CN114897225A - Accident prediction method and device for drilling operation, electronic device and storage medium - Google Patents

Accident prediction method and device for drilling operation, electronic device and storage medium Download PDF

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CN114897225A
CN114897225A CN202210437431.9A CN202210437431A CN114897225A CN 114897225 A CN114897225 A CN 114897225A CN 202210437431 A CN202210437431 A CN 202210437431A CN 114897225 A CN114897225 A CN 114897225A
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龙威
黄瑞
田愉杰
熊钊
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Icore Shenzhen Energy Technology Co ltd
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Abstract

The embodiment of the disclosure provides an accident prediction method and device for drilling operation, electronic equipment and a storage medium, and relates to the technical field of engineering abnormity detection. The accident prediction method for the drilling operation comprises the following steps: acquiring standardized safety data of drilling operation; constructing a big data regression model according to the well drilling operation normalized safety data; acquiring real-time data of a drilling operation target; according to the technical scheme provided by the embodiment of the disclosure, the prediction efficiency of the accident in the drilling operation can be improved.

Description

Accident prediction method and device for drilling operation, electronic device and storage medium
Technical Field
The invention relates to the technical field of engineering anomaly detection, in particular to an accident prediction method and device for drilling operation, electronic equipment and a storage medium.
Background
In the drilling engineering operation, the safety of the drilling operation needs to be pre-warned, such as pre-warning of a stuck drilling accident. The current early warning method comprises the following steps: the depth of a construction well is marked based on historical data of adjacent wells under similar geological structures, drilling, mud parameters and downhole drilling tool feedback data are monitored in real time during operation, and preliminary prejudgment is carried out under the condition of abnormal data.
The current early warning method highly depends on historical data of a reference well and professional experience of engineering technicians, has certain subjectivity and hysteresis, and causes low prediction efficiency of accidents in drilling operation.
Disclosure of Invention
The main purpose of the embodiments of the present disclosure is to provide an accident prediction method and apparatus for drilling operation, an electronic device, and a storage medium, which can improve the prediction efficiency of an accident in drilling operation.
To achieve the above object, a first aspect of an embodiment of the present disclosure provides an accident prediction method for drilling operation, including:
acquiring standardized safety data of drilling operation;
constructing a big data regression model according to the well drilling operation normalized safety data;
acquiring real-time data of a drilling operation target;
and performing accident prediction on the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
In some embodiments, the obtaining drilling operation normalization safety data comprises:
acquiring historical safety data of drilling operation;
and carrying out data cleaning processing on the drilling operation historical safety data to obtain the drilling operation standardized safety data.
In some embodiments, the building a big data regression model from the drilling operation normalized safety data comprises:
performing characteristic engineering processing on the well drilling operation normalized safety data to obtain characteristic parameters;
and carrying out big data regression training on the characteristic parameters to obtain a big data regression model.
In some embodiments, the acquiring real-time drilling operation target data comprises:
acquiring original real-time data of drilling operation;
and performing suspicious data elimination processing on the original real-time data of the drilling operation according to a Lauda method to obtain the real-time data of the drilling operation target.
In some embodiments, the performing the accident prediction on the real-time drilling operation target data according to the big data regression model to obtain the early warning information includes:
inputting the real-time data of the drilling operation target into the big data regression model to obtain parameter prediction interval data;
and obtaining early warning information according to the real-time data of the drilling operation target and the parameter prediction interval data.
In some embodiments, the inputting the real-time drilling operation target data into the big data regression model to obtain parameter prediction interval data comprises:
obtaining a prediction window value;
inputting the real-time data of the drilling operation target into the big data regression model;
and predicting the real-time data of the drilling operation target through the big data regression model, and setting a sliding window according to the prediction window value to obtain the parameter prediction interval data corresponding to the prediction window value.
In some embodiments, the method further comprises:
generating accident reason analysis data according to the early warning information and a preset accident experience record;
and generating intervention information of the drilling operation according to the accident reason analysis data and a preset manual intervention flow record.
To achieve the above object, a second aspect of the present disclosure provides an accident prediction apparatus for a drilling operation, including:
the normalized safety data acquisition module is used for acquiring the normalized safety data of the drilling operation;
the big data regression model building module is used for building a big data regression model according to the well drilling operation normalized safety data;
the target real-time data acquisition module is used for acquiring real-time data of a drilling operation target;
and the accident prediction module is used for predicting accidents of the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
To achieve the above object, a third aspect of the present disclosure provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in a memory and a processor executes the at least one program to implement the method of the present disclosure as described in the above first aspect.
