Quantitative safety evaluation method for whole shaft casing

Abstract

The invention discloses a full shaft casing quantitative safety evaluation method, and relates to the technical field of safety evaluation, and the method comprises the steps: carrying out the fretting corrosion coupling analysis of the fretting damage data of a threaded connection part according to the marine environment dynamic load data and shaft operation parameters, and obtaining the fretting corrosion coupling data; performing fatigue crack initiation risk evaluation based on the fretting corrosion coupling data to obtain fatigue crack initiation risk data; performing corrosion fatigue life prediction according to the fatigue crack initiation risk data and the fretting corrosion coupling data to obtain corrosion fatigue life prediction data, and performing life prediction deviation evaluation based on the corrosion fatigue life prediction data to obtain life prediction deviation data; and according to the corrosion fatigue life prediction data and the life prediction deviation data, performing full-shaft casing safety quantitative evaluation, and generating a full-shaft casing safety quantitative evaluation result. The method has the effect of improving the safety evaluation efficiency.

Description

Technical Field

[0001] This application relates to the field of safety evaluation technology, and in particular to a quantitative safety evaluation method for the entire well casing. Background Technology

[0002] In deep-sea drilling, casing is composed of multiple segments connected by threads, and the threaded joints are the weakest points in the casing. In the complex marine environment, the platform is affected by waves, currents, and temperature changes, generating dynamic loads that cause micron-level relative motion at the threaded joints. This fretting accelerates localized corrosion and fatigue crack initiation, significantly reducing the fatigue life of the casing.

[0003] In related technologies, existing quantitative safety evaluation methods for whole-well casing usually focus on macroscopic stress analysis and ignore the coupling effect of fretting corrosion, which leads to overly optimistic life predictions and may cause sudden failures. In addition, conventional detection methods are difficult to capture microscopic damage, which in turn reduces the efficiency of safety evaluation and needs to be improved. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a quantitative safety evaluation method for the entire well casing.

[0005] Firstly, this application provides a quantitative safety evaluation system for the entire well casing, comprising: The data acquisition module is used to acquire the structural parameters of the entire well casing, the dynamic load data of the marine environment, and the well casing operation parameters. Based on the structural parameters of the entire well casing, it performs fretting damage analysis at the threaded connection to obtain fretting damage data at the threaded connection. The analysis module is used to perform fretting corrosion coupling analysis on the fretting damage data at the threaded connection based on the marine environmental dynamic load data and the wellbore operation parameters to obtain fretting corrosion coupling data, and to conduct fatigue crack initiation risk assessment based on the fretting corrosion coupling data to obtain fatigue crack initiation risk data. The prediction module is used to predict corrosion fatigue life based on the fatigue crack initiation risk data and the fretting corrosion coupling data, to obtain corrosion fatigue life prediction data, and to evaluate the life prediction deviation based on the corrosion fatigue life prediction data, to obtain life prediction deviation data. The safety evaluation module is used to perform a quantitative safety evaluation of the entire well casing based on the corrosion fatigue life prediction data and the life prediction deviation data, and generate a quantitative safety evaluation result for the entire well casing.

[0006] Preferably, the structural parameters of the entire well casing include casing material, thread geometry parameters, and connection preload; the marine environmental dynamic load data include wave load, ocean current load, temperature change load, and the frequency and amplitude data of the dynamic loads derived therefrom; and the wellbore operation parameters include drilling fluid properties and its corrosivity parameters.

[0007] Preferably, the process of obtaining fretting damage data at the threaded connection specifically includes: Acquire full well casing structural parameters, marine environmental dynamic load data, and wellbore operation parameters; The structural parameters of the entire well casing are vectorized to generate casing structure vector parameters; The micro-motion requirements at the threaded connection are extracted from the vector parameters of the sleeve structure to obtain the micro-motion requirement data at the threaded connection. Based on the sleeve structure vector parameters, fretting damage analysis is performed on the fretting demand data of the threaded connection to obtain the fretting damage data of the threaded connection.

[0008] Preferably, fatigue crack initiation risk assessment is performed based on the fretting corrosion coupling data to obtain fatigue crack initiation risk data, specifically including: The frequency and amplitude of the dynamic load are obtained from the marine environmental dynamic load data; Based on the marine environmental dynamic load data, the wellbore operating parameters, and the dynamic load frequency and amplitude, a fretting corrosion coupling analysis was performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data. The fatigue crack initiation tolerance data is fitted to the fretting corrosion coupling data to generate fatigue crack initiation tolerance data. Based on the fatigue crack initiation tolerance data, the fatigue crack initiation risk is assessed by evaluating the frequency and amplitude of the dynamic load, thus obtaining fatigue crack initiation risk data.

[0009] Preferably, based on the marine environmental dynamic load data, the wellbore operating parameters, and the frequency and amplitude of the dynamic load, a fretting corrosion coupling analysis is performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data, specifically including: Obtain the drilling fluid corrosivity parameters from the wellbore operating parameters; calculate the vector angle between the threaded connection based on the dynamic load frequency and amplitude to obtain the vector angle between the threaded surface and the load; Based on the micro-motion damage data at the threaded connection, vector angle change analysis is performed to obtain the attitude change vector angle data between the threaded surface and the load. Based on the drilling fluid corrosivity parameters, the instantaneous corrosion rate during the multi-frequency micro-motion process of the attitude change vector angle data is simulated and analyzed to obtain the multi-frequency corrosion instantaneous rate field. Anisotropic corrosion behavior imbalance analysis is performed on the instantaneous rate field of the multi-frequency corrosion to obtain anisotropic corrosion behavior imbalance data; Interfacial corrosion vibration regression analysis was performed on the anisotropic corrosion behavior imbalance data to obtain interfacial corrosion vibration regression data. Based on the anisotropic corrosion behavior imbalance data and the interface corrosion vibration regression data, fretting corrosion coupling analysis was performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data.

[0010] Preferably, an anisotropic corrosion behavior imbalance analysis is performed on the instantaneous rate field of the multi-frequency corrosion to obtain anisotropic corrosion behavior imbalance data, specifically including: The corrosion flow non-uniformity vector is identified by performing corrosion flow non-uniformity vector identification on the multi-frequency corrosion instantaneous velocity field to obtain the corrosion flow non-uniformity vector; Based on the non-uniformity vector of corrosion flow, differential coupling of the corrosion time series variation of the thread surface is performed to generate corrosion time series variation differential coupling data; Based on the corrosion flow non-uniformity vector and the corrosion time series variation differential coupling data, the local variance of metal loss viscosity is calculated to obtain the local variance of metal loss viscosity. Based on the non-uniformity vector of corrosion flow, the differential coupling data of corrosion time sequence variation, and the local variance of metal loss viscosity, the difference in the ratio of latent heat release of corrosion energy between different thread orientations is deduced, and the difference in the ratio of heat release of different thread orientations is generated. Anisotropic corrosion behavior imbalance analysis is performed based on the difference in heat release ratio between different thread orientations, thereby obtaining the anisotropic corrosion behavior imbalance data.

