DRILLING WITH CASING MONITOR

Description

BACKGROUND
Field of the Disclosure

The present disclosure generally relates to the access and production of hydrocarbons (for example, oil and gas) from hydrocarbon reservoirs. More specifically, embodiments of the disclosure relate to performing drilling operations to form wells to access hydrocarbon reservoirs.

Description of the Related Art

Hydrocarbon production typically relies on wells drilled to access hydrocarbon reservoirs, such as reservoirs located in subterranean formations. Wells may be drilled using a variety of techniques to locate hydrocarbons and facilitate production of hydrocarbons via the well. In one such technique referred to as “Drilling with Casing” (also referring to as “Casing while Drilling”), casing is installed while simultaneously drilling the wellbore. Casing while drilling presents several challenges specific to this technique, such as a lack of fatigue resistance, strength, and robustness of the casing as compared to the drill pipe typically used in drilling wells.

SUMMARY

Drilling with casing (DwC) may be used in specific types of locations and formations having hydrocarbon reservoirs, such as mature fields and shale formations. However, the casing does not have the strength, robustness, fatigue resistance, and torque resistance properties of drill pipe.

To facilitate drilling with casing (DwC), casing connections having a relatively large torque capacity and fatigue resistance may be used in order to sustain the dynamics of drilling. Moreover, some techniques may also use fatigue prediction models for such casing connections. However, the existing fatigue prediction models are based on static considerations and do not accurately account for the dynamics of drilling.

Embodiments of the disclosure are directed to a drilling with casing monitoring system that receives data from a drilling operation, provides visualizations, and provides a model that accurately predicts problematic drilling loads in order to avert these loads and maintain casing and the efficiency of the drilling operation. As discussed in the disclosure, the drilling with casing monitor may determine the status of a drilling with casing operation based on the dynamic effects of drilling.

In one embodiment, a method for drilling a well using a drilling with casing operation is provided. The method includes receiving, from a transducer, data associated with a casing, the transducer data including torque, radial acceleration, tangential acceleration, and axial acceleration. The method also includes providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation. The drilling with casing machine learning model is trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration. The method further includes outputting a condition of the drilling with casing operation from the drilling with casing machine learning model.

In some embodiments, the method includes receiving data associated with a rig, the rig data including electronic drilling recorder (EDR) data including hook load, string depth, revolutions-per-minute (RPM), torque, and block height, forming a dataset having at least one datum of the transducer data and at least one datum of the EDR data, and providing the dataset to the drilling with casing machine learning model. In some embodiments, the drilling with casing machine learning model is an artificial neural network (ANN). In some embodiments, the method includes stopping the drilling with casing operation based on the determined condition. In some embodiments, the method includes adjusting the drilling with casing operation based on determined condition. In some embodiments, the condition includes a torsional vibration value above a threshold value over a threshold time period. In some embodiments, the condition includes a lateral vibration value above a threshold value over a threshold time period. In some embodiments, the transducer data is acquired at a rate of 120 hertz (Hz). In some embodiments, the EDR data is acquired at a rate of 1 hertz (Hz). In some embodiments, the method includes providing a graph of torque, radial acceleration, tangential acceleration, and axial acceleration versus time.

In another embodiment, a non-transitory computer-readable storage medium having executable code stored thereon for drilling a well using a drilling with casing operation. The executable code includes a set of instructions that causes a processor to perform operations that include receiving, from a transducer, transducer data associated with a casing, the data including torque, radial acceleration, tangential acceleration, and axial acceleration. The operations also include providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation. The drilling with casing machine learning model is trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration. The operations further include outputting a condition of the drilling with casing operation from the drilling with casing machine learning model.

In some embodiments, the operations include receiving data associated with a rig, the rig data including electronic drilling recorder (EDR) data including hook load, string depth, revolutions-per-minute (RPM), torque, and block height, forming a dataset having at least one datum of the transducer data and at least one datum of the EDR data, and providing the dataset to the drilling with casing machine learning model. In some embodiments, the drilling with casing machine learning model is an artificial neural network (ANN). In some embodiments, the operations include stopping the drilling with casing operation based on the determined condition. In some embodiments, the operations include adjusting the drilling with casing operation based on determined condition. In some embodiments, the condition includes a torsional vibration value above a threshold value over a threshold time period. In some embodiments, the condition includes a lateral vibration value above a threshold value over a threshold time period. In some embodiments, the transducer data is acquired at a rate of 120 hertz (Hz). In some embodiments, the EDR data is acquired at a rate of 1 hertz (Hz). In some embodiments, the operations include providing a graph of torque, radial acceleration, tangential acceleration, and axial acceleration versus time.

