Well drilling parameter strengthening index automatic analysis method, device, equipment and medium

Abstract

The invention discloses a drilling parameter strengthening index automatic analysis method, device and equipment and a medium. The method comprises the following steps: constructing a drilling parameter evaluation template according to a drilling acceleration template and a drilling design of a current drilling operation area; acquiring drilling operation data of the current drilling operation area in real time and importing the drilling operation data into the drilling parameter evaluation template; evaluating the drilling parameter evaluation template through a pre-trained evaluation model; and performing strengthening index processing on the drilling parameters according to an evaluation result. According to the scheme, artificial intelligence methods such as supervised machine learning and natural language recognition are introduced, a conventional method for roughly and manually counting the execution condition and the implementation effect of the drilling parameters is broken through, drilling parameter management is finer and more scientific, and the efficiency of drilling operation is improved.

Description

Technical Field

[0001] The invention relates to the technical field of oil drilling, and in particular to an automatic analysis method, device, equipment and medium for drilling parameter enhancement index. Background Art

[0002] At present, the vast majority of drilling sites are constructed according to drilling design parameters. The joint-stock company has introduced drilling speed-up templates in some key basins, key blocks, and key large well cluster platforms in China to improve operational efficiency.

[0003] Due to the complexity of various influencing factors at the drilling site, the existing drilling speed-up template cannot be applied in all cases. When the drilling speed-up template is not applicable to the current drilling site, it will not only fail to improve the operating efficiency, but may also reduce the drilling operation efficiency.

[0004] In summary, an automatic analysis method for drilling parameter enhancement index is needed to analyze and adjust the enhancement index of drilling parameters to meet the current drilling site and improve the drilling operation efficiency. Summary of the invention

[0005] The present invention provides a drilling parameter enhancement index automatic analysis method, device, equipment and medium, which can analyze and adjust the enhancement index of the drilling parameters to meet the current drilling site and improve the drilling operation efficiency.

[0006] According to one aspect of the present invention, there is provided a method for automatically analyzing drilling parameter enhancement indicators, comprising:

[0007] Construct a drilling parameter evaluation template based on the drilling speed-up template and drilling design in the current drilling operation area;

[0008] Acquire the drilling operation data of the current drilling operation area in real time and import the drilling operation data into the drilling parameter evaluation template;

[0009] Evaluating the drilling parameter evaluation template by using a pre-trained evaluation model;

[0010] The drilling parameters are subjected to enhanced index processing according to the evaluation results.

[0011] Optionally, the drilling parameter evaluation template is constructed according to the drilling speed-up template and the drilling design of the current drilling operation area, including:

[0012] Performing structural analysis on the drilling speed-up template and the drilling design by using a document analysis tool to obtain structured data of at least one dimension;

[0013] The drilling parameter evaluation template is constructed according to the structured data of the at least one dimension, wherein the drilling parameter evaluation template contains a plurality of evaluation items corresponding to the structured data of different evaluation dimensions.

[0014] Optionally, the importing the drilling operation data into the drilling parameter evaluation template includes:

[0015] Splitting the drilling operation data according to the evaluation items included in the drilling parameter evaluation template;

[0016] The split drilling operation data is filled into the evaluation item corresponding to the drilling parameter evaluation template.

[0017] Optionally, the evaluation model is trained in the following manner:

[0018] Determine the baseline value for each evaluation dimension;

[0019] Scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value;

[0020] The numerical values ​​corresponding to the evaluation items of each evaluation dimension are used as variables and the changes in drilling time are analyzed to build a database for evaluating the effect of drilling parameter enhancement indicators;

[0021] The drilling parameter enhancement index effect evaluation database is used to study a preset machine learning algorithm to obtain the evaluation model.

[0022] Optionally, scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value includes:

[0023] According to the benchmark value, three evaluation criteria of failing to meet the standard, meeting the standard and exceeding the standard are set, and the weight of each evaluation criterion is set;

[0024] The numerical value corresponding to the evaluation item of each evaluation dimension belongs to the evaluation standard and is scored according to the weight of each evaluation standard.

