, etc.).

[0034] Figure 1 The geological environment 150 is also shown, optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that can intersect one or more fractures 159. For example, consider a well in a shale formation, which can include natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination of natural and artificial fractures. As an example, a well can be drilled for a laterally extensive reservoir. In such an example, there can be lateral variations in properties, stresses, etc., and assessment of such variations can assist in planning, operation, etc. to develop the laterally extensive reservoir (e.g., through fracturing, injection, extraction, etc.). As an example, the equipment 157 and / or 158 can include components, one or more systems, etc. for fracturing, seismic sensing, seismic data analysis, assessing one or more fractures, etc.

[0035] As mentioned, the system 100 can be used to perform one or more workflows. A workflow can be a process that includes a plurality of work steps. A work step can operate on data, for example to create new data, update existing data, etc. As an example, one or more inputs can be operated on and one or more results created, for example based on one or more algorithms. As an example, the system can include a workflow editor for creating, editing, executing, etc. a workflow. In such an example, the workflow editor can provide a selection of one or more predefined work steps, one or more custom work steps, etc. As an example, a workflow can be a workflow that can be implemented in software, for example that operates on seismic data, seismic attributes, etc. As an example, a workflow can be a workflow that can be implemented in hardware, for example that operates on seismic data, seismic attributes, etc. As an example, a workflow can be a workflow that can be implemented in software, for example that operates on seismic data, seismic attributes, etc. As an example, a workflow can be a workflow that can be implemented in hardware, for example that operates on seismic data, seismic attributes, etc. Processes implemented in the framework. As an example, a workflow can include one or more work steps that access modules (e.g., plug-ins, external executable code, etc.).

[0036] Figure 2 A cross-sectional view of an example of a wellsite 200 is shown in accordance with one embodiment. The wellsite 200 can include a rig 202, which can include a rig substructure and a derrick assembly. The rig 202 can be positioned over a wellbore 204 formed in a formation 206. A drill string 208 can be supported by the rig 202 and extend downward into the wellbore 204.

[0037] A downhole tool (e.g., a bottom hole assembly) 210 can be coupled to a lower end of the drill string 208. The downhole tool 210 can be or include a logging while drilling (LWD) tool 212, a measuring while drilling (MWD) tool 214, and a drill bit 216. The LWD tool 212 can be configured to measure one or more formation properties and / or physical properties at any time while or after the wellbore 204 is being drilled. The MWD tool 214 can be configured to measure one or more physical properties at any time while or after the wellbore 204 is being drilled. The formation properties can include resistivity, density, porosity, acoustic velocity, gamma rays, etc. The physical properties can include pressure, temperature, wellbore caliper, wellbore trajectory, weight on bit, bit torque, vibration, shock, stick slip, etc.

[0038] A drilling fluid (also referred to as mud) 220 can be stored in a pit 222 at the surface 201. A pump 224 can deliver the drilling fluid 220 to an interior of the drill string 208, which causes the drilling fluid 220 to flow downward through the drill string 208 and into the downhole tool 210, as indicated by directional arrows 226. The drilling fluid 220 can flow through the downhole tool 210 (e.g., through the LWD tool 212 and / or the MWD tool 214) and exit via ports in the drill bit 216. The drilling fluid 220 can then flow through an annular space between an exterior of the drill string 208 and a wall of the wellbore 204, as shown by directional arrows 228, where it can be filtered and / or reintroduced into the pit 222.

[0039] While in the wellbore 204, the downhole tool 210 can use telemetry techniques such as mud pulse telemetry or EM telemetry to transmit measured data from the LWD tool 212 and the MWD tool 214 to a computing system 600 at the surface 201. More particularly, measured data from the LWD tool 212 can be transmitted to the MWD tool 214. The MWD tool 214 can then encode the measured data from the LWD tool 212 and / or the MWD tool 214 using any suitable modulation method (e.g., pulse position modulation, continuous phase modulation, phase shift keying, frequency shift keying, quadrature amplitude modulation, quadrature frequency division multiplexing, etc.).