To achieve the above object, a fourth aspect of the present disclosure proposes a storage medium that is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform:
a method as described in the first aspect above.
According to the accident prediction method and device for the drilling operation, the electronic equipment and the storage medium, the normalized safety data of the drilling operation are firstly obtained, then the big data regression model is built according to the normalized safety data of the drilling operation, the real-time data of the drilling operation target is further obtained, finally the accident prediction is carried out on the real-time data of the drilling operation target according to the big data regression model, and the early warning information is obtained.
Drawings
Fig. 1 is a flow chart of an incident prediction method of a drilling operation provided by an embodiment of the present disclosure.
Fig. 2 is a flowchart of step S110 in fig. 1.
Fig. 3 is a flowchart of step S120 in fig. 1.
Fig. 4 is a flowchart of step S130 in fig. 1.
Fig. 5 is a flowchart of step S140 in fig. 1.
Fig. 6 is a flowchart of step S510 in fig. 5.
Fig. 7 is a partial flow diagram of an accident prediction method for drilling operations according to another embodiment of the present disclosure.
Fig. 8 is a block diagram of an accident prediction apparatus for drilling operations provided by an embodiment of the present disclosure.
Fig. 9 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present disclosure.
Reference numerals: the system comprises a normalized safety data acquisition module 810, a big data regression model construction module 820, a target real-time data acquisition module 830, an accident prediction module 840, a processor 901, a memory 902, an input/output interface 903, a communication interface 904 and a bus 905.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
It should be noted that although functional blocks are partitioned in a schematic diagram of an apparatus and a logical order is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the partitioning of blocks in the apparatus or the order in the flowchart. The terms first, second and the like in the description and in the claims, and the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, 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 belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
In the drilling engineering operation, the safety of the drilling operation needs to be pre-warned, such as pre-warning of a stuck drilling accident. The current early warning method comprises the following steps: the depth of the construction well is marked based on historical data of adjacent wells in similar geological structures, drilling well, mud parameters and downhole drilling tool feedback data are monitored in real time in operation, and preliminary prejudgment is carried out under the condition that the data are abnormal.
The current early warning method highly depends on historical data of a reference well and professional experience of engineering technicians, has certain subjectivity and hysteresis, and causes low prediction efficiency of accidents in drilling operation.
Based on this, the embodiment of the disclosure provides an accident prediction method and device for drilling operation, an electronic device, and a storage medium, wherein drilling operation normalized safety data is obtained first, then a big data regression model is constructed according to the drilling operation normalized safety data, further, drilling operation target real-time data is obtained, and finally, accident prediction is performed on the drilling operation target real-time data according to the big data regression model, so that early warning information is obtained.
The embodiment of the present disclosure provides an accident prediction method and apparatus for drilling operation, an electronic device, and a storage medium, which are specifically described in the following embodiments.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The embodiment of the disclosure provides an accident prediction method for drilling operation, and relates to the technical field of engineering anomaly detection. The accident prediction method for the drilling operation, provided by the embodiment of the disclosure, can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smartphone, tablet, laptop, desktop computer, smart watch, or the like; the server can be an independent server, and can also be a cloud server for providing basic cloud computing services such as cloud service, a cloud database, cloud computing, cloud functions, cloud storage, Network service, cloud communication, middleware service, domain name service, security service, Content Delivery Network (CDN), big data and artificial intelligence platforms and the like; the software may be, but is not limited to, an application of an accident prediction method to implement a drilling operation, or the like.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiment of the disclosure provides an accident prediction method for drilling operation, which comprises the following steps: acquiring standardized safety data of drilling operation; constructing a big data regression model according to the drilling operation normalized safety data; acquiring real-time data of a drilling operation target; and (4) performing accident prediction on the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
Fig. 1 is an optional flowchart of an accident prediction method for drilling operation according to an embodiment of the present disclosure, where the method in fig. 1 may include, but is not limited to, steps S110 to S140, and specifically includes:
s110, acquiring standardized safety data of drilling operation;
s120, constructing a big data regression model according to the well drilling operation normalized safety data;
s130, acquiring real-time data of a drilling operation target;
and S140, performing accident prediction on the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
In step S110, the drilling operation normalized safety data is data of the drilling operation after data cleaning, and is used for building a big data regression model.
