A method and system for evaluating the reliability of a subsea wellhead-conduit

Abstract

The application relates to a kind of underwater wellhead-conduit reliability evaluation method and system, define the fatigue limit state of wellhead and conduit;According to the value range of the parameter that influences the fatigue life of underwater wellhead and conduit, random sampling is carried out in the value range of all the parameters, forming several groups of discrete data of random variables, and each group of discrete data of the random variables is composed of the single discrete point of all parameters after sampling;The stress range of underwater wellhead and conduit under the action of each group of discrete points is obtained, and then the function function of fatigue limit state represented by random variable is obtained;Solve the function function, obtain the fatigue life reliability index value of underwater wellhead and conduit, and then carry out reliability evaluation.Compared with the prior art, the reliability index value after solving can reflect the wellhead fatigue reliability under the multi-source randomness, and the accuracy of evaluation is better.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of offshore oil engineering, in particular to underwater wellhead and guide pipe prediction and evaluation technology. BACKGROUND

[0002] The underwater wellhead and guide pipe are important structural equipment in the deepwater oil and gas development link, and have been subjected to cyclic load environment directly or indirectly due to waves and ocean currents for a long time. However, due to the harsh environment, it is difficult to realize the overall structure monitoring and stability evaluation. In addition, there are many uncertain factors affecting the process from processing and manufacturing to in-service, which leads to the accumulation of error of analysis results, and makes it difficult to evaluate the accuracy of the results. The reliability of the underwater wellhead and guide pipe system is the key link to ensure the normal operation of deepwater oil and gas development. Therefore, the reliability evaluation of the underwater wellhead and guide pipe has important value for the detection and maintenance of deepwater key equipment.

[0003] With the continuous deepening of the trend of deepwater and ultra-deepwater exploration and development, the fatigue life reliability of the underwater wellhead and guide pipe is facing more severe tests. The multi-source uncertainty caused by the complexity of deepwater operation and the process concealment exist in the production and manufacturing and service process of the underwater wellhead and guide pipe structure. The uncertainty sources of the underwater wellhead and guide pipe can be divided into the following three categories: physical uncertainty, data uncertainty and model uncertainty.

[0004] Comprehensive consideration of the safety performance evaluation of the underwater wellhead and guide pipe under multi-source uncertainty has important practical significance for improving the prediction accuracy. The physical uncertainty is also called physical variability. The underwater wellhead and guide pipe are subjected to load conditions (bending moment load, current force load, bearing load, etc.), material properties (elastic modulus, Poisson's ratio, yield strength, etc.), model parameters (processing error, structure micro-variation caused by corrosion). The data uncertainty is also called statistical uncertainty, which mainly includes data source uncertainty, distribution type uncertainty and finite element analysis uncertainty. Specifically, the material distribution type and finite element result data. The model uncertainty mainly comes from the idealized assumption in mathematical modeling analysis, the difference in understanding of physical process, the difference in analysis model theory and applicability.

[0005] Therefore, in the service process of the underwater wellhead and guide pipe, the performance of the in-service equipment is greatly affected by the multi-source random factors. The existing technology does not consider the uncertainty of random factors when obtaining and evaluating the reliability index value of the underwater wellhead and guide pipe, which leads to the distortion of the evaluation results. SUMMARY

[0006] One of the purposes of the present application is to provide a method for evaluating the reliability of the underwater wellhead and guide pipe to solve the problem of distorted evaluation results in the prior art. The second purpose is to provide a system for evaluating the reliability of the underwater wellhead and guide pipe.

[0007] To achieve the above object, the technical scheme adopted by the present application is as follows:

[0008] A method for evaluating reliability of underwater wellhead-catheter,

[0009] Defining fatigue limit state of wellhead and catheter;

[0010] According to the value range of parameters affecting fatigue life of underwater wellhead and catheter, random sampling is performed in all value ranges of the parameters to form discrete data of random variables in several groups, and each group of discrete data of the random variables is composed of single discrete points of all parameters after sampling;

[0011] The stress range of underwater wellhead and catheter under the action of each group of discrete points is obtained, and then a function function representing fatigue limit state by random variables is obtained;

[0012] The function function is solved to obtain the reliability index value of fatigue life of underwater wellhead and catheter, and then reliability evaluation is performed.

[0013] According to the above technical means, the parameter uncertainty is dataized by forming discrete data of random variables in several groups through random sampling, and the reliability index value obtained by solving the function function considers the parameter uncertainty, thereby effectively improving the accuracy of evaluation.

