Method for predicting erosion deformation and service life of heavy oil thermal recovery wellhead device

Abstract

The present application relates to the technical field of safety evaluation of heavy oil thermal recovery well, in particular to a method for predicting erosion deformation and service life of heavy oil thermal recovery wellhead device. The method comprises the following steps: (1) collecting simulation erosion experiment data; (2) drawing erosion change curve between dependent variables of erosion rate and erosion mass loss, and determining erosion damage critical speed; (3) determining erosion rate formula; (4) calculating fluid mechanics simulation experiment, and obtaining erosion thinning amount distribution under different oil production conditions; (5) judging bearing capacity; (6) obtaining final erosion deformation and predicted service life. The present application has the advantages of rapidness and accuracy.

Description

Technical Field

[0001] This invention relates to the field of safety evaluation technology for heavy oil thermal recovery wells, specifically to a method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment. Background Technology

[0002] Sand and gravel are often mixed in during oil and gas extraction, which is a major problem hindering oilfield development. These sand and gravel can cause a series of problems, such as increased pressure differential, pipeline blockage, and erosion corrosion.

[0003] The damage caused to materials by high-velocity corrosive fluids, i.e., the combination of corrosive fluids and high velocity, is called erosion corrosion. Static or slowly flowing corrosive media have a relatively low corrosion rate, while high-velocity corrosive fluids not only directly contact the substrate material but also damage the protective film of surface corrosion products, forming mortar and exposing the fresh metal surface, leading to rapid scouring and corrosion. Erosion corrosion is a common problem in steel pipeline systems transporting steam containing condensate droplets; it is frequently observed at pipe bends, flow restrictors, turbines, pumps, and other locations that can alter the flow direction or velocity and enhance eddies.

[0004] Heavy oil is the residual heavy oil after gasoline and diesel are extracted from crude oil. It is characterized by its large molecular weight and high viscosity. The specific gravity of heavy oil is generally between 0.82 and 0.95, and its calorific value is between 10,000 and 11,000 kcal / kg. Its main components are hydrocarbons, with some sulfur and trace amounts of inorganic compounds.

[0005] Compared with conventional light crude oil, heavy oil has the following main characteristics: (1) High viscosity, high density, and poor fluidity. This not only increases the difficulty and cost of extraction, but also makes the final recovery rate of the oil field very low. The key to heavy oil extraction is to improve its flowability in the oil layer, wellbore, and gathering and transportation pipeline. (2) Heavy oil has a low content of light components, but a high content of gum and asphaltenes. (3) The viscosity of heavy oil is sensitive to temperature. As the temperature of heavy oil increases, its viscosity decreases significantly, which is also the main mechanism of thermal recovery of heavy oil.

[0006] Thermal treatment of oil reservoirs is a technique that provides heat energy to the reservoir, raising the temperature of the reservoir rocks and fluids. This increases the reservoir's driving force, reduces the viscosity of the reservoir fluids, prevents wax deposition, and reduces seepage resistance, ultimately leading to better extraction of heavy and high-pour-point oils. Currently, commonly used thermal treatment techniques mainly include two methods: injecting hot fluids (such as steam and hot water) and firing the reservoir. Steam injection, based on its specific production process characteristics, primarily includes steam huff and puff and steam drive.

[0007] Due to the complexity of the thermal recovery process, heavy oil wellhead equipment is often placed in harsh environments.

[0008] Erosion corrosion can lead to thinning failure, resulting in crude oil or steam leakage. Due to the high temperatures in heavy oil thermal recovery wells, such failure could cause personal injury, making it more dangerous than general wellhead equipment. Furthermore, the erosion resistance of materials weakens further at high temperatures, increasing the likelihood of erosion corrosion. Therefore, conducting erosion and life prediction studies is essential for wellhead integrity management and improving the reliability of heavy oil thermal recovery wellheads. Currently, research on erosion at heavy oil thermal recovery wellheads is limited both domestically and internationally, and no methods for predicting and assessing the life of such erosion have been proposed.