[0011] Preferably, the fatigue crack initiation tolerance data is fitted to the fretting corrosion coupling data to generate fatigue crack initiation tolerance data, specifically including: The micro-motion corrosion coupling data is subjected to corrosion morphology deviation network partitioning to obtain corrosion morphology deviation network partitioning data; Corrosion geometric deviation feature analysis is performed on the corrosion morphology deviation network segmentation data to obtain corrosion geometric deviation feature data; Based on the corrosion geometric deviation characteristic data, the stress offset distribution of crack initiation is simulated to generate stress offset distribution data. The stress distribution strain rate component decomposition is performed on the stress offset distribution data to obtain strain rate component decomposition data; Based on the strain rate component decomposition data, fatigue crack initiation tolerance is fitted to generate the fatigue crack initiation tolerance data.

[0012] Preferably, a quantitative safety evaluation of the entire well casing is performed based on the corrosion fatigue life prediction data and the life prediction deviation data, generating a quantitative safety evaluation result for the entire well casing, specifically including: Feature learning is performed on the lifetime prediction deviation data to obtain lifetime prediction deviation feature data; Based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data, a quantitative safety evaluation matching is performed to generate quantitative safety evaluation matching data. The singular value optimization adjustment is performed on the safety quantitative evaluation matching data to obtain the safety quantitative evaluation optimized data; The safety quantitative evaluation data of the entire well casing is obtained by performing a safety quantitative evaluation of the entire well casing using the optimized safety quantitative evaluation data.

[0013] Preferably, safety quantitative evaluation matching is performed based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data to generate safety quantitative evaluation matching data, specifically including: Based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data, a safety factor matching is performed to obtain safety factor matching data. Based on the safety factor matching data and the life prediction deviation characteristic data, the remaining life adaptive adjustment control is performed to obtain the remaining life adaptive control data. Risk level matching is performed on the remaining lifetime adaptive control data to obtain risk level matching data; Based on the safety factor matching data, the remaining life adaptive control data, and the risk level matching data, a safety quantitative evaluation and matching is performed on the corrosion fatigue life prediction data to generate the safety quantitative evaluation and matching data.

[0014] Secondly, this application provides a quantitative safety evaluation method for the entire well casing, comprising the following steps: Step S1: Obtain the structural parameters of the entire well casing, the dynamic load data of the marine environment, and the well operation parameters. Based on the structural parameters of the entire well casing, perform fretting damage analysis at the threaded connection to obtain fretting damage data at the threaded connection. Step S2: Perform fretting corrosion coupling analysis on the fretting damage data at the threaded connection based on the marine environmental dynamic load data and the wellbore operating parameters to obtain fretting corrosion coupling data. Based on the fretting corrosion coupling data, conduct fatigue crack initiation risk assessment to obtain fatigue crack initiation risk data. Step S3: Based on the fatigue crack initiation risk data and the fretting corrosion coupling data, perform corrosion fatigue life prediction to obtain corrosion fatigue life prediction data. Based on the corrosion fatigue life prediction data, evaluate the life prediction deviation to obtain life prediction deviation data. Step S4: Perform a quantitative evaluation of the safety of the entire well casing based on the corrosion fatigue life prediction data and the life prediction deviation data, and generate the quantitative evaluation result of the safety of the entire well casing.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application provides a quantitative safety evaluation system for the entire well casing. By integrating fretting damage analysis and fretting corrosion coupling analysis, it can deeply evaluate the damage mechanism of threaded connections under the combined action of marine dynamic loads and corrosive environment. This overcomes the limitation of traditional macro-stress analysis that ignores micro-coupling effects, significantly reduces the risk of sudden failure due to overly optimistic predictions, provides a reliable basis for the safe operation of deep-sea casing, and effectively improves the efficiency of safety evaluation. 2. Through multi-module collaboration, a full-chain evaluation system was constructed, from thread fretting damage, corrosion fatigue coupling, crack initiation risk to remaining life prediction. This system enables dynamic quantitative tracking of damage evolution, shifts safety assessment from qualitative judgment to quantitative decision-making, and improves the systematicness and precision of the assessment. 3. By analyzing the anisotropic corrosion behavior and interface vibration regression during the fretting corrosion coupling process, it is possible to capture early damage characteristics that are difficult to detect by conventional detection, realize early warning of potential crack initiation, and provide key technical support for preventive maintenance. 4. By using methods such as corrosion morphology deviation network partitioning, fatigue tolerance fitting, and life prediction deviation, the computational efficiency and interpretability of the results are improved while ensuring the depth of analysis. It can also output multi-dimensional evaluation results including risk level, remaining life, and safety factor, which significantly improves the response speed and scientific nature of on-site safety management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system for quantitative safety evaluation of the entire well casing in an embodiment of this application.

[0018] Figure 2This is a flowchart of the method for quantitative safety evaluation of the entire well casing in an embodiment of this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0020] Example 1 This application discloses a quantitative safety evaluation system for the entire well casing.

[0021] Reference Figure 1 A quantitative safety evaluation system for the entire well casing, comprising: The data acquisition module is used to acquire the structural parameters of the entire well casing, the dynamic load data of the marine environment, and the well casing operation parameters. Based on the structural parameters of the entire well casing, it performs fretting damage analysis at the threaded connection to obtain fretting damage data at the threaded connection. For example, the structural parameters of the entire well casing include casing material, thread geometry parameters, and connection preload; the dynamic load data of the marine environment includes wave load, ocean current load, temperature change load, and the frequency and amplitude data of the dynamic loads derived therefrom; and the wellbore operation parameters include drilling fluid properties and its corrosiveness parameters.

[0022] For example, the process of obtaining fretting damage data at a threaded connection specifically includes: Acquire full well casing structural parameters, marine environmental dynamic load data, and wellbore operation parameters; The structural parameters of the entire well casing are vectorized to generate casing structure vector parameters; The micro-motion requirements at the threaded connection are extracted from the vector parameters of the sleeve structure to obtain the micro-motion requirement data at the threaded connection. Based on the sleeve structure vector parameters, fretting damage analysis is performed on the fretting demand data of the threaded connection to obtain the fretting damage data of the threaded connection.

[0023] Specifically, the process of obtaining fretting damage data at threaded connections first involves vectorizing the acquired well casing structural parameters. This means mapping the mechanical properties of the casing material, the thread profile angle, taper, pitch, and other geometric parameters, as well as the connection preload value, into standardized vector components in a multidimensional feature space. Principal component analysis is then used to reduce the dimensionality and reconstruct the vectors, eliminating multicollinearity among parameters and generating casing structural vector parameters that comprehensively characterize the essential features of the casing structure. Next, the fretting damage data at the threaded connection is analyzed using these casing structural vector parameters. The extraction process, based on the principles of contact mechanics, involves inputting vector parameters into a pre-constructed finite element contact model of the threaded pair. An initial contact pressure equivalent to the connection preload is applied to the model boundary. The tangential contact stiffness distribution of the thread surface under the constraint of the normal preload is calculated iteratively, identifying threaded contact areas with relatively low contact stiffness, i.e., those with weak potential shear resistance. Simultaneously, by combining the vector dot product of the thread helix angle and the friction coefficient, the tendency index for micro-slippage of the threaded pair under external loads is quantified. This outputs a threaded connection diagram containing the location of weak areas and the slip tendency index. The analysis first obtains micromotion demand data. Then, based on the aforementioned generated casing structure vector parameters, a micromotion damage analysis is performed on the micromotion demand data of the threaded connection. The core of this analysis lies in introducing dynamic excitation environment constructed from marine environmental dynamic load data and wellbore operation parameters. Specifically, the time series of wave loads and ocean current loads are superimposed with the temperature change load spectrum in the time-frequency domain to generate a composite dynamic load spectrum. This composite dynamic load spectrum is then applied as input load to a refined threaded connection finite element model containing micromotion demand data. Transient analysis is performed using an explicit dynamic solver to calculate the cyclic tangential stress and relative displacement amplitude distributed within the weak area of ​​the threaded contact under the cyclic action of the composite dynamic load. The relative displacement amplitude is calculated by tracking the displacement difference between the nodes of the contact element pair in the tangential direction. Based on the modified Ruiz micromotion damage parameter model, the calculated tangential stress amplitude and relative displacement amplitude are multiplicatively coupled with environmental factors characterized by drilling fluid corrosivity parameters. This allows for a quantitative assessment of the micromotion damage intensity of each weak area. Finally, the assessment results of all weak areas are integrated to obtain micromotion damage data of the threaded connection containing location information, damage mode, and quantified damage values.

