Petroleum drilling blockage prediction method and device, electronic equipment and storage medium

Abstract

The invention discloses a petroleum drilling blockage prediction method and device, electronic equipment and a storage medium. The method comprises the steps that in the petroleum drilling process, shaft engineering parameters and tripping working condition parameters are obtained in real time; inputting the shaft engineering parameters and the tripping working condition parameters into a pre-trained petroleum drilling blocking prediction model; and according to an output result of the petroleum drilling blockage prediction model, determining whether a shaft blockage accident occurs in a petroleum drilling process. According to the technical scheme provided by the embodiment of the invention, the shaft engineering parameters and the tripping working condition parameters are monitored in real time, and the petroleum drilling blockage prediction model is introduced to determine whether the shaft blockage accident occurs or not, so that the risk prompt is more accurate, more efficient and more advanced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, device, electronic equipment and storage medium for predicting oil drilling obstruction. Background Art

[0002] Drilling is a key link in the oil industry, and the number of wells completed each year is very large. At the same time, accidents such as stuck during lifting, obstruction during drilling, and drill bit jamming occur frequently, resulting in a large loss of engineering time and cost. In the traditional operation mode, when it comes to finding stuck holes, the operator usually switches to manual intervention procedures when the hook load is abnormal during the upward or downward movement of the drilling tool, and early warning cannot be achieved. Summary of the invention

[0003] The present invention provides a method, device, electronic equipment and storage medium for predicting oil drilling stuckness. By real-time monitoring of wellbore engineering parameters and drilling and drilling working condition parameters, and introducing an oil drilling stuckness prediction model, it is determined whether a wellbore stuckness accident occurs, and the risk warning is more accurate, more efficient and more advanced.

[0004] According to one aspect of the present invention, a method for predicting oil drilling blockage is provided, the method comprising:

[0005] During the oil drilling process, real-time acquisition of wellbore engineering parameters and tripping parameters;

[0006] Inputting the wellbore engineering parameters and the tripping working condition parameters into a pre-trained oil drilling obstruction prediction model;

[0007] According to the output result of the oil drilling stuck prediction model, it is determined whether a wellbore stuck accident occurs during the oil drilling process.

[0008] According to another aspect of the present invention, a device for predicting oil drilling blockage is provided, the device comprising:

[0009] The parameter acquisition module is used to obtain wellbore engineering parameters and tripping parameters in real time during the oil drilling process;

[0010] A first parameter input module, used for inputting the wellbore engineering parameters and the tripping working condition parameters into a pre-trained oil drilling blockage prediction model;

[0011] The obstruction prediction module is used to determine whether a wellbore obstruction accident occurs during the oil drilling process according to the output result of the oil drilling obstruction prediction model.

[0012] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the oil drilling obstruction prediction method described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the oil drilling obstruction prediction method described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention is to obtain wellbore engineering parameters and drilling condition parameters in real time during the oil drilling process; input the wellbore engineering parameters and drilling condition parameters into a pre-trained oil drilling stuck prediction model; and determine whether a wellbore stuck accident occurs during the oil drilling process based on the output result of the oil drilling stuck prediction model. The technical solution of the embodiment of the present invention is to monitor the wellbore engineering parameters and drilling condition parameters in real time, and introduce the oil drilling stuck prediction model to determine whether a wellbore stuck accident occurs, so that the risk warning is more accurate, more efficient, and more advanced.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a method for predicting oil drilling blockage provided according to the first embodiment of the present invention;

[0021] Figure 2 This is a flow chart of a method for predicting oil drilling blockage according to Embodiment 2 of the present invention;

[0022] Figure 3 This is an interface prototype diagram of a petroleum drilling obstruction prediction model and an obstruction accident handling method determination model provided according to the second embodiment of the present invention;

[0023] Figure 4 2 is a schematic diagram of the structure of a device for predicting oil drilling blockage according to Embodiment 3 of the present invention;

[0024] Figure 5 The present invention is a schematic diagram of the structure of an electronic device for implementing the oil drilling obstruction prediction method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] Figure 1 A flowchart of a method for predicting a well blockage is provided for the first embodiment of the present invention. This embodiment is applicable to the case of predicting well blockage accidents. The method can be performed by a well blockage prediction device. The well blockage prediction device can be implemented in the form of hardware and / or software. The well blockage prediction device can be configured in an electronic device. Figure 1 As shown, the method includes:

[0029] S110. During the oil drilling process, wellbore engineering parameters and tripping parameters are obtained in real time.