[0025] Optionally, the drilling parameter enhancement index effect evaluation database is imported into a preset machine learning algorithm for learning to obtain the evaluation model, including:

[0026] The drilling parameter enhancement index effect evaluation database is used to enable the machine learning algorithm to learn the impact of the evaluation items of each evaluation dimension in the sample drilling parameters on the drilling operation, and converge into the evaluation model.

[0027] Optionally, the drilling parameters are subjected to enhanced index processing according to the evaluation results, including:

[0028] Determine the influence of the drilling parameter evaluation template on the drilling operation through the evaluation model, and determine at least one drilling parameter of the indicator to be enhanced;

[0029] Modify at least one drilling parameter of the indicator to be enhanced.

[0030] According to another aspect of the present invention, there is provided a drilling parameter enhancement index automatic analysis device, comprising:

[0031] An evaluation template building unit, used to build a drilling parameter evaluation template according to the drilling speed-up template and drilling design of the current drilling operation area;

[0032] A drilling operation data acquisition unit, used for acquiring the drilling operation data of the current drilling operation area in real time and importing the drilling operation data into the drilling parameter evaluation template;

[0033] An evaluation unit, used to evaluate the drilling parameter evaluation template by using a pre-trained evaluation model;

[0034] The drilling parameter processing unit is used to perform enhanced index processing on the drilling parameters according to the evaluation results.

[0035] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0036] At least one processor; and a memory in communication with 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 automatic analysis method of drilling parameter enhancement indicators described in any embodiment of the present invention.

[0037] 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 automatic analysis method of drilling parameter enhancement indicators described in any embodiment of the present invention when executed.

[0038] The technical solution of the embodiment of the present invention is to construct a drilling parameter evaluation template according to the drilling speed-up template and drilling design of the current drilling operation area; to obtain the drilling operation data of the current drilling operation area in real time and import the drilling operation data into the drilling parameter evaluation template; to evaluate the drilling parameter evaluation template through a pre-trained evaluation model; and to strengthen the index processing of the drilling parameters according to the evaluation results. The solution of the present invention introduces artificial intelligence methods such as supervised machine learning and natural language recognition, breaking through the conventional manual rough statistics of the execution of drilling parameters and the effect of their implementation, and the drilling parameter management is more refined and scientific. In terms of drilling parameter supervision, this method is used to automatically carry out batch well drilling parameter execution evaluation, guiding technical management personnel to more quickly locate nodes where drilling parameters are not executed in place; in terms of drilling speed-up strategy adjustment, this method is used to finely evaluate the execution effects of different drilling parameter sets, and combined with the performance of drilling equipment in the region, comprehensively recommend the drilling parameter set with the most enhanced indicators.

[0039] 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

[0040] 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.

[0041] Figure 1 This is a flow chart of a method for automatically analyzing drilling parameter enhancement indicators provided in Example 1 of the present invention;

[0042] Figure 2 It is a schematic diagram of the execution of a drilling parameter enhancement index applicable to the first embodiment of the present invention;

[0043] Figure 3 It is a schematic diagram of a drilling parameter enhancement index effect evaluation applicable to the first embodiment of the present invention;

[0044] Figure 4 It is a flow chart of a method for constructing a drilling parameter enhancement index effect applicable to the first embodiment of the present invention;

[0045] Figure 5 It is a structural schematic diagram of a drilling parameter enhancement index automatic analysis device provided by the second embodiment of the present invention;

[0046] Figure 6It is a structural schematic diagram of an electronic device for implementing the automatic analysis method of drilling parameter enhancement index according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] 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.

[0048] 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.

