CN114186767A

CN114186767A - Method and system for intelligently identifying drilling event based on drilling log

Info

Publication number: CN114186767A
Application number: CN202110850366.8A
Authority: CN
Inventors: 汪海阁; 刘梅全; 葛云华; 梅运谊; 刘继亮; 席云龙; 张彦龙
Original assignee: China National Petroleum Corp; CNPC Engineering Technology R&D Co Ltd
Current assignee: China National Petroleum Corp; CNPC Engineering Technology R&D Co Ltd
Priority date: 2021-07-27
Filing date: 2021-07-27
Publication date: 2022-03-15

Abstract

The invention provides a method and a system for intelligently identifying a drilling event based on a drilling log, wherein the method comprises the following steps: setting working state information of non-production time of petroleum drilling; creating a drilling event keyword set rule; acquiring a well drilling log, and identifying and splitting working conditions according to the well drilling log; comparing the drilling working condition recognition and splitting result with the working conditions of the event keyword groups in sequence according to the priority of each event keyword group in the drilling event keyword group rule; if the same working condition is not compared, continuously comparing the key word group with the key word group of the next event; if the same working condition is compared, judging whether a negative keyword of the event keyword group is included; if yes, judging that the drilling event corresponding to the event keyword group does not stand; if not, judging whether the keyword group is matched with the drilling working condition recognition splitting result, if so, judging that the drilling event corresponding to the keyword group of the event is established, stopping polling, and storing the matching result into a database in a structuralized manner.

Description

Method and system for intelligently identifying drilling event based on drilling log

Technical Field

The invention relates to the technical field of petroleum drilling development, in particular to a method and a system for intelligently identifying drilling events based on a drilling log.

Background

In the process of petroleum drilling engineering construction, for working conditions such as production scheduling, technical compatibility, safety supervision and the like, field engineers can record in the working content of a drilling log in a time axis narrative form and transmit back to a rear base through an information system.

At present, for the analysis application of DDR (well drilling log) working content, an off-line manual read-back mode of an engineering analyst remains, the analysis efficiency is low, and large-scale data mining cannot be carried out on batch wells; meanwhile, NPT (non-productive time) elements are mainly filled by engineers, and problems of NPT event misinformation, misreport, missing report and the like frequently occur on site, so that potential excavation and efficiency improvement of drilling and completion engineering organization is hindered.

In view of the above, there is a need for a technical solution that can overcome the above-mentioned drawbacks and can analyze well logs quickly and efficiently.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides a method and a system for intelligently identifying a drilling event based on a drilling log. According to the invention, by automatically picking up well site ground shutdown, equipment maintenance, underground complexity and drilling accidents and matching abnormal event time sequence analysis and aging analysis, NPT events can be optimized and analyzed at fixed points, technical managers of drilling engineering can be guided to adapt to technologies, production operators can adapt to materials, and production organization capability and construction performance of drilling and completion can be improved.

In a first aspect of embodiments of the present invention, a method for intelligently identifying a drilling event based on a drilling log is presented, the method comprising:

setting working state information of non-production time of petroleum drilling;

performing regular matching on the working state information of the petroleum drilling non-production time, performing secondary constraint by combining a keyword group of a natural language event of a drilling log, and creating a drilling event keyword group rule;

acquiring a drilling log, and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result;

comparing the drilling working condition recognition and splitting result with the working conditions of the event keyword groups in sequence according to the priority of each event keyword group in the drilling event keyword group rule; wherein,

if the same working condition is not compared, continuously comparing the key word group with the key word group of the next event;

if the same working condition is compared, judging whether the drilling working condition identification split result contains a negative keyword of the event keyword group; wherein,

if the drilling event comprises the negative keyword, judging that the drilling event corresponding to the keyword group of the event does not stand, and continuously comparing the drilling event with the keyword group of the next event;

if not, judging whether the keyword group is matched with the drilling working condition recognition split result, if so, judging that the drilling event corresponding to the keyword group of the event is established, stopping polling, and storing the matching result into a database in a structured manner; if not, the next event keyword group is continuously compared.

In a second aspect of embodiments of the present invention, a system for intelligently identifying drilling events based on a drilling log is presented, the system comprising:

the setting module is used for setting the working state information of the non-production time of the petroleum drilling;

the rule creating module is used for performing regular matching on the working state information of the petroleum drilling non-production time, performing secondary constraint by combining a keyword group of a natural language event of a drilling log, and creating a drilling event keyword group rule;

the log identification and splitting module is used for acquiring a drilling log, and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result;

the drilling event analysis module is used for sequentially comparing the drilling working condition identification and splitting result with the working conditions of the event keyword groups according to the priority of each event keyword group in the drilling event keyword group rule; wherein,

In a third aspect of embodiments of the present invention, a computer device is presented, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a method for intelligently identifying drilling events based on a drilling log when executing the computer program.