[0030] Among them, the wellbore engineering parameters include at least one of drilling fluid properties, formation lithology, pressure system, drilling parameters and drilling technology, and the drilling and lowering working parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure and drilling fluid density.

[0031] In the embodiment of the present invention, during the oil drilling process, sensors and other equipment can be used to obtain wellbore engineering parameters and drilling condition parameters in real time. Specific equipment and sensors include logging instruments, Doppler flowmeters, pressure sensors, temperature sensors, gravity sensors, etc. At the same time, the well site production and construction data can be transmitted back to the rear base data lake through the Internet of Things technology, and these parameters can be processed and analyzed with the help of computers and software to achieve dynamic monitoring of wellbore engineering parameters and drilling condition parameters, and timely determine whether the wellbore condition is normal.

[0032] S120, inputting wellbore engineering parameters and tripping and drilling condition parameters into a pre-trained oil drilling obstruction prediction model.

[0033] In an embodiment of the present invention, after the wellbore engineering parameters and the drilling condition parameters are acquired in real time, the wellbore engineering parameters and the drilling condition parameters can be input into a pre-trained oil drilling obstruction prediction model to obtain output results.

[0034] Optionally, before inputting the wellbore engineering parameters into a pre-trained oil drilling stuck prediction model, it also includes: obtaining a training data set of the process of sticking condition occurrence; wherein the training data set of the process of sticking condition occurrence includes at least two sample data of the process of sticking condition occurrence; extracting first drilling condition characteristic information from the training data set of the process of sticking condition occurrence; and training a pre-set first machine learning model based on the first drilling condition characteristic information to generate an oil drilling stuck prediction model.

[0035] Among them, the training data set for the process of stuck conditions can be constructed by calibrating the time domain data of the stuck conditions of different drilling tools under different operating conditions. Different drilling tools include drilling tools of different sizes and combinations, and different operating conditions include different formations, different lithologies, different well types, different pressure systems, different drilling fluid systems, different drilling processes, different drilling parameters, etc. It should be noted that the training data set for the process of stuck conditions should include at least two sample data of the process of stuck conditions.

[0036] Among them, the first drilling condition characteristic information includes the skewness, kurtosis and value range of the drilling condition parameters, wherein the drilling condition parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, cycle time, reaming process and reverse reaming process.

[0037] The first machine learning model can be at least one of K-nearest neighbor, Bayesian, logistic regression, decision tree, vector machine, and neural network, which is not limited in the embodiment of the present invention. Optionally, according to the characteristic index and the main control factor index, an algorithm that satisfies the risk prediction of oil drilling obstruction is selected, and the first machine learning model is obtained through iterative fitting and algorithm index parameter optimization.

[0038] In an embodiment of the present invention, by obtaining a training data set of the process of occurrence of the stuck condition, the first drilling condition characteristic information, that is, characteristic information such as the correlation and association between different drilling condition parameters, is extracted from the training data set of the process of occurrence of the stuck condition, and a pre-set first machine learning model is trained to generate an oil drilling stuck prediction model.

[0039] Optionally, before extracting the first drilling condition characteristic information from the training data set of the process of the stuck condition, it also includes: preprocessing the training data set of the process of the stuck condition; wherein the preprocessing includes at least one of format normalization, unit unification, noise removal, missing point filling and bad point correction.

[0040] It is understandable that the original data of the training data set of the process of jamming condition occurrence usually contains noise, outliers and missing values. By preprocessing the training data set of the process of jamming condition occurrence, erroneous data can be removed, missing values ​​can be filled and outliers can be processed, thereby reducing their adverse effects on model training and improving data quality and accuracy.

[0041] S130: Determine whether a wellbore sticking accident occurs during the oil drilling process according to the output result of the oil drilling sticking prediction model.

[0042] In the embodiment of the present invention, by connecting the wellbore engineering parameters and real-time parameters such as hook load, drilling pressure, displacement, rotation speed, etc. under drilling conditions to the prediction model, the wellbore instability risk, wellbore cleaning condition, etc. can be evaluated in real time, and the prediction results can be output to determine whether a wellbore sticking accident occurs during the oil drilling process, breaking through the limitations of conventional adjacent well statistics on sticking risks, and the risk warning is more accurate, efficient, and advanced.

