Deep-sea Christmas tree tubing hanger condition assessment method and system

Abstract

This disclosure relates to the field of deep-sea Christmas tree equipment, specifically a method and system for assessing the status of a tubing hanger in a deep-sea Christmas tree. The method comprises: obtaining at least two mechanical parameters to which the tubing hanger is subjected during operation; determining the tubing hanger's current operating condition based on the at least two mechanical parameters; and outputting a warning message to the deep-sea Christmas tree control system when the operating condition is determined to be a target condition. This disclosed solution accurately assesses the tubing hanger's operating status, improving the operational reliability and safety of deep-sea Christmas trees.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the technical field of deep-sea Christmas tree equipment, and more particularly to a method and system for evaluating the status of a tubing hanger of a deep-sea Christmas tree. Background Art

[0002] Deep-sea Christmas trees are key equipment for underwater oil and gas extraction. Because they operate in the harsh and complex ocean environment, such as at depths below 500 meters, ensuring their safe operation is crucial.

[0003] Related technologies primarily rely on dynamic monitoring and diagnosis of various underwater hydraulic valves and instruments in deepwater Christmas trees for safety protection, but overlook the tubing hanger, a device that supports the tubing string and seals the annular space between the tubing and casing. The tubing hanger is the core pressure-bearing component of the underwater wellhead, and its performance determines the continued safe and reliable operation of the oil and gas production system. Therefore, a method that can accurately assess the working status of the tubing hanger is urgently needed to improve the operational reliability and safety of deepwater Christmas trees. However, this is currently a gap that needs to be filled in the industry due to the lack of attention paid to this issue. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, embodiments of the present disclosure provide a method and system for evaluating the status of a tubing hanger of a deep-sea Christmas tree.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for evaluating the status of a tubing hanger of a deep-sea Christmas tree, comprising:

[0006] obtaining at least two mechanical parameters to which the tubing hanger is subjected during operation;

[0007] determining a current first operating condition of the tubing hanger based on the at least two mechanical parameters;

[0008] When it is determined that the first operating condition is the target operating condition, a warning message is output to a control system of the deep-sea Christmas tree.

[0009] In one embodiment, determining the current first operating condition of the tubing hanger based on the at least two mechanical parameters includes:

[0010] The at least two mechanical parameters are input into a working condition determination model to obtain a current first working condition of the tubing hanger; wherein the working condition determination model is obtained by pre-training a convolutional neural network model based on at least two sample mechanical parameters of the tubing hanger and corresponding working condition labels, and the working condition label is jointly determined based on all sample mechanical parameters of the at least two sample mechanical parameters.

[0011] In one embodiment, the at least two mechanical parameters include any two or more of the internal pressure, external pressure, tension and buoyancy of the tubing hanger; and the at least two sample mechanical parameters include any two or more of the sample internal pressure, sample external pressure, sample tension and sample buoyancy of the tubing hanger.

[0012] In one embodiment, the operating condition labels corresponding to the at least two sample mechanical parameters are determined based on the corresponding sample internal pressure, sample external pressure, sample tension and sample buoyancy, as well as the weight value of the degree of influence of each sample mechanical parameter on the operating condition label.

[0013] In one embodiment, the specific process of determining the working condition labels corresponding to the at least two sample mechanical parameters is as follows:

[0014] A calculated value is obtained by weighted summing the sample internal pressure and a first weight value, the sample external pressure and a second weight value, the sample tension and a third weight value, and the sample buoyancy and a fourth weight value; wherein the first weight value, the second weight value, the third weight value, and the fourth weight value are preset and different, the first weight value represents the degree of influence of the sample internal pressure on the working condition label, the second weight value represents the degree of influence of the sample external pressure on the working condition label, the third weight value represents the degree of influence of the sample tension on the working condition label, and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label;

[0015] Based on the calculated value, the operating condition label corresponding to the calculated value is determined by looking up the preset relationship table; different operating condition labels represent different types of operating conditions, and the preset relationship table contains the corresponding relationship between different operating condition labels and corresponding calculated values.

[0016] In one embodiment, the preset relationship table is pre-established based on experiments, and specifically includes the correspondence between different operating condition labels and corresponding different calculated value intervals; the step of looking up the preset relationship table based on the calculated value to determine the operating condition label corresponding to the calculated value includes:

[0017] When the query determines that the calculated value is within the target calculated value interval in the preset relationship table, the operating condition label corresponding to the target calculated value interval is determined to be the operating condition label corresponding to the calculated value; wherein the target calculated value interval is one of the different calculated value intervals.