[0009] Chinese patent application CN112182793A discloses a method for predicting the erosion life of a gas well sand control pipe, comprising: establishing a prediction model for the erosion rate of the gas well sand control pipe, wherein the parameters for calculating the erosion rate of the gas well sand control pipe in the prediction model include: fluid velocity, sand concentration, and correlation coefficient; determining the value of the correlation coefficient in the prediction model by combining gas-solid two-phase erosion physical simulation experiments and gas well erosion numerical simulations; determining the maximum fluid velocity of the actual downhole borehole gas and the sand control pipe filter unit based on the field data of the tested gas well sand control pipe, and combining the allowable sand concentration of the gas well in the field to predict the life of the tested downhole sand control pipe. However, this method has low prediction efficiency and its accuracy needs to be improved.

[0010] Therefore, it is essential to develop a method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment that can solve the above-mentioned technical problems. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a rapid and accurate method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment. This method considers the influence of factors such as fluid velocity, sand concentration, erosion angle, material hardness, temperature, and damage location on the erosion of heavy oil thermal recovery wellhead equipment. It can quantitatively and quickly calculate the erosion life of the wellhead equipment and rapidly assess its remaining strength to determine whether the wellhead equipment can continue to be used.

[0012] This invention is achieved through the following technical solutions:

[0013] A method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment includes the following steps:

[0014] (1) Collecting simulated scouring experimental data

[0015] Samples were taken from the wellhead and subjected to simulated erosion experiments to collect data on the experimental time and erosion mass loss.

[0016] (2) Plot the erosion variation curve

[0017] Based on the data from step (1), plot the erosion change curve between the erosion rate and the dependent variable of erosion mass loss, and determine the critical velocity of erosion failure, wherein the erosion rate is the erosion mass loss per unit time, and the dependent variable of erosion mass loss includes fluid velocity.

[0018] (3) Determine the erosion rate formula

[0019] The data from step (1) are fitted using the nonlinear fitting function of the erosion model to determine the empirical parameters, thereby determining the erosion rate formula.

[0020] (4) Computational Fluid Dynamics Simulation Experiment

[0021] Substitute the critical erosion failure velocity obtained in step (2) and the erosion rate formula obtained in step (3) into the computational fluid dynamics simulation experiment to obtain the erosion thinning distribution under different oil production conditions.

[0022] (5) Load-bearing capacity assessment

[0023] The erosion thinning amount distribution obtained in step (4) introduces the concept of virtual time step. It is assumed that the erosion rate remains unchanged within the virtual time step, and the erosion thinning amount after the virtual time step is obtained. The wall thickness of the device after erosion is obtained by subtracting the erosion thinning amount from the wall thickness. Based on the bearing capacity-wall thickness correspondence, the bearing capacity is queried and compared with the rated pressure bearing capacity of the wellhead to determine whether the wellhead device can continue to be used safely.

[0024] Wherein, erosion thinning amount = erosion rate × virtual time step;

[0025] (6) Obtain the final erosion deformation and expected service life

[0026] If the conclusion of step (5) is that the wellhead device can continue to be used, then a new production tree structure is obtained based on the erosion thinning amount, and steps (4) and (5) are repeated based on the structure until the wellhead device can no longer withstand the wellhead rated pressure, and the final erosion deformation and expected service life are obtained; if the conclusion of step (5) is that the wellhead device can no longer withstand the wellhead rated pressure, then the final erosion deformation and expected service life are obtained accordingly.

[0027] Erosion deformation is the cumulative amount of thinning from each erosion event.

[0028] The expected lifespan is the cumulative duration of each virtual time step.

[0029] Preferably, the dependent variable of the erosion quality damage further includes at least one of erosion angle, sand concentration, material hardness, and temperature.

[0030] Preferably, based on the erosion rate versus fluid velocity curve, the erosion rate increases significantly once the fluid velocity reaches the critical erosion failure velocity.

[0031] Preferably, the erosion characteristics are determined based on the relationship between the erosion rate and the dependent variable.

[0032] For example, the relationship between erosion rate and erosion angle can be used to determine whether the erosion is of brittle or ductile materials.

[0033] Brittle materials: The higher the erosion angle, the higher the erosion rate;

[0034] Plastic materials: The lower the erosion angle, the higher the erosion rate.

[0035] Different materials require different erosion models.