[0024] The analysis module is used to perform fretting corrosion coupling analysis on the fretting damage data at the threaded connection based on the marine environmental dynamic load data and the wellbore operation parameters to obtain fretting corrosion coupling data, and to conduct fatigue crack initiation risk assessment based on the fretting corrosion coupling data to obtain fatigue crack initiation risk data. For example, fatigue crack initiation risk assessment is performed based on the aforementioned fretting corrosion coupling data to obtain fatigue crack initiation risk data, specifically including: The frequency and amplitude of the dynamic load are obtained from the marine environmental dynamic load data; Based on the marine environmental dynamic load data, the wellbore operating parameters, and the dynamic load frequency and amplitude, a fretting corrosion coupling analysis was performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data. The fatigue crack initiation tolerance data is fitted to the fretting corrosion coupling data to generate fatigue crack initiation tolerance data. Based on the fatigue crack initiation tolerance data, the fatigue crack initiation risk is assessed by evaluating the frequency and amplitude of the dynamic load, thus obtaining fatigue crack initiation risk data.

[0025] Specifically, the process of obtaining fatigue crack initiation risk data based on the aforementioned fretting corrosion coupling data is as follows: First, the dynamic load frequency and amplitude are obtained from the marine environmental dynamic load data. By performing Fourier transform or power spectral density analysis on the time-series data of wave load, ocean current load, and temperature change load, the dominant load cycle frequency and its corresponding stress amplitude range acting on the threaded connection are extracted, and these characteristic parameters are quantified as the input basis for the subsequent risk probability model. Next, based on the marine environmental dynamic load data and the wellbore operation parameters... The extracted dynamic load frequency and amplitude were used to perform fretting corrosion coupling analysis on the fretting damage data at the threaded connection. The core of this analysis is to construct a damage evolution model that couples mechanical and chemical effects. Specifically, fretting damage data characterizing mechanical wear is integrated with drilling fluid corrosivity parameters characterizing environmental erosion. A "corrosion aggravation factor" based on the principle of corrosion electrochemistry is introduced to quantitatively describe the activation effect of the corrosive medium on the fretting wear surface. This factor is obtained by regressing experimental data of material loss rate under the same fretting parameters in an inert environment and a real drilling fluid environment. This factor is then used to analyze the fretting corrosion coupling data at the threaded connection. The purely mechanical fretting damage rate is amplified and corrected. Simultaneously, the dynamic load frequency and amplitude are substituted as driving variables into the corrected corrosion-fatigue coupling model. Numerical integration is used to simulate the continuous damage accumulation process of the threaded contact surface transforming from fretting wear pits to fatigue crack initiation sites under a specific marine environmental load spectrum. The final output is fretting-corrosion coupling data containing the expected cycle number of crack initiation at different locations and the probability distribution of the initial crack size. Subsequently, fatigue crack initiation tolerance fitting is performed on the fretting-corrosion coupling data. This step aims to quantify the engineering-acceptable safety boundary for crack initiation, and the operating principle is based on probabilistic statistics. The calculation method treats the predicted crack initiation cycles in the coupled data as random variables and uses a three-parameter distribution function to fit a large number of predicted values ​​to determine their statistical characteristics, especially the lower limit tolerance range. Specifically, a high reliability requirement is set and a low failure probability is selected. The lower limit confidence boundary value of the crack initiation cycles under the joint probability requirement is calculated, and this boundary value is compared with the design life or detection cycle. The difference is the fatigue crack initiation tolerance. This tolerance data characterizes the conservative safety margin that the system still has before the occurrence of detectable crack initiation after considering the fretting corrosion coupling effect.Finally, based on the fatigue crack initiation tolerance data, a fatigue crack initiation risk assessment is performed on the dynamic load frequency and amplitude. This assessment process is achieved by constructing a risk mapping matrix, using the load conditions defined by the dynamic load frequency and amplitude as one dimension of the matrix, and the calculated fatigue crack initiation tolerance value as another dimension. Multiple risk threshold ranges are set for the tolerance value (e.g., tolerance greater than 50% of the design life is low risk, between 20% and 50% is medium risk, and less than 20% is high risk). By querying the mapping matrix, the risk level of crack initiation at the threaded connection under the current marine environmental dynamic load characteristics is determined. Furthermore, considering the degree of corrosion inhomogeneity reflected by the anisotropic corrosion behavior imbalance data, the risk level is locally corrected. For example, in areas where the imbalance data indicates localized corrosion intensification, the risk level is increased by one level based on the matrix query results. Finally, a set of fatigue crack initiation risk data is generated, including the spatial distribution of risk levels, the location of key risk points, and the analysis of dominant risk factors.

[0026] For example, based on the marine environmental dynamic load data, the wellbore operating parameters, and the dynamic load frequency and amplitude, a fretting corrosion coupling analysis is performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data, specifically including: Obtain the drilling fluid corrosivity parameters from the wellbore operating parameters; calculate the vector angle between the threaded connection based on the dynamic load frequency and amplitude to obtain the vector angle between the threaded surface and the load; Based on the micro-motion damage data at the threaded connection, vector angle change analysis is performed to obtain the attitude change vector angle data between the threaded surface and the load. Based on the drilling fluid corrosivity parameters, the instantaneous corrosion rate during the multi-frequency micro-motion process of the attitude change vector angle data is simulated and analyzed to obtain the multi-frequency corrosion instantaneous rate field. Anisotropic corrosion behavior imbalance analysis is performed on the instantaneous rate field of the multi-frequency corrosion to obtain anisotropic corrosion behavior imbalance data; Interfacial corrosion vibration regression analysis was performed on the anisotropic corrosion behavior imbalance data to obtain interfacial corrosion vibration regression data. Based on the anisotropic corrosion behavior imbalance data and the interface corrosion vibration regression data, fretting corrosion coupling analysis was performed on the fretting damage data at the threaded connection to obtain fretting corrosion coupling data.