[0040] The downhole tool 210 can have multiple telemetry modes. As used herein, the term "telemetry mode" refers to the pulse duration, pulse rate, bit rate, and / or carrier frequency of a signal transmitted by the downhole tool 210. In one embodiment, each mode can correspond to a different pulse duration used to transmit encoded data. For example, the downhole tool 210 can have six modes: 0.5 seconds, 0.6 seconds, 0.8 seconds, 1.0 seconds, 1.5 seconds, and 2.0 seconds. Thus, when in the fourth mode, encoded data can be transmitted in discrete pulses, each having a duration of 1.0 seconds. When mud pulse telemetry is used to transmit encoded data, the pulses can be pressure pulses introduced into the drilling fluid 220 by the downhole tool 210 (e.g., by the MWD tool 214). When EM telemetry is used to transmit encoded data, the pulses can be EM pulses generated by the downhole tool 210 (e.g., by the MWD tool 214).

[0041] The pulses can be received by one or more sensors 230 at the surface 201, which can transmit the pulses (or encoded data therein) to the computing system 600. As described above, the computing system 600 likewise can have multiple telemetry modes. When the telemetry mode of the computing system 600 corresponds to the telemetry mode of the downhole tool 210 (e.g., a pulse having a duration of 1.0 seconds), the computing system 600 is able to decode data transmitted by the downhole tool 210. However, when the telemetry mode of the computing system 600 does not correspond to the telemetry mode of the downhole tool 210 (e.g., because the mode of the downhole tool 210 has inadvertently changed), the computing system 600 can have difficulty decoding data transmitted by the downhole tool 210.

[0042] Figure 3 A flowchart of a method 300 for predicting a telemetry mode of the downhole tool 210 is shown, in accordance with an embodiment. An illustrative order of the method 300 is provided below; however, as will be appreciated, one or more portions of the method 300 can be performed in a different order or omitted.

[0043] The method 300 can include receiving a signal from the downhole tool 210, as shown in 302. As described above, the signal can be or include a mud pulse signal or an EM signal. The signal can be received by the sensors 230 and / or the computing system 600 at the surface 201. The signal can include data from the downhole tool 210. For example, the signal can include encoded measurement data from the LWD tool 212 and / or the MWD tool 214. In at least one embodiment, the telemetry mode of the downhole tool 210 can be unknown at the surface 201. Thus, the pulse duration of the signal, the pulse rate of the signal, the carrier frequency of the signal, the bit rate of the signal, or a combination thereof can be unknown to the computing system 600 or a user at the surface 201.

[0044] Figures 4A-4D An example of a plot showing different unprocessed signals transmitted from the downhole tool 210 is shown. More particularly, Figure 4A A plot 410 of pressure versus time for a signal having a 0.6 second pulse duration is shown. Figure 4B A plot 420 of pressure versus time for a signal having a 0.8 second pulse duration is shown. Figure 4C A plot 430 of pressure versus time for a signal having a 1.0 second pulse duration is shown. Figure 4D A plot 440 of pressure versus time for a signal having a 1.5 second pulse duration is shown. The signals in plots 410, 420, 430, 440 are mud pulse telemetry signals, and data has been encoded in the signals using pulse position modulation (PPM). When analyzing plots 410, 420, 430, 440, it can be difficult to determine the duration of the pressure pulse. Thus, it can be difficult to predict the telemetry mode by analyzing plots 410, 420, 430, 440 when the telemetry mode of the downhole tool 210 is unknown. Thus, it can be difficult to decode the signals.

[0045] The signals can be processed as described in 304, 306, 308, and / or 310 below. More particularly, the method 300 can include identifying a first frequency band of the signal containing data, as described in 304. The first frequency band can include encoded measurement data from the LWD tool 212 and / or the MWD tool 214. The first frequency band can be identified by the computing system 600 by modulation type. For example, when the modulation type is PPM, the data is encoded at baseband, so a low pass filter can be applied at a predetermined frequency (e.g., 4 Hz) with a predetermined cutoff (e.g., 3 dB).

