Method for determining integrity of extra-high sulfur content production device, digital intelligent cloud platform and equipment

Abstract

The invention relates to a pipeline integrity analysis technology, and discloses a method for determining the integrity of an extra-high sulfur-containing production device, which comprises the following steps of: acquiring acquired data of the extra-high sulfur-containing production device, and extracting safety risk factors from the acquired data; according to the safety risk factors, the integrity of the ultrahigh-sulfur-content production device is predicted through a pre-constructed integrity prediction model, an integrity prediction result is obtained, the pre-constructed integrity prediction model comprises a flow erosion model, a corrosion prediction model, a rigidity and strength prediction model and a sealing life prediction model, and the flow erosion model, the corrosion prediction model, the rigidity and strength prediction model and the sealing life prediction model are used for prediction of the integrity of the ultrahigh-sulfur-content production device. The integrity prediction result is obtained by fusing an output result of the flow erosion model, an output result of the corrosion prediction model, an output result of the rigidity and strength prediction model and an output result of the sealing life prediction model. The invention further provides a digital intelligent cloud platform, equipment and a storage medium. The method can improve the prediction accuracy and efficiency of the integrity of the extra-high sulfur-containing production device.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline integrity analysis, and particularly to a method for determining the integrity of a super-high sulfur production device, a digital intelligent cloud platform, and equipment. Background Art

[0002] A natural gas field refers to an area or structure where a large amount of natural gas (mainly hydrocarbon gas) is naturally accumulated in an underground reservoir. It is an important carrier of natural gas resources. In a sulfur-containing gas field rich in hydrogen sulfide, due to the long-term exploitation of the sulfur-containing gas field and the full-scale production increase and utilization, the hydrogen sulfide content in the exploitation pipeline will rise sharply, breaking through the limit bearing range of the hydrogen sulfide content that the pipeline can withstand. Long-term and efficient production will lead to an increase in the corrosion of the exploitation pipeline by hydrogen sulfide. Therefore, to ensure the safe and efficient operation of the exploitation pipeline, it is necessary to predict the integrity of the exploitation pipeline to ensure the operation efficiency of the exploitation pipeline. Summary of the Invention

[0003] The present invention provides a method for determining the integrity of a super-high sulfur production device, a digital intelligent cloud platform, and equipment, which can improve the prediction accuracy and efficiency of the integrity of the super-high sulfur production device.

[0004] To achieve the above object, a method for determining the integrity of a super-high sulfur production device provided by the present invention includes:

[0005] Obtain the acquisition data of the super-high sulfur production device, and extract safety risk factors from the acquisition data;

[0006] According to the safety risk factors, use a pre-constructed integrity prediction model to predict the integrity of the super-high sulfur production device, and obtain an integrity prediction result. Among them, the pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a stiffness and strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the stiffness and strength prediction model, and the seal life prediction model.

[0007] Optionally, the construction process of the flow erosion model includes:

[0008] Obtain the flow channel shape parameters and flow channel material parameters of the raw gas pipeline of the super-high sulfur production device for natural gas;

[0009] Obtain the fluid pressure parameters of natural gas in the raw material pipeline, and analyze the basic parameters of the current natural gas components;

[0010] Construct a flow erosion model according to the flow channel shape parameters, flow channel material parameters, fluid pressure parameters, and basic parameters of natural gas components.

[0011] Optionally, the flow erosion model is expressed as:

[0012]

[0013] Among them, R erosion is the erosion rate per unit area of the wall in the raw gas pipeline, i is the number of the collision particle, N particle is the number of collision particles per unit area on the wall surface in the raw gas pipeline, is the mass flow rate of the i-th collision particle, c(d p ) is the shape function of the colliding particles, A face is the area of the wall calculation unit in the raw gas pipeline, f(θ1) is the correction function of the impact angle on the erosion rate, θ1 is the impact angle, that is, the angle between the particle movement direction and the wall normal, is the velocity function, d p is the diameter of the colliding particle, and α is the empirical erosion coefficient determined according to the material properties.

[0014] Optionally, the construction of the corrosion prediction model includes:

[0015] Obtain the partial pressure parameters of the component gases in natural gas, and obtain the current temperature parameters and current flow rate;

[0016] Build a partial pressure corrosion model based on the partial pressure parameters, build a temperature corrosion model based on the current temperature parameters, and build a flow rate corrosion model based on the current flow rate;

[0017] The corrosion prediction model is obtained by integrating the partial pressure corrosion model, the temperature corrosion model and the flow rate corrosion model.

[0018] Optionally, the corrosion prediction model is:

[0019]

[0020] Among them, lnv corr is the corrosion rate, A is the first influence coefficient, B is the second influence coefficient, K is the third influence coefficient, D is the fourth influence coefficient, E is the fifth influence coefficient, and C is the comprehensive correction constant. is the H2S partial pressure corrosion influence item, is the CO2 partial pressure corrosion influence term, lnv is the velocity corrosion influence term, E a is the reaction activation energy of the raw gas pipeline material, R is the universal gas constant, and T is the absolute temperature.

