A well logging acquisition construction design intelligent generation method and device

Abstract

This invention discloses an intelligent generation method and apparatus for well logging acquisition construction design. The method includes: determining a matching template from a pre-established well logging acquisition construction design template library based on the well logging task category, well type, and well shape in the well logging acquisition task notification; using a well logging acquisition construction information knowledge graph based on a pre-established well logging team information library to screen qualified well logging teams that match the well logging task category, well type, and well shape in the notification and whose relevant information is not on a set blacklist; and using the knowledge graph to extract the information of qualified well logging teams required for the template from the well logging task notification and the well logging team information library, and filling this information into the template to generate the well logging acquisition construction design. This method enables intelligent requirements analysis for well logging acquisition construction design, improving the quality and timeliness of design preparation.

Description

Technical Field

[0001] This invention relates to the field of intelligent logging data acquisition technology, and in particular to an intelligent generation method and device for logging acquisition construction design. Background Technology

[0002] With the rapid development of my country's petroleum industry, the level of automation in petroleum systems has been continuously improving. In order to improve economic efficiency and labor productivity, and to solve problems such as low timeliness, inability to achieve standardized and consistent supervision, and "too many wells and too few personnel" in well logging supervision, a large amount of well logging supervision work adopts a remote supervision mode. Well logging acquisition construction design, as an important key node in remote supervision and inspection, usually requires a lot of time for well logging teams and well logging supervisors to prepare and review, which seriously restricts the development of intelligent remote intelligent supervision systems for well logging acquisition.

[0003] See Figure 1 As shown, currently, well logging construction design is generally prepared offline. The well logging team leader needs to collect detailed information on the team's qualifications, personnel, surface equipment, and each well logging instrument downhole. Based on the well logging design requirements, they must also collect detailed information on different well logging instruments and equipment for different well logging task types, categorize and organize this information, and submit a paper construction design to the well logging supervisor for review. Only after the review is approved can the well logging team proceed to the well site to carry out the operation. Summary of the Invention

[0004] The above-mentioned well logging and acquisition construction design preparation and supervision and review model has three main problems:

[0005] First, the process of designing and compiling well logging and data acquisition systems consumes a lot of manpower, resources, and time, resulting in low work efficiency.

[0006] Secondly, incomplete or incomplete information and data collection can easily lead to rework of the design, affecting the efficiency and quality of well logging and acquisition construction design.

[0007] Third, the information after the well logging and acquisition construction design review cannot be synchronized with relevant departments in a timely manner, resulting in the inability to share construction operation information and affecting the decision-making of remote monitoring of well logging and acquisition operations.

[0008] To address the issues of low timeliness, lack of standardization, and inability to share information in well logging acquisition construction design, this invention provides an intelligent generation method and device for well logging acquisition construction design, enabling intelligent demand analysis of well logging acquisition construction design and improving the quality and timeliness of design preparation.

[0009] In a first aspect, embodiments of the present invention provide an intelligent generation method for well logging acquisition construction design, comprising:

[0010] Based on the logging task category, well type and well type in the logging acquisition task notification form, determine the matching template from the pre-established logging acquisition construction design template library;

[0011] Based on a pre-established database of logging teams, a knowledge graph of logging acquisition and construction information is used to screen logging teams that match the logging task category, well type, and well type, and whose relevant information is not on the set blacklist, and these teams are considered qualified logging teams.

[0012] Using the knowledge graph, information on qualified logging teams required for the template is extracted from the notification form and the logging team information database, and then filled into the template to generate a logging acquisition construction design.

[0013] Optionally, the knowledge graph is pre-established based on the well logging team information database through steps of knowledge system classification, ontology construction, relation extraction, and knowledge fusion. The well logging team information database includes a well logging team qualification information database, a well logging team personnel information database, a surface equipment information database, and a downhole instrument information database.

