CN115983135B - Method and system for evaluating service life of deep sea Christmas tree oil pipe hanger - Google Patents
Method and system for evaluating service life of deep sea Christmas tree oil pipe hanger Download PDFInfo
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Abstract
The invention relates to the field of deep sea Christmas tree equipment, and particularly discloses a service life assessment method of a deep sea Christmas tree oil pipe hanger, which comprises the following steps: acquiring a plurality of different marine environment data in a preset range around the oil pipe hanger; inputting a plurality of different marine environment data into a first model to obtain first predicted values corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger; inputting a plurality of different marine environment data into a second model to obtain second predicted values corresponding to each marine environment data, wherein each second predicted value represents a predicted remaining life value of the tubing hanger based on a corresponding marine environment data; and obtaining the final residual life value of the oil pipe hanger based on the weighted summation of the first predicted value and the second predicted value corresponding to each marine environment data.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of deep-sea Christmas tree equipment, in particular to a service life assessment method and system for a deep-sea Christmas tree oil pipe hanger.
Background
The current deep sea christmas tree equipment is the key equipment for offshore underwater oil and gas exploitation. Because the deep sea christmas tree equipment is positioned in the deep sea, such as the sea water below 500 meters, the marine environment is severe and complex, so that the operation safety of the deep sea christmas tree equipment is an important technology.
In the related art, safety protection is mainly carried out based on dynamic monitoring and diagnosis of various underwater hydraulic valves, meters and the like of a deep sea christmas tree, but an oil pipe hanger is omitted, the oil pipe hanger is a device for supporting an oil pipe column and sealing an annular space between the oil pipe and a casing, and the oil pipe hanger is a pressure-bearing member of an underwater wellhead core, and the performance of the oil pipe hanger determines whether an oil gas exploitation system can continuously run safely and reliably. There is therefore a need for a solution that enables accurate assessment of the life of tubing hangers in order to improve the operational reliability and safety of deep sea christmas trees.
Disclosure of Invention
To solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a method and a system for evaluating the lifetime of a deep sea tree tubing hanger.
In a first aspect, an embodiment of the present disclosure provides a method for evaluating life of a deep sea tree tubing hanger, including:
Acquiring a plurality of different marine environment data within a preset range around the oil pipe hanger;
inputting the plurality of different marine environment data into a first model to obtain a first predicted value corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger; the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data;
inputting the plurality of different marine environmental data into a second model to obtain a second predicted value corresponding to each of the marine environmental data, each of the second predicted values representing a predicted remaining life value of the tubing hanger based on a corresponding one of the marine environmental data; wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance;
and obtaining a final residual life value of the oil pipe hanger based on the weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data.
In one embodiment, the training process of the second model includes:
inputting the plurality of different sample marine environment data into a first machine learning model to output a predicted value corresponding to each sample marine environment data;
inputting the plurality of different sample marine environment data into a second machine learning model to output another predicted value corresponding to each sample marine environment data;
repeatedly updating model parameters of the second machine learning model based on a predicted value corresponding to each sample marine environment data, another predicted value corresponding to each sample marine environment data and a target loss function for a plurality of times until the loss value of the target loss function meets a preset threshold condition, and ending the training process; the target loss function indicates a difference between a specified value corresponding to each sample marine environment data and another predicted value corresponding to each sample marine environment data output by the second machine learning model in each iterative updating process, and each specified value is a product of the predicted value corresponding to each sample marine environment data and the another predicted value.
In one embodiment, the plurality of different marine environmental data includes any one or more of water pressure, water temperature, water quality, seawater flow rate, internal wave environment.
In one embodiment, the method further comprises:
judging whether the final residual life value of the oil pipe hanger is smaller than a preset life value or not;
generating prompt information when the final remaining life value of the oil pipe hanger is smaller than the preset life value;
and sending the prompt information to a control system of the deep sea christmas tree so as to enable the control system to make corresponding control operation.