[0027] Specifically, the process of performing fretting corrosion coupling analysis on the fretting damage data at the threaded connection based on the marine environmental dynamic load data, the wellbore operating parameters, and the dynamic load frequency and amplitude to obtain the fretting corrosion coupling data is as follows: First, the drilling fluid corrosivity parameters are obtained from the wellbore operating parameters. These parameters are a comprehensive set of indicators including chloride ion concentration, pH value, hydrogen sulfide content, and effective concentration of corrosion inhibitor. Next, the vector angle at the threaded connection is calculated based on the dynamic load frequency and amplitude, with the helix angle direction of the threaded pair as the reference vector. Using the direction of the composite load fluctuation determined by the frequency and amplitude of the dynamic load as the action vector, the cosine value of the spatial angle between the two vectors is calculated through the dot product and cross product operations of spatial vectors. This quantitatively obtains the vector angle between the thread surface and the load, indicating the degree to which the force direction on the thread surface does not coincide with its structural orientation. Subsequently, based on the fretting damage data at the threaded connection, a vector angle change analysis is performed. This analysis vectorically superimposes the fretting slip direction of each local contact area recorded in the fretting damage data with the macroscopic load-structure vector angle calculated in the previous step, and calculates the slip direction vector. The projection components on the macroscopic angle plane and their rate of change over time are used to analyze the dynamic deviation process of the local fretting direction of the threaded contact pair relative to the macroscopic load direction under complex alternating loads, thereby obtaining the attitude change vector angle data between the threaded surface and the load, which describes the amplitude and frequency of this dynamic deviation. Then, based on the drilling fluid corrosivity parameters, the instantaneous corrosion rate simulation analysis of the attitude change vector angle data during the multi-frequency fretting process is performed. This analysis is based on the principle of electrochemical corrosion kinetics, establishing the fretting slip velocity and contact stress characterized by the attitude change vector angle data as mechanical... A transient corrosion rate model is constructed using the corrosivity of the medium as defined by drilling fluid corrosivity parameters as the environmental input. This model modifies the reaction rate constant in the electrochemical kinetic equation by introducing a surface film rupture factor positively correlated with fretting slip velocity and a local strain activation energy term related to contact stress. This allows for the simulation and calculation of the instantaneous peak value of the corrosion current density caused by the rupture of the passivation film on the metal surface and the exposure of fresh metal due to mechanical action in each fretting cycle. The instantaneous peak value sequence under the entire load spectrum is then spatially mapped to form a multi-frequency corrosion instantaneous rate field.Subsequently, an anisotropic corrosion behavior imbalance analysis was performed on the instantaneous rate field of the multi-frequency corrosion. The core of this analysis is to identify the non-uniformity of corrosion in different orientations of the threaded connection structure. This is achieved by calculating the corrosion rate gradient between spatially adjacent points within the rate field and statistically analyzing the distribution characteristics of the gradient values ​​along the circumferential, radial, and axial directions of the thread. In particular, it identifies abrupt regions where the gradient values ​​are significantly higher than the average level. Furthermore, considering the geometric symmetry of the threaded structure, it analyzes whether these high-gradient abrupt regions exhibit specific spatial distribution patterns, thereby identifying the systemic corrosion caused by structural stress concentration, uneven flow field distribution, or differences in material texture. The study aims to identify a uniform corrosion imbalance tendency and ultimately obtain anisotropic corrosion behavior imbalance data that can quantitatively describe the location, direction, and intensity of corrosion nonuniformity. Subsequently, interfacial corrosion vibration regression analysis is performed on this anisotropic corrosion behavior imbalance data. This step aims to establish a quantitative correlation between mechanical vibration load and observed corrosion imbalance. The principle is to use the dynamic load frequency as the independent variable and the corrosion rate fluctuation amplitude extracted from the anisotropic corrosion behavior imbalance data at a specific orientation as the dependent variable. A multi-harmonic regression method is used for fitting to determine under which characteristic frequencies or frequency combinations the corrosion imbalance fluctuation amplitude will appear. The resonance amplification phenomenon is analyzed, and the corresponding regression coefficients and phase angles are calculated to obtain interface corrosion vibration regression data that can predict the excitation intensity of corrosion imbalance behavior by different load frequency components. Finally, based on the anisotropic corrosion behavior imbalance data and the interface corrosion vibration regression data, a fretting corrosion coupling analysis is performed on the fretting damage data at the threaded connection. This coupling analysis is an integration and weight allocation process. It performs tensor fusion between the anisotropic corrosion behavior imbalance data describing spatial non-uniformity and the interface corrosion vibration regression data describing frequency sensitivity to construct a multidimensional corrosion damage weight field. Each weight value comprehensively represents the amplification factor of corrosion effect on purely mechanical fretting damage at the corresponding spatial location and load frequency. This weight field is then multiplied point-by-point with the initial fretting damage data, resulting in a weighted calculation that corrects the original mechanical damage amount to an equivalent damage amount that simultaneously includes the synergistic effects of mechanical wear and chemical corrosion. The total cumulative damage of the entire threaded connection area under a specified service time or load cycle, as well as the weakest point with the most severe damage, are then calculated through integration. This completes the coupling analysis and outputs fretting corrosion coupling data containing equivalent damage distribution, damage evolution trends, and key failure locations.

[0028] For example, anisotropic corrosion behavior imbalance analysis is performed on the instantaneous rate field of the multi-frequency corrosion to obtain anisotropic corrosion behavior imbalance data, specifically including: The corrosion flow non-uniformity vector is identified by performing corrosion flow non-uniformity vector identification on the multi-frequency corrosion instantaneous velocity field to obtain the corrosion flow non-uniformity vector; Based on the non-uniformity vector of corrosion flow, differential coupling of the corrosion time series variation of the thread surface is performed to generate corrosion time series variation differential coupling data; Based on the corrosion flow non-uniformity vector and the corrosion time series variation differential coupling data, the local variance of metal loss viscosity is calculated to obtain the local variance of metal loss viscosity. Based on the non-uniformity vector of corrosion flow, the differential coupling data of corrosion time sequence variation, and the local variance of metal loss viscosity, the difference in the ratio of latent heat release of corrosion energy between different thread orientations is deduced, and the difference in the ratio of heat release of different thread orientations is generated. Anisotropic corrosion behavior imbalance analysis is performed based on the difference in heat release ratio between different thread orientations, thereby obtaining the anisotropic corrosion behavior imbalance data.