[0014] Optional knowledge system classification steps include:

[0015] Based on the workflow contained in the templates in the well logging acquisition construction design template library, the main object is extracted;

[0016] By integrating multi-source structured data and unstructured information, and taking the qualifications of logging teams, logging personnel, surface equipment, downhole instruments, and blacklist information as the main lines, a knowledge system classification is constructed based on the extracted objects.

[0017] Optional ontology construction steps include:

[0018] For each object, an ontology is constructed from information about the object, activities, and features, where the activities act on the object and the features describe the activities and the object.

[0019] Optionally, based on a pre-established logging team information database, the logging teams that match the logging task category, well type, and well type, and whose relevant information is not on a set blacklist, are selected as qualified logging teams, including:

[0020] By using the API query command of the detection model and utilizing the knowledge graph of well logging construction information, the well logging teams whose qualifications match the well logging task category, well type and well type in the notification form can be retrieved from the pre-established well logging team information database.

[0021] From the retrieved logging teams, filter out logging teams that are not on the logging team blacklist;

[0022] For each selected logging team, the API query command of the detection model is used to retrieve multiple settings information of the logging team from the logging team information database using the knowledge graph. Logging teams whose information does not match the corresponding blacklist are selected as qualified logging teams.

[0023] Optionally, if there are multiple logging teams whose information does not match the corresponding blacklist for each of the filtered items, the following may also be included:

[0024] The selected logging teams are pushed to users, and qualified logging teams are determined based on the users' selection results.

[0025] Optionally, after generating the well logging acquisition construction design, the method further includes:

[0026] Collect photos and / or video data from the well logging site and store them in the monitoring database;

[0027] For the information to be supervised in the well logging and acquisition construction design, the knowledge graph is used to extract corresponding information from the supervision database, and the intelligent recognition result of the extracted information is compared with the information to be supervised.

[0028] If the two do not match, it is determined that the execution quality supervision of the well logging and acquisition construction design is unqualified, and a corresponding unqualified notice is generated.

[0029] Secondly, embodiments of the present invention provide an intelligent generation device for well logging acquisition and construction design, comprising:

[0030] The template matching module is used to determine the matching template from the pre-established well logging and acquisition construction design template library based on the well logging task category, well type and well type in the well logging and acquisition task notification form;

[0031] The qualified logging team screening module is used to screen logging teams that match the logging task category, well type and well type, and whose relevant information is not on the set blacklist, based on a pre-established logging team information database and a knowledge graph of logging acquisition and construction information. These teams are then selected as qualified logging teams.

[0032] The well logging acquisition construction design automatic generation module is used to extract the information of qualified well logging teams required for the template from the notification form and the well logging team information database using the knowledge graph, fill in the template, and generate the well logging acquisition construction design.

[0033] Thirdly, embodiments of the present invention provide a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-mentioned intelligent generation method for well logging acquisition and construction design.

[0034] Fourthly, this disclosure provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned intelligent generation method for well logging acquisition and construction design.

[0035] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:

[0036] (1) The intelligent generation method for well logging acquisition construction design provided in this embodiment of the invention generates corresponding well logging acquisition construction design templates in advance based on multiple possible combinations of well logging task categories, well types, and well shapes; it also pre-constructs a well logging team information database and a well logging acquisition construction information knowledge graph; and uses the knowledge graph to associate the well logging task notification form and the well logging team information database with the data required by the determined matching template, automatically generating the well logging acquisition construction design. Through intelligent information reading, repetitive input is reduced, manual input errors are prevented, the timeliness and accuracy of well logging acquisition construction design compilation are improved, and standardized compilation is achieved.

[0037] (2) The intelligent generation method for well logging acquisition construction design provided in this embodiment of the invention collects photos and / or video data from the well logging acquisition construction site and stores them in a supervision database; for the information to be supervised in the well logging acquisition construction design, it uses a knowledge graph to extract corresponding information from the supervision database, and compares the intelligent recognition results of the extracted information with the information to be supervised; if the two do not match, it is determined that the execution quality supervision of the well logging acquisition construction design is unqualified, and a corresponding unqualified notification is generated. This realizes intelligent supervision of the execution quality of well logging acquisition construction design, improves the timeliness of supervision, and frees up supervision manpower.