[0021] Optionally, the construction of the seal life prediction model includes:

[0022] Obtain the gate valve structure of the raw gas pipeline and quantify the cavitation damage factor, erosion damage factor, and impact wear factor of the gate valve structure;

[0023] Construct a sealing index system by using the cavitation damage factor, erosion damage factor, and impact wear factor, and construct a sealing life prediction model based on the sealing index system.

[0024] Optionally, the construction of the stiffness and strength prediction model includes:

[0025] Divide the spatial structure of the raw material gas pipeline into a grid to obtain a raw material gas pipeline grid;

[0026] Construct a spatial model of the raw material gas pipeline based on the raw material gas pipeline grid, determine the displacement component, strain component, and stress component according to the spatial model, and add boundary constraint conditions to the displacement component, strain component, and stress component to construct the stiffness and strength prediction model, where the boundary constraint conditions include displacement boundary conditions and force boundary conditions.

[0027] To solve the above problems, the present invention also provides a digital intelligence cloud platform, which includes:

[0028] A safety risk factor extraction module for obtaining the collected data of the ultra-high sulfur production device and extracting safety risk factors from the collected data;

[0029] An integrity prediction module for predicting the integrity of the ultra-high sulfur production device according to the safety risk factors by using a pre-constructed integrity prediction model to obtain an integrity prediction result, where the pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a stiffness and strength prediction model, and a sealing life prediction model, and the integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the stiffness and strength prediction model, and the sealing life prediction model.

[0030] To solve the above problems, the present invention also provides an electronic device, which includes:

[0031] At least one processor; and,

[0032] A memory communicatively connected to the at least one processor; wherein,

[0033] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the integrity of the ultra-high sulfur production device described above.

[0034] To solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the method for determining the integrity of the ultra-high sulfur production device described above.

[0035] The present invention obtains the acquisition data of the ultra-high sulfur production device, which can provide a data basis for complete prediction. In addition, by extracting the safety risk factors from the acquisition data, key risk factors can be extracted, and the influence weight of each safety risk factor on the pipeline integrity can be quantified. In addition, by constructing a flow erosion model, the physical erosion damage of the multiphase flow (gas-liquid-solid) in the ultra-high sulfur production device to the pipe wall can be evaluated. By constructing a corrosion prediction model, the corrosion rate and remaining life can be predicted by combining the electrochemical corrosion mechanism and environmental parameters. By constructing a strength prediction model, the stress distribution of the pipeline under load can be calculated to evaluate the structural stability. By constructing a seal life prediction model, the sealing performance of the ultra-high sulfur production device can be analyzed. Finally, by integrating multiple models and combining safety risk factors, the prediction accuracy and efficiency of the integrity of the ultra-high sulfur production device can be improved. Brief Description of the Drawings

[0036] Figure 1 It is a schematic flowchart of a method for determining the integrity of an ultra-high sulfur production device provided by an embodiment of the present invention;

[0037] Figure 2 It is a functional module diagram of a digital intelligence cloud platform provided by an embodiment of the present invention;

[0038] Figure 3 It is a schematic structural diagram of an electronic device for implementing the method for determining the integrity of an ultra-high sulfur production device provided by an embodiment of the present invention;

[0039] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiments

[0040] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] An embodiment of the present application provides a method for determining the integrity of an ultra-high sulfur-containing production device. The execution subject of the method for determining the integrity of an ultra-high sulfur-containing production device includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for determining the integrity of an ultra-high sulfur-containing production device can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0042] Referring to Figure 1 As shown, it is a schematic flowchart of the method for determining the integrity of an ultra-high sulfur-containing production device provided by an embodiment of the present invention. In this embodiment, the method for determining the integrity of an ultra-high sulfur-containing production device includes:

[0043] S1. Obtain the acquisition data of the ultra-high sulfur-containing production device, and extract safety risk factors from the acquisition data.

[0044] In an embodiment of the present invention, in response to an integrity prediction request obtained from a user interface, the acquisition data of the ultra-high sulfur-containing production device is extracted from a database, or the acquisition data of the ultra-high sulfur-containing production device is obtained from a data upload interface. The user interface is preferably but not limited to an output interface of an input device (such as a keyboard, a touch screen) or an Internet application interface.

[0045] In an embodiment of the present invention, an ultra-high sulfur-containing production device refers to the general term for industrial equipment and facilities that process media with extremely high hydrogen sulfide (H2S) concentrations (usually H2S volume fraction ≥ 5% or higher) during the extraction, processing, and storage and transportation of oil and natural gas. Among them, ultra-high sulfur-containing production devices are common in scenarios such as high-sulfur gas field development, refinery acid gas treatment, and sulfur recovery. The core challenges lie in the high toxicity, strong corrosiveness, and flammable and explosive risks of H2S.

[0046] In an embodiment of the present invention, a safety risk factor refers to a factor that endangers the service life of a sulfur-containing production device. For example, safety risk factors include, but are not limited to, the flow rate of objects, the partial pressure of objects, the ambient temperature, and the shape parameters of objects in the ultra-high sulfur-containing production device.