In a fourth aspect of embodiments of the present invention, a computer readable storage medium is presented, which stores a computer program that, when executed by a processor, implements a method of intelligently identifying drilling events based on a drilling log.

The method and the system for intelligently identifying the drilling event based on the drilling log can comprehensively track the abnormal working state of the drilling operation in real time, automatically inspect and position the well in the abnormal working state, quickly analyze the drilling construction dynamics of batch drilling and self-defining service dimension, provide production data for managers, assist the managers to timely coordinate drilling tools, drilling fluid materials, drill bits, instruments, equipment, accessories and the like, adapt to the optimal treatment process, improve the overall efficiency and the benefit of the drilling and completion construction management, and provide powerful data support for complex analysis of drilling accidents and multi-dimensional large data analysis.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

FIG. 1 is a flow chart illustrating a method for intelligently identifying drilling events based on a drilling log according to an embodiment of the invention.

FIG. 2 is an interface diagram of a drilling multi-objective operational status application according to an embodiment of the present invention.

FIG. 3 is a schematic diagram of a system architecture for intelligently identifying drilling events based on a drilling log, in accordance with an embodiment of the present invention.

FIG. 4 is a block diagram of a system architecture for intelligently identifying drilling events based on a drilling log, in accordance with an embodiment of the present invention.

Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

Detailed Description

The principles and spirit of the present invention will be described with reference to a number of exemplary embodiments. It is understood that these embodiments are given solely for the purpose of enabling those skilled in the art to better understand and to practice the invention, and are not intended to limit the scope of the invention in any way. 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.

As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, device, method, or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

According to the embodiment of the invention, a method and a system for intelligently identifying a drilling event based on a drilling log are provided, and the technical field of petroleum drilling development is related. The invention intelligently picks up the events such as shaft accidents, complications, equipment maintenance and other organizations from the working content of a drilling log (DDR), is used for intelligently tracking the production organization and technical implementation of the petroleum drilling, and provides basic preparation for developing KPI (key performance indicator), NPT (network performance indicator), accident complex analysis and multidimensional big data analysis of the petroleum drilling.

The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the invention.

FIG. 1 is a flow chart illustrating a method for intelligently identifying drilling events based on a drilling log according to an embodiment of the invention. As shown in fig. 1, the method includes:

and S101, setting working state information of the non-production time of the petroleum drilling.

And S102, performing regular matching on the working state information of the petroleum drilling non-production time, performing secondary constraint by combining a keyword group of a natural language event of a drilling log, and creating a drilling event keyword group rule.

And S103, acquiring a drilling log, and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result.

And step S104, comparing the drilling working condition recognition and splitting result with the working conditions of the event keyword group in sequence according to the priority of each event keyword group in the drilling event keyword group rule.

And step S105, if the same working condition is not compared, continuing to compare with the next event keyword group.

And S106, if the same working condition is compared, judging whether the drilling working condition identification splitting result contains a negative keyword of the event keyword group.

And S107, if the negative keywords are included, judging that the drilling event corresponding to the event keyword group is not established, and continuously comparing the drilling event with the next event keyword group.

And S108, if the negative keywords are not included, judging whether the keyword group is matched with the drilling condition identification splitting result.

And step S109, if the matching is carried out, judging that the drilling event corresponding to the event keyword group is established, stopping polling, and storing the matching result into a database in a structured manner.

Step S110, if not, comparing with the next event keyword group.

Further, the method comprises: merging and splicing the drilling events; the method comprises the following specific processes:

when the drilling condition identification and splitting result shows a drilling log without identifying the drilling event, the working condition is not blank, the drilling well depth is not changed, and the drilling log without identifying the drilling event is defined as the drilling event;

when a drilling condition identification split result has a drilling log with blank working conditions, defining the drilling log as a drilling event;

when the same drilling event exists in the same well and the end time of the previous drilling event is equal to the start time of the next drilling event, the two drilling events are combined into one.

In step S101, the set operating state information of the non-productive time of the oil drilling includes: the aging category, the aging items, the working state and the operation content; wherein,

the aging is mainly non-production time; the aging term at least comprises: complex, other non-production, accident, repair, natural shutdown, organizational shutdown, special shutdown; each aging item comprises a plurality of working states, and each working state is correspondingly provided with operation contents.

In step S102, the drilling event keyword set rules include: priority, drilling event name, keyword group, negative keyword, condition, execution condition, range condition, and condition value.