[0043] The technical solution of the embodiment of the present invention is to obtain wellbore engineering parameters and drilling condition parameters in real time during the oil drilling process; input the wellbore engineering parameters and drilling condition parameters into a pre-trained oil drilling stuck prediction model; and determine whether a wellbore stuck accident occurs during the oil drilling process based on the output result of the oil drilling stuck prediction model. The technical solution of the embodiment of the present invention is to monitor the wellbore engineering parameters and drilling condition parameters in real time, and introduce the oil drilling stuck prediction model to determine whether a wellbore stuck accident occurs, so that the risk warning is more accurate, more efficient, and more advanced.

[0044] Embodiment 2

[0045] Figure 2 This is a flow chart of a method for predicting oil drilling blockage provided by the second embodiment of the present invention. The embodiment of the present invention is optimized based on the above embodiment. For solutions not described in detail in the embodiment of the present invention, please refer to the above embodiment. Figure 2 As shown, the method includes:

[0046] S210. During the oil drilling process, wellbore engineering parameters and tripping and drilling condition parameters are obtained in real time.

[0047] S220, inputting wellbore engineering parameters and drilling operation parameters into a pre-trained oil drilling blockage prediction model.

[0048] S230: Determine whether a wellbore sticking accident occurs during the oil drilling process according to the output result of the oil drilling sticking prediction model.

[0049] S240. When it is determined that a wellbore obstruction accident occurs during the oil drilling process, wellbore engineering parameters and drilling operation parameters are input into a pre-trained model for determining a method for handling an obstruction accident.

[0050] In an embodiment of the present invention, when it is determined that a wellbore obstruction accident occurs during oil drilling, wellbore engineering parameters and drilling operation parameters can be input into a pre-trained model for determining a method for handling an obstruction accident to obtain an output result.

[0051] Optionally, when it is determined that a wellbore stuck accident occurs during the oil drilling process, before the wellbore engineering parameters and the drilling condition parameters are input into the pre-trained stuck accident handling method determination model, it also includes: obtaining a stuck accident handling process training data set; wherein the stuck accident handling process training data set includes at least two stuck accident handling process data, and the stuck accident handling process data includes stuck condition occurrence process data and corresponding stuck condition handling methods; extracting second drilling condition characteristic information from the stuck condition occurrence process data contained in the stuck accident handling process training data set; training a pre-set second machine learning model based on the second drilling condition characteristic information and the stuck condition handling method contained in the stuck accident handling process training data set to generate a stuck accident handling method determination model.

[0052] Among them, the training data set for the process of handling the jam accident is constructed by calibrating the time domain data under the conditions of handling the jam and the jam. It should be noted that the training data set for the process of handling the jam accident should include at least two process data of handling the jam accident, and the process data of handling the jam accident should include the process data of the jam condition and the corresponding jam handling method.

[0053] Among them, the second drilling condition characteristic information includes the skewness, kurtosis and value range of the drilling condition parameters, wherein the drilling condition parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, cycle time, reaming process and reverse reaming process.

[0054] The second machine learning model can be at least one of K-nearest neighbor, Bayesian, logistic regression, decision tree, vector machine, and neural network, which is not limited in the embodiment of the present invention. Optionally, according to the characteristic index and the main control factor index, an algorithm that satisfies the processing method of the jam accident is selected, and the second machine learning model is obtained through iterative fitting and algorithm index parameter optimization.

[0055] In an embodiment of the present invention, by obtaining a training data set of the stuck accident handling process, the second drilling condition characteristic information is extracted from the training data set of the stuck accident handling process, that is, characteristic information such as the correlation and association between different drilling condition parameters, the correspondence between accidents and handling methods, etc., and a pre-set second machine learning model is trained to generate a model for determining the stuck accident handling method.

[0056] S250, determining the output result of the model according to the method for handling the wellbore stuck accident, and determining the target method for handling the wellbore stuck accident.

[0057] In the embodiment of the present invention, based on the actual wellbore conditions, drilling rig capabilities, etc., comprehensive optimization is performed to recommend the best wellbore cleaning, reaming / back-reaming processes and parameters, and the processing method determination results are output to determine the target processing method for the wellbore blockage accident, breaking through the limitations of conventional experience-guided reaming and other measures, avoiding the timeliness of invalid auxiliary processes, and improving the efficiency of blockage treatment.