In one embodiment, the method further comprises:
acquiring material data of the oil pipe hanger;
determining a corrosion degree value of the tubing hanger based on the water temperature, water quality, seawater flow rate and the material data;
and correcting the final residual life value of the tubing hanger based on the corrosion degree value of the tubing hanger to obtain a corrected residual life value.
In one embodiment, said modifying the final remaining life value of the tubing hanger based on the corrosion level value of the tubing hanger to obtain a modified remaining life value comprises:
when the corrosion degree value is determined to be larger than a first threshold value and smaller than or equal to a second threshold value, reducing the final residual life value by a first value to obtain a corrected residual life value, wherein the second threshold value is larger than the first threshold value;
When the corrosion degree value is determined to be larger than the second threshold value and smaller than or equal to a third threshold value, reducing the final residual life value by a second value, and correcting the residual life value, wherein the third threshold value is larger than the second threshold value, and the second value is larger than the first value;
and when the corrosion degree value is determined to be larger than a third threshold value and smaller than or equal to a fourth threshold value, reducing the final residual life value by a third value to obtain a corrected residual life value, wherein the fourth threshold value is larger than the third threshold value, and the third value is larger than the second value.
In one embodiment, the water temperature, water quality, seawater flow rate, and the material data and the corrosion degree value of the tubing hanger satisfy a preset functional relationship, and the preset functional relationship is determined in advance through experimental fitting.
In a second aspect, embodiments of the present disclosure provide a deep sea tree tubing hanger life assessment system comprising:
the data acquisition module is used for acquiring a plurality of different marine environment data in a preset range around the oil pipe hanger;
the first prediction module is used for inputting the plurality of different marine environment data into a first model to obtain a first predicted value corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger; the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data;
A second prediction module for inputting the plurality of different marine environment data into a second model to obtain a second predicted value corresponding to each of the marine environment data, each of the second predicted values representing a predicted remaining life value of the tubing hanger based on a corresponding one of the marine environment data; wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance;
and the data processing module is used for obtaining a final residual life value of the oil pipe hanger based on weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data.
In a third aspect, embodiments of the present disclosure provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a deep sea tree tubing hanger life assessment method according to any of the above embodiments.
In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:
a processor; and
a memory for storing a computer program;
wherein the processor is configured to perform the deep sea tree tubing hanger life assessment method of any of the embodiments described above via execution of the computer program.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the deep sea Christmas tree tubing hanger service life assessment method, a plurality of different marine environment data in a preset range around the tubing hanger are obtained; inputting a plurality of different marine environment data into a first model to obtain a first predicted value corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger, and the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data; inputting a plurality of different marine environment data into a second model to obtain a second predicted value corresponding to each marine environment data, wherein each second predicted value represents a predicted remaining life value of the tubing hanger based on a corresponding marine environment data, and the second model is obtained by training a second machine learning model based on a plurality of different sample marine environment data in advance; and obtaining the final residual life value of the tubing hanger based on the weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data. In this way, in this embodiment, a second predicted value corresponding to each marine environmental data, that is, a remaining life value, is obtained by inputting a plurality of different marine environmental data based on a second model obtained by pre-training, then a first predicted value corresponding to each marine environmental data, that is, a degree of influence, that is, a weight, of the marine environment identified by the marine environmental data on the life of the oil pipe hanger is obtained by inputting a plurality of different marine environmental data based on a first model obtained by pre-training, and finally a final remaining life value of the oil pipe hanger is obtained by weighted summation based on the first predicted value corresponding to each marine environmental data and the second predicted value, so that the life of the oil pipe hanger can be accurately estimated, so as to improve the reliability and safety of operation of the deep-sea oil tree.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a method for evaluating the life of a deep sea tree tubing hanger according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a training process for a second model in an embodiment of the present disclosure;
FIG. 3 is a flow chart of a method for evaluating the life of a deep sea tree tubing hanger according to yet another embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a deep sea tree tubing hanger life assessment system according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be understood that, hereinafter, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe association relationships of associated objects, meaning that there may be three relationships, e.g., "a and/or B" may mean: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). 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, c may be single or plural.