[0047] In an embodiment of the present invention, the acquisition data refers to the data collected by sensors installed in the ultra-high sulfur-containing production device.

[0048] S2. According to the safety risk factors, use the pre-constructed integrity prediction model to predict the integrity of the ultra-high sulfur content production device, and obtain the integrity prediction result. The pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the strength prediction model, and the seal life prediction model.

[0049] In the embodiment of the present invention, after obtaining the integrity prediction result, it is fed back to the user through the integrity result feedback interface, and the integrity result feedback interface is not limited to the display interface or the user device communication interface.

[0050] In the embodiment of the present invention, the flow erosion model refers to a model constructed by using the data of the ultra-high sulfur content production device eroded by flowing objects in the ultra-high sulfur content production device.

[0051] As an embodiment of the present invention, the construction process of the flow erosion model includes:

[0052] Obtain the flow channel shape parameters and flow channel material parameters of the raw gas pipeline of the ultra-high sulfur content production device for natural gas;

[0053] Obtain the fluid pressure parameters of natural gas in the raw material pipeline, and analyze the basic parameters of the current natural gas components;

[0054] Construct a flow erosion model according to the flow channel shape parameters, flow channel material parameters, fluid pressure parameters, and basic parameters of natural gas components.

[0055] In the embodiment of the present invention, the basic parameters of the current natural gas components include but are not limited to the pressure, temperature, flow rate, components, and particle size of natural gas.

[0056] In the embodiment of the present invention, the raw gas pipeline refers to the pipeline system that transports untreated natural gas from the wellhead of the gas well to the gas gathering station or treatment plant.

[0057] In the embodiment of the present invention, the flow channel shape parameters can be obtained by actual measurement of the pipeline system, the flow channel material parameters can be directly retrieved from the database, the basic parameters of natural gas components can be obtained by analyzing the produced natural gas, and the fluid pressure parameters can be obtained by sensors or simulated by fluid dynamics.

[0058] Further, the flow erosion model is expressed as:

[0059]

[0060] Where R erosionis the erosion rate per unit area of the wall surface in the raw material gas pipeline, i is the number of the colliding particulate matters, N particle is the number of the colliding particulate matters per unit area of the wall surface in the raw material gas pipeline, is the mass flow rate of the i-th colliding particulate matter, c(d p ) is the shape function of the colliding particulate matter, A face is the area of the wall surface calculation unit in the raw material gas pipeline, f(θ1) is the correction function of the erosion rate with respect to the impact angle, θ1 is the impact angle, that is, the angle between the particle motion direction and the wall surface normal direction, is the velocity function, d p is the diameter of the colliding particulate matter, α is the empirical erosion coefficient determined according to the material property.

[0061] As an embodiment of the present invention, the construction of the corrosion prediction model includes:

[0062] Obtain the partial pressure parameters of the component gases in the natural gas, and obtain the current temperature parameter and the current flow rate;

[0063] Construct a partial pressure corrosion model according to the partial pressure parameters, construct a temperature corrosion model according to the current temperature parameter, and construct a flow rate corrosion model according to the current flow rate;

[0064] Fuse the partial pressure corrosion model, the temperature corrosion model and the flow rate corrosion model to obtain the corrosion prediction model.

[0065] Further, the corrosion prediction model is:

[0066]

[0067] where, lnv corr is the corrosion rate, A is the first influence coefficient, B is the second influence coefficient, K is the third influence coefficient, D is the fourth influence coefficient, E is the fifth influence coefficient, C is the comprehensive correction constant, is the influence term of H2S partial pressure corrosion, is the influence term of CO2 partial pressure corrosion, lnv is the influence term of flow rate corrosion, E a is the reaction activation energy of the raw material gas pipeline material, R is the universal gas constant, T is the absolute temperature.

[0068] In the embodiment of the present invention, the comprehensive correction constant C is the sum of the partial pressure corrosion constant, the temperature corrosion constant and the flow rate corrosion constant.

[0069] Further, in the partial pressure corrosion model, the following formula is used to calculate the partial pressure corrosion rate:

[0070]

[0071] where, is the partial pressure corrosion rate, and C1 is the partial pressure corrosion constant.

[0072] Further, in the temperature corrosion model, the following formula can be used to calculate the temperature corrosion rate:

[0073]

[0074] where, is the temperature corrosion rate, and C2 is the temperature corrosion constant.

[0075] Further, in the flow velocity corrosion model, the following formula can be used to calculate the flow velocity corrosion rate:

[0076]

[0077]

[0078] where n is the flow velocity constant, v is the flow velocity, is the flow velocity corrosion rate, and C3 is the flow velocity corrosion constant.

[0079] As an embodiment of the present invention, the construction of the seal life prediction model includes:

[0080] Obtain the gate valve structure of the raw material gas pipeline, and quantify the cavitation damage factor, erosion damage factor, and impact wear factor of the gate valve structure;

[0081] Construct a seal index system using the cavitation damage factor, erosion damage factor, and impact wear factor, and construct a seal life prediction model according to the seal index system.