In another embodiment, a system for drilling a well using a drilling with casing operation is provided. The system includes a processor and a non-transitory computer-readable storage memory accessible by the processor and having executable code stored thereon for drilling a well using a drilling with casing operation. The executable code includes a set of instructions that causes a processor to perform operations that include receiving, from a transducer, data associated with a casing, the transducer data including torque, radial acceleration, tangential acceleration, and axial acceleration. The operations also include providing transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation. The drilling with casing machine learning model is trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration. The operations further include outputting a condition of the drilling with casing operation from the drilling with casing machine learning model.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a well environment and a drilling with casing monitor in accordance with an embodiment of the disclosure;

FIG. 2 is a block diagram of a process for a drilling with casing operation and monitoring a drilling with casing operation in accordance with an embodiment of the disclosure;

FIGS. 3-7 are screens of a drilling with casing monitor in accordance with an embodiment of the disclosure; and

FIG. 8 is a block diagram of a casing installation monitoring system in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION

The present disclosure will be described more fully with reference to the accompanying drawings, which illustrate embodiments of the disclosure. This disclosure may, however, be embodied in many different forms and should not be construed as limited to the illustrated embodiments. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

FIG. 1 is diagram that illustrates a well environment 100 in accordance with one or more embodiments. In the illustrated embodiment, the well environment 100 includes a hydrocarbon reservoir (“reservoir”) 102 located in a subsurface formation (“formation”) 104 and a well system (“well”) 106. The formation 104 may include a porous or fractured rock formation that resides underground, beneath the earth's surface (“surface”) 108. In the case of the well 106 being a hydrocarbon well, the reservoir 102 may include a portion of the formation 104 that contains (or is at least determined to or expected to contain) a subsurface pool of hydrocarbons, such as oil and gas. The formation 104 and the reservoir 102 may each include different layers of rock having varying characteristics, such as varying degrees of permeability, porosity, and resistivity. In the case of the well 106 being operated as a production well, the well 106 may facilitate the extraction (or “production”) of hydrocarbons from the reservoir 102. In the case of the well 106 being operated as an injection well, the well 106 may facilitate the injection of fluids, such as water, into the reservoir 102. In the case of the well 106 being operated as a monitoring well, the well 106 may facilitate the monitoring of characteristics of the reservoir 102, such as reservoir pressure or water encroachment.

The well 106 may include a wellbore 110, a well control system (or “control system”) 112 and a rig 114. The control system 112 may control various operations of the well 106, such as well drilling or workover operations, well completion operations, well production operations, or well and formation monitoring operations. In some embodiments, the control system 112 includes a computer system that is the same as or similar to that of computer system 800 described with regard to at least FIG. 8.

The wellbore 110 may include a drilling (or “bored”) hole that extends from the surface 108 into a target zone of the formation 104, such as the reservoir 102. An upper end of the wellbore 110 at or near the surface 108 may be referred to as the “uphole” end of the wellbore 110, and a lower end of the wellbore 110 terminating in the formation 104 may be referred to as the “downhole” end of the wellbore 110. The wellbore 110 may be created, for example, by a drilling with casing operation that uses a drill bit 116 boring through the formation 104 and the reservoir 102 while simultaneously installing casing 118. Other components used in a drilling with casing operation may be included, such a bottom hole assembly, connectors, centralizers, etc. The wellbore 110 can provide for the circulation of drilling fluids during drilling operations, the flow of hydrocarbons (for example, oil and gas) from the reservoir 102 to the surface 108 during production operations, the injection of substances (for example, water) into one or both of the formation 104 and the reservoir 102 during injection operations, or the communication of monitoring devices (for example, logging tools) into one or both of the formation 104 and the reservoir 102 during monitoring operations (for example, during in situ logging operations).