FIG. 1 is a flowchart of a method for deep sea tree tubing hanger life assessment, which may be performed by, but is not limited to, a computing device such as a control device for a deep sea tree, in accordance with an embodiment of the present disclosure. The deep sea tree tubing hanger life assessment method may include the steps of:
Step S101: and acquiring a plurality of different marine environment data in a preset range around the tubing hanger.
The predetermined range around the tubing hanger may be, for example, within 100 meters around, but is not limited to such. In one embodiment, the plurality of different marine environmental data includes any one or more of water pressure, water temperature, water quality, seawater flow rate, internal wave environment. In this embodiment, a plurality of different marine environment data such as water pressure, water temperature, water quality, and seawater flow rate may be obtained simultaneously. The specific acquisition mode can be acquired by related sensors such as a temperature sensor, a water quality analyzer and the like and transmitted to the control equipment on water by the underwater control module, and the specific acquisition mode can be understood by referring to the prior art, and is not repeated herein.
Step S102: and inputting the plurality of different marine environment data into a first model to obtain first predicted values corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger. Wherein the first model is trained on a first machine learning model based on a plurality of different sample marine environmental data in advance.
The first machine learning model may be, for example, but not limited to, a convolutional neural network model. In this embodiment, the first model may be obtained by training the first machine learning model in advance based on a plurality of different sample marine environment data and corresponding tag values, where the sample marine environment data may include water pressure, water temperature, water quality, and sea water flow rate, and each tag value represents a degree of influence of a marine environment identified by the corresponding sample marine environment data on a life of the tubing hanger, and may be obtained through a simulation test. After training, the input data of the first model are a plurality of acquired different marine environment data, and the output is a first predicted value corresponding to each marine environment data, namely the influence degree, namely the influence weight, of the marine environment identified by each marine environment data on the service life of the oil pipe hanger. The first model may end the training when, for example, the loss function value of the convolutional neural network model is less than a preset value during the training process, which may be understood with reference to the prior art and will not be described here again.
Step S103: and inputting the plurality of different marine environment data into a second model to obtain second predicted values corresponding to each marine environment data, wherein each second predicted value represents a predicted remaining life value of the oil pipe hanger based on a corresponding marine environment data. Wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance.
For example, in this embodiment, the second model, that is, the model for predicting the remaining life of the tubing hanger, may also be obtained by training the second machine learning model in advance based on a plurality of different sample marine environment data, such as water pressure, water temperature, water quality, and seawater flow rate. Wherein the second machine learning model may include, but is not limited to, a convolutional neural network model. After training, the input of the second model is a plurality of acquired different marine environment data, and the output is a second predicted value corresponding to each marine environment data, namely a residual life value of the oil pipe hanger, namely the predicted residual life value of the oil pipe hanger is obtained for a plurality of marine environment data of different types.
Step S104: and obtaining a final residual life value of the oil pipe hanger based on the weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data.
Illustratively, after obtaining a first predicted value corresponding to each marine environmental data and a second predicted value corresponding to each marine environmental data, a weighted sum is obtained to obtain a final remaining life value of the tubing hanger.
According to the scheme, based on the second model obtained through pre-training, a plurality of different marine environment data are input to obtain a second predicted value corresponding to each marine environment data, namely a residual life value corresponding to single type marine environment data, based on the first model obtained through pre-training, a first predicted value corresponding to each marine environment data, namely the influence degree, namely the weight, of the marine environment identified by the single type marine environment data on the life of the oil pipe hanger is obtained, and finally, based on the first predicted value corresponding to each marine environment data and the second predicted value, the final residual life value of the oil pipe hanger is obtained through weighted summation, so that the influence of different types of marine environment data can be fused to accurately evaluate the life of the oil pipe hanger, and the operation reliability and safety of a deep-sea oil production tree can be improved.
On the basis of the above embodiments, in one embodiment, in order to more accurately evaluate the life of the tubing hanger, in connection with the process shown in fig. 2, the training process of the second model may include the following steps:
step S201: the plurality of different sample marine environment data are input into a first machine learning model to output a predicted value corresponding to each sample marine environment data.