[0038] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0039] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0040] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0041] Figure 1 A flowchart for the design of well logging and data acquisition operations using existing technologies is provided.

[0042] Figure 2 This is an example image of a well logging acquisition construction design template page in an embodiment of the present invention;

[0043] Figure 3 This is a description of the ontological structure features of the knowledge graph of well logging and construction information in this embodiment of the invention;

[0044] Figure 4 This is an example diagram of a knowledge graph for well logging and construction information in an embodiment of the present invention;

[0045] Figure 5 This is a flowchart of the intelligent generation method for well logging acquisition construction design in an embodiment of the present invention;

[0046] Figure 6 This is an example image of the intelligent supervision page for the design and execution quality of well logging and data acquisition in an embodiment of the present invention.

[0047] Figure 7 This is a technical flowchart illustrating the intelligent generation and supervision of well logging acquisition construction design in an embodiment of the present invention.

[0048] Figure 8 This is a schematic diagram of the intelligent generation device for well logging acquisition and construction design in an embodiment of the present invention. Detailed Implementation

[0049] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0050] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0051] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0052] An embodiment of the present invention provides a method and device for intelligently generating a logging acquisition construction design, which realizes intelligent requirement analysis of the logging acquisition construction design and improves the quality and timeliness of design compilation.

[0053] In an embodiment of the present invention, it is first necessary to pre - establish a logging acquisition construction design template library, a logging team information library, and a logging acquisition construction information knowledge graph.

[0054] 1. Establish a logging acquisition construction design template library

[0055] Set the logging acquisition construction design specifications, and formulate a standard logging acquisition construction design template according to the construction design specifications. Among them, the logging acquisition construction design specifications can be formulated based on the analysis results of existing logging acquisition construction designs.

[0056] An embodiment of the present invention selects the construction acquisition design of a cased - hole logging for illustration. Figure 2 As shown, the content of this design includes a cover, a table of contents, a measurement method, the situation of operation team personnel, surface equipment information, downhole instrument information, depth system and auxiliary tool information, and an HSE emergency plan. An embodiment of the present invention uses XML to formulate the design template and configures the data attributes that need to be read from the database into the template, specifically as follows:

[0057] (1) Define the styles of each level of headings, body texts, and tables in the design. These styles will be applied to the corresponding element node tags. Only need to specify the style attribute for the element nodes.

[0058] <element name="Titlel" fontsize="14" alias="四号" fontname="宋体" bold="true" level="1" linespacing="18" / >

[0059] <element name="BaseText" fontsize="14" alias="四号" fontname="宋体" leftindent="2" linespacing="18" / >

[0060] (2) Define the formats of the design cover, table of contents, headings, and body texts.

[0061] The cover uses <cover>Tags, directory usage <catalogue>Tags, titles use <title> Tags, used in the main text< / title> <element>Labels, the text content uses the content attribute. The text content can be static content without reading data from the database, or it can be dynamic content, identified by #{} for dynamic attributes. For example, #{wellname} represents a dynamic attribute and will automatically read data from the database.

[0062] <element content="#{wellname} Well Logging Evaluation Report" style="Three" / >

[0063] (3) Define the design table and picture formats.

[0064] The table format adopts Tags, used in table header rows Labels, header columns use Tags. Data rows use <row> Labels, data columns< / row> Tags can be used to configure the relationship between tabular data and database tables.

[0065] Image format used <picture>Tags can be configured to store system images or to configure image index relationships in the database, such as core photos and result images.

[0066] Based on various possible combinations of logging task categories, well types, and well shapes, corresponding logging acquisition construction design templates are pre-generated, and a template library is established, providing a foundation for the automated generation and standardization of logging acquisition construction designs.

[0067] 2. Establish a database of logging teams.