[0029] Specifically, the process of performing anisotropic corrosion behavior imbalance analysis on the multi-frequency corrosion instantaneous rate field to obtain anisotropic corrosion behavior imbalance data is as follows: First, the corrosion flow non-uniformity vector is identified in the multi-frequency corrosion instantaneous rate field. This step aims to convert the corrosion instantaneous rate value at each spatial point into a vector with direction and magnitude. The operating principle is based on the gradient change of the corrosion rate field between spatially adjacent points. By calculating the difference in corrosion rate values ​​between each grid point and all its adjacent points, the direction with the largest difference is determined as the vector direction of the corrosion flow at that point, and the magnitude of the largest difference is used as the modulus of the vector. This results in a corrosion flow non-uniformity vector field covering the entire threaded connection area, which can simultaneously characterize the spatial differences in corrosion intensity and its dominant direction. Next, the time-series variation of thread surface corrosion is differentially coupled based on the corrosion flow non-uniformity vector. The essence of this process is to combine the spatial vector field with time series analysis. Specifically, along the local dominant direction indicated by the corrosion flow non-uniformity vector, a series of spatial points along the time-series variation of the corrosion rate is extracted. Then, numerical differentiation is performed on these time series data to calculate the rate of change of corrosion rate at each spatial point at each time point. This time differential rate of change is then coupled with the original vector direction of that point by a dot product, thereby generating corrosion time series differential coupling data that simultaneously contains information on "the direction of spatial corrosion intensification" and "the degree of drastic change of corrosion rate with time in that direction". Subsequently, the local variance of metal loss viscosity is calculated based on the corrosion flow non-uniformity vector and the corrosion time series differential coupling data. Here, metal loss viscosity is introduced to describe the spatial non-uniformity of the resistance to material loss due to corrosion. The calculation method is to divide the magnitude of the corrosion flow non-uniformity vector at each spatial point by the absolute value of the time change rate in the corrosion time series differential coupling data at that point to obtain a dimensionless ratio, which is defined as the local metal loss viscosity at that point. Then, within a local window composed of multiple adjacent spatial points, the statistical variance of the metal loss viscosity of all points is calculated. This variance value quantifies the non-uniformity of the material's resistance to corrosion loss in that local area, thereby obtaining a local variance distribution map of metal loss viscosity.Subsequently, based on the corrosion flow non-uniformity vector, the corrosion time-series variation differential coupling data, and the metal loss viscosity local variance, the ratio difference of corrosion latent heat energy release between different thread orientations is extrapolated. The physical basis of this step is that the corrosion electrochemical reaction is accompanied by heat release, and the release rate is closely related to the corrosion rate and the local environment. The extrapolation process first establishes an empirical correlation model, multiplying the magnitude of the corrosion flow non-uniformity vector at each point by an electrochemical thermal effect coefficient determined by the drilling fluid corrosivity parameter to estimate its corrosion latent heat energy release rate per unit time. Then, the thread structure is divided according to the circumferential orientation. Within each orientation sector, the estimated heat energy release rate of all points is averaged by area weighting. At the same time, the corrosion time-series variation differential coupling data is introduced as a time-varying correction factor, and the metal loss viscosity local variance is introduced as a spatial non-uniformity damping factor to correct the average value. Finally, the corrected average heat energy release between different orientation sectors is calculated. The relative difference in the rate is the difference in the heat release ratio between different thread orientations. This difference reveals the non-uniformity of corrosion heat generation caused by different structural orientations. Finally, an anisotropic corrosion behavior imbalance analysis is performed based on the difference in the heat release ratio between different thread orientations. This analysis judges the significance of the imbalance by setting a critical threshold. Specifically, the calculated heat release ratio differences between all orientations are compared with a benchmark threshold determined based on the material's thermal diffusivity and structural symmetry. This identifies which orientation differences exceed the threshold, indicating a significant risk of thermal-mechanical imbalance caused by anisotropic corrosion behavior in these orientations. The information on these exceeding orientation differences, the corresponding spatial regions, and the magnitude of their exceeding the threshold are integrated to form comprehensive anisotropic corrosion behavior imbalance data. This data clearly indicates the hotspot orientation of corrosion non-uniformity and the severity of the imbalance, providing key differentiated input for subsequent coupled analysis.

[0030] For example, fatigue crack initiation tolerance fitting is performed on the fretting corrosion coupling data to generate fatigue crack initiation tolerance data, specifically including: The micro-motion corrosion coupling data is subjected to corrosion morphology deviation network partitioning to obtain corrosion morphology deviation network partitioning data; Corrosion geometric deviation feature analysis is performed on the corrosion morphology deviation network segmentation data to obtain corrosion geometric deviation feature data; Based on the corrosion geometric deviation characteristic data, the stress offset distribution of crack initiation is simulated to generate stress offset distribution data. The stress distribution strain rate component decomposition is performed on the stress offset distribution data to obtain strain rate component decomposition data; Based on the strain rate component decomposition data, fatigue crack initiation tolerance is fitted to generate the fatigue crack initiation tolerance data.

[0031] Specifically, the process of fitting fatigue crack initiation tolerance data to the fretting corrosion coupling data is as follows: First, the fretting corrosion coupling data undergoes corrosion morphology deviation network partitioning. This process, based on the geometric complexity and damage severity of the corrosion morphology, uses adaptive meshing technology to map the coupled damage data of the entire threaded connection area into a non-uniform topological network. The partitioning principle is to automatically subdivide areas with drastic changes in corrosion pit depth and large gradients of equivalent damage into denser network units, while dividing areas with gentle morphology and uniform damage into sparser units. A corrosion morphology deviation network partitioning data is generated that can capture local features while controlling the overall computational load. Next, corrosion geometric deviation feature analysis is performed on the corrosion morphology deviation network partitioning data. This step extracts the geometric descriptors of the corrosion damage area covered by each unit in the network, including but not limited to the average depth, aspect ratio, statistical variance of the contour curvature of the corrosion pits within the unit, and the maximum difference in the pit bottom inclination angle between adjacent units. These multidimensional geometric features are then dimensionality-reduced and fused using principal component analysis to form a scalar index that comprehensively characterizes the irregularity and sharpness of the corrosion morphology of that unit, i.e., the corrosion geometric deviation feature. The data is then used to simulate the stress offset distribution for crack initiation. The physical basis of this simulation is to treat the corrosion geometric deviation characteristics as an amplifier of stress concentration effects. By establishing an empirical mapping relationship between the corrosion characteristic index and the local stress concentration factor, the theoretical stress peak position of each network element under external load is calculated. Furthermore, considering the contact stress redistribution caused by fretting slip, an iterative algorithm is used to simulate the small offset of the stress peak point in the actual complex stress field, ultimately generating stress offset distribution data describing the most likely crack initiation location and its offset direction and distance. Subsequently, the stress offset distribution is further simulated for… The stress offset distribution data is decomposed into strain rate components. This decomposition aims to quantify the dynamic response of the material near the crack initiation point under dynamic load. The principle is to decompose the cyclic stress history at this point into hydrostatic stress and deviatoric stress components, and to focus on calculating the rate of change of the deviatoric stress component in each load cycle, i.e. strain rate. At the same time, the strain rate is further decomposed into elastic strain rate components that promote brittle fracture and plastic strain rate components that promote plastic passivation. By calculating the proportion and phase difference of the two in the total strain rate, a set of strain rate component decomposition data that characterizes the dynamic deformation mechanism of the material at the corrosion notch is obtained.Finally, fatigue crack initiation tolerance is fitted based on the strain rate component decomposition data. This fitting process uses the strain rate component decomposition data as the core input and, through a trained material fatigue response model, predicts the number of cycles required to expand from a detectable initial micro-defect to the critical engineering crack size under the specific dynamic deformation mechanism. This predicted number of cycles is then compared with the design life or detection interval, and the difference is the theoretical tolerance. Finally, a reduction factor based on the uncertainty analysis of corrosion geometric deviation characteristic data is introduced to conservatively correct the theoretical tolerance, thereby outputting the fatigue crack initiation tolerance data.

[0032] The prediction module is used to predict corrosion fatigue life based on the fatigue crack initiation risk data and the fretting corrosion coupling data, to obtain corrosion fatigue life prediction data, and to evaluate the life prediction deviation based on the corrosion fatigue life prediction data, to obtain life prediction deviation data. For example, life prediction deviation evaluation is performed based on the corrosion fatigue life prediction data to obtain life prediction deviation data, specifically including: Based on the crack initiation life prediction results in the fatigue crack initiation risk data and the fretting corrosion coupling data, the preset benchmark fatigue crack propagation model is modified to generate a modified fatigue life model. Based on the modified fatigue life model and the dynamic load frequency and amplitude, corrosion fatigue life cycle calculation is performed, and preliminary corrosion fatigue life prediction data is output. Based on the distribution characteristics of the anisotropic corrosion behavior imbalance data, the confidence level of the prediction results of the preliminary corrosion fatigue life prediction data is evaluated to obtain the corrosion fatigue life prediction data and its corresponding prediction confidence level. Obtain baseline fatigue life data for the sleeve thread connection; The corrosion fatigue life prediction data is compared with the baseline fatigue life data and weighted according to the prediction confidence level to calculate the absolute deviation value of the life prediction, thereby obtaining the life prediction deviation data.

