Emergency early warning and disposal method for integrity of wellbore of offshore gas well

Abstract

The invention discloses an offshore gas well shaft integrity emergency early warning and disposal method which comprises the following steps that S1, data related to shaft integrity are collected in real time through an underground sensor and Internet of Things equipment, and the data comprise underground temperature, pressure, stress, casing pressure and wellhead lifting height; s2, detecting abnormal points in the data, and marking missing data; s3, the missing data are cleaned; s4, establishing a related model according to the data; s5, analysis is carried out in combination with the related model, and graded early warning is carried out on cement sheath integrity failure, pipe column buckling deformation, annulus belt pressure and wellhead lifting; and S6, giving a corresponding emergency disposal method according to the early warning level. According to the method, discreteness, complexity, correlation, readability, nonlinearity and timeliness of field production data are fully considered, early warning is carried out on the integrity of the wellbore of the high-temperature and high-pressure gas well based on the data driving technology, and corresponding emergency disposal methods are provided for different early warning results.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas drilling and production, and more specifically, to an emergency warning and disposal method for the integrity of an offshore gas wellbore. Background Art

[0002] At present, the conventional offshore oil and gas exploration and development technologies have been relatively mature, the degree of oil and gas exploration is relatively high, the newly discovered oil and gas scale gradually becomes smaller, the oil and gas production growth is slow, and the exploration and development direction is gradually shifting to the development of oil and gas resources in deep and high-temperature and high-pressure formations. Under such extreme service conditions, the integrity of the gas wellbore faces severe challenges and is extremely vulnerable to problems such as annulus pressure buildup, wellhead uplift, tubing buckling deformation, and damage to the tubing structure stability.

[0003] One of the important reasons for annulus pressure buildup is caused by the damage to the physical structure of the wellbore due to corrosion and wear of the wellbore; wellhead uplift is mainly caused by the cementing failure of the casing and the cement sheath due to temperature and pressure changes, resulting in the generation of the free section of the casing; tubing buckling deformation is mainly caused by excessive axial compressive load, resulting in the loss of stability of the tubing; the damage to the wellbore structure stability is mainly caused by the action of various external loads.

[0004] In actual production, the failure of the integrity of the high-temperature and high-pressure gas wellbore caused by annulus pressure buildup, wellhead uplift, tubing buckling deformation, and damage to the wellbore structure stability may lead to serious accidents such as liquid leakage, formation pollution, and well blowout. Therefore, it is urgent to make an intelligent warning in advance for the failure of the integrity of the high-temperature and high-pressure gas wellbore and take effective emergency disposal methods. Summary of the Invention

[0005] To solve the above problems, the present invention proposes an emergency warning and disposal method for the integrity of an offshore gas wellbore, which can provide scientific guidance for the prevention of the integrity of the high-temperature and high-pressure gas wellbore and avoid safety accidents caused by the failure of the wellbore integrity.

[0006] To achieve the above object, an emergency warning and disposal method for the integrity of an offshore gas wellbore according to the present invention is characterized by including the following steps:

[0007] S1: Real-time collect data related to the integrity of the wellbore through downhole sensors and Internet of Things devices, and the data includes: downhole temperature, pressure, stress, casing pressure, and wellhead uplift height;

[0008] S2: Detect abnormal points in the data and mark missing data;

[0009] S3: Clean the missing data;

[0010] S4: Establish a wellbore temperature-pressure field model, an elastoplastic mechanics model for the multi-tubular string cement sheath assembly, a pipe string buckling form discrimination model, a pipe string triaxial strength verification model, an annulus allowable pressure calculation model, and a wellhead lift height calculation model based on the said data;

[0011] S5: Analyze by combining the wellbore temperature-pressure field model, the elastoplastic mechanics model for the multi-tubular string cement sheath assembly, the pipe string buckling form discrimination model, the pipe string triaxial strength verification model, the annulus allowable pressure calculation model, and the wellhead lift height calculation model, and conduct hierarchical early warnings for cement sheath integrity failure, pipe string buckling deformation, annulus pressure holding, and wellhead lift;

[0012] S6: Give corresponding emergency disposal methods according to the early warning level.

[0013] In this technical solution, downhole sensors and Internet of Things devices transmit data to the database for storage in real time through a high-speed network, process the data to detect abnormal points and mark missing data to ensure the accuracy of the data. Subsequently, the data is cleaned to ensure the accuracy, reliability, and readability of the data, preparing for subsequent data processing. After establishing relevant data models, hierarchical early warnings can be carried out for common problems affecting wellbore integrity, such as cement sheath integrity failure, pipe string buckling deformation, annulus pressure holding, and wellhead lift, and corresponding measures are taken for the problems at each level of early warning, thus avoiding safety accidents caused by wellbore integrity failure.

[0014] As a preferred solution, in order to consider the discreteness, complexity, correlation, readability, non-linearity, and timeliness of the data, in step S2, the detection methods for data abnormal points include: local outlier factor algorithm, isolation forest algorithm, and box plot statistical method.