[0049] Embodiment 1

[0050] Figure 1 This is a flow chart of a method for automatically analyzing drilling parameter enhancement indicators provided by the first embodiment of the present invention. This embodiment is applicable to the case where the enhancement indicators of drilling parameters are analyzed and adjusted. The method can be executed by an automatic analysis device for drilling parameter enhancement indicators. The automatic analysis device for drilling parameter enhancement indicators can be implemented in the form of hardware and / or software. The automatic analysis device for drilling parameter enhancement indicators can be configured in an electronic device. Figure 1 As shown, the method includes:

[0051] S110: construct a drilling parameter evaluation template according to the drilling speed-up template and the drilling design in the current drilling operation area.

[0052] Among them, the drilling speed-up template is a scheme for strengthening the drilling parameters in the corresponding drilling operation area when implementing drilling operations. However, the impact of the drilling speed-up template on drilling operations depends on multiple dimensions, so it may appear that the currently used drilling speed-up template has a poor effect on the strengthening indicators of drilling parameters, or even has a negative effect. Therefore, it is necessary to evaluate the currently used drilling speed-up template to determine whether its impact on drilling operations meets expectations.

[0053] In an embodiment of the present invention, the drilling parameter evaluation template is constructed according to the drilling speed-up template and the drilling design of the current drilling operation area, including:

[0054] Performing structural analysis on the drilling speed-up template and the drilling design by using a document analysis tool to obtain structured data of at least one dimension;

[0055] The drilling parameter evaluation template is constructed according to the structured data of the at least one dimension, wherein the drilling parameter evaluation template contains a plurality of evaluation items corresponding to the structured data of different evaluation dimensions.

[0056] Among them, document parsing tools can be used to carry out structured analysis of drilling design and regional drilling speed-up templates, data governance can be carried out on the analysis results according to corresponding business rules, and a benchmark drilling parameter template database can be built according to the block unit to which the well belongs, including basic wellbore information, drilling parameters, drilling fluid parameters and speed-up tool parameters.

[0057] S120, acquiring the drilling operation data of the current drilling operation area in real time and importing the drilling operation data into the drilling parameter evaluation template.

[0058] In an embodiment of the present invention, the importing of the drilling operation data into the drilling parameter evaluation template includes:

[0059] Splitting the drilling operation data according to the evaluation items included in the drilling parameter evaluation template;

[0060] The split drilling operation data is filled into the evaluation item corresponding to the drilling parameter evaluation template.

[0061] Among them, when conducting drilling operations, drilling operation data is obtained in real time, the drilling conditions of the connected real-time data are automatically identified in real time, the data noise points of parameters such as drilling pressure, rotation speed, displacement, drilling fluid density and viscosity are eliminated, and the values ​​that conform to the normal distribution are extracted as whole meter parameters with whole meters as intervals, and the plots are automatically drawn in real time on the drilling parameter enhancement indicator execution tracking system interface. Figure 2 It is a schematic diagram of the execution of a drilling parameter enhancement index applicable to the first embodiment of the present invention.

[0062] S130: Evaluate the drilling parameter evaluation template using a pre-trained evaluation model.

[0063] In the embodiment of the present invention, the evaluation model is trained in the following manner:

[0064] Determine the baseline value for each evaluation dimension;

[0065] Scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value;

[0066] The numerical values ​​corresponding to the evaluation items of each evaluation dimension are used as variables and the changes in drilling time are analyzed to build a database for evaluating the effect of drilling parameter enhancement indicators;

[0067] The drilling parameter enhancement index effect evaluation database is used to study a preset machine learning algorithm to obtain the evaluation model.

[0068] In the embodiment of the present invention, scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value includes:

[0069] According to the benchmark value, three evaluation criteria of failing to meet the standard, meeting the standard and exceeding the standard are set, and the weight of each evaluation criterion is set;

[0070] The numerical value corresponding to the evaluation item of each evaluation dimension belongs to the evaluation standard and is scored according to the weight of each evaluation standard.

