Method, system, apparatus and storage medium for identifying a critical point of a drilling operating condition change

Abstract

The present disclosure belongs to the technical field of oil and gas drilling, and particularly relates to a method, system, device and storage medium for identifying a critical point of drilling operation condition change. The method comprises repeating the following steps: collecting real-time logging engineering data at a critical point of a target drilling and performing inspection; determining a current operation condition of the target drilling according to a judgment condition based on the real-time logging engineering data; updating the real-time logging engineering data of the target drilling, obtaining a critical point of operation condition change based on the updated real-time logging engineering data; recording a critical point time, and determining an operation condition of the target drilling at the critical point time based on the judgment condition. The present disclosure has the advantages of meeting the actual needs of field application, improving the accuracy of operation condition identification, adapting to complex operation conditions, and the like, and can effectively improve the safety and efficiency of drilling operation, reduce costs, and has important practical value.

Description

Technical Field

[0001] This disclosure pertains to the field of oil and gas drilling technology, and particularly relates to methods, systems, equipment, and storage media for identifying critical points of changes in drilling conditions. Background Technology

[0002] In today's oil drilling industry, real-time monitoring and accurate identification of drilling conditions are crucial for ensuring operational safety and efficiency. Real-time identification of these conditions is essential for accident prevention, optimizing operational parameters, and improving drilling efficiency. Different drilling conditions, such as drilling, tripping, running in, and circulation, have varying impacts on drilling parameters (such as drilling pressure, rotational speed, and torque). Accurate identification of these conditions allows for timely adjustments to operational parameters, avoiding unnecessary risks and improving operational efficiency.

[0003] Publication No. CN117404081A relates to a drilling condition and stuck pipe condition simulation bench and its application. It can simulate all working conditions, including single joint connection, running in, tripping out, reaming, circulation, overflow, drilling, and drill collar connection, as well as stuck pipe risk conditions. This facilitates the collection and analysis of corresponding numerical data from various sensor devices to analyze complex downhole conditions and identify changes in parameters such as drill pressure, torque, and pump pressure associated with stuck pipe and other risk conditions from an indoor simulation perspective. Publication No. CN116307010A relates to a drilling condition identification method based on a random forest algorithm, which collects drilling data... A database is established using drilling monitoring data and drilling operating parameters from the well operation site. A large amount of accumulated drilling monitoring data and operating parameters are used as sample data for preprocessing. Selectable characteristic parameters, such as hook load, riser pressure, torque, rotational speed, and drilling pressure, are used as input samples, while operating condition categories are used as output samples. An operating condition identification model is then trained on the sample data to obtain a random forest operating condition identification model based on the random forest algorithm. Measured drilling characteristic parameters from the drilling site are then input into the random forest operating condition identification model to obtain the operating condition identification results.

[0004] However, the aforementioned existing technologies also have drawbacks. The automatic working condition identification method in publication number CN117404081A relies on pre-stored fixed thresholds, i.e., static thresholds. Static thresholds may not be able to cope with real-time variations in drilling parameters, which can easily lead to misjudgments. Especially under complex working conditions, the thresholds are difficult to calibrate accurately. Furthermore, this application directly uses raw data collected by sensors for working condition identification without introducing data smoothing or filtering mechanisms. It may misjudge due to fluctuations in drilling data (such as parameter jumps during tripping / running out of the well), and lacks anti-interference capabilities. The random forest algorithm in publication number CN116307010A relies on a large amount of historical data for training. In drilling operations, formation conditions, drill string combinations, and working conditions are complex and variable. If the training data is insufficient or lacks representativeness, the model may overfit, leading to a decrease in identification accuracy in new wells or under changing conditions. Moreover, this application requires data preprocessing and model training before prediction (steps include data acquisition, preprocessing, model training, and verification). This process is time-consuming and cannot achieve true real-time response. In drilling safety scenarios, delays may lead to the risk of misjudgment accidents.

[0005] Current research on real-time drilling condition identification, both domestically and internationally, often relies on judgment conditions. However, these conditions struggle to handle fluctuations in drilling data, frequently leading to misjudgments. Current research in the oil and gas drilling engineering field primarily focuses on using judgment conditions to identify different drilling conditions, such as drilling, tripping, running in, and circulation. These judgment conditions typically set thresholds based on changes in drilling parameters (such as drilling pressure, rotational speed, and torque). When a parameter exceeds or falls below a threshold, the drilling condition is considered to have changed.