In step S103, obtaining a well drilling log, and performing working condition identification and splitting according to the well drilling log to obtain a well drilling working condition identification and splitting result, further comprising:

and collecting well drilling logs, and storing the well drilling logs into a background data center Oracle database.

And splitting the drilling log according to the execution conditions, the range conditions and the condition values in the drilling event keyword group rule to obtain one or more drilling working condition identification splitting results.

In step S109, the step of storing the matching result into the database further includes:

the matching result is stored in an Oracle database of a background data center in a structuralized mode;

and when a query instruction is received, querying the drilling log in the database according to the query instruction, the drilling event matched with the drilling log, and the working state and the operation content corresponding to each working condition in the drilling event.

The engineering technician can call the well log, well events and related content in the database as needed.

It should be noted that although the operations of the method of the present invention have been described in the above embodiments and the accompanying drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the operations shown must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

For a more clear explanation of the above method for intelligently identifying drilling events based on a drilling log, a specific embodiment is described below.

And S1, setting the working state information of the petroleum drilling in the non-production time.

The petroleum drilling non-production time working state information comprises: the aging category, the aging items, the working state and the operation content; wherein,

Specifically, the operating state in the "complex case" includes: measuring leakage speed, treating water invasion, measuring leakage point, killing well, etc.

The operating state in the "accident" includes: downhole junk, drill string, etc.

The working conditions in "repair" include: repairing drilling tools, modifying well control equipment, etc.

The working state in "natural shutdown" includes: the shutdown caused by weather, natural disasters, and the like.

The working states in "tissue shutdown" include: waiting for the device, waiting for the material, waiting for the event to be completed, waiting for the event such as contract waiting, pressure testing, instruction waiting, and the like.

The working state in "special shutdown" includes: due to environmental protection shutdown, epidemic situation shutdown, road control, oil-land coordination and the like.

The above examples are used to illustrate the non-productive time working status information of oil drilling, and can be set according to actual situations in practical application.

S2, create DDR natural language accident complex keyword group rules (drilling event keyword group rules).

On the basis of automatic regular matching of the working state standard rule in non-production time, performing secondary constraint by using the DDR natural language accident complex keyword group to create the DDR natural language accident complex keyword group rule.

The DDR natural language accident complex keyword group rule is provided with a priority, an event (accident complex), a keyword (keyword group), a negative keyword, a working condition, an execution condition, a condition range and a condition value.

For example, the highest priority event is: a blowout accident; the key words are: killing the well and overflowing; the negative keywords are: recovering normal construction, normal drilling and drilling, and circulating without overflow; the working conditions are as follows: suppressing pressure, measuring after effect, treating complicated, treating gas invasion, treating overflow and the like; the execution conditions are as follows: recording before; the range conditions are as follows: well depth; the condition values are: 0.

after a blowout accident, the priority according to the event includes: handling overflow, handling stuck pipe, handling falling objects in the well, handling lost circulation, etc. The events are not limited to the above, and may be set and adjusted according to actual situations.

Among them, in the event of "processing lost circulation", there are many kinds of keywords, and the priority of the keywords is set from high to low, including: and (3) finding loss, finding lost circulation, treating lost circulation, generating lost circulation, losing and returning lost circulation of a well head, preparing leakage-stopping slurry, drilling leakage-stopping slurry, injecting leakage-stopping slurry and the like. The keywords are not limited to the above, and may be set and adjusted according to actual situations.

Each keyword is correspondingly provided with a negative keyword, a working condition, an execution condition, a range condition and a condition value.

For example, a negative key for "finding a miss" is: 8:00-8:00 circulation, recovering normal construction and normal drilling; the working conditions are as follows: suppressing pressure, measuring after effect, treating complicatedly, treating borehole wall instability, treating lost circulation; the execution conditions are as follows: recording before; the range conditions are as follows: well depth; the condition values are: 0.

the negative keywords corresponding to the 'slurry plugging and leaking preparation' are as follows: 8:00-8:00 circulation, recovering normal construction and normal drilling; the working conditions are as follows: building pressure, treating complicated, treating well leakage, circulating engineering, closing a well, observing and standing; the execution conditions are as follows: current recording; the range conditions are as follows: well depth; the condition values are: 0.

the above example is used to illustrate the complex keyword group rule of the DDR natural language accident, and can be set according to actual situations in practical applications.

S3, DDR intelligent recognition accident is complex.

In S3, the specific process is:

s301, extracting DDR working condition intelligent recognition results from the Oracle database.

And S302, extracting DDR natural language accident complex keyword group rules.