[0082] In the embodiment of the present invention, the cavitation damage factor refers to the situation where in a working condition with a large pressure difference, the flow velocity of the working medium increases when flowing through the throttling surface between the valve core and the valve bottom, the pressure drops rapidly to form bubbles, and the bubbles in the medium burst due to the pressure recovery, resulting in local hydraulic shock, leading to corrosion and pitting of the seal surface.

[0083] In the embodiment of the present invention, the erosion damage factor refers to the fact that the fluid flowing in the internal flow channel is a mixed medium of gas, liquid, and solid particles. The erosion of solid particles will have a strong erosion effect on the gate valve seal, resulting in seal failure. The erosion of the gate valve mainly occurs in areas with large flow field changes, including the gate valve sealing surface, resulting in many deep grooves scoured by the medium on the gate valve seat sealing surface.

[0084] In the embodiments of the present invention, the impact wear factor refers to that during the opening and closing process of the gate valve, the sealing surface will be impacted and damaged, and the frictional wear caused by the long-term relative frictional movement of the sealing interface is also a great test for the sealing performance of the gate valve. The amount of seal wear is affected by factors such as the hardness matching of the wear pair, the wear resistance of the material, and the processing technology of the seal pair. The impact and wear of the gate valve seal are common seal failure forms during the long-term operation of the gate valve.

[0085] In the embodiments of the present invention, the seal index system is constructed by using the PCA principal component analysis method.

[0086] As an embodiment of the present invention, the construction of the stiffness prediction model includes:

[0087] The spatial structure of the raw material gas pipeline is divided into grids to obtain the raw material gas pipeline grid;

[0088] According to the raw material gas pipeline grid, a spatial model of the raw material gas pipeline is constructed, and the displacement component, strain component, and stress component are determined according to the spatial model. After adding boundary constraint conditions to the displacement component, strain component, and stress component, a stiffness prediction model is constructed, where the boundary constraint conditions include displacement boundary conditions and force boundary conditions.

[0089] In the embodiments of the present invention, the displacement component refers to the displacement of any point in the raw material gas pipeline having displacement components in the x, y, and z directions, that is, the displacement component is (u, v, w), and there are 9 stress components. Due to the reciprocity of shear stresses, there is τ xy =τ yx ,τ yz =τ zy ,τ zx =τ xz . Therefore, the independent stress components are 6. The situation of the strain components is the same as that of the stress. Then the three major types of variables are as follows: (1) Displacement components: u, v, w; (2) Strain components: ε xx ε yy ε zz γ xy γ yz γ zx ; (3) Stress components: σ xx σ yy σ zz τ xy τ yz τ zx ; where u is the displacement component along the x-axis direction, v is the displacement component along the y-axis direction, w is the displacement component along the z-axis direction, ε xx is the unit length change rate along the x-axis direction, ε yy is the unit length change rate along the y-axis direction, ε zz is the unit length change rate along the z-axis direction, γxy is the change in the right angle in the x-y plane, γ yz is the change in the right angle in the y-z plane, γ zx is the change in the right angle in the z-x plane, σ xx is the normal stress in the x-axis direction, σ yy is the normal stress in the y-axis direction, σ zz is the normal stress in the z-axis direction, τ xy is the shear stress in the x-y plane, τ yz is the shear stress in the y-z plane, τ zx is the shear stress in the z-x plane.

[0090] In the embodiments of the present invention, the displacement boundary condition refers to the boundary condition used to constrain the displacement of a structure at a specific position or direction in mechanical analysis, and can be expressed by the following formula:

[0091]

[0092] where, S u is the boundary region where the displacement boundary condition is applied, is the displacement value specified in the x-axis direction on the boundary region, is the displacement value specified in the y-axis direction on the boundary region, is the displacement value specified in the z-axis direction on the boundary region.

[0093] In the embodiments of the present invention, the force boundary condition refers to the boundary condition used to specify the external forces (such as concentrated forces, distributed forces or surface forces) acting on a structure in a specific boundary region in mechanical analysis, and can be expressed by the following formula:

[0094]

[0095] where, S p is the boundary region where the force boundary condition is applied, is the external force component applied in the x-axis direction on the boundary region, is the external force component applied in the y-axis direction on the boundary region, is the external force component applied in the z-axis direction on the boundary region, n x is the cosine of the external normal direction of the boundary in the x-axis, n y is the cosine of the external normal direction of the boundary in the y-axis, n z is the cosine of the external normal direction of the boundary in the z-axis.

[0096] In the embodiments of the present invention, after normalizing the output results of the flow erosion model, the corrosion prediction model, the rigidity and strength prediction model, and the seal life prediction model into dimensionless prediction data, the integrity prediction result is output by using a fully connected layer network.

[0097] In the embodiments of the present invention, the integrity prediction results include safe no leakage, dangerous no leakage, and dangerous leakage.