[0006] Current technology has the following problems: 1. Data fluctuations leading to misjudgments: Drilling data, especially when the drill string is raised / lowered (corresponding to the working conditions of tripping and running), often fluctuates. This fluctuation can lead to misjudgments of judgment conditions, such as misjudging tripping as running, or vice versa.

[0007] 2. Difficulty in setting thresholds: Due to the influence of different well conditions, formations and other factors, it is difficult to accurately set the thresholds for drilling parameters, which leads to a decrease in the accuracy of working condition identification.

[0008] 3. Inability to identify subtle changes: Existing judgment methods can usually only identify large changes in operating conditions, but they are difficult to identify subtle changes, such as slow drift of parameters.

[0009] To address the above issues, existing research has attempted the following improvement methods: 1. Filtering algorithm: The filtering algorithm removes noise and fluctuations from the data, thereby improving the accuracy of operating condition identification.

[0010] 2. Machine learning algorithms: Machine learning algorithms, such as support vector machines and neural networks, are used to learn from drilling data in order to identify different working conditions.

[0011] 3. Multi-parameter fusion: Multiple drilling parameters, such as drilling pressure, rotational speed, torque, and vibration, are fused together to improve the accuracy of working condition identification.

[0012] Although the above methods have improved the accuracy of working condition identification to some extent, there are still some shortcomings. For example, filtering algorithms may remove useful information from the data, machine learning algorithms require a large amount of training data and are difficult to generalize, and the trained models are difficult to adapt to changes in formation or drilling tools. Multi-parameter fusion requires consideration of the correlation between parameters and manual parameter adjustment.

[0013] Therefore, developing a method based on real-time logging data that can effectively identify critical points of changes in drilling conditions is of great significance for improving drilling safety and efficiency. Summary of the Invention

[0014] To address the above problems, this disclosure provides a method for identifying critical points of change in drilling conditions, the method comprising repeating the following steps: Collect and verify real-time logging engineering data at the critical point of the target well. Based on real-time logging data, the current working condition of the target well is determined according to the judgment conditions. Update the real-time logging data of the target well, and based on the updated real-time logging data, obtain the critical point of change in operating conditions; Record the critical point time and determine the operating conditions of the target well at the critical point time based on the judgment conditions.

[0015] According to some embodiments of this disclosure, the verification of real-time logging engineering data includes: Confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

[0016] According to some embodiments of this disclosure, based on real-time logging engineering data, the current working condition of the target well is determined according to judgment conditions, including: When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

[0017] According to some embodiments of this disclosure, obtaining the critical point of operating condition change includes: The index-weighted moving average of the structural load was calculated based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

[0018] According to some embodiments of this disclosure, the expression for calculating the exponentially weighted moving average of the load on a large structure is as follows: ; in, Let r be the weighted moving average of the structural load index at time j; r is a constant. For the first Weighted moving average of the structural load index at any given time; This represents the data collected at time j.

[0019] According to some embodiments of this disclosure, a critical value for the change in operating conditions is set, and compared with the exponentially weighted moving average of the main structural load to determine the critical point of the change in operating conditions, including: Set the critical value for the change in operating conditions as and ; when or If so, a critical point will appear and be recorded; in, The mean of the first n data points; L is the threshold calculation parameter; Let be the standard deviation of the first n data points.

[0020] According to some embodiments of this disclosure, the sample mean of the first n data points The expression is: ; in, The sample mean of the first n data points; This represents the value of the hook load at time t.

[0021] According to some embodiments of this disclosure, the expression for the threshold calculation parameter L is: ; in, This represents the maximum value of the hook load in the currently collected data.

[0022] According to some embodiments of this disclosure, the standard deviation of the first n data points The expression is: .

[0023] This disclosure also provides a system for identifying critical points of changes in drilling conditions, the system comprising repeating the following modules: The data acquisition and verification module is used to acquire and verify real-time logging engineering data at the critical point of the target drilling. The first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions. The acquisition module is used to update the real-time logging engineering data of the target well and, based on the updated real-time logging engineering data, to obtain the critical point of change in working conditions. The second judgment module is used to record the critical point time and determine the working condition of the target drilling at the critical point time based on the judgment conditions.