And S303, judging that the accident is complicated.

In S303, the specific process is:

s3031, aiming at each DDR working condition, intelligently identifying the splitting result, and circularly comparing DDR natural language accident complex keyword group rules.

S3032, matching the working condition in each group of accident complex keyword rules with the working condition in the DDR working condition intelligent recognition split result, and searching whether the group of accident complex keyword rules have the same working condition as the DDR working condition intelligent recognition split result;

if not, matching the rule of the complex keyword group of the next accident;

if yes, continuing to perform identification logic downwards by utilizing the rule;

s3033, obtaining corresponding DDR working condition intelligent recognition split result (S) according to the execution condition, the range condition and the condition value in the accident complex keyword group rule.

S3034, circularly executing the DDR working condition intelligent recognition splitting result set obtained in the step S3033 according to the execution condition in the accident complex keyword group rule, and sequentially judging whether log contents in the splitting result contain negative keywords in the accident complex keyword group rule;

if yes, judging that the complex accident corresponding to the accident complex keyword group does not stand, and judging the rule of the next accident complex keyword group;

if not, making keyword group judgment (forward keyword group); and if the keyword group is matched with the log content in the splitting result, judging that the accident complexity corresponding to the accident complex keyword rule is satisfied, stopping polling, and storing the result into an Oracle database in a structuralized manner.

Furthermore, according to the result, the accident complexity can be merged and spliced.

For example, if logs with no recognized accident complexity occur in the same accident complexity of the same well and the well depth does not change, the logs with no recognized accident complexity are defined as the accident complexity event.

If the log with empty working condition occurs in the same accident complexity (event) of the same well, the log with empty working condition is defined as the accident complexity (event), and the rule does not consider whether the well depth changes or not.

The same accident complex (event) of the same well, the end time of the previous accident complex (event) is equal to the start time of the next accident, and the two accident complexes (events) are merged into one.

For example, 16 oil and gas field companies and 5 drilling enterprises which belong to a certain company are provided with 20DJ-90DJ various drilling machines. By utilizing the method for intelligently identifying the drilling event based on the drilling log, disclosed by the invention, the drilling construction dynamic state can be tracked in real time, and the abnormal progress dynamic monitoring of single-well and multi-well drilling construction can be realized, so that the method is suitable for the application in the drilling well field, the remote operation center, the mobile phone APP and other scenes.

Firstly, acquiring data by using an information system of a drilling and completion project, and automatically and remotely transmitting the data back to an Oracle database of a rear base data center in real time;

and starting the split log of the drilling multi-target working state application software. Referring to fig. 2, an interface diagram of the drilling multi-target operation status application software according to an embodiment of the present invention is shown.

And intelligently identifying multiple accident complex application software by using the well drilling log to pick up the accident complex and develop the user-defined statistical analysis application.

By adopting the method for intelligently identifying the drilling events based on the drilling logs, disclosed by the invention, the abnormal working states of all the working wells governed by an operating company can be comprehensively tracked in real time, the wells in abnormal working states can be automatically patrolled and positioned, the drilling construction dynamics of batch drilling and self-defining service dimensions can be rapidly analyzed, managers can be assisted to timely coordinate production data such as drilling tools, drilling fluid materials, drill bits, instruments, equipment, accessories and the like, an optimal disposal process is adapted, and the overall efficiency and benefit of drilling and completion construction management are improved.

Furthermore, the intelligent identification result can be used for constructing a big data artificial intelligent analysis model, and the intelligent analysis capability of production organization and process technology is improved.

Having described the method of an exemplary embodiment of the present invention, a system for intelligently identifying drilling events based on a drilling log of an exemplary embodiment of the present invention is next described with reference to FIG. 5.

The implementation of the system for intelligently identifying the drilling event based on the drilling log can be referred to the implementation of the method, and repeated details are omitted. The term "module" or "unit" used hereinafter may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.

Based on the same inventive concept, the invention also provides a system for intelligently identifying drilling events based on a drilling log, as shown in fig. 3, the system comprises:

the setting module 110 is used for setting the working state information of the non-production time of the petroleum drilling;

the rule creating module 120 is used for performing regular matching on the working state information of the petroleum drilling non-production time, performing secondary constraint by combining a keyword group of a natural language event of a drilling log, and creating a drilling event keyword group rule;

the log identification and splitting module 130 is used for acquiring a drilling log, and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result;

the drilling event analysis module 140 is configured to compare the drilling condition recognition and splitting result with the conditions of the event keyword groups in sequence according to the priority of each event keyword group in the drilling event keyword group rule; wherein,

In this embodiment, referring to fig. 4, a schematic diagram of a system architecture for intelligently identifying a drilling event based on a drilling log according to an embodiment of the present invention is shown. As shown in fig. 4, the system further includes:

the drilling event processing module 150 is used for defining the drilling log without the drilling event as the drilling event when the drilling condition recognition and splitting result has the drilling log without the drilling event recognized, the working condition is not blank, and the drilling well depth is not changed;

In the embodiment, the set operating state information of the non-productive time of the oil drilling comprises: the aging category, the aging items, the working state and the operation content; wherein,

The drilling event keyword set rules include: priority, drilling event name, keyword group, negative keyword, condition, execution condition, range condition, and condition value.