[0033] Specifically, the process of evaluating life prediction deviation based on the corrosion fatigue life prediction data and obtaining life prediction deviation data is as follows: First, the preset benchmark fatigue crack propagation model is modified according to the crack initiation life prediction results in the fatigue crack initiation risk data and the fretting corrosion coupling data. This benchmark model is usually the Paris formula describing the expansion of the crack from the initial size to the critical size. The key to the modification is to convert the quantified "corrosion equivalent damage increment" in the fretting corrosion coupling data into an amplification factor acting on the stress intensity factor range at the crack tip. At the same time, the crack initiation life prediction results are used as the initial condition input for the modified model. The reverse calibration method adjusts the material constants in the model to ensure that the calculated crack initiation stage life matches the predicted results, thereby generating a customized modified fatigue life model that reflects both the specific corrosion damage environment and the specific crack initiation point. Next, based on the modified fatigue life model and the dynamic load frequency and amplitude, a corrosion fatigue life cyclic calculation is performed. This calculation uses the load spectrum defined by the dynamic load frequency and amplitude as the cyclic input, taking the current state of each potential crack point at the threaded connection as the starting point. The modified model is used to recursively calculate the increment of crack size under each load cycle, continuously accumulating until the crack size reaches a preset failure threshold. The accumulated value at this point... The total number of cycles is the predicted lifetime at that point. After performing this calculation on all potential points, preliminary corrosion fatigue lifetime prediction data covering the entire threaded connection area and including the predicted lifetime values ​​for each point is output. Then, the confidence level of the preliminary corrosion fatigue lifetime prediction data is evaluated based on the distribution characteristics of the anisotropic corrosion behavior imbalance data. This evaluation is based on a core principle: the more spatially non-uniform the corrosion behavior, the greater the uncertainty of the lifetime prediction in the local area. Specifically, the index characterizing the intensity of local imbalance in the anisotropic corrosion behavior imbalance data is extracted, normalized, and mapped to an uncertainty coefficient between 0 and 1. The higher the coefficient, the greater the uncertainty. The more unreliable the prediction is in a certain area, the less the uncertainty coefficient is subtracted from 1 to obtain the prediction confidence level of that local area. By performing this operation on all areas, the prediction confidence level distribution corresponding to the preliminary life prediction data in space is obtained. Then, the baseline fatigue life data of the sleeve thread connection is obtained. This baseline data comes from the traditional engineering evaluation method that ignores the coupling effect of fretting corrosion. It is usually obtained by simplifying the thread connection into an equivalent continuum and applying the standard fatigue curve based on the nominal stress of the net section, or by obtaining the hot spot stress through macroscopic finite element analysis and then querying the corresponding curve. The result represents a conventional life prediction value that does not consider the micro-damage mechanism.Finally, the corrosion fatigue life prediction data is compared with the baseline fatigue life data, and weighted according to the prediction confidence level to calculate the absolute deviation value of the life prediction. This calculation is done on a regional basis. First, the simple difference between the corrected predicted life and the baseline life is calculated for each region. Then, this difference is multiplied by the prediction confidence level of that region. For regions with high confidence levels, the difference is fully adopted in the final deviation, while for regions with low confidence levels, the difference is partially discounted. This yields the local absolute deviation value after reliability weighting. The weighted deviation values ​​of all regions are integrated, and their central tendency is statistically analyzed to obtain the life prediction deviation data.

[0034] The safety evaluation module is used to perform a quantitative safety evaluation of the entire well casing based on the corrosion fatigue life prediction data and the life prediction deviation data, and generate a quantitative safety evaluation result for the entire well casing.

[0035] For example, a quantitative safety evaluation of the entire well casing is performed based on the corrosion fatigue life prediction data and the life prediction deviation data, generating a quantitative safety evaluation result for the entire well casing, specifically including: Feature learning is performed on the lifetime prediction deviation data to obtain lifetime prediction deviation feature data; Based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data, a quantitative safety evaluation matching is performed to generate quantitative safety evaluation matching data. The singular value optimization adjustment is performed on the safety quantitative evaluation matching data to obtain the safety quantitative evaluation optimized data; The safety quantitative evaluation data of the entire well casing is obtained by performing a safety quantitative evaluation of the entire well casing using the optimized safety quantitative evaluation data.

[0036] Specifically, the process of generating a quantitative safety evaluation result for the entire well casing based on the corrosion fatigue life prediction data and the life prediction deviation data is as follows: First, feature learning is performed on the life prediction deviation data. This process uses a hybrid model based on principal component analysis and kernel density estimation to extract key patterns of statistical distribution from the spatial distribution matrix of the deviation data, including the global mean and variance of the deviation values ​​to describe the overall level of systematic prediction deviation. Moran's index is calculated through spatial autocorrelation analysis to identify whether there are clustered high or low value regions of the deviation. Wavelet transform is then used to extract the characteristic scale and dominant frequency of the deviation variation along the thread axial and circumferential directions, thereby... These multi-dimensional and multi-scale statistical features are fused into a high-dimensional feature vector that can comprehensively characterize the internal structure and patterns of the deviation data, namely, the life prediction deviation feature data. Next, a quantitative safety evaluation matching is performed based on the life prediction deviation feature data and the corrosion fatigue life prediction data. The core of the above steps is to construct a multi-dimensional decision rule engine. The input to this engine is the aforementioned deviation feature vector and the corrosion fatigue life prediction value for the corresponding region. Its internal logic first performs preliminary classification based on the predicted life value against a preset industry safety life threshold, and then introduces the deviation feature data for dynamic correction. For example, when the predicted life of a certain region is close to the threshold and its deviation features show that the deviation at that location is high variance and spatially isolated, the engine may be corrected. If the prediction uncertainty is high, the safety level of that location should be downgraded. Conversely, if the deviation characteristics show low variance and change in tandem with surrounding areas, the safety level should be maintained or upgraded. By traversing all regions and applying this rule engine, a preliminary regional safety level map combining lifetime predictions and prediction uncertainties is generated, i.e., safety quantitative evaluation matching data. Subsequently, the safety quantitative evaluation matching data undergoes singular value optimization adjustment. This adjustment aims to handle individual abnormal safety ratings that may arise during the rule matching process and significantly conflict with engineering physics common sense or overall data trends. The method is to calculate the difference between the safety level of each region and the average safety level of all its neighboring regions. A dynamic threshold based on the statistical three sigma principle is set, and regions exceeding this threshold are marked as singularities to be adjusted. For these singularities, instead of simply replacing them with the neighborhood average, they are input into a backtracking analysis model. This model will re-examine the original corrosion fatigue life prediction data, fretting corrosion coupling data, and local load conditions of the point, and check whether there are special damage mechanisms or extreme load conditions that were previously ignored by the rule engine and can reasonably explain their abnormal ratings. If they exist, the original rating is retained and special annotations are added. If they do not exist, their safety level is smoothly adjusted to the neighborhood average level, thereby eliminating unreasonable data noise and obtaining logically consistent and spatially continuous safety quantitative evaluation optimization data.Finally, a quantitative safety evaluation of the entire well casing is conducted using the optimized safety evaluation data. This evaluation is a comprehensive decision-making process that integrates optimized zonal safety level information, the overall predicted life distribution of the entire well casing, and the location of critical weak areas. The overall structural integrity is quantified by calculating the proportion of areas meeting the highest safety level requirements to the total assessed area. The most dangerous potential failure segments are identified by recognizing the lowest safety level contiguous areas and evaluating their size and location. Combined with the drilling operation plan, priority and timelines for preventative maintenance recommendations are provided. The final output is a structured quantitative safety evaluation result for the entire well casing. This result not only includes qualitative conclusions of "safety / early warning / danger" but also provides quantitative evidence for each conclusion, spatial location indications, and action recommendations based on life prediction and deviation analysis, thus completing a closed loop from data prediction to engineering decision-making.