[0071] Among them, the evaluation of the effect of drilling parameter enhancement indicators mentioned above can be used as a training sample for the evaluation model. Specifically, taking the interval defined by the speed-up template of the block to which the well belongs as the reference value, the parameters such as drilling pressure, rotation speed, displacement, pump pressure, drilling fluid density and viscosity are automatically scored in real time according to the three standards of not meeting the standard, meeting the standard and exceeding the standard. The weight of meeting the standard is 1, and the weights of not meeting the standard and exceeding the standard are the influence indicators of the change of the single parameter on the drilling time. The weighted average of the parameters in the selected range is used as the execution result of the drilling parameter enhancement indicators for each well section, each drilling and each opening.

[0072] Taking any of the six groups of parameters, including well type, speed-increasing tools, wellbore size, lithology, drilling fluid system, and drill bit model, as variables, the changes in drilling time after different degrees of drilling parameter enhancement indicators are analyzed respectively, and a database for evaluating the effects of drilling parameter enhancement indicators is constructed. Figure 3 It is a schematic diagram of a drilling parameter enhancement index effect evaluation applicable to the first embodiment of the present invention.

[0073] In an embodiment of the present invention, the drilling parameter enhancement index effect evaluation database is imported into a preset machine learning algorithm for learning to obtain the evaluation model, including:

[0074] The drilling parameter enhancement index effect evaluation database is used to enable the machine learning algorithm to learn the impact of the evaluation items of each evaluation dimension in the sample drilling parameters on the drilling operation, and converge into the evaluation model.

[0075] Among them, from the machine learning algorithm library such as cluster analysis, BP neural network, exponential distribution, convolution, vector weighted average, gradient forest, etc., the algorithms that meet the requirements of drilling parameter enhancement index optimization are selected and substituted into the drilling parameter enhancement index effect evaluation database, and the training fitting and algorithm index parameter enhancement indicators are converged into a drilling parameter recommendation model (evaluation model) for optimal drilling time.

[0076] S140, performing enhanced index processing on the drilling parameters according to the evaluation results.

[0077] In an embodiment of the present invention, the processing of enhanced indicators of the drilling parameters according to the evaluation results includes: determining the impact of the drilling parameter evaluation template on the drilling operation through the evaluation model, and determining at least one drilling parameter of the indicator to be enhanced; modifying at least one drilling parameter of the indicator to be enhanced.

[0078] Among them, the drilling parameter recommendation model automatically recommends drill bit models, speed-up tools, drilling parameters and drilling fluid parameters that meet the needs of different blocks, well types and wellbore sizes in real time.

[0079] Figure 4 It is a construction flow chart of a drilling parameter enhancement index effect method applicable to the first embodiment of the present invention. The intelligent analysis, evaluation and optimization model of drilling parameter execution formed in the embodiment of the present invention covers 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 (rock) gas, geothermal and other drilling and completion projects, covering 20 to 120 drilling rigs. The document parsing software is used to parse the drilling parameter design data in documents such as speed-up templates and drilling engineering designs in different regions, and the structured data after parsing is managed and stored to form an evaluation basic data ledger. The oil engineering technology Internet of Things system is used to access the drilling pressure, speed, displacement, torque, vertical pressure and other drilling parameters of the drill bit of the batch well in real time, and the micro-drilling time data is accessed to automatically extract the data table of the whole meter drilling time of the wellbore. The drilling parameter tracking and evaluation system developed by the method of the present invention tracks the execution of batch well drilling parameter enhancement in real time, automatically evaluates the implementation effect of drilling parameter enhancement, and automatically recommends drilling parameter template enhancement index suggestions for different openings, different layers, and different wellbores.