[0014] Further, the method for defining fatigue limit state of wellhead and catheter is:

[0015] logN=A-mlogΔS

[0016] In the formula, A and m are the intercept and slope of the S-N curve, respectively, which can be obtained from the DNV standard; and ΔS is the stress range.

[0017] Further, the fatigue limit state of underwater wellhead and catheter is obtained according to the following formula:

[0018] g f,SN =log(N)-log(N t )

[0019] Where f represents the fatigue limit state; SN represents the S-N curve method; N is the failure load cycle number determined by the S-N curve formula in the DNV standard; and N t is the expected loading cycle number in a given time period.

[0020] Further, the discrete data of the random variables are taken as independent variables, and the stress range is taken as dependent variables, the independent variables and dependent variables are one-to-one corresponding, and the function function is obtained by multiple regression method.

[0021] Further, the function function is solved by the FORM method, so as to obtain the reliability index value.

[0022] Further, the types of the parameters at least include bending moment load, sea current force load, bearing load, elastic modulus, Poisson's ratio, yield strength, processing error and structure micro-variation caused by corrosion.

[0023] Further, the random sampling method is a Latin hypercube sampling method.

[0024] An underwater wellhead-catheter reliability evaluation system based on the above method comprises a fatigue limit state definition module configured to define fatigue limit states of the underwater wellhead and the catheter.

[0025] A value interval selection module configured to determine a value interval in the value range of the input parameters affecting the fatigue life of the underwater wellhead and the catheter;

[0026] An independent variable determination module configured to perform random sampling in the value interval of all the parameters, to form discrete data of a plurality of groups of random variables, and each group of the discrete data of the random variables is composed of a single discrete point after sampling of all the parameters;

[0027] A dependent variable determination module configured to obtain a stress range of the underwater wellhead and the catheter corresponding to the discrete data of each group of random variables in combination with the discrete data of the random variables;

[0028] A function function determination module configured to obtain a function function representing the fatigue limit state by using the random variables in combination with the parameters obtained by the independent variable determination module and the dependent variable determination module;

[0029] A reliability evaluation module configured to solve the function function, to obtain a fatigue life reliability index of the underwater wellhead and the catheter, and to perform reliability evaluation by using the reliability index.

[0030] Further, the function function determination module takes the discrete data of the random variables as independent variables, takes the stress range as dependent variables, and the independent variables and the dependent variables are one-to-one corresponding, and the function function is obtained by using a multiple regression method.

[0031] Advantages of the present application:

[0032] Compared with the prior art, the present application parameterizes the uncertainty degree of the parameters affecting the fatigue life of the underwater wellhead and the catheter, and represents the relationship between the fatigue life and the parameters under the condition of considering the uncertainty by using a function function. The reliability index value after solving can reflect the wellhead fatigue reliability under the condition of multiple source randomness, and the evaluation accuracy is better. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 This is a flowchart of the method described in Embodiment 1 of the present invention;

[0034] Figure 2 This is a structural diagram of the system described in Embodiment 2 of the present invention.

[0035] The module includes: 1- Fatigue limit state definition module; 2- Value range selection module; 3- Independent variable determination module; 4- Dependent variable determination module; 5- Functional function determination module; and 6- Reliability assessment module. Detailed Implementation

[0036] The following description, with reference to the accompanying drawings and preferred embodiments, illustrates the implementation of the technical solution of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0037] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0038] Example 1

[0039] This embodiment proposes a method for evaluating the reliability of underwater wellhead-conductor connections. The method is as follows:

[0040] S1: Defines the fatigue limit state of the wellhead and guide tube.

[0041] According to DNV standards, the S-N curve can be expressed as follows:

[0042] logN=A-mlogΔS (1)

[0043] --------------------

[0044] In the formula, A and m are the intercept and slope of the SN curve on the logarithmic plot, respectively. The two parameters of the SN curve for the subsea wellhead and the guide tube can be obtained from the DNV standard.

[0045] The function for fatigue reliability assessment in the above steps is shown in the following formula:

[0046] g f,SN =log(N)-log(N)t (2)

[0047] ----------------------

[0048] Where f represents the fatigue limit state; SN represents the S-N curve method; N is the number of failure load cycles determined by the S-N curve formula in the DNV standard; N t It represents the expected number of loading loops within a given time period.

[0049] This formula is the solution function for the reliability assessment FORM. By combining equations (1) and (2) and then fitting ΔS, the function of the fatigue limit state can be obtained.

[0050] S2: Based on the value range of the parameters affecting the fatigue life of the subsea wellhead and guide pipe, random sampling is performed within the value range of all the parameters to form several sets of discrete data of random variables.