[0037] For example, safety quantitative evaluation matching is performed based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data to generate safety quantitative evaluation matching data, specifically including: Based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data, a safety factor matching is performed to obtain safety factor matching data. Based on the safety factor matching data and the life prediction deviation characteristic data, the remaining life adaptive adjustment control is performed to obtain the remaining life adaptive control data. Risk level matching is performed on the remaining lifetime adaptive control data to obtain risk level matching data; Based on the safety factor matching data, the remaining life adaptive control data, and the risk level matching data, a safety quantitative evaluation and matching is performed on the corrosion fatigue life prediction data to generate the safety quantitative evaluation and matching data.

[0038] Specifically, the process of generating safety quantitative evaluation matching data based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data is as follows: First, safety factor matching is performed based on the life prediction deviation characteristic data and the corrosion fatigue life prediction data. This matching operation is achieved by constructing a two-input mapping function. One input is the corrosion fatigue life prediction value of a local area, and the other input is a key indicator extracted from the life prediction deviation characteristic data that reflects the prediction uncertainty of that area. Based on the engineering conservative principle that "the shorter the predicted life and the higher the prediction uncertainty, the larger the required safety factor," the function calculates a dynamic safety factor for each area. This factor indicates how many times the predicted life needs to be divided in subsequent decisions to obtain a conservative life value that takes into account uncertainty and can be directly used for design or maintenance, thus obtaining safety factor matching data covering the entire structure. Next, residual life adaptive adjustment control is performed based on the safety factor matching data and the life prediction deviation characteristic data. This step aims to convert the theoretical predicted life into an engineering-usable residual life. The control logic is to convert the original life of each area into a residual life that is suitable for engineering use. The corrosion fatigue life prediction value is divided by its corresponding dynamic safety factor to obtain a conservative initial value of remaining life that has been "discounted". Then, the time trend component in the deviation feature data is introduced to adjust the discounted remaining life a second time. If the trend shows that the prediction deviation has been continuously reduced in recent assessments, the remaining life value is appropriately increased to reflect the improvement in prediction accuracy; otherwise, it is further decreased. Finally, a set of more robust adaptive control data for remaining life is generated, which has been calibrated by both the safety factor and the prediction trend. Subsequently, the adaptive control data for remaining life is matched for risk levels. This matching is based on a preset threshold range associated with drilling operations and maintenance cycles. For example, areas with remaining life greater than twice the planned drilling cycle are classified as "low risk", areas with remaining life between 1.5 and 2 times the planned cycle are classified as "medium risk", and areas with remaining life less than 1.5 times the planned cycle are classified as "high risk". At the same time, areas in the high risk level whose safety factor matching data shows an extremely high safety factor are additionally marked as "urgent concern" subclasses, thereby transforming continuous life values ​​into discrete risk level matching data that is easy to manage and respond to.Finally, based on the safety factor matching data, the remaining life adaptive control data, and the risk level matching data, a quantitative safety evaluation and matching of the corrosion fatigue life prediction data is performed. This is an information fusion and decision generation step, which synthesizes a structured safety profile for each evaluation unit. This profile not only records the original corrosion fatigue life prediction data as a theoretical reference, but also integrates the safety factor adjusted according to uncertainty, the calibrated remaining life, the clearly defined risk level, and the confidence evaluation from deviation characteristic data. This information is linked and bound through a specific data encapsulation protocol, thereby generating a set of quantitative safety evaluation and matching data containing multi-level, traceable decision-making basis, which can directly guide differentiated inspections, maintenance priority ranking, and work plan optimization.

[0039] Example 2 This application also discloses a quantitative safety evaluation method for the entire well casing.

[0040] Reference Figure 2 A quantitative safety evaluation method for the entire well casing includes the following steps: Step S1: Obtain the structural parameters of the entire well casing, the dynamic load data of the marine environment, and the well operation parameters. Based on the structural parameters of the entire well casing, perform fretting damage analysis at the threaded connection to obtain fretting damage data at the threaded connection. Step S2: Perform fretting corrosion coupling analysis on the fretting damage data at the threaded connection based on the marine environmental dynamic load data and the wellbore operating parameters to obtain fretting corrosion coupling data. Based on the fretting corrosion coupling data, conduct fatigue crack initiation risk assessment to obtain fatigue crack initiation risk data. Step S3: Based on the fatigue crack initiation risk data and the fretting corrosion coupling data, perform corrosion fatigue life prediction to obtain corrosion fatigue life prediction data. Based on the corrosion fatigue life prediction data, evaluate the life prediction deviation to obtain life prediction deviation data. Step S4: Perform a quantitative evaluation of the safety of the entire well casing based on the corrosion fatigue life prediction data and the life prediction deviation data, and generate the quantitative evaluation result of the safety of the entire well casing.

[0041] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0042] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0043] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A full bore casing quantitative safety evaluation system, characterized in that, The method comprises the following steps: a data acquisition module is used to acquire full wellbore casing structure parameters, marine environment dynamic load data and wellbore operation parameters, and micro-motion damage analysis is performed on the thread connection based on the full wellbore casing structure parameters, so as to obtain micro-motion damage data of the thread connection; an analysis module is used to perform micro-motion corrosion coupling analysis on the micro-motion damage data of the thread connection according to the marine environment dynamic load data and the wellbore operation parameters, to obtain micro-motion corrosion coupling data, perform fatigue crack initiation risk evaluation based on the micro-motion corrosion coupling data, and obtain fatigue crack initiation risk data; a prediction module is used to perform corrosion fatigue life prediction according to the fatigue crack initiation risk data and the micro-motion corrosion coupling data, to obtain corrosion fatigue life prediction data, perform life prediction deviation evaluation based on the corrosion fatigue life prediction data, and obtain life prediction deviation data; a safety evaluation module is used to perform full wellbore casing safety quantitative evaluation according to the corrosion fatigue life prediction data and the life prediction deviation data, and generate full wellbore casing safety quantitative evaluation results.

2. The system for quantitative safety evaluation of a full wellbore casing according to claim 1, wherein, The full wellbore casing structure parameters include casing material, thread geometric parameters and connection pre-tightening force, the marine environment dynamic load data includes wave load, current load, temperature change load and derived dynamic load frequency and amplitude data, and the wellbore operation parameters include drilling fluid properties and corrosion parameters thereof.