[0036] Preferably, the erosion model in step (3) includes at least one of the Finnie erosion model, Bitter erosion model, Oka et al. erosion model, Tabakoff et al. erosion model, Zhang et al. erosion model and Ahlert erosion model.

[0037] More preferably, the erosion model in step (3) is the Finnie erosion model.

[0038] (1) Finnie erosion model

[0039] Finnie proposed the first erosion model for materials. The model is primarily based on micro-cutting wear theory, addressing the erosion wear caused by a single particle impacting the material. It employs various fundamental assumptions and has been continuously developed and improved in subsequent research. The expression for the Finnie erosion model is shown in equation (1-1):

[0040]

[0041] In the formula, V represents the volume loss of the material wall; m s Indicates the mass of a single particle; v s σ represents the velocity of a single particle. f D represents fluid stress; c Let f(α) represent the cutting depth; f(α) represent the impact angle function of the particle with respect to α; and K represent the ratio of the vertical force to the horizontal force acting on the solid particle. However, this model has some limitations. Finnie's erosion model underestimates material loss during large-angle impacts on the wall and overestimates material loss during small-angle impacts.

[0042] (2) Bitter erosion model

[0043] Based on Finnie's erosion model, Bitter conducted experiments on particles impacting material walls at large impact angles and proposed the deformation wear theory. Deformation wear of materials occurs when particles continuously collide with the material wall, causing deformation and fracture of the wall material. Its starting point is the energy balance during erosion. Bitter believes that erosion includes cutting wear and deformation wear, therefore classifying the erosion loss of materials into cutting loss and deformation loss.

[0044] Deformation and wear:

[0045]

[0046] Cutting wear:

[0047]

[0048]

[0049] In the formula, V represents the volume loss of the material; M represents the total mass of the solid particles; v s α represents the particle impact velocity; α is the impact angle; α0 represents the impact angle when the particle's horizontal velocity is exactly zero upon leaving the object; C, k, and k1 are constants. ε s Deformation wear constant; ξ is the cutting wear constant.

[0050] (3) Oka et al. Erosion model

[0051] Based on extensive wear experiments and several key factors, Oka et al. proposed a comprehensive erosion equation. The equation considers factors such as particle impact angle, impact velocity, material hardness, particle size, and particle shape. The specific form of the model is shown in equations (1-5) and (1-6):

[0052]

[0053]

[0054] In the formula, HV is the Vickers number, representing the material hardness; d s Indicates particle size; d c Indicates the reference particle size; v s Indicates particle impact velocity; v c Indicates the particle reference velocity; k0, k1, k2, k3, n1, n2, d c and v c It is related to materials and is obtained through material erosion experiments.

[0055] (4) Tabakoffetal. Erosion Model

[0056] Based on the volume loss caused by coal ash impacting the material wall, Tabakoff et al. proposed an erosion model equation that includes erosion influence parameters such as particle impact velocity and impact angle. The erosion wear rate V is expressed as the ratio of the mass of material lost to the mass of the impacting particles. This erosion model equation is shown in equation (1-7):

[0057]

[0058] In the formula, W1 represents the particle impact velocity; β1 represents the particle impact angle; β0 represents the impact angle at maximum wear, 20°; R1 = 1 - 0.0016W1 sinβ1; C K C is a constant, when β1≤3β0. K =1; when β1>3β0, C K =0; K1, K2, and K3 are constants determined by the wall material, in Tabakoff

[36] In the study, K1 = 1.505 × 10 -6 K2 = 0.296, K3 = 5.0 × 10 -12 .

[0059] (5) Zhang et al. Erosion model

[0060] Researchers at the Erosion / Corrosion Research Laboratory at the University of Tulsa conducted erosion experiments on particles of different shapes at different impact angles, and proposed an erosion model to predict the erosion rate of dry and wet material surfaces. The equations given by Zhang et al. are shown in equations (1-8) and (1-9):

[0061] V = K(BH) -0.59 F S u p 2.41 F(α) (1-8)

[0062]

[0063] In the formula, K is a constant; for steel pipes, K = 2.17 × 10⁻⁶. -7 BH indicates the Brinell hardness of the material; F S F is the particle shape factor; for sharp-angled particles S The value is 1, while for spherical particles F S R is 0.2; i The values ​​are 5.4, -10.11, 10.93, -6.33, and 1.42, respectively.