[0046] The method 300 can also include removing one or more second frequency bands in the signal outside of the first frequency band, as described in 306. In other words, the second frequency bands not including data (e.g., encoded measurement data) can be removed by the computing system 600.

[0047] The method 300 can also include removing noise from the first frequency band, as described in 308. The computing system 600 can remove noise from the first frequency band including data (e.g., encoded measurement data). The noise can be generated by equipment around the wellsite 100, such as the pump 224.

[0048] The method 300 can also include segmenting the first frequency band of the signal into one or more time series having a predetermined duration, as described in 310. The predetermined duration can be from about 1 second to about 3 seconds, from about 2 seconds to about 5 seconds, from about 3 seconds to about 10 seconds, from about 5 seconds to about 30 seconds, or indefinitely. As shown, the predetermined duration of the time series is 20 seconds. Figures 4A-4D ​

[0049] Figures 5A-5D It shows including Figures 4A-4D Examples of graphs of the signals after they have been processed (e.g., as described in 304, 306, 308, and / or 310). More specifically, Figure 5A Shown after processing Figure 4A The curve of the signal (with a pulse duration of 0.6 seconds) in Figure 510. Figure 5B Shown after processing Figure 4B The curve of the signal (with a pulse duration of 0.8 seconds) in Figure 520. Figure 5C Shown after processing Figure 4C The curve of the signal (with a pulse duration of 1.0 second) in Figure 530. Figure 5D Shown after processing Figure 4D The curve of the signal (with a pulse duration of 1.5 seconds) in Figure 540.

[0050] Due to processing (e.g., as described in 304, 306, 308, and / or 310), graphs 510, 520, 530, and 540 may have less distortion and be easier to analyze by the computing system 600 compared to the corresponding graphs 410, 420, 430, and 440. However, the duration of the pulse at the surface may differ from the duration of the downhole pulse. Figure 5A In one example, downhole tool 210 can transmit signals with a pulse length of 0.6 seconds. However, Figure 5A This indicates that the signal is received at the ground (e.g., via sensor 230 and / or computing system 600), and as in Figure 5A As can be seen, the pulse duration of the signal may be different from (for example, greater than) 0.6 seconds.

[0051] As described below, the computing system 600 can analyze signals received on the surface to predict the duration of pulses transmitted from the downhole tool 600, and signal processing (e.g., as described in 304, 306, 308 and / or 310) can increase the accuracy of this analysis.

[0052] Method 300 may further include predicting telemetry patterns of the downhole tool 210 used for transmitting signals (e.g., from the downhole tool 210 to the sensor 230 and / or the computing system 600), as described in 312. The computing system 600 may use machine learning (ML) techniques (e.g., neural networks, support vector machines, clustering, and / or hard computing) to analyze the processed signals to predict telemetry patterns.

[0053] In embodiments where the signal uses PPM encoding, the computing system 600 can use a neural network to predict telemetry patterns by comparing processed signals from downhole tool 210 (e.g., in Figure 530) with a signal library. The signals in the library may also be signals transmitted from downhole tool 210 or other downhole tools. The signals in the library may have been previously analyzed (e.g., by a field engineer) to determine the telemetry patterns of those signals, so the telemetry patterns of the signals in the library are known. For example, a field engineer may determine the telemetry pattern of each signal in the library based at least in part on specific pulse characteristics transmitted immediately after the downhole tool 210 is opened, analysis of the Fourier spectrum of the pressure signal, appropriate decoding of the signal, pulse duration, energy concentration at a specific frequency / bandwidth, or combinations thereof. Based on the comparison, the computing system 600 can identify one or more signals in the library that are most similar to the signal from downhole tool 210, and the computing system 600 can then predict that the telemetry pattern of the signal from downhole tool 210 is the same as the telemetry pattern of similar signals in the library.

[0054] The computing system 600 can predict the telemetry pattern of a signal from the downhole tool 210 by identifying a single pattern (e.g., the downhole tool 210 is in mode 4, with a pulse duration of 1 second). However, in another embodiment, the computing system 600 can predict the telemetry pattern of a signal from the downhole tool 210 by providing the probability of the downhole tool 210 being in each of multiple modes (e.g., six possible modes). An example of this embodiment is shown in Table 1 below.