[0058] For example, Figure 3 The following is a prototype diagram of an interface for an oil well obstruction prediction model and an obstruction accident handling method determination model, as shown in FIG. Figure 3 As shown in the figure, this interface shows the prediction results of the blockage situation under different operating conditions and the corresponding wellbore cleaning measures.

[0059] It should be noted that the oil drilling obstruction prediction model and the obstruction accident handling method determination model provided in the embodiments of the present invention can be applicable to conventional sandstone oil and gas reservoirs and carbonate oil and gas reservoirs, unconventional shale oil and gas reservoirs and tight oil and gas reservoirs, conglomerate oil reservoirs, coalbed methane, geothermal and other drilling and completion projects, as well as 20-120 model drilling rigs.

[0060] The technical solution of the embodiment of the present invention is to obtain wellbore engineering parameters and drilling condition parameters in real time during oil drilling; input the wellbore engineering parameters and drilling condition parameters into a pre-trained oil drilling obstruction prediction model; determine whether a wellbore obstruction accident occurs during oil drilling according to the output result of the oil drilling obstruction prediction model; when it is determined that a wellbore obstruction accident occurs during oil drilling, input the wellbore engineering parameters and drilling condition parameters into a pre-trained obstruction accident handling method determination model; determine the target handling method for the wellbore obstruction accident according to the output result of the obstruction accident handling method determination model. The technical solution of the embodiment of the present invention is to monitor the wellbore engineering parameters and drilling condition parameters in real time, and introduce the oil drilling obstruction prediction model to determine whether a wellbore obstruction accident occurs, so that the risk warning is more accurate, more efficient and more advanced. At the same time, the best handling method is determined by comprehensively considering various factors through the obstruction accident handling method determination model, avoiding the timeliness of invalid auxiliary processes, and the obstruction handling efficiency is higher.

[0061] Embodiment 3

[0062] Figure 4 This is a schematic diagram of the structure of a device for predicting oil drilling blockage provided by the third embodiment of the present invention. Figure 4 As shown, the device comprises:

[0063] The parameter acquisition module 310 is used to acquire wellbore engineering parameters and tripping parameters in real time during the oil drilling process;

[0064] A first parameter input module 320, for inputting the wellbore engineering parameters and the tripping condition parameters into a pre-trained oil drilling blockage prediction model;

[0065] The obstruction prediction module 330 is used to determine whether a wellbore obstruction accident occurs during the oil drilling process according to the output result of the oil drilling obstruction prediction model.

[0066] Optionally, the wellbore engineering parameters include at least one of drilling fluid properties, formation lithology, pressure system, drilling parameters and drilling technology, and the drilling and lowering working condition parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure and drilling fluid density.

[0067] Optionally, the device further comprises:

[0068] A first data set acquisition module is used to acquire a training data set of a process of a stuck condition; wherein the training data set of a process of a stuck condition includes at least two sample data of a process of a stuck condition;

[0069] A first drilling condition characteristic information extraction module, used to extract the first drilling condition characteristic information from the training data set of the blocking condition occurrence process;

[0070] A prediction model generation module is used to train a pre-set first machine learning model based on the first drilling condition characteristic information to generate an oil drilling obstruction prediction model.

[0071] Optionally, the device further comprises:

[0072] A data preprocessing module is used to preprocess the training data set of the blocking condition occurrence process; wherein the preprocessing includes at least one of format normalization, unit unification, noise removal, missing point filling and bad point correction.

[0073] Optionally, the device further comprises:

[0074] A second parameter input module is used to input the wellbore engineering parameters and the drilling condition parameters into a pre-trained model for determining a handling method for a wellbore stuck accident when it is determined that a wellbore stuck accident occurs during the oil drilling process;

[0075] The processing method determination module is used to determine the target processing method for the wellbore blocking accident according to the output result of the model for determining the processing method of the wellbore blocking accident.

[0076] Optionally, the device further comprises:

[0077] A second data set acquisition module is used to acquire a training data set for a jam accident handling process; wherein the training data set for a jam accident handling process includes at least two jam accident handling process data, and the jam accident handling process data includes jam condition occurrence process data and a corresponding jam handling method;

[0078] A second drilling condition characteristic information extraction module is used to extract the second drilling condition characteristic information from the stuck condition occurrence process data contained in the stuck accident handling process training data set;

[0079] The processing method determination model generation module is used to train a pre-set second machine learning model based on the second drilling condition characteristic information and the blockage processing method contained in the blockage accident processing process training data set to generate a blockage accident processing method determination model.