The drilling rig 114 may include various components for operating a drilling string (that is, a casing in a drilling with casing operation), such as a top drive, as well as components for installing sections of tubulars that form the casing. As shown in FIG. 1, the drilling rig 114 may include a transducer 120 (as used herein, the term “a” transducer may refer to multiple transducers) installed beneath the top drive that may be used to acquire and transmit (for example, wirelessly transmit) drilling data to an external device or system (for example, the control system 112). The transducer 120 may also be referred to as Torque Turn Sub (TTS). The transducer 120 may include one or more transducers configured to measure different parameters associated with the casing. In some embodiments, the transducer 120 may acquire and transmit data such as acceleration in each axis (that is, x-axis (radial acceleration), y-axis (tangential acceleration), and z-axis (axial acceleration), torque, hook load, and drilling fluid pressure. The rig 114 may include other sensors (not shown) that transmit (for example, wirelessly transmit) additional data acquired from the drilling rig 114. For example, the rig 114 may include or be coupled to an electronic drilling recorder (EDR) 122 at the wellsite that receives sensor data from components of the rig 114 and transmits such data to an external device or system (for example, the control system 112). The EDR may acquire and transmit data such as drilling time, string depth, speed (in revolutions-per-minute (RPM), drilling fluid pressure, block height, and block weight.

The well control system 112 may include or be in communication with a drilling with a casing monitor 124 that may receive transducer data 126 from the transducer 120 and other rig data 128 from the EDR 122, such as via a network 130, to monitor the drilling with casing operation according to the techniques described in the disclosure. As discussed in the disclosure, the drilling with casing monitor 124 may include a drilling with casing machine learning (ML) model 132 that received may receive measurements from the data 126 and 128 and generate an output used to perform the drilling with casing operation. The drilling with casing monitor 126 may output a condition indicating in real-time the possibility of damage to the casing 118 based on the most recent data corresponding to the drilling with casing operation.

In some embodiments, the transducer data 126 may be acquired and transmitted at a relatively greater rate as compared to the rig data 128 (such transducer data may be referred to as “high frequency” data). For example, in some embodiments, the transducer data 126 may be acquired and transmitted at a rate of 120 Hz. In such embodiments, the other rig data 128 may be acquired and transmitted at a relatively lesser rate as compared to the transducer data 126 (such rig data may be referred to as “low frequency” data). In some embodiments, the other rig data 128 may be acquired and transmitted at a rate of 1 Hz. In other embodiments, other rates may be acquisition and transmission rates may be used.

FIG. 2 depicts a process 200 for a drilling with casing operation and monitoring a drilling with casing operation in accordance with an embodiment of the disclosure. Initially, the drilling with casing operation may begin at a wellsite (block 202). Data may be received from a transducer on a drilling rig at the wellsite (block 204). As discussed herein, in some embodiments the transducer data may include acceleration in all axes, torque, hook load, and drilling fluid pressure. Additionally, other rig data may be received (block 206), such as from an EDR, during the drilling with casing operation. As discussed in the disclosure, in some embodiments the transducer data may be acquired and received at relatively greater rate as compared to the other rig data, and the other rig data may be acquired and received at a relatively lesser rate as compared to the transducer data.

In some embodiments, the transducer data and the other rig data may be synchronized (block 208). In some embodiments, the transducer data, the other rig data, or both (for example, in synchronized form) may be provided as one or more visualizations (block 210). For example, as described below and shown in FIGS. 3-5, variables such as torque, well depth, bit depth, block height, average rate of penetration (ROP), drilling fluid pressure, flow, and acceleration may be displayed in various user interface elements as a graph versus time. In some embodiments, the other rig data (e.g., the EDR data) may be used to set a common reference point between different data sets and may be used to set a zero baseline. Additionally, the visualizations may enable the correlation of the determinations described in the disclosure with the EDR data to further improve control of the drilling rig 114.

Next, as shown in FIG. 2, a machine learning model for the drilling with casing operation may be generated using the synchronized data (block 212). Using the model, a condition of the drilling with casing operation may be determined (block 214). For example, moments of instability or relatively large dynamic loads during the drilling with casing operation may be identified. In embodiments, the machine learning model is an artificial neural network (ANN). In other embodiments, the machine learning model may be or use a support vector machine (SVM), a radial basis function (RBF), or other suitable techniques.

In some embodiments, the drilling with casing machine learning model may be trained using transducer data from multiple wellsites having drilling with casing operations. In some embodiments, the drilling with casing machine learning model may additionally be trained with rig data, such as EDR data, from wellsites having drilling with casing operations. In some embodiments, the machine learning model may be trained using supervised learning. In some embodiments, the machine learning model may be trained using a relationship between acceleration (in one or more axes) and torque. In some embodiments, certain values in the acceleration, torque, or identified acceleration-torque relationship may correspond to conditions of a drilling with casing operation. Such values and conditions may be used to train the drilling with casing machine learning model based on the acceleration and torque inputs. In some embodiments, vibrations (e.g., torsional vibrations) may be determined from the measured accelerations. By way of example, torsional vibrations above a threshold occurring longer than a threshold time period may indicate an increased risk to a drilling with casing operation. In another example, lateral vibrations above a threshold occurring longer than a threshold time period may indicate an increased risk of buckling conditions and potential damage to the casing.