The predicted value corresponding to each sample marine environment data is a first predicted value output by a model in the training process, namely the influence degree of the marine environment identified by each marine environment data on the service life of the oil pipe hanger.
Step S201: the plurality of different sample marine environment data is input into a second machine learning model to output another predicted value corresponding to each sample marine environment data.
The other predicted value corresponding to each sample marine environment data is a second predicted value output by the model in the training process, namely a predicted residual life value of the oil pipe hanger corresponding to each marine environment data.
Step S203: and iteratively updating model parameters of the second machine learning model for a plurality of times based on the predicted value corresponding to each sample marine environment data, another predicted value corresponding to each sample marine environment data and a target loss function until the loss value of the target loss function meets a preset threshold condition, and ending the training process. The target loss function indicates a difference between a specified value corresponding to each sample marine environment data and another predicted value corresponding to each sample marine environment data output by the second machine learning model in each iterative updating process, and each specified value is a product of the predicted value corresponding to each sample marine environment data and the another predicted value.
In this embodiment, the training of the second model is combined with the training of the first model, and the output result of the first model, such as a predicted value, is added as the input data of the second model, so as to increase the type and quantity of the input data of the second model, that is, the fusion training of the second model based on the data of multiple dimensions, the training effect is better, and when the loss value of the target loss function is smaller than a specified value (which can be set as required), the training can be ended, so that the accuracy of predicting the residual life value of the tubing hanger by the second model obtained by training is improved, and the life of the tubing hanger can be estimated more accurately.
In one embodiment, the method may further comprise the steps of: judging whether the final residual life value of the oil pipe hanger is smaller than a preset life value or not; generating prompt information when the final remaining life value of the oil pipe hanger is smaller than the preset life value; and sending the prompt information to a control system of the deep sea christmas tree so as to enable the control system to make corresponding control operation.
For example, the preset lifetime value may be preset in advance, which is not limited. When the final remaining life value of the tubing hanger is determined to be less than the preset life value, a prompt message such as a text or a voice prompt message may be generated, but the invention is not limited thereto. And then sending the prompt information to a control system of the deep-sea Christmas tree to enable the control system to perform corresponding control operations such as suspending oil extraction work and the like so as to avoid production safety accidents, thereby improving the operation safety of the deep-sea Christmas tree.
On the basis of any one of the above embodiments, in one embodiment, as shown in fig. 3, the method may further include the steps of:
step S301: and acquiring material data of the oil pipe hanger.
Illustratively, the tubing hanger is typically a metal material, such as an alloy, and the material data may be pre-stored at the control device of the deep sea tree for reading during use.
Step S302: and determining the corrosion degree value of the oil pipe hanger based on the water temperature, the water quality, the seawater flow rate and the material data.
For example, the corrosion results of the marine environments such as the water temperature, the water quality, the seawater flow rate are different according to different materials, so that the corrosion degree value of the oil pipe hanger can be determined based on the acquired material data and the water temperature, the water quality, the seawater flow rate in the embodiment.
In an exemplary embodiment, the water temperature, the water quality, the seawater flow rate, and the material data satisfy a preset functional relationship with the corrosion degree value Y of the tubing hanger, where the preset functional relationship may be determined by fitting in advance through experiments, and the specific fitting process may be understood with reference to the prior art and will not be repeated herein. The corrosion degree value corresponding to the water temperature, the water quality, the seawater flow rate and the material data can be accurately determined based on the preset functional relation.
Step S303: and correcting the final residual life value of the tubing hanger based on the corrosion degree value of the tubing hanger to obtain a corrected residual life value.
Illustratively, after determining the corrosion level value of the tubing hanger, the final remaining life value of the tubing hanger determined in accordance with the above-described scheme may be corrected based on the corrosion level value, thereby obtaining a corrected remaining life value. In this embodiment, the corrosion action of the marine environment on the tubing hanger is also considered, and the final remaining life value of the tubing hanger determined by the prediction calculation is corrected based on the relatively accurately calculated corrosion degree value to obtain a corrected remaining life value, so that the life of the tubing hanger can be further accurately estimated.