In step S120, the big data regression model is constructed from the well drilling operation normalized safety data, thereby ensuring correlation between important parameters and ensuring the rationality of accident prediction, such as the rationality of parameter prediction interval data.
In step S130, the real-time drilling operation target data is the original real-time drilling operation data subjected to the suspicious data elimination processing, and is used for inputting the trained big data regression model to realize accident prediction.
In step S140, the warning information is a warning about parameter changes of the drilling operation, and the warning information includes, but is not limited to, normal parameters and abnormal parameters, for example, the contents of the warning information are: "average weight-on-bit value is abnormal".
According to the accident prediction method for the drilling operation, the normalized safety data of the drilling operation are firstly obtained, then the big data regression model is built according to the normalized safety data of the drilling operation, the real-time data of the drilling operation target is further obtained, and finally the accident prediction is carried out on the real-time data of the drilling operation target according to the big data regression model to obtain the early warning information.
In some embodiments, obtaining drilling operation normalization safety data comprises: acquiring historical safety data of drilling operation; and cleaning the data of the historical safety data of the drilling operation to obtain the standardized safety data of the drilling operation.
Fig. 2 is a flow chart of step S110 in some embodiments, and step S110 illustrated in fig. 2 includes, but is not limited to, steps S210 to S220:
s210, obtaining historical safety data of drilling operation;
and S220, performing data cleaning processing on the historical safety data of the drilling operation to obtain the standardized safety data of the drilling operation.
In step S210, the drilling operation history safety data is data of safety operations in which no accident has occurred in the history of drilling operations, for example, relevant parameters in the drilling data of a stuck-bit-free accident, and specifically, the drilling operation history safety data includes, but is not limited to, WOBA (weight on bit Average), MFIA (Mud Flow Inlet Average), DMEA (depth measured Average), TQA (torque Average, Average rotating disc torque), SPPA (Average riser pressure), and the like.
In step S220, the drilling operation normalized safety data is the drilling operation data after data cleaning, and the drilling operation historical safety data is subjected to normalized cleaning and sorting to meet the subsequent model calculation requirements.
In some embodiments, constructing a big data regression model from the drilling operation normalized safety data includes: performing characteristic engineering processing on the well drilling operation normalized safety data to obtain characteristic parameters; and carrying out big data regression training on the characteristic parameters to obtain a big data regression model.
Fig. 3 is a flow chart of step S120 in some embodiments, and step S120 illustrated in fig. 3 includes, but is not limited to, steps S310 to S320:
s310, performing characteristic engineering processing on the well drilling operation normalized safety data to obtain characteristic parameters;
and S320, performing big data regression training on the characteristic parameters to obtain a big data regression model.
In step S310, after the cleaned drilling operation normalized safety data is obtained, feature engineering processing is required, where the feature engineering processing includes, but is not limited to, feature correlation analysis and feature importance analysis, and a target for performing the feature engineering processing on the drilling operation normalized safety data is to extract feature parameters, and the feature parameters are used in a subsequent big data regression model building process.
The purpose of feature engineering processing is to extract features from raw data to the maximum extent for use by algorithms and models, and feature processing is a core part of feature engineering, and specifically, feature engineering processing of the present application can be completed by using a feature processing method provided by a machine learning tool sklern, where the feature processing method includes specific steps of data preprocessing, feature selection, dimension reduction, and the like.
In step S320, after the characteristic parameters are obtained, the characteristic parameters are trained by using a random forest algorithm to obtain a big data regression model, and the big data regression model is used in the prediction process, where it should be noted that the random forest algorithm has the characteristics of no need of characteristic normalization, parallelization, high regression fitting degree, short program time consumption, and the like, and is suitable for training the characteristic parameters obtained by normalizing the safety data in the drilling operation.
In some embodiments, obtaining drilling operation target real-time data comprises: acquiring original real-time data of drilling operation; and performing suspicious data elimination processing on the original real-time data of the drilling operation according to the Lauda rule to obtain the target real-time data of the drilling operation.
Fig. 4 is a flowchart of step S130 in some embodiments, and step S130 illustrated in fig. 4 includes, but is not limited to, step S410 to step S420:
s410, acquiring original real-time data of drilling operation;
and S420, performing suspicious data elimination processing on the original real-time data of the drilling operation according to the Lauda rule to obtain the target real-time data of the drilling operation.