[0064] (6) Ahlert erosion model

[0065] Ahlert constructed an erosion model that can be used for AISI1018 material, and the equations are shown in equations (1-10)(1-11)(1-12):

[0066] V = A(BH) -0.59 F S u p 1.73 F(α) (1-10)

[0067] F(α)=aα 2 +bα, α≤α0 (1-11)

[0068] F(α) = xcos 2 αsin(wα)+ysin 2 α+z, α>α0 (1-12)

[0069] In the formula, A represents the wall material constant; for carbon steel, A is 15.59 × 10⁻⁶. -7 BH indicates the Brinell hardness of the material; F S α represents the particle shape coefficient; α0 represents the critical angle, which is 15°; the values ​​of a, b, x, w, y and z are -38.4, 22.71, 3.147, 0.3609 and 2.532, respectively.

[0070] Preferably, the simulated erosion experiment in step (1) is conducted using a high-temperature, high-velocity erosion experimental apparatus.

[0071] Preferably, the fluid dynamics simulation experiment in step (4) is to solve the control conservation equations (mass, momentum and energy) of multiphase flow, such as the Navier-Stokes equation, and add boundary condition constraints of the computational domain to obtain the flow field distribution in the target area.

[0072] Preferably, the bearing capacity-wall thickness correspondence in step (5) is the correspondence between the pressure and minimum wall thickness of the wellhead device obtained through plastic limit analysis.

[0073] Regarding the acquisition of samples, since most well trees are cast or forged, micro-samples can be taken from the load-bearing or non-load-bearing parts to conduct laboratory erosion experiments, which can realistically simulate the scouring process of the well tree.

[0074] Preferably, in step (4), samples can also be obtained at different locations of the well tree, and steps (1)-(4) can be repeated to obtain the erosion and thinning distribution of different locations of the well tree under different oil production conditions.

[0075] The beneficial effects of this invention are:

[0076] This invention uses indoor erosion experiments with micro-samples to correct erosion models, significantly improving the accuracy of erosion thinning prediction. This method can quantitatively calculate the erosion corrosion rate and erosion thinning amount of heavy oil thermal recovery wellhead equipment. By querying the bearing capacity-wall thickness correspondence, the safety status of heavy oil thermal recovery wellhead equipment can be quickly determined, greatly improving efficiency. This method provides stronger safety assurance for non-stop inspection of heavy oil thermal recovery wellheads. Furthermore, this method can focus on monitoring easily eroded areas based on deformation distribution and shorten the wellhead inspection interval when the expected service life is approaching, ensuring the safety of heavy oil thermal recovery wellhead equipment. Attached Figure Description

[0077] Figure 1 This is an analysis diagram of the sand sample from the field in Example 1.

[0078] Figure 2 The graph shows the relationship between the erosion rate, erosion angle, and fluid velocity obtained in Example 1.

[0079] Figure 3 This is a schematic diagram of the erosion thinning distribution obtained from computational fluid dynamics calculations in Example 1.

[0080] Figure 4 This is a schematic diagram of the wellhead structure in Example 1. Wherein:

[0081] Four-way body dimensions: D: outer diameter of the main pipe; Defect dimensions: L: length of the defect in the main pipe;

[0082] d: Outer diameter of the branch pipe; l: Length of the branch pipe defect;

[0083] T: Main pipe wall thickness; B: Half of the circumferential angle of the branch pipe defect;

[0084] t: Branch pipe wall thickness. C: Remaining thickness. Detailed Implementation

[0085] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0086] Example 1

[0087] This embodiment provides a method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment, including the following steps:

[0088] (1) Simulated scouring experiment

[0089] The wellhead (model: KR14-337, structural diagram as shown) Figure 4 As shown, samples were taken from the bearing area (flange location of the wellhead, wire-cut sampling, sample size 40mm×30mm×4mm) of the wellhead bearing area (including the locally thinned shoulder). A simulated erosion test of the wellhead was conducted. The erosion medium was a solid-liquid two-phase flow. The solid phase was determined based on the analysis results of the sand samples from the field, and the liquid phase was tap water with a sand content of 5% by mass. The outlet diameter of the erosion test was 8mm, the erosion time was 8 hours, and the erosion angles were 20°, 35°, 50°, 70°, and 90°, with fluid velocities of 1m / s, 5m / s, 10m / s, 20m / s, and 30m / s, respectively. The collected experimental data are shown in Table 1, and the calculated erosion rate data are shown in Table 2.