[0015] As a preferred solution, in step S2, the detection method for data abnormal points is specifically: adopt the local outlier factor algorithm for sparsely distributed data; adopt the isolation forest algorithm for high-dimensional complex data; adopt the box plot statistical method for data subject to a specific distribution, replace the detected abnormal data with NaN characters, and mark the missing data through Microsoft Excel formulas, and keep the rest of the data.

[0016] As a preferred solution, to ensure the accuracy, reliability, and readability of the data, in step S3, the data cleaning is specifically: according to the data type, use linear interpolation method to fill in the missing values for data with good linearity, and use BP neural network method to predict and fill in the missing values for complex non-linear data.

[0017] As a preferred solution, step S4 specifically includes: constructing a wellbore temperature-pressure field model based on the laws of thermodynamics and the theorem of conservation of energy; constructing an elastoplastic mechanical model of a multi-string cement sheath assembly based on elastoplastic theory and the Mohr-Coulomb criterion; constructing a pipe buckling mode discrimination model based on the discriminant formula for the critical axial compression load of the pipe string; constructing a pipe string triaxial strength checking model based on the thick-walled cylinder theory and the Von-Mises strength criterion; constructing an annulus allowable pressure calculation model based on the API RP90 and NORSOK D-010 standards; and constructing a wellhead lift height calculation model in combination with the generation conditions of the free section of the casing.

[0018] As a preferred solution, in step S5, based on the Matlab APP Designer platform, in combination with the wellbore temperature-pressure field model, the elastoplastic mechanical model of the multi-string cement sheath assembly, the pipe buckling mode discrimination model, the pipe string triaxial strength checking model, the annulus allowable pressure calculation model, and the wellhead lift height calculation model respectively, construct modules including temperature-pressure field prediction, cement sheath integrity prediction, pipe buckling mode discrimination, pipe string integrity checking, annulus allowable pressure prediction, and wellhead lift prediction, and then calculate the critical axial compression load value of the pipe string when buckling occurs, the casing-cement sheath interface contact force, the micro-annulus, the casing strength, the annulus allowable pressure value, and the wellhead lift allowable height value during the temperature loading and unloading stages through the modules respectively.

[0019] As a preferred solution, based on the degree and cause of wellbore integrity failure, the hierarchical early warning is specifically as follows: based on the casing-cement sheath interface contact force and the size of the micro-annulus respectively, the cement sheath integrity early warning is divided into three levels; based on the critical axial compression load value of the pipe string and the casing strength respectively, the pipe string risk early warning is divided into three levels; based on the annulus pressure range value and the duration respectively, the annulus pressure warning is divided into three levels; based on the wellhead lift allowable height value respectively, the wellhead lift early warning is divided into three levels.

[0020] As a preferred solution, when the casing-cement sheath interface contact force is less than the radial bonding strength and the displacement of the outer wall of the casing is equal to the displacement of the inner wall of the cement sheath, the cement sheath integrity is a first-level early warning. When the casing-cement sheath interface contact force is greater than the radial bonding strength or there is a small micro-annulus <100μm, the cement sheath integrity is a second-level early warning. When the casing-cement sheath interface contact force is greater than the radial bonding strength or there is a large micro-annulus >100μm, the cement sheath integrity is a third-level early warning;

[0021] When the pipe string does not buckle and the external load is less than the casing strength, the pipe string failure risk is a first-level early warning. When the pipe string undergoes sinusoidal buckling or the external load is greater than the casing strength, the pipe string failure risk is a second-level early warning. When the pipe string undergoes helical buckling or the external load is greater than the casing strength, the pipe string failure risk is a third-level early warning;

[0022] When the casing pressure is lower than 0.69 MPa, the annulus pressure is at the first-level warning. When the casing pressure is greater than 0.69 MPa and less than 5 MPa, the annulus pressure is at the second-level warning. When the casing pressure is greater than 5 MPa and continuous venting for 24 hours fails to reduce to zero, the annulus pressure is at the third-level warning;

[0023] When the wellhead lifting height is lower than 50 mm, the wellhead lifting is at the first-level warning. When the wellhead lifting height is higher than 50 mm, the wellhead lifting is at the second-level warning. When the wellhead lifting height exceeds the maximum allowable wellhead lifting value and continues to rise, the wellhead lifting is at the third-level warning.

[0024] As a preferred solution, in step S6, the corresponding emergency response methods given according to the warning level are specifically as follows:

[0025] When there is a first-level warning for the cement sheath integrity, regularly detect the micro-annulus. When there is a second-level warning for the cement sheath integrity, regularly detect the micro-annulus and inject a plugging agent to repair the cement sheath. When there is a third-level warning for the cement sheath integrity, conduct acoustic logging or gamma-ray logging and analyze and judge the cause of the failure of the cement sheath integrity;

[0026] When there is a first-level warning for the risk of tubing failure, regularly detect the buckling condition and stress distribution of the tubing. When there is a second-level warning for the tubing risk, regularly detect the buckling condition and stress distribution of the tubing and reinforce the tubing or reduce the load. When there is a third-level warning for the tubing risk, conduct downhole vibration monitoring or acoustic logging and analyze and judge the cause of the failure of the tubing stability;