In step S410, the raw real-time data of the drilling operation is the raw real-time data collected in the historical operation of the drilling operation.
In step S420, the suspicious data removing process is to remove suspicious data from the original real-time data of the drilling operation, so as to ensure the accuracy of the real-time data, and thus ensure the accuracy of the prediction. It should be noted that the law of Layda specifically includes: assuming that a group of detection data only contains random errors, calculating the group of data to obtain a standard deviation, determining an interval according to a certain probability, wherein the errors exceeding the interval do not belong to the random errors but are gross errors, and the data containing the errors are removed.
In some embodiments, performing accident prediction on real-time data of a drilling operation target according to a big data regression model to obtain early warning information, including: inputting real-time data of a drilling operation target into a big data regression model to obtain parameter prediction interval data; and obtaining early warning information according to the real-time data of the drilling operation target and the parameter prediction interval data.
Fig. 5 is a flowchart of step S140 in some embodiments, and step S140 illustrated in fig. 5 includes, but is not limited to, step S510 to step S520:
s510, inputting real-time data of a drilling operation target into a big data regression model to obtain parameter prediction interval data;
and S520, obtaining early warning information according to the real-time data of the drilling operation target and the parameter prediction interval data.
In step S510, the real-time data of the drilling operation target obtained after the real-time acquisition and the suspicious data elimination are input into a big data regression model which is constructed and trained to predict, so as to obtain parameter prediction interval data, where the meaning of the parameter prediction interval data is: and taking the acquisition time of the real-time data of the drilling operation target as a reference, and setting a reasonable range of parameters in a section of interval after the time reference.
Specifically, for example, for the drilling operation target real-time data collected for "5-10 kn per centimeter diameter", a reasonable range of the average weight-on-bit over a time interval of "12 hours 00 minutes 01 seconds to 12 hours 00 minutes 59 seconds" is "5 to 10 kn per centimeter diameter".
In step S520, if the real-time data of the drilling operation target does not fall within the interval range of the parameter prediction interval data, generating early warning information to warn the constructor and guide the subsequent reason analysis and manual intervention process.
Specifically, if the real-time data of the drilling operation target is that the real-time acquired value of the average weight-on-bit is '11 kilonewtons per centimeter diameter', and the reasonable range of the parameter prediction interval data of the average weight-on-bit is '5 to 10 kilonewtons per centimeter diameter', generating early warning information, wherein the content of the early warning information is as follows: "average weight-on-bit value is abnormal".
In a specific embodiment, with reference to fig. 4 to 5, it can be known that the accident prediction method for drilling operation provided by the present application utilizes real-time data in drilling engineering, and combines with a ralda criterion and a sliding time window manner, so as to more accurately predict a reasonable interval of the relevant real-time data.
In some embodiments, inputting the real-time data of the drilling operation target into a big data regression model to obtain parameter prediction interval data comprises: obtaining a prediction window value; inputting real-time data of a drilling operation target into a big data regression model; and predicting real-time data of the drilling operation target through a big data regression model, and setting a sliding window according to a prediction window value to obtain parameter prediction interval data corresponding to the prediction window value.
Fig. 6 is a flowchart of step S510 in some embodiments, and step S510 illustrated in fig. 6 includes, but is not limited to, step S610 to step S630:
s610, obtaining a prediction window value;
s620, inputting the real-time data of the drilling operation target into a big data regression model;
and S630, predicting the real-time data of the drilling operation target through the big data regression model, and setting a sliding window according to the prediction window value to obtain parameter prediction interval data corresponding to the prediction window value.
In step S610, the purpose of the prediction window value is to define the length of the resulting prediction interval, such as when the prediction window value is "5 minutes", the parameter prediction interval data is "12 hours 00 minutes to 12 hours 05 minutes" at a certain time.
In steps S620 to S630, the real-time data of the drilling operation target is input into the trained big data regression model, and according to the prediction window value setting of "5 minutes", the parameter prediction interval data generated at a certain time is "12 hours 00 minutes to 12 hours 05 minutes", after one minute, the parameter prediction interval data and the test window slide for 1 minute along the positive direction of the time line, at this time, the parameter prediction interval data generated is "12 hours 01 minutes to 12 hours 06 minutes", and through the mechanism of the sliding window, the parameter prediction in a specific time interval in the future is realized, so that the probability of occurrence of drilling sticking accidents is reduced, the economic loss is reduced, and the safe development of the drilling operation is ensured.