[0080] The solution of the embodiment of the present invention introduces artificial intelligence methods such as supervised machine learning and natural language recognition, breaking through the conventional method of manually roughly counting the execution of drilling parameters and their implementation effects, making drilling parameter management more precise and scientific. In terms of drilling parameter supervision, this method is used to automatically carry out batch well drilling parameter execution evaluation, guiding technical management personnel to more quickly locate nodes where drilling parameters are not implemented in place; in terms of drilling speed-up strategy adjustment, this method is used to finely evaluate the execution effects of different drilling parameter sets, combined with the performance of drilling equipment in the region, and comprehensively recommend the drilling parameter set with the most enhanced indicators.

[0081] Embodiment 2

[0082] Figure 5 Schematic diagram of the structure of an automatic analysis device for drilling parameter enhancement index provided by the second embodiment of the present invention. Figure 5 As shown, the device comprises:

[0083] An evaluation template building unit 510 is used to build a drilling parameter evaluation template according to the drilling speed-up template and the drilling design in the current drilling operation area;

[0084] A drilling operation data acquisition unit 520, configured to acquire the drilling operation data of the current drilling operation area in real time and import the drilling operation data into the drilling parameter evaluation template;

[0085] An evaluation unit 530, configured to evaluate the drilling parameter evaluation template using a pre-trained evaluation model;

[0086] The drilling parameter processing unit 540 is used to perform enhanced index processing on the drilling parameters according to the evaluation results.

[0087] Optionally, the evaluation template construction unit 510 is used to execute:

[0088] Performing structural analysis on the drilling speed-up template and the drilling design by using a document analysis tool to obtain structured data of at least one dimension;

[0089] The drilling parameter evaluation template is constructed according to the structured data of the at least one dimension, wherein the drilling parameter evaluation template contains a plurality of evaluation items corresponding to the structured data of different evaluation dimensions.

[0090] Optionally, when the drilling operation data acquisition unit 520 imports the drilling operation data into the drilling parameter evaluation template, specifically performs:

[0091] Splitting the drilling operation data according to the evaluation items included in the drilling parameter evaluation template;

[0092] The split drilling operation data is filled into the evaluation item corresponding to the drilling parameter evaluation template.

[0093] Optionally, the device further includes: a model training unit 550;

[0094] The model training unit 550 is used to train the evaluation model in the following manner:

[0095] Determine the baseline value for each evaluation dimension;

[0096] Scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value;

[0097] The numerical values ​​corresponding to the evaluation items of each evaluation dimension are used as variables and the changes in drilling time are analyzed to build a database for evaluating the effect of drilling parameter enhancement indicators;

[0098] The drilling parameter enhancement index effect evaluation database is used to study a preset machine learning algorithm to obtain the evaluation model.

[0099] Optionally, when the model training unit 550 performs the step of scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value, the model training unit 550 specifically performs:

[0100] According to the benchmark value, three evaluation criteria of failing to meet the standard, meeting the standard and exceeding the standard are set, and the weight of each evaluation criterion is set;

[0101] The numerical value corresponding to the evaluation item of each evaluation dimension belongs to the evaluation standard and is scored according to the weight of each evaluation standard.

[0102] Optionally, when the model training unit 550 imports the drilling parameter enhancement index effect evaluation database into a preset machine learning algorithm for learning to obtain the evaluation model, the following is specifically performed:

[0103] The drilling parameter enhancement index effect evaluation database is used to enable the machine learning algorithm to learn the impact of the evaluation items of each evaluation dimension in the sample drilling parameters on the drilling operation, and converge into the evaluation model.

[0104] Optionally, the drilling parameter processing unit 540 is configured to execute:

[0105] Determine the influence of the drilling parameter evaluation template on the drilling operation through the evaluation model, and determine at least one drilling parameter of the indicator to be enhanced;

[0106] Modify at least one drilling parameter of the indicator to be enhanced.

[0107] The drilling parameter enhancement index device provided in the embodiment of the present invention can execute the drilling parameter enhancement index method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0108] Embodiment 3

[0109] Figure 6A 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.

[0110] like Figure 6 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.

[0111] 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.

[0112] 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 the automatic analysis method of drilling parameter enhancement indicators.