[0068] Different logging teams have different qualification levels, application specialties, and management systems. In the early stages, it is necessary to collect a large amount of logging team qualification information and build a logging team qualification information database. At the same time, the personnel information associated with the logging teams, including their positions, education levels, work experience, and technical titles, also differ, so a logging team personnel information database needs to be built. The information on the surface equipment and downhole instruments of the logging teams also differs, so it is necessary to collect, organize, and summarize the information to form a complete surface equipment information database and downhole instrument information database.

[0069] The well logging team information database is composed of the aforementioned databases of well logging team qualifications, well logging team personnel, surface equipment, and downhole instruments, forming a well logging team information database that is associated with well logging acquisition and construction design. This strengthens structured data storage and management, and provides a data source for the automatic generation of well logging acquisition and construction design.

[0070] 3. Establish a knowledge graph of well logging and acquisition construction information.

[0071] The construction of a knowledge graph for well logging acquisition and construction information includes the steps of knowledge system classification, ontology construction, relation extraction, and knowledge fusion.

[0072] Knowledge system classification is the foundation for building knowledge graphs and realizing knowledge management. Its construction follows the principle of business-driven design. Based on the workflow contained in different templates in the well logging acquisition and construction design template library, the main objects are extracted. Data such as well logging team qualifications, well logging team personnel, surface equipment and downhole instruments are structured data, which can be structured data with well logging teams as the main line. Data such as pictures and videos collected at the construction site are unstructured data. Multi-source structured data and unstructured materials such as documents, pictures and videos are integrated. The knowledge system classification is constructed based on well logging team qualifications, well logging team personnel, surface equipment, downhole instruments and blacklist information as the main lines.

[0073] Ontology construction is the foundation for building a knowledge graph of well logging acquisition and construction information. For each object, an ontology is constructed by acquiring information about the object, activities, and features. Activities act on objects, and features are used to describe activities and objects.

[0074] The most basic ontology includes concepts, concept hierarchy, attributes, attribute value types, relations, relation domain concept set, and relation value domain concept set. It can be constructed using top-down and / or bottom-up approaches.

[0075] This embodiment proposes a knowledge graph ontology based on objects, activities, and features, based on the business processes, datasets, and technical requirements involved in well logging and acquisition construction design. The ontology's structural features are described in [link to documentation]. Figure 3 As shown.

[0076] After completing ontology modeling, deep knowledge extraction and management can be performed on the knowledge outcomes, experience, and structured data from well logging acquisition and construction design. This mainly includes named entity recognition, relation extraction, attribute classification, and attribute value extraction. Named entity recognition is the foundation of information extraction; its task is to find named entities from unstructured data such as field HSE photos, videos, and documents, and label their types. Relation extraction requires extracting the semantic relationships between two or more entities from text. Attributes generally refer to the attributes of an entity or the components that make up the entity; they need to be extracted and classified according to the composition of the knowledge and the purpose of the task.

[0077] The data sources for the knowledge graph of well logging acquisition construction information mainly come from well logging task notification forms, well logging team qualification information databases, well logging team personnel information databases, surface equipment information databases, and downhole instrument information databases. In the actual construction of the knowledge management system, in order to reduce the ambiguity of the knowledge graph and achieve the integration and deep fusion of unstructured knowledge results and structured data.

[0078] This invention constructs a knowledge graph of well logging acquisition and construction information, including professional knowledge, technical specifications, expert knowledge, and management requirements. This enables intelligent judgment and analysis, and remote interactive management in well logging acquisition and construction design preparation, thereby improving the quality and timeliness of well logging acquisition and construction design preparation. See also... Figure 4 The image shown is an example diagram of a knowledge graph.

[0079] Based on the knowledge graph of well logging acquisition construction information, the required information and the relationships between information can be easily found in the associated database, which makes it possible to make intelligent judgment on the qualification of the initial well logging acquisition construction design and intelligent quality supervision of the design execution in the later construction process.