[0090] Figure 1 The results of the on-site sand sample analysis were obtained by... Figure 1 It can be seen that the sand particle size is mainly distributed around 100 micrometers, accounting for 64% of the volume. Based on the sand sample analysis results, the sand diameter ratio was determined. The sand used in the experiment was quartz sand with a particle size of 100 micrometers.

[0091] Table 1 Erosion Experiment Data

[0092]

[0093]

[0094] Table 2 Erosion Rate Data

[0095]

[0096] (2) Plot the curve of erosion rate versus dependent variable.

[0097] Based on the experimental data obtained in step (1), plot the erosion variation curve between the erosion rate and the dependent variable of erosion mass loss (e.g., Figure 2 (as shown);

[0098] The critical velocity of erosion can be obtained from the erosion rate and the erosion variation curve. It is defined as the erosion rate will increase significantly after the fluid velocity reaches this value. The critical velocity of erosion obtained by the flange sample in this embodiment under the above experimental conditions is 10 m / s.

[0099] (3) Obtaining the erosion rate formula

[0100] The experimental data obtained in step (1) are fitted using a nonlinear fitting function to determine the empirical parameters in the wellhead erosion rate formula applicable to the target oil well, thereby determining the tree erosion rate formula applicable to the target oil well.

[0101] This embodiment uses the Finnie erosion model.

[0102]

[0103] The corrected erosion rate model expression

[0104] E = 1.211 × e -6 ×V P 1.74 (1.042×e -8 θ 5 -2.577×e -6 θ 4 +2.369×e -4 θ 3 -0.010θ 2 +0.174θ);

[0105] Where E is the dimensionless erosion rate, g / g; k is an empirical constant; V p γ is the particle impact velocity; γ is the impact angle; and f(γ) is a dimensionless function. The experiments primarily corrected for the empirical constants k and V. p The exponents n and f(γ).

[0106] (4) Computational Fluid Dynamics Simulation Experiment

[0107] Substituting the critical failure velocity obtained in step (2) and the erosion rate formula obtained in step (3) into the computational fluid dynamics simulation experiment, the erosion thinning distribution of the sample under different erosion angles and erosion velocities was obtained (e.g., Figure 3 (as shown);

[0108] (5) Load-bearing capacity assessment

[0109] The erosion thinning amount distribution obtained in step (4) introduces the concept of virtual time step. It is assumed that the erosion rate remains unchanged within the virtual time step, and the erosion thinning amount after the virtual time step is obtained. The wall thickness after erosion is obtained by subtracting the erosion thinning amount from the wall thickness. The erosion thinning amount of the wellhead device is estimated based on the erosion deformation. Based on the pre-calculated bearing capacity-erosion thinning amount-wall thickness correspondence, the bearing capacity is queried and compared with the rated pressure bearing capacity of the wellhead to determine whether the wellhead device can continue to be used safely.

[0110] Specifically Figure 4 The following diagram illustrates the four-way structure as an example:

[0111] Simulation experiments were conducted using different d / D and D / T data of the four-way valve to obtain different results. The value was fitted to obtain the following estimation formula for the dimensionless plastic limit internal pressure of a constant wall thickness tee:

[0112]

[0113] in The internal pressure of the defect-free four-way valve, calculated by finite element method, is the rated pressure, in MPa.

[0114] In this embodiment, the KR14-337 oil well tree has d / D = 1 and D / T = 10.

[0115] Then we can obtain,

[0116] The operating pressure of the KR14-337 wellhead is P = 0.7 MPa;

[0117] The four-way valve cannot be used anymore. If it is to be used, the pressure needs to be reduced or further detailed numerical analysis and evaluation should be carried out. (6) Obtain the final erosion deformation and expected service life.