[0018] In one embodiment, the target operating condition is a condition in which the danger level of the tubing hanger is greater than or equal to a preset level.

[0019] In a second aspect, an embodiment of the present disclosure provides a deep-sea Christmas tree tubing hanger status assessment system, comprising:

[0020] a data acquisition module, configured to acquire at least two mechanical parameters to which the tubing hanger is subjected during operation;

[0021] a working condition determination module, configured to determine a current working condition of the tubing hanger based on the at least two mechanical parameters;

[0022] An information processing module is configured to output warning information to the control system of the deep-sea Christmas tree when determining that the operating condition is the target operating condition.

[0023] In a third aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for evaluating the status of a deep-sea Christmas tree tubing hanger as described in any of the above embodiments.

[0024] In a fourth aspect, an embodiment of the present disclosure provides an electronic device, including:

[0025] processor; and

[0026] memory for storing computer programs;

[0027] The processor is configured to execute the method for evaluating the state of a tubing hanger of a deep-sea Christmas tree according to any one of the above embodiments by executing the computer program.

[0028] The technical solution provided by the embodiments of the present disclosure has the following advantages over the prior art:

[0029] The disclosed embodiments provide a method for assessing the status of a tubing hanger in a deep-sea tree. This method obtains at least two mechanical parameters experienced by the tubing hanger during operation, determines the tubing hanger's current first operating condition based on these parameters, and outputs a warning message to the deep-sea tree's control system when the first operating condition is determined to be a target operating condition. This method accurately assesses the tubing hanger's operating status, enabling the deep-sea tree's control system to take appropriate measures. This improves the reliability and safety of deep-sea tree operations, filling a gap in the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0031] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 This is a flow chart of a method for evaluating the status of a tubing hanger of a deep-sea Christmas tree according to an embodiment of the present disclosure;

[0033] Figure 2 This is a flow chart of a method for evaluating the status of a tubing hanger of a deep-sea Christmas tree according to another embodiment of the present disclosure;

[0034] Figure 3 This is a flow chart of a method for determining a working condition label in an embodiment of the present disclosure;

[0035] Figure 4 This is a schematic diagram of a deep-sea Christmas tree tubing hanger status assessment system according to an embodiment of the present disclosure;

[0036] Figure 5 Schematic diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0037] In order to more clearly understand the above-mentioned objectives, features and advantages of the present disclosure, the scheme of the present disclosure will be further described below. It should be noted that the embodiments of the present disclosure and the features therein can be combined with each other in the absence of conflict.

[0038] In the following description, many specific details are set forth to facilitate a full understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present disclosure, rather than all of the embodiments.

[0039] It should be understood that, in the following text, "at least one (item)" refers to one or more, and "plurality" refers to two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0040] Figure 1 This is a flow chart of a method for evaluating the status of a tubing hanger in a deep-sea Christmas tree according to an embodiment of the present disclosure. This method can be executed by a control system above the surface of the deep-sea Christmas tree or by a separate processing unit in communication with the control system, but is not limited thereto. Specifically, the method for evaluating the status of a tubing hanger in a deep-sea Christmas tree may include the following steps:

[0041] Step S101: obtaining at least two mechanical parameters to which the tubing hanger is subjected during operation.

[0042] For example, in one embodiment, the at least two mechanical parameters may include, but are not limited to, any two or more of the internal pressure, external pressure, tension, and buoyancy experienced by the tubing hanger. Specifically, the internal pressure may be the pressure of the liquid within the tubing hanger, such as the pressure of produced oil, such as hydraulic pressure. The external pressure may be the pressure exerted by seawater on the tubing hanger. Since the tubing hanger supports the tubing string, the tension may be the tension exerted by the tubing string on the tubing hanger, while the buoyancy may be the buoyancy exerted by seawater on the tubing hanger. These various mechanical parameters can be pre-calculated or acquired through relevant sensor acquisition. These can be understood with reference to prior art and will not be further elaborated here.

[0043] Step S102: determining a current first working condition of the tubing hanger based on the at least two mechanical parameters.