[0080] Example

[0081] This invention provides an intelligent generation method for well logging acquisition construction design, the process of which is as follows: Figure 5 As shown, it includes the following steps:

[0082] Step S51: Based on the logging task category, well type and well type in the logging acquisition task notification form, determine the matching template from the pre-established logging acquisition construction design template library.

[0083] Establish a matching relationship between well logging acquisition task categories, well types, well types and well logging acquisition construction design templates, and find the template with the highest matching degree based on the matching relationship.

[0084] It may also include allowing construction personnel to confirm whether to use the template; if not, allowing them to select other design templates and complete the loading of the design template.

[0085] Step S52: Based on the pre-established logging team information database, use the logging acquisition construction information knowledge graph to screen logging teams that match the logging task category, well type and well type, and whose relevant information is not on the set blacklist, and these teams are qualified logging teams.

[0086] Based on key node data information from well logging acquisition and construction design, API query instructions are generated for an AI-powered detection model. Using this API query, and leveraging a well logging acquisition and construction information knowledge graph, the model retrieves well logging team qualification information from a database, filtering out teams whose qualification information matches the well logging task category, well type, and well type specified in the notification. For example, a Class A qualified team can design and construct risk exploration wells and horizontal wells, while a Class B qualified team may partially meet the requirements.

[0087] Then, from the logging teams read, those logging teams that are not on the logging team blacklist are filtered out.

[0088] Furthermore, for each selected logging team, the API query command of the detection model is used to retrieve multiple settings information of the logging team from the logging team information database using a knowledge graph. Logging teams whose information does not match the corresponding blacklist are selected as qualified logging teams.

[0089] The method can be to read the logging team personnel, surface equipment, and downhole instrument information from the logging team information database, and compare them with the logging personnel blacklist, surface equipment blacklist, and downhole instrument blacklist respectively to select the final qualified logging team.

[0090] If there are multiple logging teams whose information does not match the corresponding blacklist, the process can also include pushing the selected logging teams to the user and determining the qualified logging team based on the user's selection.

[0091] Step S53: Using a knowledge graph, extract the information of qualified logging teams required for the template from the notification form and the logging team information database, fill it into the template, and generate the logging acquisition construction design.

[0092] The intelligent generation method for well logging acquisition construction design provided in this invention pre-generates corresponding well logging acquisition construction design templates based on multiple possible combinations of well logging task categories, well types, and well shapes; it pre-constructs a well logging team information database and a well logging acquisition construction information knowledge graph; and using the knowledge graph, it associates the well logging task notification form and the well logging team information database with the data required by the determined matching templates to automatically generate the well logging acquisition construction design. Through intelligent information reading, it reduces repetitive input, prevents manual input errors, improves the timeliness and accuracy of well logging acquisition construction design preparation, and achieves standardized preparation.

[0093] In some embodiments, after generating the well logging acquisition construction design, the process may further include: collecting photos and / or video data from the well logging acquisition construction site and storing them in a monitoring database; using a knowledge graph to extract corresponding information from the monitoring database for the information to be monitored in the well logging acquisition construction design; comparing the intelligent recognition results of the extracted information with the information to be monitored; if the two do not match, determining that the execution quality supervision of the well logging acquisition construction design is unqualified and generating a corresponding unqualified notification.

[0094] Intelligent monitoring of the execution quality of well logging and acquisition construction design improves monitoring efficiency and frees up manpower for supervision. (See also...) Figure 6 The image shown is an example of an intelligent monitoring page for the execution quality of well logging and data acquisition design.