In some embodiments, the method further comprises: generating accident reason analysis data according to the early warning information and a preset accident experience record; and generating intervention information of the drilling operation according to the accident reason analysis data and a preset manual intervention flow record.
As shown in fig. 7, fig. 7 is a flowchart of an accident prediction method for a drilling operation according to another embodiment, where the accident prediction method for a drilling operation further includes:
s710, generating accident reason analysis data according to the early warning information and preset accident experience records;
and S720, generating intervention information of the drilling operation according to the accident reason analysis data and the preset manual intervention flow records.
In step S710, the early warning information includes, but is not limited to, normal parameters and abnormal parameters, and when the parameters are abnormal, matching is performed in a preset accident experience record according to the content of the specific parameter abnormality pointed by the early warning information to obtain accident cause analysis data, where the accident cause analysis data is a cause of the parameter abnormality of the early warning at this time.
In step S720, after the accident cause analysis data is obtained, the job intervention information in the manual intervention flow record is matched for the specific cause of the occurrence of the abnormality, wherein the job intervention information is an intervention means for handling or dealing with the abnormality.
Specifically, if the warning information is "average bit pressure is too high" in step S710, the matched accident cause analysis data is "drill bit slipping or scraper blade breaking", and at this time, the matched operation intervention information in step S720 is "stop operation, pull-out to overhaul the drill bit".
In a specific embodiment, the accident prediction method for drilling operation not only realizes accident prediction and generates early warning information, but also realizes source tracing analysis on the early warning information and generates operation intervention information so as to implement necessary preventive measures to process accidents and reduce the probability of subsequent drilling sticking accidents.
The embodiment of the present disclosure provides an accident prediction device for drilling operation, including: the normalized safety data acquisition module is used for acquiring the normalized safety data of the drilling operation; the big data regression model building module is used for building a big data regression model according to the well drilling operation normalized safety data; the target real-time data acquisition module is used for acquiring real-time data of a drilling operation target; and the accident prediction module is used for predicting accidents of the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
Referring to fig. 8, fig. 8 illustrates an accident prediction apparatus for drilling operation according to an embodiment, the accident prediction apparatus for drilling operation including: the system comprises a normalized safety data acquisition module 810, a big data regression model construction module 820, a target real-time data acquisition module 830 and an accident prediction module 840. The normalized safety data obtaining module 810 is connected with the big data regression model building module 820, the big data regression model building module 820 is connected with the target real-time data obtaining module 830, and the target real-time data obtaining module 830 is connected with the accident prediction module 840.
In a specific embodiment, the normalized safety data acquisition module acquires the well drilling operation normalized safety data, the big data regression model construction module constructs a big data regression model according to the well drilling operation normalized safety data, the target real-time data acquisition module acquires real-time target data of the well drilling operation, and the accident prediction module performs accident prediction on the real-time target data of the well drilling operation according to the big data regression model to obtain early warning information.
The specific implementation of the accident prediction apparatus for drilling operation of this embodiment is substantially the same as the specific implementation of the accident prediction method for drilling operation described above, and belongs to the same inventive concept, and is not described herein again.
An embodiment of the present disclosure further provides an electronic device, including:
at least one memory;
at least one processor;
at least one program;
the program is stored in the memory and the processor executes the at least one program to implement the disclosed incident prediction method for performing the drilling operation described above. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a Personal Digital Assistant (PDA for short), a vehicle-mounted computer and the like.
Referring to fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
the processor 901 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, and is configured to execute a relevant program to implement the technical solution provided by the embodiment of the present disclosure;
the memory 902 may be implemented in a form of a ROM (read only memory), a static storage device, a dynamic storage device, or a RAM (random access memory). The memory 902 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present disclosure is implemented by software or firmware, the relevant program codes are stored in the memory 902 and called by the processor 901 to execute the accident prediction method of the drilling operation of the embodiments of the present disclosure;
an input/output interface 903 for implementing information input and output;
a communication interface 904, configured to implement communication interaction between the device and another device, where communication may be implemented in a wired manner (e.g., USB, network cable, etc.), or in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.); and
a bus 905 that transfers information between various components of the device (e.g., the processor 901, the memory 902, the input/output interface 903, and the communication interface 904);
wherein the processor 901, the memory 902, the input/output interface 903 and the communication interface 904 enable a communication connection within the device with each other through a bus 905.