[0098] The present invention obtains the acquisition data of the ultra-high sulfur-containing production device, which can provide a data basis for integrity prediction. In addition, by extracting the safety risk factors in the acquisition data, key risk factors can be extracted, and the influence weight of each safety risk factor on pipeline integrity can be quantified. In addition, by constructing a flow erosion model, the physical erosion damage of multiphase flow (gas-liquid-solid) to the pipe wall in the ultra-high sulfur-containing production device can be evaluated. By constructing a corrosion prediction model, the corrosion rate and remaining life can be predicted by combining the electrochemical corrosion mechanism and environmental parameters. By constructing a strength prediction model, the stress distribution of the pipeline under load can be calculated to evaluate the structural stability. By constructing a seal life prediction model, the sealing performance of the ultra-high sulfur-containing production device can be analyzed. Finally, by integrating multiple models and combining safety risk factors, the prediction accuracy and efficiency of the integrity of the ultra-high sulfur-containing production device can be improved.

[0099] As Figure 2 shown, it is a functional module diagram of the digital intelligence cloud platform provided by an embodiment of the present invention.

[0100] The digital intelligence cloud platform 100 of the present invention can be installed in an electronic device. According to the functions implemented, the digital intelligence cloud platform 100 can include a safety risk factor extraction module 101 and an integrity prediction module 102.

[0101] The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in the memory of the electronic device.

[0102] In this embodiment, the functions of each module / unit are as follows:

[0103] The safety risk factor extraction module 101 is used to obtain the acquisition data of the ultra-high sulfur-containing production device and extract safety risk factors from the acquisition data.

[0104] In the embodiments of the present invention, the ultra-high sulfur-containing production device refers to the general term of industrial equipment and facilities that process media with extremely high hydrogen sulfide (H2S) concentration (usually the volume fraction of H2S ≥ 5% or higher) during the exploitation, processing, and storage and transportation of petroleum and natural gas. Among them, the ultra-high sulfur-containing production device is commonly found in scenarios such as the development of high-sulfur gas fields, the treatment of refinery acid gas, and sulfur recovery. Its core challenge lies in the high toxicity, strong corrosiveness, and flammable and explosive risks of H2S.

[0105] In the embodiments of the present invention, the safety risk factor refers to a factor that endangers the service life of a sulfur-containing production device. For example, the safety risk factors include, but are not limited to, the flow velocity of objects, the partial pressure of objects, the ambient temperature, and the shape parameters of objects in a super-high sulfur-containing production device.

[0106] In the embodiments of the present invention, the collected data refers to the data collected by sensors installed in a super-high sulfur-containing production device.

[0107] The integrity prediction module 102 is configured to predict the integrity of a super-high sulfur-containing production device according to the safety risk factors by using a pre-constructed integrity prediction model, and obtain an integrity prediction result. The pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a stiffness and strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the stiffness and strength prediction model, and the seal life prediction model.

[0108] In the embodiments of the present invention, the flow erosion model refers to a model constructed by using data on the erosion of a super-high sulfur-containing production device by flowing objects in the super-high sulfur-containing production device.

[0109] As an embodiment of the present invention, the construction process of the flow erosion model includes:

[0110] Obtain the flow channel shape parameters and flow channel material parameters of the raw gas pipeline of the super-high sulfur-containing production device for natural gas;

[0111] Obtain the fluid pressure parameters of natural gas in the raw material pipeline, and analyze the basic parameters of the current natural gas composition;

[0112] Construct a flow erosion model according to the flow channel shape parameters, flow channel material parameters, fluid pressure parameters, and basic parameters of the natural gas composition.

[0113] In the embodiments of the present invention, the basic parameters of the current natural gas composition include, but are not limited to, the pressure, temperature, flow rate, composition, and particle size of the natural gas.

[0114] In the embodiments of the present invention, the raw gas pipeline refers to a pipeline system that transports untreated natural gas from the wellhead of a gas well to a gas gathering station or a treatment plant.

[0115] In the embodiments of the present invention, the flow channel shape parameters can be obtained by actual measurement of the pipeline system, the flow channel material parameters can be directly retrieved from a database, the basic parameters of the natural gas composition can be obtained by analyzing the extracted natural gas, and the fluid pressure parameters can be obtained by sensors or simulated by fluid dynamics.

[0116] Further, the flow erosion model is expressed as:

[0117]

[0118] Among them, R erosion is the erosion rate per unit area of the wall surface in the raw material gas pipeline, i is the number of the colliding particulate matters, N particle is the number of colliding particulate matters per unit area of the wall surface in the raw material gas pipeline, is the mass flow rate of the i-th colliding particulate matter, c(d p ) is the shape function of the colliding particulate matter, A face is the area of the wall surface calculation unit in the raw material gas pipeline, f(θ1) is the correction function of the impact angle on the erosion rate, θ1 is the impact angle, that is, the included angle between the particle movement direction and the wall surface normal direction, is the velocity function, d p is the diameter of the colliding particulate matter, and α is the empirical erosion coefficient determined according to the material properties.

[0119] As an embodiment of the present invention, the construction of the corrosion prediction model includes:

[0120] Obtain the partial pressure parameters of the component gases in the natural gas, and obtain the current temperature parameter and the current flow rate;

[0121] Construct a partial pressure corrosion model according to the partial pressure parameters, construct a temperature corrosion model according to the current temperature parameter, and construct a flow rate corrosion model according to the current flow rate;

[0122] Fuse the partial pressure corrosion model, the temperature corrosion model and the flow rate corrosion model to obtain the corrosion prediction model.