[0095] See Figure 7 The diagram shown is a technical flowchart of an embodiment of the present invention. First, a well logging team qualification information database (hereinafter referred to as the team qualification database), a well logging team personnel information database (hereinafter referred to as the team personnel database), a surface equipment information database (hereinafter referred to as the surface equipment database), and a downhole instrument information database (hereinafter referred to as the downhole instrument database) are created. Based on the above information databases, a well logging acquisition construction information knowledge graph is constructed through ontology creation, feature extraction, and relationship extraction. A well logging acquisition construction design compliance intelligent judgment model is created, which integrates API query instructions and compliance judgment rules. Based on unstructured data such as pictures, videos, and documents collected at the construction site, combined with intelligent judgment and expert judgment, intelligent supervision of well logging acquisition construction design is jointly completed.

[0096] Based on the inventive concept of this invention, embodiments of this invention also provide an intelligent generation device for well logging acquisition and construction design, the structure of which is as follows: Figure 8 As shown, it includes:

[0097] The template matching module 81 is used to determine the matching template from the pre-established well logging and acquisition construction design template library based on the well logging task category, well type and well type in the well logging and acquisition task notification form;

[0098] The qualified logging team screening module 82 is used to screen logging teams that match the logging task category, well type and well type, and whose relevant information is not on the set blacklist, based on a pre-established logging team information database and a knowledge graph of logging acquisition construction information, as qualified logging teams.

[0099] The well logging acquisition construction design automatic generation module 83 is used to extract the information of qualified well logging teams required for the template from the notification form and the well logging team information database using the knowledge graph, fill in the template, and generate the well logging acquisition construction design.

[0100] In some embodiments, the above-described apparatus further includes a design execution quality monitoring module 84, for:

[0101] Photos and / or video data are collected from the well logging construction site and stored in the supervision database. For the information to be supervised in the well logging construction design, the knowledge graph is used to extract the corresponding information from the supervision database. The intelligent recognition result of the extracted information is compared with the information to be supervised. If the two do not match, it is determined that the execution quality supervision of the well logging construction design is unqualified, and a corresponding unqualified notice is generated.

[0102] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0103] Based on the inventive concept of the present invention, the embodiments of the present invention also provide a computer storage medium, wherein the computer storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the above-mentioned intelligent generation method for well logging acquisition construction design is realized.

[0104] Based on the inventive concept of this invention, this embodiment of the invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned intelligent generation method for well logging acquisition and construction design.

[0105] Unless otherwise specifically stated, terms such as processing, calculation, operation, determination, display, etc., may refer to the actions and / or processes of one or more processing or computing systems or similar devices that represent the manipulation and conversion of data representing physical (e.g., electronic) quantities within the registers or memory of the processing system into other data similarly representing physical quantities within the memory, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltage, current, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0106] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process may be rearranged without departing from the scope of this disclosure. The appended method claims provide elements of various steps in an exemplary order and are not intended to limit the scope to the specific order or hierarchy described.

[0107] In the detailed description above, various features are combined together in a single embodiment to simplify this disclosure. This approach to disclosure should not be construed as reflecting an intention that embodiments of the claimed subject matter require more features than those stated in each claim. Rather, as reflected in the appended claims, the invention is presented with fewer features than all of the features in a single disclosed embodiment. Therefore, the appended claims are hereby clearly incorporated into the detailed description, wherein each claim stands alone as a preferred embodiment of the invention.

[0108] Those skilled in the art will also understand that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments herein can be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in alternative ways for each specific application; however, such implementation decisions should not be construed as departing from the scope of this disclosure.

[0109] The steps of the methods or algorithms described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by a processor, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, the processor and storage medium can exist as discrete components in the user terminal.

[0110] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory units and executed by a processor. The memory units can be implemented within the processor or outside the processor; in the latter case, they are communicatively coupled to the processor via various means, as is well known in the art.