In one embodiment, the correcting the final remaining life value of the tubing hanger based on the corrosion level value of the tubing hanger in step S303 to obtain a corrected remaining life value comprises: when the corrosion degree value is determined to be larger than a first threshold value and smaller than or equal to a second threshold value, reducing the final residual life value by a first value to obtain a corrected residual life value, wherein the second threshold value is larger than the first threshold value; when the corrosion degree value is determined to be larger than the second threshold value and smaller than or equal to a third threshold value, reducing the final residual life value by a second value, and correcting the residual life value, wherein the third threshold value is larger than the second threshold value, and the second value is larger than the first value; and when the corrosion degree value is determined to be larger than a third threshold value and smaller than or equal to a fourth threshold value, reducing the final residual life value by a third value to obtain a corrected residual life value, wherein the fourth threshold value is larger than the third threshold value, and the third value is larger than the second value.
The first threshold, the second threshold, the third threshold, and the fourth threshold may be set as needed, for example, without limitation. The first value, the second value and the third data can be set according to the requirement, so long as the negative correlation with the corrosion degree value is satisfied, namely, the greater the corrosion degree value is, the smaller the corresponding value is. In the embodiment, the corrosion action of the marine environment on the oil pipe hanger is considered, a specific correction scheme is provided based on the relatively accurate calculated corrosion degree value, and the final residual life value of the oil pipe hanger determined by specific correction prediction calculation is used for obtaining a corrected residual life value, so that the service life of the oil pipe hanger can be further accurately estimated.
It should be noted that although the steps of the methods of the present disclosure are illustrated in the accompanying drawings in a particular order, this does not require or imply that the steps must be performed in that particular order or that all of the illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc. In addition, it is also readily understood that these steps may be performed synchronously or asynchronously, for example, in a plurality of modules/processes/threads.
As shown in fig. 4, an embodiment of the present disclosure provides a deep sea tree tubing hanger life assessment system comprising:
the data acquisition module 401 is configured to acquire a plurality of different marine environment data within a preset range around the tubing hanger;
a first prediction module 402, configured to input the plurality of different marine environment data into a first model, so as to obtain a first predicted value corresponding to each of the marine environment data, where each of the first predicted values represents a degree of influence of a marine environment identified by a corresponding one of the marine environment data on a life of the tubing hanger; the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data;
a second prediction module 403, configured to input the plurality of different marine environment data into a second model, so as to obtain a second predicted value corresponding to each of the marine environment data, where each of the second predicted values represents a predicted remaining life value of the tubing hanger based on a corresponding one of the marine environment data; wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance;
The data processing module 404 is configured to obtain a final remaining life value of the tubing hanger based on weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data.
In one embodiment, the training process of the second model includes: inputting the plurality of different sample marine environment data into a first machine learning model to output a predicted value corresponding to each sample marine environment data; inputting the plurality of different sample marine environment data into a second machine learning model to output another predicted value corresponding to each sample marine environment data; repeatedly updating model parameters of the second machine learning model based on a predicted value corresponding to each sample marine environment data, another predicted value corresponding to each sample marine environment data and a target loss function for a plurality of times until the loss value of the target loss function meets a preset threshold condition, and ending the training process; the target loss function indicates a difference between a specified value corresponding to each sample marine environment data and another predicted value corresponding to each sample marine environment data output by the second machine learning model in each iterative updating process, and each specified value is a product of the predicted value corresponding to each sample marine environment data and the another predicted value.
In one embodiment, the plurality of different marine environmental data includes any one or more of water pressure, water temperature, water quality, seawater flow rate, internal wave environment.
In one embodiment, the system may further comprise an alarm processing module for: judging whether the final residual life value of the oil pipe hanger is smaller than a preset life value or not; generating prompt information when the final remaining life value of the oil pipe hanger is smaller than the preset life value; and sending the prompt information to a control system of the deep sea christmas tree so as to enable the control system to make corresponding control operation.