[0044] It is understood that the tubing hanger is simultaneously subjected to multiple different types of forces, such as internal pressure, external pressure, tension, and buoyancy. These different types of forces are variable and act simultaneously on the tubing hanger, affecting its operating conditions. For example, the tubing hanger's operating conditions can be categorized into multiple different types of operating conditions, such as normal operating conditions and hazardous operating conditions. These different types of operating conditions can be determined in advance based on test results of simulation tests when the tubing hanger is simultaneously subjected to multiple different types of forces, such as internal pressure, external pressure, tension, and buoyancy. For example, given a value for each of the internal pressure, external pressure, tension, and buoyancy, a normal operating condition is when the corresponding test results indicate no deformation or rupture of the tubing hanger, while a hazardous operating condition is when the tubing hanger is deformed or ruptured. In other words, a relationship can be pre-established between different types of operating conditions and the magnitudes of the corresponding multiple mechanical parameters. Based on this, the current first operating condition of the tubing hanger can be determined based on two or more acquired mechanical parameters.

[0045] Step S103: outputting warning information to the control system of the deep-sea Christmas tree when determining that the first operating condition is the target operating condition.

[0046] In one embodiment, the target operating condition is a condition in which the danger level of the tubing hanger is greater than or equal to a preset level, i.e., a dangerous operating condition. When the determined first operating condition is the target operating condition, i.e., the dangerous operating condition, a warning message, such as a text prompt message, is generated and sent to the control system of the deep-sea Christmas tree so that the control system can take corresponding measures, such as suspending oil production.

[0047] The above-mentioned solution provided by the embodiment of the present disclosure can determine the current working condition of the tubing hanger based on at least two mechanical parameters that the tubing hanger is subjected to during operation, and then accurately evaluate the working status of the tubing hanger. If it is in the target working condition, the control system of the deep-sea oil tree will take corresponding measures, thereby improving the operating reliability and safety of the deep-sea oil tree and filling the current gap in the industry.

[0048] In one embodiment, in order to more accurately evaluate the working status of the tubing hanger to improve the operational reliability and safety of the deep-sea Christmas tree. Figure 2 As shown in , on the basis of the above embodiment, in step S102, the current first working condition of the tubing hanger is determined based on the at least two mechanical parameters, specifically, the at least two mechanical parameters are input into a working condition determination model to obtain the current first working condition of the tubing hanger; wherein the working condition determination model is obtained by pre-training a convolutional neural network model based on at least two sample mechanical parameters of the tubing hanger and corresponding working condition labels, and the working condition label is jointly determined based on all sample mechanical parameters of the at least two sample mechanical parameters.

[0049] Exemplarily, different working condition labels represent different types of working conditions, for example, working condition label 0 represents a dangerous working condition, and working condition label 1 represents a normal working condition. The convolutional neural network model can be any of the existing convolutional neural networks, and there is no limitation on this. In this embodiment, at least two sample mechanical parameters can include but are not limited to any two or more of the sample internal pressure, sample external pressure, sample tension, and sample buoyancy of the tubing hanger. At the same time, the working condition label is jointly determined based on all the sample mechanical parameters of the at least two sample mechanical parameters. In this way, training sample data can be constructed, and then the convolutional neural network model is iteratively trained based on the training sample data. After the training is completed, the working condition determination model is obtained, wherein the training can be terminated when the loss function value of the convolutional neural network model is less than a preset value. The preset value can be set as needed and there is no limitation on this.

[0050] The operating condition determination model takes as input at least two currently acquired mechanical parameters of the tubing hanger, such as any two or more of internal pressure, external pressure, tension, and buoyancy. Its output is the label value of the corresponding current first operating condition, with 0 representing a hazardous operating condition and 1 representing a normal operating condition. Step S103 can then be executed.

[0051] In this embodiment, the current operating condition of the tubing hanger is determined based on at least two mechanical parameters that the tubing hanger is subjected to during operation and a pre-trained operating condition determination model. This allows for a more accurate assessment of the tubing hanger's operating status. If the tubing hanger is in the target operating condition, the deep-sea Christmas tree control system takes corresponding measures, further improving the operational reliability and safety of the deep-sea Christmas tree.