Additionally, in embodiments, one or more visualizations of the output from the drilling with casing model may be provided (block 216). For example, as shown in FIG. 7 and as discussed below, the output from the drilling with casing model may be provided in a visualization that indicates conditions of stability and conditions of increased risk to the drilling with casing operation.

In some embodiments, the drilling with casing operation may be adjusted or stopped based on the determinations (block 218). For example, in some instances the drilling speed (in revolutions-per-minute (RPM)) may be adjusted. In another example, the weight on bit may be adjusted. In yet another example, the drilling fluid circulation rate may be adjusted. Advantageously, the drilling with casing monitor may enable the avoidance of undesirable drilling zones, prevent failure, and maximize life of the casing, and provide for optimization of drilling speeds.

As discussed in the disclosure, embodiments of the drilling with casing monitor may include interactive visualizations and associated user interfaces of a drilling with casing operation for monitoring and responding to the operation. FIGS. 3-7 depict example screens of example interactive visualizations in accordance with an embodiment of the disclosure. The interactive visualizations may be updated at set time intervals to enable real-time visualization and decision making.

FIGS. 3, 4, and 5 display screens 300, 400, and 500 respectively that depict certain variables measured by the drilling with casing monitor. For example, the screen 300 depicted in FIG. 3 illustrates measurements 302 acquired by the relatively high sample rate transducer and from the low sample rate data sources. The screen 300 further includes a graph 304 that displays the measurements (y-axis) vs time (x-axis) in real-time. In the embodiment shown in FIG. 3, the graph 304 has been filtered (via a user-selection in the user interface) to show “high frequency” torque acquired from the transducer and “low frequency” torque acquired from a low sample sensor on the rig. As shown in this figure, the measurement of torque at a higher rate enables more accurate evaluation of the measurement in real-time.

In another example, the screen 400 depicts measurements 402 acquired by low sample rate data sources of the drilling rig, such as from an EDR. In the embodiment shown in FIG. 4, these measurements include well depth (in meters (m)), bit depth (in m), block height (in m), average ROP (in meters/hour (m/h), drilling fluid pressure (in pounds-per-square inch (psi)), and flow (in gallons per minute (gpm)). The screen 400 further includes a graph 404 that displays the measurements (y-axis) vs time (x-axis) in real-time. As mentioned, the user interface provided in the visualization may enable a user to filter one or more variables for display on a graph 404.

FIG. 5 depicts another example of a screen 500 that depicts an acceleration measurement 502 from the drilling rig. The acceleration measurements include an acceleration measurement for each axis of the casing (that is, x-axis (radial acceleration), y-axis (tangential acceleration), and z-axis (axial acceleration)). The screen 500 further includes a graph 504 that displays the measurements (y-axis) vs time (x-axis) of the graph 504 in real-time.

FIG. 6 depicts a screen 600 illustrating a “dashboard” of a user interface of the drilling with casing monitor in accordance with an embodiment of the disclosure. As shown in FIG. 6, the dashboard may include various elements, including element 602 depicting measurements in real-time (for example, acceleration), element 604 depicting user annotations, element 606 depicting alerts, element 608 depicting data received from the transducer, and element 610 depicting the battery level of the transducer. It should be appreciated that other embodiments may include additional or alternative elements in the user interface depicted in screen 600.

FIG. 7 is a screen 700 depicting various measurements and corresponding determinations from the drilling with casing monitor in accordance with an embodiment of the disclosure. For example, the screen 700 includes a torque measurement graph 702 illustrating the torque measurements (in pound-force ft (lbf.t) acquired from the components of the drilling rig. The screen 700 also includes an acceleration measurement graph 704 depicting acceleration measurements (in g) acquired from the components of the drilling rig. FIG. 7 also depicts an output 706 of the drilling with casing model that that provides indications of a status of the drilling with casing operation. In some embodiments, the output 706 may include color-coding or other visual indications that correspond with a condition of the drilling with casing operation. For example, as shown in FIG. 7, a green color in the output 706 may correspond to a stable condition of a drilling with casing operation. In another example, a yellow color in the output 706 may correspond to an increased risk condition. In some embodiments, the yellow color may correspond to torsional vibrations above a threshold over a specific time period, as such vibrations could damage the drill string (that is the casing and drill bit). In some embodiments, an increased risk condition may also result in an alarm indicator on a user interface screen of the drilling with casing monitor.