In one embodiment, the system may further comprise a correction module for: acquiring material data of the oil pipe hanger; determining a corrosion degree value of the tubing hanger based on the water temperature, water quality, seawater flow rate and the material data; and correcting the final residual life value of the tubing hanger based on the corrosion degree value of the tubing hanger to obtain a corrected residual life value.
In one embodiment, the modifying module modifies the final remaining life value of the tubing hanger based on the corrosion level value of the tubing hanger to obtain a modified remaining life value comprises: when the corrosion degree value is determined to be larger than a first threshold value and smaller than or equal to a second threshold value, reducing the final residual life value by a first value to obtain a corrected residual life value, wherein the second threshold value is larger than the first threshold value; when the corrosion degree value is determined to be larger than the second threshold value and smaller than or equal to a third threshold value, reducing the final residual life value by a second value, and correcting the residual life value, wherein the third threshold value is larger than the second threshold value, and the second value is larger than the first value; and when the corrosion degree value is determined to be larger than a third threshold value and smaller than or equal to a fourth threshold value, reducing the final residual life value by a third value to obtain a corrected residual life value, wherein the fourth threshold value is larger than the third threshold value, and the third value is larger than the second value.
In one embodiment, the water temperature, water quality, seawater flow rate, and the material data and the corrosion degree value of the tubing hanger satisfy a preset functional relationship, and the preset functional relationship is determined in advance through experimental fitting.
The specific manner in which the respective modules perform the operations and the corresponding technical effects thereof have been described in corresponding detail in relation to the embodiments of the method in the above embodiments, which will not be described in detail herein.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied. The components shown as modules or units may or may not be physical units, may be located in one place, or may be distributed across multiple network elements. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood disclosure scheme. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The disclosed embodiments also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the deep sea tree tubing hanger life assessment method of any of the above embodiments.
By way of example, the readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The embodiment of the disclosure also provides an electronic device comprising a processor and a memory, wherein the memory is used for storing a computer program. Wherein the processor is configured to perform the deep sea tree tubing hanger life assessment method of any of the above embodiments via execution of the computer program.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 600 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present invention.
As shown in fig. 5, the electronic device 600 is embodied in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs steps according to various exemplary embodiments of the present invention described in the above method examples section of the present specification. For example, the processing unit 610 may perform the steps of the method as shown in fig. 1.
The memory unit 620 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, or a network device, etc.) to perform the method steps according to the embodiments of the present disclosure.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. The service life assessment method of the deep sea Christmas tree oil pipe hanger is characterized by comprising the following steps of:
acquiring a plurality of different marine environment data within a preset range around the oil pipe hanger;
inputting the plurality of different marine environment data into a first model to obtain a first predicted value corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger; the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data;
Inputting the plurality of different marine environmental data into a second model to obtain a second predicted value corresponding to each of the marine environmental data, each of the second predicted values representing a predicted remaining life value of the tubing hanger based on a corresponding one of the marine environmental data; wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance;
obtaining a final residual life value of the oil pipe hanger based on weighted summation of a first predicted value corresponding to each marine environment data and a second predicted value corresponding to each marine environment data;
wherein the training process of the second model comprises:
inputting the plurality of different sample marine environment data into a first machine learning model to output a predicted value corresponding to each sample marine environment data;
inputting the plurality of different sample marine environment data into a second machine learning model to output another predicted value corresponding to each sample marine environment data;
repeatedly updating model parameters of the second machine learning model based on a predicted value corresponding to each sample marine environment data, another predicted value corresponding to each sample marine environment data and a target loss function for a plurality of times until the loss value of the target loss function meets a preset threshold condition, and ending the training process; the target loss function indicates a difference between a specified value corresponding to each sample marine environment data and another predicted value corresponding to each sample marine environment data output by the second machine learning model in each iterative updating process, and each specified value is a product of the predicted value corresponding to each sample marine environment data and the another predicted value.
2. The deep sea tree tubing hanger life assessment method of claim 1, wherein the plurality of different marine environmental data comprises any one or more of water pressure, water temperature, water quality, seawater flow rate, internal wave environment.