[0080] Optionally, the drilling condition characteristic information includes skewness, kurtosis, and value range of drilling condition parameters, wherein the drilling condition parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure, drilling fluid density, cycle time, reaming process, and reverse reaming process.

[0081] The oil drilling obstruction prediction device provided in the embodiment of the present invention can execute the oil drilling obstruction prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0082] Embodiment 4

[0083] Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0084] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0085] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0086] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as an oil drilling blockage prediction method.

[0087] In some embodiments, the oil drilling obstruction prediction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the oil drilling obstruction prediction method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the oil drilling obstruction prediction method in any other appropriate manner (for example, by means of firmware).

[0088] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0089] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0090] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0091] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0092] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0093] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0094] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0095] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting oil drilling blockage, characterized in that: include: During the oil drilling process, real-time acquisition of wellbore engineering parameters and tripping parameters; Inputting the wellbore engineering parameters and the tripping condition parameters into a pre-trained oil drilling blockage prediction model; According to the output result of the oil drilling stuck prediction model, it is determined whether a wellbore stuck accident occurs during the oil drilling process.

2. The method according to claim 1, characterized in that The wellbore engineering parameters include at least one of drilling fluid performance, formation lithology, pressure system, drilling parameters and drilling technology, and the drilling and lowering working condition parameters include at least one of hook load, drilling pressure, torque, displacement, vertical pressure and drilling fluid density.

3. The method according to claim 1, characterized in that Before inputting the wellbore engineering parameters into the pre-trained oil drilling blockage prediction model, the method further includes: Obtain a training data set of a process of occurrence of a blocking condition; wherein the training data set of a process of occurrence of a blocking condition includes at least two sample data of the process of occurrence of a blocking condition; Extracting first drilling condition characteristic information from the training data set of the process of the stuck condition; A pre-set first machine learning model is trained based on the first drilling condition characteristic information to generate an oil drilling obstruction prediction model.

4. The method according to claim 1, characterized in that: Before extracting the first drilling condition characteristic information from the training data set of the stuck condition occurrence process, the method further includes: The training data set of the process of the blocking condition occurring is preprocessed; wherein the preprocessing includes at least one of format normalization, unit unification, noise removal, missing point filling and bad point correction.

5. The method according to claim 1, characterized in that After determining whether a wellbore sticking accident occurs during the oil drilling process according to the output result of the oil drilling sticking prediction model, the method further includes: When it is determined that a wellbore obstruction accident occurs during the oil drilling process, the wellbore engineering parameters and the drilling operation parameters are input into a pre-trained model for determining a method for handling an obstruction accident; The output result of the model is determined according to the method for handling the sticking accident, and the target method for handling the wellbore sticking accident is determined.

6. The method according to claim 5, characterized in that When it is determined that a wellbore stuck accident occurs during the oil drilling process, before the wellbore engineering parameters and the drilling condition parameters are input into the pre-trained stuck accident handling method determination model, the method further includes: Obtain a training data set for a jam accident handling process; wherein the training data set for a jam accident handling process includes at least two jam accident handling process data, and the jam accident handling process data includes jam condition occurrence process data and a corresponding jam handling method; Extracting the second drilling condition characteristic information from the stuck condition occurrence process data included in the stuck accident handling process training data set; Based on the second drilling condition characteristic information and the stuck accident handling method contained in the stuck accident handling process training data set, a pre-set second machine learning model is trained to generate a stuck accident handling method determination model.

7. The method according to claim 3 or claim 6, characterized in that: The drilling condition characteristic information includes the skewness, kurtosis and value range of the drilling condition parameters, wherein the drilling condition parameters include at least one of hook load, bit pressure, torque, displacement, vertical pressure, drilling fluid density, cycle time, reaming process and reverse reaming process.

8. A device for predicting oil drilling blockage, characterized in that: include: The parameter acquisition module is used to obtain wellbore engineering parameters and tripping parameters in real time during the oil drilling process; A first parameter input module, used for inputting the wellbore engineering parameters and the tripping condition parameters into a pre-trained oil drilling blockage prediction model; The obstruction prediction module is used to determine whether a wellbore obstruction accident occurs during the oil drilling process according to the output result of the oil drilling obstruction prediction model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the oil drilling obstruction prediction method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the oil drilling obstruction prediction method described in any one of claims 1-7 when executed.

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