[0123] Furthermore, the corrosion prediction model is:

[0124]

[0125] Among them, lnv corr is the corrosion rate, A is the first influence coefficient, B is the second influence coefficient, K is the third influence coefficient, D is the fourth influence coefficient, E is the fifth influence coefficient, C is the comprehensive correction constant, is the influence term of H2S partial pressure corrosion, is the influence term of CO2 partial pressure corrosion, lnv is the influence term of flow rate corrosion, E a is the reaction activation energy of the raw material gas pipeline material, R is the universal gas constant, and T is the absolute temperature.

[0126] In the embodiment of the present invention, the comprehensive correction constant C is the sum of the partial pressure corrosion constant, the temperature corrosion constant and the flow rate corrosion constant.

[0127] Furthermore, in the partial pressure corrosion model, the following formula is used to calculate the partial pressure corrosion rate:

[0128]

[0129] Among them, is the partial pressure corrosion rate, and C1 is the partial pressure corrosion constant.

[0130] Furthermore, in the temperature corrosion model, the following formula can be used to calculate the temperature corrosion rate:

[0131]

[0132] Among them, is the temperature corrosion rate, and C2 is the temperature corrosion constant.

[0133] Furthermore, in the flow velocity corrosion model, the following formula can be used to calculate the flow velocity corrosion rate:

[0134]

[0135] Among them, n is the flow velocity constant, v is the flow velocity, is the flow velocity corrosion rate, and C3 is the flow velocity corrosion constant.

[0136] As an embodiment of the present invention, the construction of the seal life prediction model includes:

[0137] Obtain the gate valve structure of the raw material gas pipeline, and quantify the cavitation damage factor, erosion damage factor, and impact wear factor of the gate valve structure;

[0138] Use the cavitation damage factor, erosion damage factor, and impact wear factor to construct a seal index system, and construct a seal life prediction model according to the seal index system.

[0139] In the embodiment of the present invention, the cavitation damage factor refers to the situation where, in a working condition with a large pressure difference, when the working medium flows through the throttling surface between the valve core and the valve bottom, the flow velocity increases, the pressure drops rapidly to form bubbles, and the bubbles in the medium burst due to the pressure recovery, resulting in local hydraulic shock, leading to corrosion and pitting of the seal surface.

[0140] In the embodiment of the present invention, the erosion damage factor refers to the fact that the fluid flowing in the internal flow channel is a mixed medium of gas, liquid, and solid particles. The erosion of the solid particles will have a strong erosion effect on the gate valve seal, resulting in seal failure. The erosion of the gate valve mainly occurs in the area with large flow field changes, including the gate valve seal surface, resulting in many deep grooves scoured by the medium on the gate valve seat seal surface.

[0141] In the embodiment of the present invention, the impact wear factor means that during the opening and closing process of the gate valve, the sealing surface will be impacted and damaged, and the frictional wear caused by the long-term relative frictional movement of the sealing interface is also a great test for the sealing performance of the gate valve. The amount of seal wear is affected by factors such as the hardness matching of the wear pair, the wear resistance of the material, and the processing technology of the seal pair. The impact and wear of the gate valve seal are common seal failure forms during the long-term operation of the gate valve.

[0142] In the embodiment of the present invention, the seal index system is constructed by using the PCA principal component analysis method.

[0143] As an embodiment of the present invention, the construction of the stiffness prediction model includes:

[0144] The spatial structure of the raw material gas pipeline is divided into grids to obtain the raw material gas pipeline grid;

[0145] According to the raw material gas pipeline grid, a spatial model of the raw material gas pipeline is constructed, and the displacement component, strain component, and stress component are determined according to the spatial model. After adding boundary constraint conditions to the displacement component, strain component, and stress component, a stiffness prediction model is constructed, where the boundary constraint conditions include displacement boundary conditions and force boundary conditions.

[0146] In the embodiment of the present invention, the displacement component means that the displacement of any point in the raw material gas pipeline has displacement components in the x direction, y direction, and z direction, that is, the displacement component is (u, v, w), and there are 9 stress components. Due to the reciprocity of shear stresses, there is τ xy =τ yx ,τ yz =τ zy ,τ zx =τ xz , so the independent stress components are 6. The situation of the strain component is the same as that of the stress. Then the three major types of variables are as follows: (1) Displacement component: u, v, w; (2) Strain component: ε xx ε yy ε zz γ xy γ yz γ zx ; (3) Stress component: σ xx σ yy σ zz τ xy τ yz τ zx ; where u is the displacement component in the x-axis direction, v is the displacement component in the y-axis direction, w is the displacement component in the z-axis direction, ε xx is the unit length change rate in the x-axis direction, ε yy is the unit length change rate in the y-axis direction, ε zz is the unit length change rate in the z-axis direction, γxy is the change in the right angle in the x-y plane, γ yz is the change in the right angle in the y-z plane, γ zx is the change in the right angle in the z-x plane, σ xx is the normal stress in the x-axis direction, σ yy is the normal stress in the y-axis direction, σ zz is the normal stress in the z-axis direction, τ xy is the shear stress in the x-y plane, τ yz is the shear stress in the y-z plane, τ zx is the shear stress in the z-x plane.