[0052] Based on the above embodiments, in one embodiment, to more accurately assess the operating status of a tubing hanger and improve the operational reliability and safety of a deep-sea Christmas tree, the operating condition labels corresponding to the at least two sample mechanical parameters are determined based on the corresponding sample internal pressure, sample external pressure, sample tension, and sample buoyancy, as well as weighted values ​​for the degree of influence of each sample mechanical parameter on the operating condition label. Specifically, when preparing training sample data, the corresponding operating condition category, i.e., the operating condition label, is determined based on the multiple sample mechanical parameters simultaneously experienced by the tubing hanger and their corresponding weighted values. The input of the operating condition determination model obtained through training in this manner is all of the currently acquired at least two mechanical parameters of the tubing hanger, such as internal pressure, external pressure, tension, and buoyancy, and the output is the label value corresponding to the current first operating condition.

[0053] Specifically, in one embodiment, combined with Figure 3 As shown in , the specific process of determining the working condition labels corresponding to the at least two sample mechanical parameters may include the following steps:

[0054] Step S301: A calculated value is obtained by weighted summing the sample pressure and a first weight value, the sample pressure and a second weight value, the sample tension and a third weight value, and the sample buoyancy and a fourth weight value. The first weight value, the second weight value, the third weight value, and the fourth weight value are pre-set and different. The first weight value represents the degree of influence of the sample pressure on the working condition label, the second weight value represents the degree of influence of the sample pressure on the working condition label, the third weight value represents the degree of influence of the sample tension on the working condition label, and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label.

[0055] Exemplarily, the calculated value X is obtained by weighted summation based on the sample internal pressure X1 and the first weight value w1, the sample external pressure X2 and the second weight value w2, the sample tension X3 and the third weight value w3, and the sample buoyancy X4 and the fourth weight value w4:

[0056] X=(X1*w1+X2*w2+X3*w3+X4*w4).

[0057] The first weight value represents the degree of influence of the in-sample pressure on the working condition label, i.e., the working condition category; the second weight value represents the degree of influence of the out-sample pressure on the working condition label, i.e., the working condition category; the third weight value represents the degree of influence of the sample tension on the working condition label, i.e., the working condition category; and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label, i.e., the working condition category. In other words, different types of mechanical parameters typically have different degrees of influence on the working condition label, i.e., the working condition category, and this influence is taken into account in this embodiment. As an example, the first, second, third, and fourth weight values ​​corresponding to different types of mechanical parameters can be determined in advance, but are not limited to, through simulation experiments. For example, the simulation experiment obtains multiple different mechanical parameter values ​​for each of the in-sample pressure, out-sample pressure, sample tension, and sample buoyancy of the tubing hanger, as well as a set of mechanical data in which the mechanical parameter values ​​of the other three types remain constant. The weight value corresponding to a particular type of mechanical parameter is then determined based on experimental results, such as a comparison between hazardous and normal working conditions. This approach is of course not limited to this.

[0058] Step S302: Based on the calculated value, a preset relationship table is searched to determine the operating condition label corresponding to the calculated value; different operating condition labels represent different types of operating conditions, and the preset relationship table contains the corresponding relationship between different operating condition labels and corresponding calculated values.

[0059] For example, a preset relationship table contains the correspondence between different working condition labels and corresponding different calculated values. Based on the calculated value X, the working condition label corresponding to the calculated value X can be determined by looking up the table in the preset relationship table. Subsequently, the convolutional neural network model can be iteratively trained based on the sample pressure, sample pressure, sample tension, and sample buoyancy, as well as the working condition labels determined based on the sample pressure, sample pressure, sample tension, and sample buoyancy as training sample data to obtain a working condition determination model. This model considers the degree to which different types of mechanical parameters affect the working condition label, i.e., the working condition type, making the determination of the working condition label more consistent with actual conditions. Therefore, the working condition determination model trained based on the above scheme can more accurately determine the current working condition of the tubing hanger, and further more accurately evaluate the working status of the tubing hanger to improve the operational reliability and safety of the deep-sea Christmas tree.

[0060] For example, in one embodiment, the preset relationship table is pre-established based on experiments and specifically includes corresponding relationships between different operating condition labels and corresponding different calculated value intervals. For example, operating condition label 0 corresponds to a calculated value interval, while operating condition label 1 corresponds to another calculated value interval, and each calculated value interval includes multiple different calculated values. Accordingly, in step S302, determining the operating condition label corresponding to the calculated value based on the calculated value in the preset relationship table includes: when the query determines that the calculated value X is within a target calculated value interval in the preset relationship table, determining the operating condition label corresponding to the target calculated value interval as the operating condition label corresponding to the calculated value X; wherein the target calculated value interval is one of the different calculated value intervals.