[0024] According to some embodiments of this disclosure, the data acquisition and verification module is used for verifying the real-time logging engineering data, including: The data acquisition and verification module is used to confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

[0025] According to some embodiments of this disclosure, the first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions, including: The first judgment module considers the time difference of well depth data. , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

[0026] According to some embodiments of this disclosure, the acquisition module is used to acquire the critical point of operating condition change, including: The acquisition module is used to calculate the exponentially weighted moving average of the structural load based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

[0027] This disclosure also provides an electronic device, including: Processor and memory; The processor invokes the computer program stored in the memory to execute the method for identifying critical points of changes in drilling conditions.

[0028] This disclosure also provides a computer-readable storage medium, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the method for identifying critical points of changes in drilling conditions.

[0029] This disclosure also provides a computer program product, The computer program product includes a computer program containing computer program code means stored on a computer-readable medium or carrier wave, the computer program code means being configured to cause a computer or processor to control the execution of steps of the method for identifying critical points of changes in drilling conditions as described above.

[0030] This disclosure has the following beneficial effects: (1) The method steps of this disclosure include collecting real-time logging engineering data, using judgment conditions to preliminarily determine whether the current working condition is tripping / running, calculating dynamic threshold, using the EWMA algorithm to calculate and compare with the threshold, recording the critical point time if it exceeds the threshold, and using judgment conditions to determine the working condition at the critical point time based on the critical point time. The method of this disclosure has the advantages of meeting the actual needs of field applications, improving the accuracy of working condition identification, and adapting to complex working conditions. It can effectively improve the safety and efficiency of drilling operations, reduce costs, and has important practical value.

[0031] (2) This disclosure utilizes real-time data from current wellbore integrated logging and combines it with the EWMA algorithm for online critical point detection. Compared with traditional methods, the EWMA algorithm is not sensitive to data fluctuations and can effectively avoid misjudgment. At the same time, it can identify subtle changes in parameters, thereby more accurately identifying subtle changes in working conditions. In addition, it can identify alternating changes and transitional states of various working conditions and can adapt to complex working conditions.

[0032] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description and the accompanying drawings. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 A diagram illustrating a method for identifying critical points of changes in drilling conditions in an embodiment of this disclosure; Figure 2 This invention discloses a detailed flowchart of a method for identifying critical points of changes in drilling conditions in an embodiment of the present disclosure. Figure 3 This diagram illustrates the real-time data of the hook load over a given time period in Embodiment 1 of this disclosure. Figure 4 A comparison graph of EWMA values ​​and critical values ​​in Embodiment 1 of this disclosure is shown; Figure 5 This diagram illustrates the real-time data of the hook load over a given time period in Embodiment 2 of this disclosure. Figure 6 A comparison graph of EWMA values ​​and critical values ​​is shown in Embodiment 2 of this disclosure; Figure 7 This diagram illustrates a system for identifying critical points of change in drilling conditions in an embodiment of this disclosure. Figure 8 A diagram of an electronic device according to an embodiment of this disclosure is shown. Detailed Implementation

[0035] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0036] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware units or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0037] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0038] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein.

[0039] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or device that includes a series of steps or sub-modules is not necessarily limited to those steps or sub-modules that are explicitly listed, but may include other steps or sub-modules that are not explicitly listed or that are inherent to such process, method, product, or device.

[0040] Currently, research on real-time drilling condition identification in the oil and gas drilling engineering field mainly focuses on using judgment conditions to identify different drilling conditions, such as drilling, tripping, running in, and circulation. These judgment conditions are usually based on thresholds set according to changes in drilling parameters (such as drilling pressure, rotational speed, torque, etc.). When the parameters exceed or fall below the threshold, the condition is considered to have changed. However, this traditional identification method has some limitations: it is sensitive to data fluctuations, cannot identify subtle changes, and is difficult to adapt to complex conditions. To overcome these limitations, this disclosure proposes a method for identifying critical points of drilling condition changes based on real-time logging data. The core idea is to use the EWMA (Exponentially Weighted Moving-Average) algorithm for real-time online critical point detection. EWMA calculates a moving average by assigning exponentially decreasing weights to historical data, thus reflecting data trend changes more flexibly. When the calculated value of EWMA exceeds the threshold, the process is considered to have changed significantly, i.e., the drilling condition has changed. The advantages of the EWMA algorithm are that it is insensitive to data fluctuations, can identify subtle changes, and can adapt to complex conditions.

[0041] This disclosure includes collecting real-time logging engineering data, using judgment conditions to initially determine whether the current working condition is tripping / running in, dynamically calculating thresholds, using the EWMA algorithm to calculate the moving average of the hook load, comparing it with the threshold, recording the critical point time if it exceeds the threshold, and using judgment conditions based on the critical point time to determine the working condition at the critical point. The method of this disclosure has advantages such as meeting the actual needs of field applications and improving the accuracy of working condition identification.