[0113] In some embodiments, the automatic analysis method of drilling parameter enhancement indicators may 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 may 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 automatic analysis method of drilling parameter enhancement indicators described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the automatic analysis method of drilling parameter enhancement indicators in any other appropriate manner (e.g., by means of firmware).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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. The automatic analysis method of drilling parameter enhancement index is characterized by: include: Construct a drilling parameter evaluation template based on the drilling speed-up template and drilling design in the current drilling operation area; Acquire the drilling operation data of the current drilling operation area in real time and import the drilling operation data into the drilling parameter evaluation template; Evaluating the drilling parameter evaluation template by using a pre-trained evaluation model; The drilling parameters are subjected to enhanced index processing according to the evaluation results.

2. The method according to claim 1, characterized in that: The drilling parameter evaluation template is constructed according to the drilling speed-up template and the drilling design of the current drilling operation area, including: Performing structural analysis on the drilling speed-up template and the drilling design by using a document analysis tool to obtain structured data of at least one dimension; The drilling parameter evaluation template is constructed according to the structured data of the at least one dimension, wherein the drilling parameter evaluation template contains a plurality of evaluation items corresponding to the structured data of different evaluation dimensions.

3. The method according to claim 2, characterized in that The step of importing the drilling operation data into the drilling parameter evaluation template comprises: Splitting the drilling operation data according to the evaluation items included in the drilling parameter evaluation template; The split drilling operation data is filled into the evaluation item corresponding to the drilling parameter evaluation template.

4. The method according to claim 2, characterized in that: The evaluation model is trained in the following way: Determine the baseline value for each evaluation dimension; Scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value; The numerical values ​​corresponding to the evaluation items of each evaluation dimension are used as variables and the changes in drilling time are analyzed to build a database for evaluating the effect of drilling parameter enhancement indicators; The drilling parameter enhancement index effect evaluation database is used to study a preset machine learning algorithm to obtain the evaluation model.

5. The method according to claim 4, characterized in that Scoring the numerical value corresponding to the evaluation item of each evaluation dimension in each sample drilling parameter according to the benchmark value includes: According to the benchmark value, three evaluation criteria of failing to meet the standard, meeting the standard and exceeding the standard are set, and the weight of each evaluation criterion is set; The numerical value corresponding to the evaluation item of each evaluation dimension belongs to the evaluation standard and is scored according to the weight of each evaluation standard.

6. The method according to claim 4, characterized in that The step of importing the drilling parameter enhancement index effect evaluation database into a preset machine learning algorithm for learning to obtain the evaluation model includes: The drilling parameter enhancement index effect evaluation database is used to enable the machine learning algorithm to learn the impact of the evaluation items of each evaluation dimension in the sample drilling parameters on the drilling operation, and converge into the evaluation model.

7. The method according to claim 1, characterized in that The step of performing enhanced index processing on the drilling parameters according to the evaluation results includes: Determine the influence of the drilling parameter evaluation template on the drilling operation through the evaluation model, and determine at least one drilling parameter of the indicator to be enhanced; Modify at least one drilling parameter of the indicator to be enhanced.

8. Automatic analysis device for drilling parameter enhancement index, characterized in that: include: An evaluation template building unit, used to build a drilling parameter evaluation template according to the drilling speed-up template and drilling design of the current drilling operation area; A drilling operation data acquisition unit, used for acquiring the drilling operation data of the current drilling operation area in real time and importing the drilling operation data into the drilling parameter evaluation template; An evaluation unit, used to evaluate the drilling parameter evaluation template by using a pre-trained evaluation model; The drilling parameter processing unit is used to perform enhanced index processing on the drilling parameters according to the evaluation results.

9. An electronic device, characterized in that The electronic device includes: 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 automatic analysis method of drilling parameter enhancement indicators 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 automatic analysis method of drilling parameter enhancement indicators according to any one of claims 1-7 when executed.

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