The disclosed embodiments also provide a storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the above-described method of incident prediction for a drilling operation.
According to the accident prediction method and device for the drilling operation, the electronic equipment and the storage medium, the normalized safety data of the drilling operation are firstly obtained, then the big data regression model is built according to the normalized safety data of the drilling operation, the real-time data of the drilling operation target is further obtained, finally the accident prediction is carried out on the real-time data of the drilling operation target according to the big data regression model, the early warning information is obtained, and the prediction efficiency of the accident in the drilling operation is improved through the technical scheme provided by the embodiment of the disclosure.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present disclosure are for more clearly illustrating the technical solutions of the embodiments of the present disclosure, and do not constitute a limitation to the technical solutions provided in the embodiments of the present disclosure, and it is obvious to those skilled in the art that the technical solutions provided in the embodiments of the present disclosure are also applicable to similar technical problems with the evolution of technology and the emergence of new application scenarios.
It will be appreciated by those skilled in the art that the solutions shown in fig. 1-7 are not intended to limit the embodiments of the present disclosure, and may include more or fewer steps than those shown, or some of the steps may be combined, or different steps.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and the above-described figures, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" is used to describe the association relationship of the associated object, indicating that there may be three relationships, for example, "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b and c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing programs, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, and therefore do not limit the scope of the claims of the embodiments of the present disclosure. Any modifications, equivalents and improvements within the scope and spirit of the embodiments of the present disclosure should be considered within the scope of the claims of the embodiments of the present disclosure by those skilled in the art.

Claims (10)

1. A method of predicting an incident for a drilling operation, comprising:
acquiring standardized safety data of drilling operation;
constructing a big data regression model according to the well drilling operation normalized safety data;
acquiring real-time data of a drilling operation target;
and performing accident prediction on the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
2. The method of claim 1, wherein the obtaining drilling operation normalization safety data comprises:
acquiring historical safety data of drilling operation;
and carrying out data cleaning processing on the drilling operation historical safety data to obtain the drilling operation standardized safety data.
3. The method of claim 1, wherein the constructing a big data regression model from the drilling operation normalized safety data comprises:
performing characteristic engineering processing on the well drilling operation normalized safety data to obtain characteristic parameters;
and carrying out big data regression training on the characteristic parameters to obtain a big data regression model.
4. The method of claim 1, wherein the acquiring drilling operation target real-time data comprises:
acquiring original real-time data of drilling operation;
and performing suspicious data elimination processing on the original real-time data of the drilling operation according to a Lauda method to obtain the target real-time data of the drilling operation.
5. The method of claim 1, wherein the predicting the accident of the real-time data of the drilling operation target according to the big data regression model to obtain early warning information comprises:
inputting the real-time data of the drilling operation target into the big data regression model to obtain parameter prediction interval data;
and obtaining early warning information according to the real-time data of the drilling operation target and the parameter prediction interval data.
6. The method of claim 5, wherein the inputting the drilling operation target real-time data into the big data regression model to obtain parametric prediction interval data comprises:
obtaining a prediction window value;
inputting the real-time data of the drilling operation target into the big data regression model;
and predicting the real-time data of the drilling operation target through the big data regression model, and setting a sliding window according to the prediction window value to obtain the parameter prediction interval data corresponding to the prediction window value.
7. The method according to any one of claims 1 to 6, further comprising:
generating accident reason analysis data according to the early warning information and a preset accident experience record;
and generating intervention information of drilling operation according to the accident reason analysis data and a preset manual intervention flow record.
8. An accident prediction device for a drilling operation, comprising:
the normalized safety data acquisition module is used for acquiring the normalized safety data of the drilling operation;
the big data regression model building module is used for building a big data regression model according to the well drilling operation normalized safety data;
the target real-time data acquisition module is used for acquiring real-time data of a drilling operation target;
and the accident prediction module is used for predicting accidents of the real-time data of the drilling operation target according to the big data regression model to obtain early warning information.
9. An electronic device, comprising:
at least one memory;
at least one processor;
at least one program;
the programs are stored in a memory, and a processor executes the at least one program to implement:
the method of any one of claims 1 to 7.
10. A storage medium that is a computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform:
the method of any one of claims 1 to 7.
CN202210437431.9A 2022-04-22 2022-04-22 Accident prediction method and device for drilling operation, electronic device and storage medium Pending CN114897225A (en)

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