[0055]

[0056] Method 300 may also include notifying the user of the predicted telemetry pattern and / or probability, as described in 314.

[0057] Method 300 may further include switching the telemetry mode of computing system 600 to match the predicted telemetry mode of downhole tool 210, as described in 316. Due to the switching, computing system 600 may now have the same mode as downhole tool 210. In one instance, computing system 600 may automatically switch to the predictive telemetry mode when the probability is greater than a predetermined threshold (e.g., 80%). In another embodiment, instead of switching computing system 600 from the first telemetry mode to the second telemetry mode (e.g., the predictive telemetry mode), a second computing system 600 (or a second receiver within the first / existing computing system 600) running in the predictive telemetry mode may be activated. In yet another embodiment, computing system 600 may be switched to run multiple modes in parallel.

[0058] Method 300 may further include demodulating the signal, as described in 318. More specifically, a first portion of the signal received before the telemetry mode of the computing system 600 is switched may be demodulated and / or a second portion of the signal received after the telemetry mode of the computing system 600 is switched may be demodulated. After the telemetry mode of the computing system 600 has been switched to match the telemetry mode of the downhole tool 210, the computing system 600 may demodulate the first and / or second portions of the signal received from the downhole tool 210. The demodulated signal may allow the computing system 600 to decode and recover measurement data from the LWD tool 212 and / or the MWD tool 214.

[0059] After at least one demodulation of the signal, the recovered measurement data can be used to determine the confidence level of the accuracy of the predicted telemetry pattern (e.g., check or double check). For example, the recovered measurement data may include the inclination of wellbore 204 and / or downhole tool 210. In this example, the operator knows that downhole tool 210 is in a substantially vertical portion of wellbore 204 (e.g., with an inclination of approximately 90°). If the recovered measurement data indicates an inclination within a predetermined range (e.g., 90° + / - 10°), this increases the confidence level of the accuracy of the predicted telemetry pattern. However, if the recovered measurement data indicates that the measured inclination is outside this predetermined range (e.g., the measured inclination is 10°), this decreases the confidence level of the accuracy of the predicted telemetry pattern. In effect, it can confirm that the predicted telemetry pattern is inaccurate, and method 300 can loop back to a previous section (e.g., 302). As will be understood, inclination is merely one type of illustrative data that can be used to determine the confidence level. Other types of data that can be independently known to be within a predetermined range may include temperature, pressure, etc.

[0060] In response to the decoded data, method 300 may include performing physical actions at well site 100, as described in 320. Physical actions may include modifying the volumetric flow rate, pressure, and / or composition of drilling fluid 114 pumped into wellbore 130. Physical actions may also, or alternatively, include modifying the weight on bit 216 (WOB). Physical actions may also, or alternatively, include modifying the trajectory of wellbore 130.

[0061] In some embodiments, the methods disclosed herein may be performed by a computing system. Figure 6An example of such a computing system 600 according to some embodiments is shown. The computing system 600 may include a computer or computer system 601A, which may be a standalone computing system 601A or an arrangement of distributed computing systems. The computing system 601A includes one or more analysis modules 602 configured to perform various tasks according to some embodiments, such as one or more methods disclosed herein. To perform these various tasks, the analysis modules 602 independently or collaboratively execute one or more processors 604 connected to one or more storage media 606. The processor 604 is also connected to a network interface 607 to allow the computer system 601A to communicate with one or more other computer systems and / or computing systems (such as 601B, 601C and / or 601D) via a data network 609. (Note that computer systems 601B, 601C and / or 601D may or may not share the same architecture as computer system 601A and may be located in different physical locations. For example, computer systems 601A and 601B may be located in a processing facility when communicating with one or more computer systems (such as 601C and / or 601D) located in one or more data centers and / or in different countries on different continents.)

[0062] The processor may include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array or other control or computing device.