[0061] In order to more accurately determine the current operating condition of the tubing hanger and thereby more accurately assess the working state of the tubing hanger to improve the operational reliability and safety of deep-sea Christmas trees, while also improving the real-time performance of tubing hanger state assessment to increase assessment speed, which is crucial for the safe operation of deep-sea Christmas trees. Failure to quickly assess the state may result in a major safety incident. Based on any of the above embodiments, in one embodiment, before training the convolutional neural network model, a first loss function and a second loss function are constructed. During training, the convolutional neural network model is trained based on the first loss function and training data, i.e., at least two sample mechanical parameters of the tubing hanger and corresponding working condition labels, to obtain a first model. The first model is then pruned based on the second loss function to obtain a second model, wherein the second loss function is configured to ensure that the estimation accuracy of the second model is greater than or equal to the estimation accuracy of the first model. The at least two mechanical parameters are then input into the second model to obtain the current first working condition of the tubing hanger. Finally, execution may jump to step S103. Training of the convolutional neural network model may terminate when the loss function value of the first loss function is less than another preset value. For more details about model pruning, please refer to the existing technology and will not be repeated here.

[0062] The above-mentioned solution of the present disclosure can perform pruning based on the trained first model, and ensure that the estimation accuracy of the pruned second model is greater than or equal to the estimation accuracy of the first model. This ensures the estimation accuracy of the second model, that is, the current operating condition of the tubing hanger can still be accurately determined, thereby more accurately evaluating the working status of the tubing hanger to improve the operational reliability and safety of the deep-sea Christmas tree. At the same time, the pruned second model has a smaller model size and reduces computational overhead, thereby improving the real-time performance of tubing hanger status assessment and increasing the assessment speed, thereby further improving the overall operational reliability and safety of the deep-sea Christmas tree.

[0063] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be decomposed into multiple steps. Furthermore, it is readily understood that these steps may be executed synchronously or asynchronously, for example, in multiple modules / processes / threads.

[0064] like Figure 4 As shown, the embodiment of the present disclosure further provides a deep-sea Christmas tree tubing hanger status assessment system, comprising:

[0065] The data acquisition module 401 is used to obtain at least two mechanical parameters that the tubing hanger is subjected to during operation;

[0066] a working condition determination module 402 for determining a current working condition of the tubing hanger based on the at least two mechanical parameters;

[0067] The information processing module 403 is configured to output warning information to the control system of the deep-sea Christmas tree when determining that the operating condition is the target operating condition.

[0068] In one embodiment, the operating condition determination module determines the current first operating condition of the tubing hanger based on the at least two mechanical parameters, including: inputting the at least two mechanical parameters into a operating condition determination model to obtain the current first operating condition of the tubing hanger; wherein the operating condition determination model is obtained by pre-training a convolutional neural network model based on at least two sample mechanical parameters of the tubing hanger and corresponding operating condition labels, and the operating condition label is jointly determined based on all sample mechanical parameters of the at least two sample mechanical parameters.

[0069] In one embodiment, the at least two mechanical parameters include any two or more of the internal pressure, external pressure, tension and buoyancy of the tubing hanger; and the at least two sample mechanical parameters include any two or more of the sample internal pressure, sample external pressure, sample tension and sample buoyancy of the tubing hanger.

[0070] In one embodiment, the operating condition labels corresponding to the at least two sample mechanical parameters are determined based on the corresponding sample internal pressure, sample external pressure, sample tension and sample buoyancy, as well as the weight value of the degree of influence of each sample mechanical parameter on the operating condition label.

[0071] In one embodiment, the specific determination process of the working condition label corresponding to the at least two sample mechanical parameters is as follows: a calculated value is obtained by weighted summation based on the sample internal pressure and a first weight value, the sample external pressure and a second weight value, the sample tension and a third weight value, and the sample buoyancy and a fourth weight value; wherein, the first weight value, the second weight value, the third weight value and the fourth weight value are pre-set and different, the first weight value represents the degree of influence of the sample internal pressure on the working condition label, the second weight value represents the degree of influence of the sample external pressure on the working condition label, the third weight value represents the degree of influence of the sample tension on the working condition label, and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label; based on the calculated value, the working condition label corresponding to the calculated value is determined by looking up a table in a preset relationship table; wherein different working condition labels represent different categories of working conditions, and the preset relationship table contains the corresponding relationship between different working condition labels and corresponding calculated values.