3. The deep sea tree tubing hanger life assessment method of claim 2, further comprising:
judging whether the final residual life value of the oil pipe hanger is smaller than a preset life value or not;
generating prompt information when the final remaining life value of the oil pipe hanger is smaller than the preset life value;
and sending the prompt information to a control system of the deep sea christmas tree so as to enable the control system to make corresponding control operation.
4. The deep sea tree tubing hanger life assessment method of claim 2, further comprising:
acquiring material data of the oil pipe hanger;
determining a corrosion degree value of the tubing hanger based on the water temperature, water quality, seawater flow rate and the material data;
and correcting the final residual life value of the tubing hanger based on the corrosion degree value of the tubing hanger to obtain a corrected residual life value.
5. The deep sea tree tubing hanger life assessment method of claim 4, wherein the modifying the final remaining life value of the tubing hanger based on the corrosion level value of the tubing hanger to obtain a modified remaining life value comprises:
when the corrosion degree value is determined to be larger than a first threshold value and smaller than or equal to a second threshold value, reducing the final residual life value by a first value to obtain a corrected residual life value, wherein the second threshold value is larger than the first threshold value;
when the corrosion degree value is determined to be larger than the second threshold value and smaller than or equal to a third threshold value, reducing the final residual life value by a second value, and correcting the residual life value, wherein the third threshold value is larger than the second threshold value, and the second value is larger than the first value;
and when the corrosion degree value is determined to be larger than a third threshold value and smaller than or equal to a fourth threshold value, reducing the final residual life value by a third value to obtain a corrected residual life value, wherein the fourth threshold value is larger than the third threshold value, and the third value is larger than the second value.
6. The deep sea tree tubing hanger life assessment method of claim 4, wherein the water temperature, water quality, seawater flow rate, and the material data satisfy a predetermined functional relationship with the corrosion level value of the tubing hanger, the predetermined functional relationship being determined in advance by a trial fit.
7. A deep sea tree tubing hanger life assessment system, comprising:
the data acquisition module is used for acquiring a plurality of different marine environment data in a preset range around the oil pipe hanger;
the first prediction module is used for inputting the plurality of different marine environment data into a first model to obtain a first predicted value corresponding to each marine environment data, wherein each first predicted value represents the influence degree of the marine environment identified by the corresponding marine environment data on the service life of the oil pipe hanger; the first model is obtained by training a first machine learning model in advance based on a plurality of different sample marine environment data;
a second prediction module for inputting the plurality of different marine environment data into a second model to obtain a second predicted value corresponding to each of the marine environment data, each of the second predicted values representing a predicted remaining life value of the tubing hanger based on a corresponding one of the marine environment data; wherein the second model is trained on a second machine learning model based on a plurality of different sample marine environmental data in advance; wherein the training process of the second model comprises: inputting the plurality of different sample marine environment data into a first machine learning model to output a predicted value corresponding to each sample marine environment data; inputting the plurality of different sample marine environment data into a second machine learning model to output another predicted value corresponding to each sample marine environment data; repeatedly updating model parameters of the second machine learning model based on a predicted value corresponding to each sample marine environment data, another predicted value corresponding to each sample marine environment data and a target loss function for a plurality of times until the loss value of the target loss function meets a preset threshold condition, and ending the training process; wherein, the target loss function indicates a difference between a specified value corresponding to each sample marine environment data and another predicted value corresponding to each sample marine environment data output by the second machine learning model in each iterative updating process, and each specified value is a product of the predicted value corresponding to each sample marine environment data and the another predicted value;
And the data processing module is used for obtaining a final residual life value of the oil pipe hanger based on weighted summation of the first predicted value corresponding to each marine environment data and the second predicted value corresponding to each marine environment data.
8. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the deep sea tree tubing hanger life assessment method of any one of claims 1-6.
9. An electronic device, comprising:
a processor; and
a memory for storing a computer program;
wherein the processor is configured to perform the deep sea tree tubing hanger life assessment method of any of claims 1-6 via execution of the computer program.
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