As shown in FIG. 7, in another example, a red color in the output 706 may correspond to a second increased risk condition (that is, a condition more severe than the condition associated with the yellow color). In some embodiments, the red color may correspond to lateral vibrations above a threshold over a specific time period that indicate a buckling condition capable of damaging the casing.

FIG. 8 depicts components of a control system 800 in accordance with an embodiment of the disclosure. In some embodiments, control system 800 may be in communication with other components for obtaining data from a rig or providing data to another system. Such other components may include, for example, a transducer and an electronic drilling recorder. As shown in FIG. 8, the control system 800 may include a processor 802, a memory 804, a display 806, and a network interface 808. It should be appreciated that the control system 800 may include other components that are omitted for clarity. In some embodiments, control system 800 may include or be a part of a computer cluster, cloud-computing system, a data center, a server rack or other server enclosure, a server, a virtual server, a desktop computer, a laptop computer, a tablet computer, or the like.

The processor 802 (as used the disclosure, the term “processor” encompasses microprocessors) may include one or more processors having the capability to receive and process data, such as torque and turn data from a connection make-up operation. In some embodiments, the processor 802 may include an application-specific integrated circuit (AISC). In some embodiments, the processor 802 may include a reduced instruction set (RISC) processor or a complex instruction set (CISC) processor. Additionally, the processor 802 may include a single-core processors and multicore processors and may include graphics processors. Multiple processors may be employed to provide for parallel or sequential execution of one or more of the techniques described in the disclosure. The processor 802 may receive instructions and data from a memory (for example, memory 804).

The memory 804 (which may include one or more tangible non-transitory computer readable storage mediums) may include volatile memory, such as random access memory (RAM), and non-volatile memory, such as ROM, flash memory, a hard drive, any other suitable optical, magnetic, or solid-state storage medium, or a combination thereof. The memory 804 may be accessible by the processor 802. The memory 804 may store executable computer code. The executable computer code may include computer program instructions for implementing one or more techniques described in the disclosure. For example, the executable computer code may include drilling with casing monitor instructions 812 that define the various modules and processes to implement embodiments of the present disclosure. In some embodiments, the drilling with casing monitor instructions 812 may implement a machine learning model as described in the disclosure. In some embodiments, the drilling with casing monitor instructions 812 may receive, as input, transducer data 814. In some embodiments, the instructions 812 may receive, as input, other rig data 816, as described in the disclosure. The outputs from the drilling with casing monitor 812, such as output from the machine learning model, may be provided on the display 806.

The display 806 may include a cathode ray tube (CRT) display, liquid crystal display (LCD), an organic light emitting diode (OLED) display, or other suitable display. The display 806 may display a user interface (for example, a graphical user interface) that may display information. In accordance with some embodiments, the display 806 may be a touch screen and may include or be provided with touch sensitive elements through which a user may interact with the user interface.

The network interface 808 may provide for communication between the control system 800 and other devices. The network interface 808 may include a wired network interface card (NIC), a wireless (e.g., radio frequency) network interface card, or combination thereof. The network interface 808 may include circuitry for receiving and sending signals to and from communications networks, such as an antenna system, an RF transceiver, an amplifier, a tuner, an oscillator, a digital signal processor, and so forth. The network interface 808 may communicate with networks, such as the Internet, an intranet, a wide area network (WAN), a local area network (LAN), a metropolitan area network (MAN) or other networks. Communication over networks may use suitable standards, protocols, and technologies, such as Ethernet Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11 standards), and other standards, protocols, and technologies. In some embodiments, for example, data from an electronic drilling recorder (EDR) may be received over a network via the network interface 808. In some embodiments, for example, outputs from the control system 800 may be provided to other devices over the network via the network interface 808.

In some embodiments, the control system 800 may be coupled to an input device 820 (for example, one or more input devices). The input devices 820 may include, for example, a keyboard, a mouse, a microphone, or other input devices. In some embodiments, the input device 820 may enable interaction with a user interface displayed on the display 806. For example, in some embodiments, the input devices 820 may enable the entry of inputs that control the acquisition of data, the processing of rig data, acknowledgement of alarms, and so on.