[0111] The foregoing description includes examples of one or more embodiments. It is certainly impossible to describe all possible combinations of components or methods in order to describe the above embodiments, but those skilled in the art will recognize that further combinations and arrangements of the various embodiments are possible. Therefore, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. Furthermore, the term "comprising" as used in the specification or claims is interpreted in a manner similar to the term "including," as it is understood when used as a conjunction in the claims. Additionally, the use of any term "or" in the specification of the claims is intended to mean "non-exclusive or."< / picture> < / element> < / catalogue> < / cover>

Claims

1. A method for intelligently generating well logging acquisition construction design, characterized in that, include: Based on the logging task category, well type and well type in the logging acquisition task notification form, determine the matching template from the pre-established logging acquisition construction design template library; Based on a pre-established database of logging teams, a knowledge graph of logging acquisition and construction information is used to screen logging teams that match the logging task category, well type, and well type, and whose relevant information is not on the set blacklist, and these teams are considered qualified logging teams. Using the knowledge graph, information on qualified logging teams required for the template is extracted from the notification form and the logging team information database, and then filled into the template to generate a logging acquisition construction design.

2. The method as described in claim 1, characterized in that, The knowledge graph is pre-established based on the well logging team information database through steps of knowledge system classification, ontology construction, relation extraction, and knowledge fusion. The well logging team information database includes a well logging team qualification information database, a well logging team personnel information database, a surface equipment information database, and a downhole instrument information database.

3. The method as described in claim 2, characterized in that, The steps for classifying a knowledge system include: Based on the workflow contained in the templates in the well logging acquisition construction design template library, the main object is extracted; By integrating multi-source structured data and unstructured information, and taking the qualifications of logging teams, logging personnel, surface equipment, downhole instruments, and blacklist information as the main lines, a knowledge system classification is constructed based on the extracted objects.

4. The method as described in claim 3, characterized in that, Ontology construction steps include: For each object, an ontology is constructed from information about the object, activities, and features, where the activities act on the object and the features describe the activities and the object.

5. The method as described in claim 2, characterized in that, The aforementioned method, based on a pre-established logging team information database, utilizes a knowledge graph of logging acquisition and construction information to screen logging teams that match the logging task category, well type, and well type, and whose relevant information is not on a set blacklist. These teams are considered qualified logging teams, including: By using the API query command of the detection model and utilizing the knowledge graph of well logging construction information, the well logging teams whose qualifications match the well logging task category, well type and well type in the notification form can be retrieved from the pre-established well logging team information database. From the retrieved logging teams, filter out logging teams that are not on the logging team blacklist; For each selected logging team, the API query command of the detection model is used to retrieve multiple settings information of the logging team from the logging team information database using the knowledge graph. Logging teams whose information does not match the corresponding blacklist are selected as qualified logging teams.

6. The method as described in claim 5, characterized in that, If there are multiple logging teams whose information does not match the corresponding blacklist, these also include: The selected logging teams are pushed to users, and qualified logging teams are determined based on the users' selection results.

7. The method as described in claim 1, characterized in that, After generating the well logging acquisition construction design, it also includes: Collect photos and / or video data from the well logging site and store them in the monitoring database; For the information to be supervised in the well logging and acquisition construction design, the knowledge graph is used to extract corresponding information from the supervision database, and the intelligent recognition result of the extracted information is compared with the information to be supervised. If the two do not match, it is determined that the execution quality supervision of the well logging and acquisition construction design is unqualified, and a corresponding unqualified notice is generated.

8. A smart generation device for well logging acquisition and construction design, characterized in that, include: The template matching module is used to determine the matching template from the pre-established well logging and acquisition construction design template library based on the well logging task category, well type and well type in the well logging and acquisition task notification form; The qualified logging team screening module is used to screen logging teams that match the logging task category, well type and well type, and whose relevant information is not on the set blacklist, based on a pre-established logging team information database and a knowledge graph of logging acquisition and construction information. These teams are then selected as qualified logging teams. The well logging acquisition construction design automatic generation module is used to extract the information of qualified well logging teams required for the template from the notification form and the well logging team information database using the knowledge graph, fill in the template, and generate the well logging acquisition construction design.

9. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the intelligent generation method for well logging acquisition construction design as described in any one of claims 1 to 7.

10. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the intelligent generation method for well logging acquisition construction design as described in any one of claims 1 to 7.

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