[0118] If the conclusion of step (5) is that the wellhead device can continue to be used, then a new production tree structure is obtained based on the erosion thinning amount, and steps (4) and (5) are repeated based on the structure until the wellhead device can no longer withstand the wellhead rated pressure, and the final erosion deformation and expected service life are obtained; if the conclusion of step (5) is that the wellhead device can no longer withstand the wellhead rated pressure, then the final erosion deformation and expected service life are obtained accordingly.

[0119] Erosion deformation is the cumulative amount of thinning from each erosion event.

[0120] The expected lifespan is the cumulative total of all virtual time steps;

[0121] In this embodiment, when the uniform flow velocity of the sand-containing fluid is 5 m / s, each virtual time step is specifically the time for calculating the erosion thinning amount under a certain fixed erosion rate. The final erosion deformation results in a thinning of 2.42 mm / year per year. After ultrasonic testing, the remaining wall thickness is 8.4 mm. The minimum bearing wall thickness under this pressure is 5 mm, so the expected service life is 1.4 years.

[0122] The above detailed description is a specific description of one of the feasible embodiments of the present invention. This embodiment is not intended to limit the patent scope of the present invention. All equivalent implementations or modifications that do not depart from the present invention should be included within the scope of the technical solution of the present invention.

Claims

1. A method for predicting the erosion deformation and service life of heavy oil thermal recovery wellhead equipment, characterized in that, Includes the following steps: (1) Collect simulated scour experimental data Samples were taken from the wellhead and subjected to simulated erosion experiments to collect data on the experimental time and erosion mass loss. (2) Draw the erosion variation curve. Based on the data from step (1), plot the erosion change curve between the erosion rate and the dependent variable of erosion mass loss, and determine the critical erosion failure velocity, wherein the erosion rate is the erosion mass loss per unit time, and the dependent variable of erosion mass loss includes fluid velocity. (3) Determine the erosion rate formula The data from step (1) are fitted using the nonlinear fitting function of the erosion model to determine the empirical parameters, thereby determining the erosion rate formula. (4) Computational Fluid Dynamics Simulation Experiment Substitute the critical erosion failure velocity obtained in step (2) and the erosion rate formula obtained in step (3) into the computational fluid dynamics simulation experiment to obtain the erosion thinning distribution under different oil production conditions. (5) Bearing capacity assessment The erosion thinning amount distribution obtained in step (4) introduces the concept of virtual time step. It is assumed that the erosion rate remains unchanged within the virtual time step, and the erosion thinning amount after the virtual time step is obtained. The wall thickness of the device after erosion is obtained by subtracting the erosion thinning amount from the wall thickness. Based on the bearing capacity-wall thickness correspondence, the bearing capacity is queried and compared with the rated pressure bearing capacity of the wellhead to determine whether the wellhead device can continue to be used safely. Wherein, erosion thinning amount = erosion rate × virtual time step; (6) Obtain the final erosion deformation and expected service life. If the conclusion of step (5) is that the wellhead device can continue to be used, then a new production tree structure is obtained based on the amount of erosion thinning, and steps (4) and (5) are repeated based on the structure until the wellhead device can no longer withstand the wellhead rated pressure, and the final erosion deformation and expected service life are obtained; if the conclusion of step (5) is that the wellhead device can no longer withstand the wellhead rated pressure, then the final erosion deformation and expected service life are obtained accordingly. Erosion deformation is the cumulative amount of thinning from each erosion event. The expected lifespan is the cumulative duration of each virtual time step.

2. The prediction method according to claim 1, characterized in that, The dependent variables for the erosion mass loss also include at least one of the following: erosion angle, sand concentration, material hardness, and temperature.

3. The prediction method according to claim 1, characterized in that, The erosion characteristics are determined based on the relationship between the erosion rate and the dependent variable.

4. The prediction method according to claim 1, characterized in that, The erosion model in step (3) includes at least one of the Finnie erosion model, Bitter erosion model, Oka erosion model, Tabakoff erosion model and Ahlert erosion model.

5. The prediction method according to claim 1, characterized in that, The simulated erosion experiment described in step (1) was conducted using a high-temperature, high-velocity erosion experimental apparatus.

6. The prediction method according to claim 1, characterized in that, The bearing capacity-wall thickness correspondence mentioned in step (5) is the correspondence between the pressure and minimum wall thickness of the wellhead device obtained through plastic limit analysis.

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