[0072] In one embodiment, the preset relationship table is established in advance based on experiments, and specifically includes the correspondence between different operating condition labels and corresponding different calculation value intervals; the table lookup in the preset relationship table based on the calculation value to determine the operating condition label corresponding to the calculation value includes: when the query determines that the calculation value is within the target calculation value interval in the preset relationship table, determining that the operating condition label corresponding to the target calculation value interval is the operating condition label corresponding to the calculation value; wherein the target calculation value interval is one of the different calculation value intervals.

[0073] In one embodiment, the target operating condition is a condition in which the danger level of the tubing hanger is greater than or equal to a preset level.

[0074] Based on any one of the above embodiments, in one embodiment, a first loss function and a second loss function are constructed before training the above-mentioned convolutional neural network model. During training, the convolutional neural network model is trained according to the first loss function and the training data, i.e., at least two sample mechanical parameters of the tubing hanger and the corresponding working condition labels to obtain a first model; then, the first model is pruned based on the second loss function and preset constraints to obtain a second model, and the second loss function and the preset constraints are used to constrain the estimation accuracy of the second model to be greater than or equal to the estimation accuracy of the first model; then, the at least two mechanical parameters are input into the second model to obtain the current first working condition of the tubing hanger.

[0075] Regarding the system in the above embodiment, the specific manner in which each module performs operations and the corresponding technical effects brought about have been described in detail in the embodiment of the method, and will not be elaborated here.

[0076] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed scheme. Those of ordinary skill in the art can understand and implement it without paying any creative work.

[0077] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for evaluating the state of a tubing hanger of a deep-sea Christmas tree described in any one of the above embodiments is implemented.

[0078] Exemplarily, the readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0079] The computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, wherein the readable program code is carried. The data signal propagated may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or component. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0080] The present disclosure also provides an electronic device comprising a processor and a memory for storing a computer program, wherein the processor is configured to execute the computer program to perform the deep-sea Christmas tree tubing hanger status assessment method according to any of the above embodiments.

[0081] Refer to the following Figure 5An electronic device 600 according to this embodiment of the present invention will be described. Figure 5 The electronic device 600 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0082] like Figure 5 As shown, electronic device 600 is implemented as 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 storage unit 620, a bus 630 connecting various system components (including storage unit 620 and processing unit 610), a display unit 640, and the like.

[0083] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method embodiment section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 The steps of the method shown in .

[0084] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0085] 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 of which or some combination may include an implementation of a network environment.

[0086] Bus 630 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0087] The electronic device 600 can also communicate with one or more external devices 700 (e.g., a keyboard, a pointing device, a Bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and / or any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., a router, a modem, etc.). Such communication can occur via an input / output (I / O) interface 650. Furthermore, the electronic device 600 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 660. The network adapter 660 can communicate with other modules of the electronic device 600 via the bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with the electronic device 600, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0088] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, or a network device, etc.) to execute the method steps of the above-mentioned embodiments according to the embodiments of the present disclosure.

[0089] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0090] The foregoing description is intended only to provide specific embodiments of the present disclosure, intended to enable those skilled in the art to understand and implement the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the embodiments described herein, but rather to be construed in the broadest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating the status of a tubing hanger of a deep-sea Christmas tree, characterized in that: include: obtaining at least two mechanical parameters to which the tubing hanger is subjected during operation; determining a current first operating condition of the tubing hanger based on the at least two mechanical parameters; outputting warning information to the control system of the deep-sea Christmas tree when determining that the first operating condition is the target operating condition; Wherein, determining the current first working condition of the tubing hanger based on the at least two mechanical parameters includes: inputting the at least two mechanical parameters into a working condition determination model to obtain a current first working condition of the tubing hanger; wherein the working condition determination model is obtained by pre-training a convolutional neural network model based on the at least two sample mechanical parameters of the tubing hanger and corresponding working condition labels, and the working condition label is determined based on all sample mechanical parameters of the at least two sample mechanical parameters; The at least two mechanical parameters include any two or more of the internal pressure, external pressure, tension and buoyancy of the tubing hanger; the at least two sample mechanical parameters include any two or more of the sample internal pressure, sample external pressure, sample tension and sample buoyancy of the tubing hanger; The operating condition labels corresponding to the at least two sample mechanical parameters are determined based on the corresponding sample internal pressure, sample external pressure, sample tension, and sample buoyancy, as well as a weighted value of the degree of influence of each sample mechanical parameter on the operating condition label. The specific process of determining the operating condition labels corresponding to the at least two sample mechanical parameters is as follows: A calculated value is obtained by weighted summing the sample internal pressure and a first weight value, the sample external pressure and a second weight value, the sample tension and a third weight value, and the sample buoyancy and a fourth weight value; wherein the first weight value, the second weight value, the third weight value, and the fourth weight value are preset and different, the first weight value represents the degree of influence of the sample internal pressure on the working condition label, the second weight value represents the degree of influence of the sample external pressure on the working condition label, the third weight value represents the degree of influence of the sample tension on the working condition label, and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label; Based on the calculated value, the operating condition label corresponding to the calculated value is determined by looking up the preset relationship table; different operating condition labels represent different types of operating conditions, and the preset relationship table contains the corresponding relationship between different operating condition labels and corresponding calculated values.