[0147] In the embodiments of the present invention, the displacement boundary condition refers to the boundary condition used to constrain the displacement of a structure at a specific position or direction in mechanical analysis, and can be expressed by the following formula:

[0148]

[0149] Among them, S u is the boundary region where the displacement boundary condition is applied, is the displacement value specified in the x-axis direction on the boundary region, is the displacement value specified in the y-axis direction on the boundary region, is the displacement value specified in the z-axis direction on the boundary region.

[0150] In the embodiments of the present invention, the force boundary condition refers to the boundary condition used to specify the external forces (such as concentrated forces, distributed forces or surface forces) acting on a structure in a specific boundary region in mechanical analysis, and can be expressed by the following formula:

[0151]

[0152] Among them, S p is the boundary region where the force boundary condition is applied, is the external force component applied in the x-axis direction on the boundary region, is the external force component applied in the y-axis direction on the boundary region, is the external force component applied in the z-axis direction on the boundary region, n x is the cosine of the external normal direction of the boundary in the x-axis, n y is the cosine of the external normal direction of the boundary in the y-axis, n z is the cosine of the external normal direction of the boundary in the z-axis.

[0153] In the embodiments of the present invention, after normalizing the output results of the flow erosion model, the corrosion prediction model, the rigidity and strength prediction model, and the seal life prediction model into dimensionless prediction data, the integrity prediction result is output using a fully connected layer network.

[0154] In the embodiments of the present invention, the integrity prediction results include safe non-leakage, dangerous non-leakage, and dangerous leakage.

[0155] As Figure 3 shown, it is a schematic structural diagram of an electronic device for implementing a method for determining the integrity of an ultra-high sulfur-containing production device provided by an embodiment of the present invention.

[0156] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a program for a method for determining the integrity of an ultra-high sulfur-containing production device.

[0157] Among them, the processor 10 may be composed of integrated circuits in some embodiments. For example, it may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same or different functions, including a combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting various components of the entire electronic device through various interfaces and lines, and by running or executing programs or modules stored in the memory 11 (such as executing a program for a method for determining the integrity of an ultra-high sulfur-containing production device, etc.), and calling data stored in the memory 11, to perform various functions of the electronic device and process data.

[0158] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disks, multimedia cards, card-type memories (such as SD or DX memories, etc.), magnetic memories, magnetic disks, optical disks, etc. The memory 11 may be an internal storage unit of the electronic device in some embodiments, such as the mobile hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Further, the memory 11 may include both an internal storage unit and an external storage device of the electronic device. The memory 11 can not only be used to store application software installed on the electronic device and various types of data, such as the code of a program for a method for determining the integrity of an ultra-high sulfur-containing production device, etc., but also be used to temporarily store data that has been output or will be output.

[0159] The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to implement connection communication between the memory 11 and at least one processor 10, etc.

[0160] The communication interface 13 is used for communication between the above electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is generally used to establish a communication connection between this electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, and is used to display the information processed in the electronic device and to display a visual user interface.

[0161] Figure 3 Only an electronic device with components is shown. Those skilled in the art can understand that Figure 3 the shown structure does not constitute a limitation on the electronic device, and it may include fewer or more components than shown, or combine some components, or have different component arrangements.

[0162] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering each component. Preferably, the power source can be logically connected to the at least one processor 10 through a power management device, so as to implement functions such as charge management, discharge management, and power consumption management through the power management device. The power source may also include any components such as one or more DC or AC power sources, a recharge device, a power failure detection circuit, a power converter or an inverter, and a power status indicator. The electronic device may also include various sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0163] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0164] The program of the method for determining the integrity of an ultra-high sulfur content production device stored in the memory 11 of the electronic device is a combination of multiple instructions, which can be implemented when running in the processor 10:

[0165] Obtain the acquisition data of the ultra-high sulfur content production device, and extract safety risk factors from the acquisition data;

[0166] According to the safety risk factors, use the pre-constructed integrity prediction model to predict the integrity of the ultra-high sulfur content production device, and obtain the integrity prediction result. Among them, the pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the strength prediction model, and the seal life prediction model.

[0167] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiments of the attached drawings, which will not be elaborated here.

[0168] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory).

[0169] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor of the electronic device, it can:

[0170] Obtain the acquisition data of the ultra-high sulfur content production device, and extract safety risk factors from the acquisition data;

[0171] According to the safety risk factors, use the pre-constructed integrity prediction model to predict the integrity of the ultra-high sulfur content production device, and obtain the integrity prediction result. Among them, the pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the strength prediction model, and the seal life prediction model.

[0172] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation.

[0173] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional modules.