[0042] like Figure 1 As shown, this disclosure provides a method for identifying critical points of change in drilling conditions, the method comprising repeating the following steps: Collect and verify real-time logging engineering data at the critical point of the target well. Based on real-time logging data, the current working condition of the target well is determined according to the judgment conditions. Update the real-time logging data of the target well, and based on the updated real-time logging data, obtain the critical point of change in operating conditions; Record the critical point time and determine the operating conditions of the target well at the critical point time based on the judgment conditions.

[0043] Specifically, the verification of real-time logging engineering data includes: Confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

[0044] Specifically, based on real-time logging data, the current operating condition of the target well is determined according to judgment criteria, including: When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

[0045] Specifically, obtaining the critical point of change in operating conditions includes: The index-weighted moving average of the structural load was calculated based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

[0046] Specifically, the expression for calculating the exponentially weighted moving average of the load on major structures is as follows: ; in, Let r be the weighted moving average of the structural load index at time j; r is a constant. For the first Weighted moving average of the structural load index at any given time; This represents the data collected at time j.

[0047] Specifically, a critical value for the change in operating conditions is set and compared with the exponentially weighted moving average of the main structure load to determine the critical point for the change in operating conditions, including: Set the critical value for the change in operating conditions as and ; when or If so, a critical point will appear and be recorded; in, The mean of the first n data points; L is the threshold calculation parameter; Let be the standard deviation of the first n data points.

[0048] Specifically, the The expression is: ; in, The sample mean of the first n data points; This represents the value of the hook load at time t.

[0049] In this disclosure, t is a time unit, and n is an integer used as an array index. The data here is usually spaced 5 seconds apart, so n is different from t.

[0050] Specifically, the expression for the threshold calculation parameter L is: ; in, This represents the maximum value of the hook load in the currently collected data.

[0051] Specifically, the The expression is: .

[0052] like Figure 2 As shown, the detailed technical solution of this disclosure is as follows: (1) Collect real-time logging data from the last 2 minutes, or at least 10 data points prior to the current time; (2) Confirm that each data point includes: time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load; (3) Since the real-time drilling data fluctuates when the drill string is raised / lowered (corresponding to the working conditions: tripping and running), using the judgment conditions may cause misjudgment. Therefore, when the judgment conditions determine that the current working condition is tripping / running, the method disclosed in this publication is used to determine the critical point of the working condition. If the current working condition is other working conditions such as drilling, since the data fluctuations are less frequent, this method can be used to determine the critical point.

[0053] Using decision conditions, the current working condition is determined as either starting or stopping the drill. These decision conditions can be expressed as follows: ; in: This represents the time difference of the well depth data, i.e., the MD of the next time step minus the MD of the previous time step; MD is the well depth data; Bitpos is the drill bit position; RPM is the rotary table rotation speed. This represents the timing difference in drill bit position.

[0054] (4) Acquire a new real-time logging data point to determine if a critical point has occurred. Use the hook load as a parameter to determine whether the tripping / running conditions have changed. Calculate the EWMA of the hook load in the time series data and compare it with a threshold. When the EWMA exceeds this threshold, a critical point is detected. The calculation formula for this type of algorithm is shown below:

[0055]

[0056]

[0057]

[0058] or

[0059] in: represents the data collected at time j; Max(x) is the maximum value of the hook load in the current collected data; L is the threshold calculation parameter; r is a constant, which takes values ​​in the range of [0.05, 1] ​​in this invention, and 0.2 is used in the example; The calculated EWMA value at time j; and The values ​​of the hook load at times t and t-1 are given.

[0060] (5) Based on the above algorithm, calculate With critical value and If the threshold is exceeded, a critical point is considered to have occurred. The judgment condition program is then invoked to determine the operating conditions at the critical point in time, and at least 10 data points are collected again from the critical point to begin a new round of critical point calculation. If the value falls within the critical range, a new real-time logging data point is acquired, and the previous step is repeated.

[0061] Example This explanation uses a horizontal well as an example. After acquiring a segment of logging data in real time, the calculations are performed using this method.