2. The deep-sea Christmas tree tubing hanger status assessment method according to claim 1, characterized in that: The preset relationship table is established in advance based on experiments, and specifically includes the correspondence between different working condition labels and corresponding different calculation value intervals; The step of determining the operating condition label corresponding to the calculated value by looking up the preset relationship table based on the calculated value includes: When the query determines that the calculated value is within the target calculated value interval in the preset relationship table, the operating condition label corresponding to the target calculated value interval is determined to be the operating condition label corresponding to the calculated value; wherein the target calculated value interval is one of the different calculated value intervals.

3. The deep-sea Christmas tree tubing hanger status assessment method according to claim 1 or 2, characterized in that: The target operating condition is an operating condition in which the danger level of the tubing hanger is greater than or equal to a preset level.

4. A deep-sea Christmas tree tubing hanger status assessment system, characterized in that: include: a data acquisition module, configured to acquire at least two mechanical parameters to which the tubing hanger is subjected during operation; a working condition determination module, configured to determine a current first working condition of the tubing hanger based on the at least two mechanical parameters; an information processing module, configured to output warning information to a control system of the deep-sea Christmas tree when determining that the first operating condition is a target operating condition; The operating condition determination module determines the current first operating condition of the tubing hanger based on the at least two mechanical parameters, including: inputting the at least two mechanical parameters into a working condition determination model to obtain a current first working condition of the tubing hanger; wherein the working condition determination model is obtained by pre-training a convolutional neural network model based on the at least two sample mechanical parameters of the tubing hanger and corresponding working condition labels, and the working condition label is determined based on all sample mechanical parameters of the at least two sample mechanical parameters; The at least two mechanical parameters include any two or more of the internal pressure, external pressure, tension and buoyancy of the tubing hanger; the at least two sample mechanical parameters include any two or more of the sample internal pressure, sample external pressure, sample tension and sample buoyancy of the tubing hanger; The operating condition labels corresponding to the at least two sample mechanical parameters are determined based on the corresponding sample internal pressure, sample external pressure, sample tension, and sample buoyancy, as well as a weighted value of the degree of influence of each sample mechanical parameter on the operating condition label. The specific process of determining the operating condition labels corresponding to the at least two sample mechanical parameters is as follows: A calculated value is obtained by weighted summing the sample internal pressure and a first weight value, the sample external pressure and a second weight value, the sample tension and a third weight value, and the sample buoyancy and a fourth weight value; wherein the first weight value, the second weight value, the third weight value, and the fourth weight value are preset and different, the first weight value represents the degree of influence of the sample internal pressure on the working condition label, the second weight value represents the degree of influence of the sample external pressure on the working condition label, the third weight value represents the degree of influence of the sample tension on the working condition label, and the fourth weight value represents the degree of influence of the sample buoyancy on the working condition label; Based on the calculated value, the operating condition label corresponding to the calculated value is determined by looking up the preset relationship table; different operating condition labels represent different types of operating conditions, and the preset relationship table contains the corresponding relationship between different operating condition labels and corresponding calculated values.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for evaluating the state of a tubing hanger of a deep-sea Christmas tree according to any one of claims 1 to 3 is implemented.

6. An electronic device, characterized in that: include: processor; as well as memory for storing computer programs; The processor is configured to execute the deep-sea Christmas tree tubing hanger status assessment method according to any one of claims 1 to 3 by executing the computer program.

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