3. The system of claim 2, wherein, The process of obtaining the micro-motion damage data of the thread connection specifically comprises the following steps: acquiring full wellbore casing structure parameters, marine environment dynamic load data and wellbore operation parameters; vectorizing the full wellbore casing structure parameters to generate casing structure vector parameters; extracting thread connection micro-motion demand data from the casing structure vector parameters; performing micro-motion damage analysis on the thread connection micro-motion demand data based on the casing structure vector parameters, so as to obtain the micro-motion damage data of the thread connection.

4. The system of claim 2, wherein, The fatigue crack initiation risk evaluation based on the micro-motion corrosion coupling data to obtain the fatigue crack initiation risk data specifically comprises the following steps: acquiring the dynamic load frequency and amplitude from the marine environment dynamic load data; performing micro-motion corrosion coupling analysis on the micro-motion damage data of the thread connection according to the marine environment dynamic load data, the wellbore operation parameters and the dynamic load frequency and amplitude, to obtain micro-motion corrosion coupling data; performing fatigue crack initiation tolerance fitting on the micro-motion corrosion coupling data to generate fatigue crack initiation tolerance data; performing fatigue crack initiation risk evaluation on the dynamic load frequency and amplitude based on the fatigue crack initiation tolerance data, to obtain the fatigue crack initiation risk data.

5. The system for quantitative safety evaluation of a full wellbore casing according to claim 4, wherein, The micro-motion corrosion coupling analysis on the micro-motion damage data of the thread connection according to the marine environment dynamic load data, the wellbore operation parameters and the dynamic load frequency and amplitude to obtain micro-motion corrosion coupling data specifically comprises the following steps: obtaining the drilling fluid corrosivity parameter from the wellbore operation parameter; performing vector angle calculation of the threaded connection according to the dynamic load frequency and amplitude, so as to obtain the vector angle between the threaded surface and the load; performing vector angle change analysis based on the micro-motion damage data of the threaded connection, so as to obtain the posture change vector angle data between the threaded surface and the load; performing corrosion instantaneous rate simulation analysis of the posture change vector angle data in the multi-frequency micro-motion process according to the drilling fluid corrosivity parameter, so as to obtain the multi-frequency corrosion instantaneous rate field; performing anisotropic corrosion behavior imbalance analysis on the multi-frequency corrosion instantaneous rate field, so as to obtain the anisotropic corrosion behavior imbalance data; performing interface corrosion vibration regression analysis on the anisotropic corrosion behavior imbalance data, so as to obtain the interface corrosion vibration regression data; performing micro-motion corrosion coupling analysis on the micro-motion damage data of the threaded connection according to the anisotropic corrosion behavior imbalance data and the interface corrosion vibration regression data, so as to obtain the micro-motion corrosion coupling data.

6. The system for quantitative safety evaluation of a full wellbore casing according to claim 5, wherein, The anisotropic corrosion behavior imbalance data is obtained by performing anisotropic corrosion behavior imbalance analysis on the multi-frequency corrosion instantaneous rate field, and specifically includes: performing corrosion flow non-uniformity vector identification on the multi-frequency corrosion instantaneous rate field, so as to obtain the corrosion flow non-uniformity vector; performing threaded surface corrosion time sequence change differential coupling based on the corrosion flow non-uniformity vector, so as to generate corrosion time sequence change differential coupling data; performing metal loss viscosity local variance calculation according to the corrosion flow non-uniformity vector and the corrosion time sequence change differential coupling data, so as to obtain the metal loss viscosity local variance; performing threaded orientation difference corrosion latent heat thermal energy release ratio difference deduction based on the corrosion flow non-uniformity vector, the corrosion time sequence change differential coupling data and the metal loss viscosity local variance, so as to generate the threaded orientation difference thermal energy release ratio difference; the anisotropic corrosion behavior imbalance data is obtained by performing anisotropic corrosion behavior imbalance analysis according to the threaded orientation difference thermal energy release ratio difference.

7. The system of claim 4, wherein, The fatigue crack initiation tolerance data is generated by performing fatigue crack initiation tolerance fitting on the micro-motion corrosion coupling data, and specifically includes: performing corrosion morphology deviation network division processing on the micro-motion corrosion coupling data, so as to obtain corrosion morphology deviation network division data; performing corrosion geometry deviation feature analysis on the corrosion morphology deviation network division data, so as to obtain corrosion geometry deviation feature data; performing crack initiation stress offset distribution simulation based on the corrosion geometry deviation feature data, so as to generate stress offset distribution data; performing stress distribution strain rate component decomposition on the stress offset distribution data, so as to obtain strain rate component decomposition data; performing fatigue crack initiation tolerance fitting based on the strain rate component decomposition data, so as to generate the fatigue crack initiation tolerance data.

8. The system of claim 1, wherein, The full-wellbore casing safety quantitative evaluation result is generated by performing full-wellbore casing safety quantitative evaluation according to the corrosion fatigue life prediction data and the life prediction deviation data, and specifically includes: performing feature learning on the life prediction deviation data, so as to obtain life prediction deviation feature data; According to the life prediction deviation characteristic data and the corrosion fatigue life prediction data, safety quantitative evaluation matching is performed to generate safety quantitative evaluation matching data; The safety quantitative evaluation matching data is subjected to singular value optimization adjustment to obtain safety quantitative evaluation optimization data; Full wellbore casing safety quantitative evaluation is performed through the safety quantitative evaluation optimization data to obtain full wellbore casing safety quantitative evaluation data.

9. The full bore casing quantitative safety evaluation system of claim 8, wherein, According to the life prediction deviation characteristic data and the corrosion fatigue life prediction data, safety quantitative evaluation matching is performed to generate safety quantitative evaluation matching data, specifically including: According to the life prediction deviation characteristic data and the corrosion fatigue life prediction data, safety coefficient matching is performed to obtain safety coefficient matching data; According to the safety coefficient matching data and the life prediction deviation characteristic data, residual life self-adaptive adjustment control is performed to obtain residual life self-adaptive control data; The residual life self-adaptive control data is subjected to risk level matching to obtain risk level matching data; Based on the safety coefficient matching data, the residual life self-adaptive control data and the risk level matching data, the corrosion fatigue life prediction data is subjected to safety quantitative evaluation matching to generate the safety quantitative evaluation matching data.

10. A full wellbore casing quantitative safety evaluation method applied to the full wellbore casing quantitative safety evaluation system of any one of claims 1-9, characterized in that, The method comprises the following steps: Step S1: Obtain full wellbore casing structure parameters, marine environment dynamic load data and wellbore operation parameters, perform micro-motion damage analysis at the threaded connection based on the full wellbore casing structure parameters to obtain micro-motion damage data at the threaded connection; Step S2: Perform micro-motion corrosion coupling analysis on the micro-motion damage data at the threaded connection according to the marine environment dynamic load data and the wellbore operation parameters to obtain micro-motion corrosion coupling data, and perform fatigue crack initiation risk evaluation based on the micro-motion corrosion coupling data to obtain fatigue crack initiation risk data; Step S3: Perform corrosion fatigue life prediction according to the fatigue crack initiation risk data and the micro-motion corrosion coupling data to obtain corrosion fatigue life prediction data, and perform life prediction deviation evaluation based on the corrosion fatigue life prediction data to obtain life prediction deviation data; Step S4: Perform full wellbore casing safety quantitative evaluation according to the corrosion fatigue life prediction data and the life prediction deviation data to generate full wellbore casing safety quantitative evaluation results.

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