[0062] Example 1: Critical Point Analysis of Drilling Conditions (0:30 – 0:32) The implementation steps are as follows: 1. Obtain real-time data on hook load over a given time period, such as... Figure 3 As shown; 2. Apply algorithms to calculate EWMA values ​​and critical values, such as... Figure 4 As shown; 3. Based on the calculation results, the critical points of drilling conditions occur at 30:15, 31:33, and 31:46 (minutes:seconds). The corresponding conditions are obtained by calling the judgment conditions, as shown in Table 1.

[0063] Table 1 Operating Condition-Time Record Sheet

[0064] Example 2: 11:36 – 0:38 Critical Point Analysis of Drilling Conditions The implementation steps are as follows: 1. Obtain real-time data on hook load over a given time period, such as... Figure 5 As shown; 2. Apply algorithms to calculate EWMA values ​​and critical values, such as... Figure 6 As shown; 3. The data change point occurred at 11:37:31 (minutes:seconds). The corresponding working condition was obtained by calling the judgment condition: drilling down.

[0065] The innovative achievements disclosed herein have significant practical value in drilling production and operation, specifically reflected in the following aspects: In terms of cost reduction and efficiency improvement, by accurately identifying critical points in drilling conditions, it effectively reduces equipment wear and operational interruptions caused by misjudgments due to traditional methods. For example, during tripping in / out of the well, it can avoid operational errors caused by misjudgments due to data fluctuations, reduce drill string wear by more than 15%, and reduce equipment maintenance costs. At the same time, by adjusting operating parameters (such as drilling pressure and rotation speed) in real time based on critical points, drilling efficiency can be improved by 8%-10%, the single-well operation cycle can be shortened by 3-5 days, and labor and time costs can be significantly reduced. Regarding performance improvements and intelligent upgrades, this invention can be directly integrated into existing real-time logging systems without requiring additional hardware. Software algorithm optimization enhances the system's adaptability to complex working conditions. The combination of dynamic threshold calculation and the EWMA algorithm improves the system's accuracy in identifying working conditions in deep and ultra-deep formations by over 30%, providing reliable data support for intelligent drilling decisions and strengthening the company's competitiveness in the high-end drilling technology and equipment field.

[0066] like Figure 7 As shown, this disclosure also provides a system for identifying critical points of change in drilling conditions, the system comprising repeating the following modules: The data acquisition and verification module is used to acquire and verify real-time logging engineering data at the critical point of the target drilling. The first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions. The acquisition module is used to update the real-time logging engineering data of the target well and, based on the updated real-time logging engineering data, to obtain the critical point of change in working conditions. The second judgment module is used to record the critical point time and determine the working condition of the target drilling at the critical point time based on the judgment conditions.

[0067] Specifically, the data acquisition and verification module, used for verifying the real-time logging engineering data, includes: The data acquisition and verification module is used to confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

[0068] Specifically, the first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions, including: The first judgment module considers the time difference of well depth data. , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

[0069] Specifically, the acquisition module is used to acquire the critical points of changes in operating conditions, including: The acquisition module is used to calculate the exponentially weighted moving average of the structural load based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

[0070] like Figure 8 As shown, corresponding to the method for identifying critical points of drilling condition changes provided above, this disclosure also provides an electronic device. Since the embodiment of this device is similar to the method embodiment described above, the description is relatively simple; relevant details can be found in the description of the method embodiment section above. The device described below is merely illustrative. This device may include: a processor 1, a memory 2, a communication bus (i.e., the aforementioned device bus), and a lookup engine. The processor 1 and memory 2 communicate with each other via the communication bus and communicate with external systems via a communication interface. The processor 1 can call logical instructions in the memory 2 to execute the method for identifying critical points of drilling condition changes.

[0071] Furthermore, the logical instructions in the aforementioned memory 2 can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as memory chips, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] On the other hand, this disclosure also provides a processor-readable storage medium storing a computer program 3, which, when executed by a processor 1, implements the method for identifying critical points of changes in drilling conditions provided in the above embodiments.

[0073] The processor-readable storage medium can be any available medium or data storage device that the processor 1 can access, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO)), optical memory (e.g., CD, DVD, BD, HVD), and semiconductor memory (e.g., ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drive (SSD)).

[0074] Embodiments of this disclosure also provide a computer program product comprising a computer program that includes computer program code means stored on a computer-readable medium or carrier wave, the computer program code means being configured to cause a computer or processor to control the execution of steps of a method according to any embodiment of this disclosure.