[0063] Storage medium 606 may be implemented as one or more computer-readable or machine-readable storage media. It should be noted that, although in Figure 6 In an exemplary embodiment, storage medium 606 is depicted within computer system 601A; however, in some embodiments, storage medium 606 may be distributed within and / or across multiple internal and / or external enclosures of computing system 601A and / or other computing systems. Storage medium 606 may include one or more different forms of memory, including semiconductor memory devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM) and flash memory, magnetic disks (such as fixed disks, floppy disks, and removable disks), other magnetic media (including magnetic tape), optical media (such as optical discs (CDs) or digital video disks (DVDs)). Disk, or other types of optical storage, or other types of storage devices. It should be noted that the instructions discussed above may be provided on a single computer-readable or machine-readable storage medium, or on multiple computer-readable or machine-readable storage media distributed across a large system with potentially multiple nodes. Such computer-readable or machine-readable storage media or medium is considered part of an article (or article of manufacture). An article or article of manufacture may refer to any single or multiple manufactured components. One or more storage media may be located either in a machine executing the machine-readable instructions or at a remote site from which the machine-readable instructions can be downloaded via a network for execution.

[0064] In some embodiments, computing system 600 includes one or more pattern prediction modules 608 that can perform at least a portion of the methods 300 disclosed herein. It should be understood that computing system 600 is merely one example of a computing system, and computing system 600 may have more or fewer components than shown, and may combine components not shown herein. Figure 6 The additional components depicted in the example embodiments, and / or the computing system 600 may have Figure 6 The different configurations or arrangements of the components depicted. Figure 6 The various components shown can be implemented in hardware, software, or a combination of both, including one or more signal processing and / or application-specific integrated circuits.

[0065] Furthermore, the steps in the processing methods described herein can be implemented by running one or more functional modules in an information processing device (e.g., a general-purpose processor or a dedicated chip such as an ASIC, FPGA, PLD, or other suitable device). These modules, combinations of these modules, and / or combinations thereof with general hardware are included within the scope of this disclosure.

[0066] Computational interpretations, models, and / or other interpretive aids can be improved iteratively; this concept applies to the methods discussed herein. This may include the use of feedback loops based on algorithmic execution, such as in a computing device (e.g., computing system 600). Figure 6 This can be performed at the location and / or through manual control by the user, who can determine whether a given step, action, template, model, or curve set has become sufficiently accurate to evaluate the underground three-dimensional geological structure under consideration.

[0067] For purposes of explanation, the foregoing description has been given with reference to specific embodiments. However, the above illustrative discussion is not intended to exhaustively describe or limit the precise forms disclosed. Many modifications and variations may be made in accordance with the foregoing teachings. Furthermore, the order in which the elements of the methods described herein are illustrated and described may be rearranged, and / or two or more elements may appear simultaneously. The embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to best utilize the disclosed embodiments and various embodiments with various modifications suitable for the intended particular use.

Claims

1. A method for predicting telemetry patterns of downhole tools, comprising: The system receives signals from the downhole tool at the ground-based computing system. Based on the aforementioned signals, predict the telemetry mode of downhole tools; Switch the telemetry mode of the computing system to match the telemetry mode of the downhole tool; After the telemetry mode of the computing system is switched, the computing system is used to demodulate the signal. When the signal is received, the telemetry mode of the downhole tool is unknown.

2. The method according to claim 1, wherein, The signal includes coded measurement data captured by a measurement-while-drilling tool, a logging-while-drilling tool, or both in the downhole tool, and wherein demodulating the signal decodes the coded measurement data.

3. The method of claim 2, further comprising identifying a first frequency band of the signal by modulation type, wherein, The first frequency band of the signal includes the coded measurement data.

4. The method according to claim 3, wherein, Identifying the first frequency band of the signal includes applying a low-pass filter to the signal at a predetermined frequency with a predetermined cutoff when the modulation type includes pulse position modulation.

5. The method of claim 3, further comprising removing one or more second frequency bands outside the first frequency band from the signal.

6. The method according to claim 4, further comprising: Remove noise from the first frequency band of the signal; as well as The first frequency band of the signal is divided into time series with a predetermined duration.