[0175] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0176] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

[0177] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0178] In addition, obviously the word "including" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or devices stated in the system claims can also be implemented by one unit or device through software or hardware. Words such as first, second, etc. are used to represent names and do not indicate any specific order.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. Method for determining the integrity of a high-sulfur production unit, characterized in that, The method includes: Obtaining the collected data of the ultra-high sulfur production device and extracting safety risk factors from the collected data; According to the safety risk factors, using a pre-built integrity prediction model to predict the integrity of the ultra-high sulfur production device, obtaining an integrity prediction result. Among them, the pre-built integrity prediction model includes a flow erosion model, a corrosion prediction model, a stiffness and strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the stiffness and strength prediction model, and the seal life prediction model.

2. The method for determining the integrity of a super-high sulfur content production device according to claim 1, characterized in that The construction process of the flow erosion model includes: Obtaining the flow channel shape parameters and flow channel material parameters of the raw gas pipeline of the ultra-high sulfur production device; Obtaining the fluid pressure parameters of the natural gas in the raw material pipeline and analyzing the basic parameters of the current natural gas components; Constructing a flow erosion model according to the flow channel shape parameters, flow channel material parameters, fluid pressure parameters, and basic parameters of natural gas components.

3. The method for determining the integrity of an ultra-high sulfur content production device according to claim 1 or 2, characterized in that, The flow erosion model is expressed as: Among them, Rerosion is the erosion rate per unit area of the wall surface in the raw material gas pipeline, i is the number of the colliding particulate matters, Nparticle is the number of colliding particulate matters per unit area of the wall surface in the raw material gas pipeline, is the mass flow rate of the i-th colliding particulate matter, c(dp) is the shape function of the colliding particulate matter, Aface is the area of the wall surface calculation unit in the raw material gas pipeline, f(θ1) is the correction function of the impact angle on the erosion rate, θ1 is the impact angle, that is, the angle between the particle movement direction and the wall surface normal direction, is the velocity function, dp is the diameter of the colliding particulate matter, and α is the empirical erosion coefficient determined according to the material properties.

4. The method for determining the integrity of a super-high sulfur-containing production device according to claim 1, characterized in that, The construction of the corrosion prediction model includes: Obtaining the partial pressure parameters of the component gases in the natural gas, and obtaining the current temperature parameter and the current flow rate; Constructing a partial pressure corrosion model according to the partial pressure parameters, constructing a temperature corrosion model according to the current temperature parameter, and constructing a flow rate corrosion model according to the current flow rate; Fusing the partial pressure corrosion model, the temperature corrosion model, and the flow rate corrosion model to obtain the corrosion prediction model.

5. The method for determining the integrity of a super-high sulfur-containing production device according to claim 4, characterized in that, The corrosion prediction model is: where lnvcorr is the corrosion rate, A is the first influence coefficient, B is the second influence coefficient, K is the third influence coefficient, D is the fourth influence coefficient, E is the fifth influence coefficient, and C is the comprehensive correction constant. is the corrosion influence term of H2S partial pressure. is the corrosion influence term of CO2 partial pressure, lnv is the corrosion influence term of flow velocity, Ea is the reaction activation energy of the raw material gas pipeline material, R is the universal gas constant, and T is the absolute temperature.

6. The method for determining the integrity of a super-high sulfur-containing production device according to claim 1, characterized in that, The construction of the seal life prediction model includes: Obtaining the gate valve structure of the raw gas pipeline and quantifying the cavitation damage factor, erosion damage factor, and impact wear factor of the gate valve structure; Using the cavitation damage factor, erosion damage factor, and impact wear factor to construct a seal index system, and constructing a seal life prediction model according to the seal index system.

7. The method for determining the integrity of an extra-high sulfur content production device as claimed in claim 1, wherein The construction of the stiffness and strength prediction model includes: Dividing the spatial structure of the raw gas pipeline into grids to obtain the raw gas pipeline grid; 8. A digital intelligence cloud platform, characterized in that, Constructing a spatial model of the raw gas pipeline according to the raw gas pipeline grid, determining the displacement component, strain component, and stress component according to the spatial model, and adding boundary constraint conditions to the displacement component, strain component, and stress component to construct the stiffness and strength prediction model. Among them, the boundary constraint conditions include displacement boundary conditions and force boundary conditions. The digital intelligent cloud platform is used to implement the method for determining the integrity of the ultra-high sulfur production device according to any one of claims 1 to 7. The digital intelligent cloud platform includes: A safety risk factor extraction module, which is used to obtain the collected data of the ultra-high sulfur production device and extract safety risk factors from the collected data; An integrity prediction module is configured to predict the integrity of an extra-high sulfur production device according to safety risk factors by using a pre-constructed integrity prediction model, so as to obtain an integrity prediction result. The pre-constructed integrity prediction model includes a flow erosion model, a corrosion prediction model, a strength prediction model, and a seal life prediction model. The integrity prediction result is obtained by fusing the output results of the flow erosion model, the corrosion prediction model, the strength prediction model, and the seal life prediction model.

9. An electronic device, characterized in that, The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the integrity of an extra-high sulfur production device according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the integrity of an extra-high sulfur production device according to any one of claims 1 to 7.

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