[0051] As described in the background section, numerous uncertainties exist in the manufacturing and service processes of subsea wellheads and guide pipe structures. These uncertainties can be categorized into three types: physical uncertainties, data uncertainties, and model uncertainties. Therefore, the coefficient of variation is used to select the range of values ​​for parameters evaluating the reliability of subsea wellheads and guide pipes; that is, the coefficient of variation characterizes the degree of dispersion of discrete points within the value range.

[0052] The formula for calculating the coefficient of variation (COV) is as follows:

[0053]

[0054] Where u is the mean and s is the standard deviation; the coefficient of variation is a measure of the uncertainty and dispersion of a random variable.

[0055] In this embodiment, after selecting an initial value range, the coefficient of variation for that range is calculated.

[0056] The parameter selection in this embodiment is related to the load conditions, material properties, and model parameters of the underwater wellhead and guide tube. More specifically, these include bending moment load, ocean current load, load-bearing load, elastic modulus, Poisson's ratio, yield strength, processing error, and structural micro-deformation caused by corrosion.

[0057] Taking ocean current load as an example, in this embodiment, the range of discrete points representing the ocean current load applied to the wellhead and guide pipe in the sea area where this study is located, obtained from publicly available literature, is [F1, F2...F...]. nAfter statistically analyzing the data from the literature, the coefficient of variation is calculated using the method described above, and outliers are removed. The coefficient of variation reflects the dispersion of a random variable. The range of values ​​is represented by discrete points. The dispersion of each random variable is then determined using the above method.

[0058] Further, the value ranges of all parameters are determined using the above method. Then, the value ranges of each parameter are randomly sampled using the Latin hypercube sampling method. The randomly sampled data are then combined to form several sets of discrete data for random variables. Each set of discrete data includes a single discrete point of all parameters. For example, the discrete data of a certain set of random variables is...

[0059]

[0060] S3: Obtain the stress range of the underwater wellhead and guide tube under the action of each set of discrete points, and then obtain the function representing the fatigue limit state using random variables.

[0061] In this step, a finite element model of the underwater wellhead-conduit is established. In this finite element model, the soil reaction force of the seabed mudline is replaced by a spring model. Then, the discrete data of each set of random variables are input into the finite element model to obtain the stress range corresponding to the discrete data of each set of random variables. The bending moment load, ocean current load, load-bearing load, elastic modulus, Poisson's ratio, yield strength, processing error, and structural microvariables caused by corrosion are the independent variables (x). 1n x 2n ...x 8n The calculated stress range is the dependent variable ΔS. Then, a multivariate regression method is used to fit the stress, thereby obtaining a function that represents the fatigue limit state using random variables.

[0062] In this embodiment, the specific steps for fitting the fatigue life reliability function of the subsea wellhead and guide tube are as follows:

[0063] 1. The independent variable (random variable x) 1n x 2n ......x 8n The dependent variable ΔS and the dependent variable ΔS are sorted in columns;

[0064] 2. Import the sorted results into relevant software that can be used for fitting, and construct the functional relationship between the random independent variables and the dependent variables;

[0065] 3. Obtain the fitting function for random factors and establish the fuzzy fatigue reliability function for the underwater wellhead and guide tube.

[0066] S4: Solve the function for the fatigue limit state to obtain the reliability index of the fatigue life of the subsea wellhead and guide tube, and then conduct a reliability assessment based on the reliability index. In this embodiment, the specific solution method uses the first-order reliability method (FORM) as an example to solve the reliability index of the fatigue life of the subsea wellhead and guide tube.

[0067] Specifically, the reliability metric is calculated using FORM through the following iterative procedure:

[0068] 1. Define the function and use FORM to solve for the fatigue limit state function represented by random variables.

[0069] 2. Take the average value point as the initial design point and calculate the gradient of the limit state function at the design point.

[0070] 3. Estimate the initial reliability index β (using the mean method) and direction cosine α:

[0071]

[0072]

[0073] 4. Calculate a new design point;

[0074] x i,k =μ xi +βσ xi α i

[0075]

[0076] 5. Update the reliability index β and direction cosine α using the following two equations;

[0077]

[0078]

[0079] Repeat steps 4 and 5 until the reliability index values ​​converge, thus obtaining the reliability index values ​​for the fatigue life of the subsea wellhead and guide tube. Then, evaluate the reliability of the subsea wellhead-guide tube based on the reliability index values.