In this embodiment, the log identification splitting module 130 is specifically configured to:

In this embodiment, referring to fig. 4, the system further includes:

and the log acquisition module 160 is used for acquiring the well drilling logs and storing the well drilling logs into the background data center Oracle database.

In this embodiment, the drilling event analysis module 140 is further configured to:

and storing the matching result into a background data center Oracle database in a structured manner.

In this embodiment, referring to fig. 4, the system further includes:

the query module 170 is configured to receive a query instruction, query the drilling log in the database according to the query instruction, the drilling event matched with the drilling log, and the working state and the operation content corresponding to each working condition in the drilling event.

It should be noted that although several modules of the system for intelligently identifying drilling events based on a drilling log are mentioned in the detailed description above, such partitioning is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the modules described above may be embodied in one module according to embodiments of the invention. Conversely, the features and functions of one module described above may be further divided into embodiments by a plurality of modules.

Based on the aforementioned inventive concept, as shown in fig. 5, the present invention further proposes a computer device 500, which comprises a memory 510, a processor 520 and a computer program 530 stored on the memory 510 and executable on the processor 520, wherein the processor 520 executes the computer program 530 to implement the aforementioned method for intelligently identifying a drilling event based on a drilling log.

Based on the foregoing inventive concept, the present invention proposes a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the aforementioned method for intelligently identifying drilling events based on a drilling log.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for intelligently identifying drilling events based on a drilling log, the method comprising:

setting working state information of non-production time of petroleum drilling;

2. The method of claim 1, wherein the method comprises:

3. The method of claim 1, wherein the set operating state information for non-productive time of oil drilling comprises: the aging category, the aging items, the working state and the operation content; wherein,

4. The method of claim 1, wherein the drilling event keyword set rules comprise: priority, drilling event name, keyword group, negative keyword, condition, execution condition, range condition, and condition value.

5. The method of claim 4, wherein the method for intelligently identifying the drilling event based on the drilling log comprises the steps of obtaining the drilling log, and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result, and further comprises:

6. The method for intelligently identifying drilling events based on the drilling log as claimed in claim 1, wherein the step of obtaining the drilling log and performing working condition identification and splitting according to the drilling log to obtain a drilling working condition identification and splitting result comprises the following steps:

7. The method for intelligently identifying drilling events based on a drilling log of claim 5, wherein the matching results are structured into a database, further comprising:

8. A system for intelligently identifying drilling events based on a drilling log, the system comprising:

9. The system for intelligently identifying drilling events based on a drilling log of claim 8, further comprising:

the drilling event processing module is used for defining the drilling log without the drilling event identified as the drilling event when the drilling log without the drilling event identified appears in the drilling working condition identification and splitting result, the working condition is not blank, and the drilling well depth is not changed;

10. The system for intelligently identifying drilling events based on a drilling log of claim 8, wherein the set operating state information for non-productive time of oil drilling comprises: the aging category, the aging items, the working state and the operation content; wherein,

11. The system for intelligently identifying drilling events based on a drilling log of claim 8, wherein the drilling event keyword set rules comprise: priority, drilling event name, keyword group, negative keyword, condition, execution condition, range condition, and condition value.

12. The system for intelligently identifying drilling events based on a drilling log of claim 11, wherein the log identification splitting module is specifically configured to:

13. The system for intelligently identifying drilling events based on a drilling log of claim 8, further comprising:

and the log acquisition module is used for acquiring the well drilling log and storing the well drilling log into the background data center Oracle database.

14. The system for intelligently identifying drilling events based on a drilling log of claim 12, wherein the drilling event analysis module is further configured to:

15. The system for intelligently identifying drilling events based on a drilling log of claim 8, further comprising:

and the query module is used for receiving a query instruction, querying the drilling log in the database according to the query instruction, the drilling event matched with the drilling log, and the working state and the operation content corresponding to each working condition in the drilling event.

16. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 7 when executing the computer program.

17. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the method of any one of claims 1 to 7.