Ranges may be expressed in the disclosure as from about one particular value, to about another particular value, or both. When such a range is expressed, it is to be understood that another embodiment is from the one particular value, to the other particular value, or both, along with all combinations within said range.

Further modifications and alternative embodiments of various aspects of the disclosure will be apparent to those skilled in the art in view of this description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the general manner of carrying out the embodiments described in the disclosure. It is to be understood that the forms shown and described in the disclosure are to be taken as examples of embodiments. Elements and materials may be substituted for those illustrated and described in the disclosure, parts and processes may be reversed or omitted, and certain features may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description. Changes may be made in the elements described in the disclosure without departing from the spirit and scope of the disclosure as described in the following claims. Headings used in the disclosure are for organizational purposes only and are not meant to be used to limit the scope of the description.

Claims

1. A method for drilling a well using a drilling with casing operation, comprising: receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration;providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; andoutputting a condition of the drilling with casing operation from the drilling with casing machine learning model.
2. The method of claim 1, comprising: receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; andproviding the dataset to the drilling with casing machine learning model.
3. The method of claim 1, wherein the drilling with casing machine learning model comprises an artificial neural network (ANN).
4. The method of claim 1, comprising stopping the drilling with casing operation based on the condition.
5. The method of claim 1, comprising adjusting the drilling with casing operation based on the condition.
6. The method of claim 1, wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period.
7. The method of claim 1, wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period.
8. The method of claim 1, wherein the transducer data is acquired at a rate of 120 hertz (Hz).
9. The method of claim 1, comprising providing a graph of torque, radial acceleration, tangential acceleration, and axial acceleration versus time.
10. A non-transitory computer-readable storage medium having executable code stored thereon for drilling a well using a drilling with casing operation, the executable code comprising a set of instructions that causes a processor to perform operations comprising: receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration;providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; andoutputting a condition of the drilling with casing operation from the drilling with casing machine learning model.
11. The non-transitory computer-readable storage medium of claim 10, the operations comprising: receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; andproviding the dataset to the drilling with casing machine learning model.
12. The non-transitory computer-readable storage medium of claim 10, wherein the drilling with casing machine learning model comprises an artificial neural network (ANN).
13. The non-transitory computer-readable storage medium of claim 10, the operations comprising stopping the drilling with casing operation based on the condition.
14. The non-transitory computer-readable storage medium of claim 10, the operations comprising adjusting the drilling with casing operation based on the condition.
15. The non-transitory computer-readable storage medium of claim 10, wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period.
16. The non-transitory computer-readable storage medium of claim 10, wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period.
17. A system for drilling a well using a drilling with casing operation, comprising: a processor;a non-transitory computer-readable storage memory accessible by the processor and having executable code stored thereon for drilling a well using a drilling with casing operation, the executable code comprising a set of instructions that causes the processor to perform operations comprising: receiving, from a transducer, data associated with a casing, the transducer data comprising torque, radial acceleration, tangential acceleration, and axial acceleration;providing the transducer data to a drilling with casing machine learning model configured to output a condition of the drilling with casing operation, the drilling with casing machine learning model trained using a relationship between torque, radial acceleration, tangential acceleration, and axial acceleration; andoutputting a condition of the drilling with casing operation from the drilling with casing machine learning model.
18. The system of claim 17, the operations comprising: receiving data associated with a rig, the rig data comprising electronic drilling recorder (EDR) data comprising hook load, string depth, revolutions-per-minute (RPM), torque, and block height;forming a dataset comprising at least one datum of the transducer data and at least one datum of the EDR data; andproviding the dataset to the drilling with casing machine learning model.
19. The system of claim 17, wherein the drilling with casing machine learning model comprises an artificial neural network (ANN).
20. The system of claim 17, the operations comprising stopping the drilling with casing operation based on the condition.
21. The system of claim 17, wherein the condition comprises a torsional vibration value above a threshold value over a threshold time period.
22. The system of claim 17, wherein the condition comprises a lateral vibration value above a threshold value over a threshold time period.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from U.S. Provisional Application No. 63/503,249 filed May 19, 2023, and titled “DRILLING WITH CASING MONITOR.” For purposes of United States patent practice, this application incorporates the contents of the Provisional Application by reference in its entirety.

Provisional Applications (1)

	Number	Date	Country
	63503249	May 2023	US

DRILLING WITH CASING MONITOR

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CPC

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Abstract

Description

Claims

CROSS-REFERENCE TO RELATED APPLICATIONS

Provisional Applications (1)