[0080] The evaluation parameter considers all random variables (load conditions (bending moment load, ocean current load, load-bearing load, etc.), material properties (elastic modulus, Poisson's ratio, yield strength, etc.), and model parameters (processing error, structural micro-changes caused by corrosion). The larger this value is, the greater the reliability of fatigue life.

[0081] Example 2

[0082] This embodiment, based on Embodiment 1, proposes a subsea wellhead-conduit reliability assessment system, such as... Figure 2As shown, it includes:

[0083] Fatigue limit state definition module 1 is configured to define the fatigue limit states of the subsea wellhead and the guide tube;

[0084] The value range selection module 2 is configured to determine the value range within the range of values ​​of the input parameters that affect the fatigue life of the subsea wellhead and guide pipe;

[0085] The independent variable determination module 3 is configured to randomly sample within the value range of all the parameters to form discrete data of several sets of random variables.

[0086] The dependent variable determination module 4 is configured to combine the discrete data of random variables to obtain the stress range of the underwater wellhead and the guide pipe corresponding to the discrete data of each set of random variables.

[0087] Function determination module 5 is configured to combine the parameters obtained by the independent variable determination module and the dependent variable determination module to obtain a function representing the fatigue limit state using random variables;

[0088] Reliability assessment module 6 is configured as a solution function to obtain reliability indicators of underwater wellhead and guide tube fatigue life, and then conducts reliability assessment through reliability indicators.

[0089] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A method for assessing the reliability of a subsea wellhead-conductor, characterized in that: Define the fatigue limit states of the wellhead and the guide pipe; Based on the value range of the parameters affecting the fatigue life of the underwater wellhead and guide tube, random sampling is performed within the value range of all the parameters to form several sets of discrete data of random variables. The discrete data of each set of random variables consists of a single discrete point after sampling of all the parameters. The stress range of the underwater wellhead and guide tube under the action of each set of discrete points is obtained, and then the function representing the fatigue limit state using random variables is obtained. Solve the aforementioned function to obtain the reliability index values ​​of the fatigue life of the subsea wellhead and guide tube, and then conduct a reliability assessment.

2. The method according to claim 1, characterized in that: The method for defining the fatigue limit state of the wellhead and the guide tube is as follows: logN = A - mlogΔS In the formula, A and m are the intercept and slope of the SN curve, respectively, which can be obtained from the DNV standard; ΔS is the stress range. The fatigue limit state of the submersible wellhead and guide tube can then be obtained using the following formula: g f,SN =log(N)-log(N t ) Where f represents the fatigue limit state; SN represents the S-N curve method; N is the number of failure load cycles determined by the S-N curve formula in the DNV standard; N t It represents the expected number of loading loops within a given time period.

3. The method according to claim 1, characterized in that: The discrete data of the random variable are used as independent variables, and the stress range is used as the dependent variable. The independent variables and the dependent variables are in one-to-one correspondence. The functional function is obtained by multiple regression.

4. The method according to claim 3, characterized in that: The reliability index value is obtained by solving the functional function using the FORM method.

5. The method according to claim 1, characterized in that: The types of parameters include at least bending moment load, ocean current load, load-bearing load, elastic modulus, Poisson's ratio, yield strength, processing error, and structural micro-deformation caused by corrosion.

6. The method according to claim 1, characterized in that: The random sampling method used is the Latin hypercube sampling method.

7. A subsea wellhead-conduit reliability assessment system based on the method described in any one of claims 1-6, characterized in that: include: The fatigue limit state definition module is configured to define the fatigue limit states of the subsea wellhead and the guide tube. The value range selection module is configured to determine the value range within the range of values ​​of the input parameters that affect the fatigue life of the subsea wellhead and guide pipe; The independent variable determination module is configured to randomly sample within the value range of all the parameters to form several sets of discrete data of random variables. Each set of discrete data of random variables consists of a single discrete point after sampling of all the parameters. The dependent variable determination module is configured to combine the discrete data of random variables to obtain the stress range of the underwater wellhead and guide pipe corresponding to the discrete data of each set of random variables. The function determination module is configured to combine the parameters obtained by the independent variable determination module and the dependent variable determination module to obtain a function representing the fatigue limit state using random variables; The reliability assessment module is configured to solve the aforementioned function to obtain the reliability index of the fatigue life of the subsea wellhead and guide tube, and to conduct a reliability assessment based on the reliability index.

8. The system according to claim 7, characterized in that: The function determination module uses the discrete data of the random variable as the independent variable and the stress range as the dependent variable, with a one-to-one correspondence between the independent and dependent variables, and obtains the function through a multiple regression method.

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