[0075] Those skilled in the art should understand that, despite the detailed description of this disclosure with reference to the foregoing embodiments, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for identifying critical points of change in drilling operating conditions, characterized in that, The method includes repeating the following steps: Collect and verify real-time logging engineering data at the critical point of the target well. Based on real-time logging data, the current working condition of the target well is determined according to the judgment conditions. Update the real-time logging data of the target well, and based on the updated real-time logging data, obtain the critical point of change in operating conditions; Record the critical point time and determine the operating conditions of the target well at the critical point time based on the judgment conditions.

2. The method for identifying critical points of drilling condition changes according to claim 1, characterized in that, The real-time logging data for testing includes: Confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

3. The method for identifying critical points of drilling condition changes according to claim 2, characterized in that, Based on real-time logging data, the current operating condition of the target well is determined according to the following criteria: When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

4. The method for identifying critical points of drilling condition changes according to claim 2, characterized in that, The critical points for changes in operating conditions include: The index-weighted moving average of the structural load was calculated based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

5. The method for identifying critical points of drilling condition changes according to claim 4, characterized in that, The expression for calculating the exponentially weighted moving average of the load on major structures is: ； in, Let r be the weighted moving average of the structural load index at time j; r is a constant. For the first Weighted moving average of the structural load index at any given time; This represents the data collected at time j.

6. The method for identifying critical points of drilling condition changes according to claim 5, characterized in that, Set a critical value for the change in operating conditions, and compare it with the exponentially weighted moving average of the main structure load to determine the critical point for the change in operating conditions, including: Set the critical value for the change in operating conditions as and ; when or If so, a critical point will appear and be recorded; in, The mean of the first n data points; L is the threshold calculation parameter; Let be the standard deviation of the first n data points.

7. The method for identifying critical points of drilling condition changes according to claim 6, characterized in that, The sample mean of the first n data points The expression is: ； in, The sample mean of the first n data points; This represents the value of the hook load at time t.

8. The method for identifying critical points of drilling condition changes according to claim 6, characterized in that, The expression for the threshold calculation parameter L is: ； in, This represents the maximum value of the hook load in the currently collected data.

9. The method for identifying critical points of drilling condition changes according to claim 7, characterized in that, ； The standard deviation of the first n data points The expression is: 。 10. A system for identifying critical points of change in drilling conditions, characterized in that, The system includes the following modules: The data acquisition and verification module is used to acquire and verify real-time logging engineering data at the critical point of the target drilling. The first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions. The acquisition module is used to update the real-time logging engineering data of the target well and, based on the updated real-time logging engineering data, to obtain the critical point of change in working conditions. The second judgment module is used to record the critical point time and determine the working condition of the target drilling at the critical point time based on the judgment conditions.

11. The system for identifying critical points of drilling condition changes according to claim 10, characterized in that, The data acquisition and verification module, used for verifying the real-time logging engineering data, includes: The data acquisition and verification module is used to confirm that the real-time logging engineering data includes the real-time logging engineering parameter set; The real-time logging engineering parameter set includes time, drill bit position, drilling pressure, hook height, rotary table speed, riser pressure, and hook load.

12. The system for identifying critical points of drilling condition changes according to claim 11, characterized in that, The first judgment module is used to determine the current working condition of the target well based on real-time logging engineering data and judgment conditions, including: The first judgment module considers the time difference of well depth data. , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current operating condition of the target well is drilling down; When the time difference of well depth data , Jing Shen MD Drill bit position (Bitpos), rotary table speed (RPM) = 0, and drill bit position timing difference At that time, the current working condition of the target well is tripping.

13. The system for identifying critical points of drilling condition changes according to claim 10, characterized in that, The acquisition module is used to acquire the critical points of changes in operating conditions, including: The acquisition module is used to calculate the exponentially weighted moving average of the structural load based on the updated real-time logging engineering data; Set a critical value for the change in operating conditions and compare it with the exponentially weighted moving average of the load on the main structure to determine the critical point of the change in operating conditions.

14. An electronic device, characterized in that, include: Processor and memory; The processor invokes the computer program stored in the memory to execute the method for identifying critical points of changes in drilling conditions as described in any one of claims 1 to 9.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the processor to perform the method for identifying critical points of changes in drilling conditions as described in any one of claims 1 to 9.

16. A computer program product, characterized in that, The computer program product includes a computer program containing computer program code means stored on a computer-readable medium or carrier wave, the computer program code means being configured to cause a computer or processor to control the execution of the steps of the method for identifying critical points of changes in drilling conditions according to any one of claims 1 to 9.

Images