7. The method according to claim 1, wherein, The signal is encoded by the downhole tool using pulse position modulation, wherein predicting the telemetry pattern includes comparing the signal with a signal library, wherein the signals in the library have known telemetry patterns.

8. The method according to claim 1, wherein, Predicting the telemetry pattern includes predicting the probability of multiple different telemetry patterns.

9. The method of claim 1, wherein the signal includes measurement parameters, and wherein after the signal is demodulated, the method further includes determining whether the measurement parameters are within a predetermined range to confirm that the predicted telemetry pattern is accurate.

10. A method for predicting telemetry patterns of downhole tools, comprising: The system receives signals from the downhole tool at a surface computing system, wherein the signals include coded measurement data captured by a measurement while drilling tool, a logging while drilling tool, or both in the downhole tool, and wherein the telemetry mode of the downhole tool is unknown when the signals are received. Identify a first frequency band of the signal, wherein the first frequency band of the signal includes the coded measurement data; When the modulation type of the signal includes pulse position modulation, a low-pass filter is applied to the signal at a predetermined frequency with a predetermined cutoff to remove one or more second frequency bands outside the first frequency band of the signal; The first frequency band of the signal is compared with a signal library, wherein the signals in the library have known telemetry patterns; Based on the aforementioned comparison, predict the telemetry patterns of downhole tools; Switch the telemetry mode of the computing system to match the telemetry mode of the downhole tool; After the telemetry mode of the computing system is switched, the computing system is used to demodulate the signal.

11. The method according to claim 10, wherein, Predicting the telemetry pattern includes predicting the probability of multiple different telemetry patterns for downhole tools.

12. The method of claim 11 further includes automatically switching the telemetry mode of the computing system in response to a probability greater than a predetermined threshold in one of the different telemetry modes.

13. The method of claim 10, further comprising performing a physical action at the well site in response to demodulating the signal.

14. The method of claim 13, wherein, The physical action to be performed is selected from the group consisting of the following items: Modify the volumetric flow rate of the fluid pumped into the wellbore; Modify the pressure of the fluid pumped into the wellbore; Modify the composition of the fluid pumped into the wellbore; Modify the weight of the drill bit in the wellbore; and Modify the trajectory of the wellbore.

15. A system comprising: Downhole tools, which are configured as follows: Run into the wellbore; Measurement data is captured when the well is located inside the wellbore. The measurement data is encoded when the well is located inside the wellbore. as well as When located inside the wellbore, a signal including the coded measurement data is transmitted; and a computing system located on the ground, wherein the computing system is configured to perform operations including: Receive signals, wherein, upon receiving the signals, the computing system is unaware of the telemetry mode of the downhole tool; Identify a first frequency band of the signal, wherein the first frequency band of the signal includes the coded measurement data; When the modulation type of the signal includes pulse position modulation, a low-pass filter is applied to the signal at a predetermined frequency with a predetermined cutoff to remove one or more second frequency bands in the signal that are outside the first frequency band; The first frequency band of the signal is compared with a signal library, wherein the signals in the library have known telemetry patterns; Based on the aforementioned comparison, predict the telemetry patterns of downhole tools; Switch the telemetry mode of the computing system to match the telemetry mode of the downhole tool; and The signal is demodulated after the telemetry mode of the computing system is switched.

16. The system according to claim 15, wherein, Predicting the telemetry pattern includes predicting the probability of multiple different telemetry patterns for downhole tools.

17. The system according to claim 16, wherein, The operation also includes automatically switching the telemetry mode of the computing system in response to a probability greater than a predetermined threshold in one of the different telemetry modes.

18. The system according to claim 15, wherein, The operation also includes performing physical actions at the well site in response to demodulating the signal.

19. The system according to claim 18, wherein, The physical action is selected from the group consisting of the following items: Modify the volumetric flow rate of the fluid pumped into the wellbore; Modify the pressure of the fluid pumped into the wellbore; Modify the composition of the fluid pumped into the wellbore; Modify the weight of the drill bit in the wellbore; and Modify the trajectory of the wellbore.

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