[0027] When there is a first-level warning for the annulus pressure, regularly monitor the casing pressure. When there is a second-level warning for the annulus pressure, regularly monitor the casing pressure and install an automatic pressure relief needle valve, and vent until the pressure is lower than the maximum allowable annulus pressure value at the wellhead. When there is a third-level warning for the annulus pressure, conduct a pressure restoration test or leak point detection and analyze and judge the wellbore leakage condition and cause;

[0028] When there is a first-level warning for the wellhead lifting, regularly monitor the wellhead lifting height. When there is a second-level warning for the wellhead lifting, regularly monitor the casing pressure and install an automatic production control valve to reduce production until the lifting height is lower than the maximum allowable wellhead lifting value. When there is a third-level warning for the wellhead lifting, shut in the well and conduct a wellhead lifting height restoration test, and analyze and judge the cause of the wellhead lifting.

[0029] As a preferred solution, the tubing buckling shape discrimination model includes the following calculation formula:

[0030]

[0031] F 0 is the critical axial compression load of the tubing, N; I is the elastic modulus of the tubing, N; E is the moment of inertia of the tubing, MPa; W is the weight per unit length of the tubing, N / m; α is the well deviation angle, °; r is the tubing-casing annulus clearance, m; F is the axial compression load of the tubing, N;

[0032] When F < F 0 no buckling occurs;

[0033] When 2F 0 < F < 4.71F 0 sinusoidal buckling occurs;

[0034] When F ≥ 4.71F 0 helical buckling occurs.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. The problems of wellbore integrity failure caused by cement sheath integrity failure, pipe string buckling deformation, pipe string integrity failure, annulus pressure buildup, and wellhead lift are comprehensively considered. At the same time, based on data-driven technology, the accuracy, reliability, and readability of on-site production data are ensured, forming an intelligent early warning and emergency disposal method for the wellbore integrity of high-temperature and high-pressure gas wells, reducing the risks and derivative problems brought by wellbore integrity failure.

[0037] 2. Using model calculations, hierarchical early warnings are carried out for the problems caused by cement sheath integrity failure, pipe string buckling deformation, pipe string integrity failure, annulus pressure buildup, and wellhead lift that affect wellbore integrity, and corresponding countermeasures for reference are given for the early warning levels of each problem, thereby avoiding safety accidents caused by wellbore integrity failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flowchart of the emergency early warning and disposal method for the wellbore integrity of the offshore gas well of the present invention;

[0039] Figure 2 is a flowchart of the emergency early warning method for the wellbore integrity of the offshore gas well of the present invention;

[0040] Figure 3 is a schematic diagram of the temperature and pressure field calculation module;

[0041] Figure 4 is a schematic diagram of the cement sheath integrity prediction module;

[0042] Figure 5 is a schematic diagram of the annulus allowable pressure prediction module;

[0043] Figure 6 is a schematic diagram of the wellhead lift prediction module. DETAILED DESCRIPTION OF THE INVENTION

[0044] The accompanying drawings are only for illustrative purposes and should not be construed as limiting the patent; for better illustration of this embodiment, some components in the drawings may be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted. The positional relationships described in the drawings are only for illustrative purposes and should not be construed as limiting the patent.

[0045] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "long", "short", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as limiting the patent. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0046] The technical solutions of the present invention will be further specifically described below through specific embodiments in conjunction with the accompanying drawings:

[0047] Embodiment 1:

[0048] As Figures 1 to 6 shown, this embodiment provides an emergency warning method for the integrity of an offshore wellbore, which is characterized by including the following steps:

[0049] S1: Real-time collect data related to the integrity of the wellbore through downhole sensors and Internet of Things devices, and the data includes: downhole temperature, pressure, stress, casing pressure, and wellhead lift height;

[0050] S2: Detect abnormal points in the data and mark missing data;

[0051] S3: Clean the missing data;

[0052] S4: According to the data, establish a wellbore temperature-pressure field model, an elastoplastic mechanics model of a multi-tubular cement sheath assembly, a pipe buckling form discrimination model, a pipe triaxial strength checking model, an annular allowable pressure calculation model, and a wellhead lift height calculation model;

[0053] S5: Combine the wellbore temperature-pressure field model, the elastoplastic mechanics model of the multi-tubular cement sheath assembly, the pipe buckling form discrimination model, the pipe triaxial strength checking model, the annular allowable pressure calculation model, and the wellhead lift height calculation model for analysis, and conduct hierarchical warnings for the failure of cement sheath integrity, pipe buckling deformation, annular pressure, and wellhead lift.

[0054] Specifically, step S4 specifically includes: constructing a wellbore temperature-pressure field model based on the laws of thermodynamics and the theorem of conservation of energy; constructing an elastoplastic mechanics model of a multi-tubular string cement sheath assembly based on elastoplastic theory and the Mohr-Coulomb criterion; constructing a pipe string buckling mode discrimination model based on the discriminant formula for the critical axial compression load of the pipe string; constructing a pipe string triaxial strength check model based on the thick-walled cylinder theory and the Von-Mises strength criterion; constructing an annulus allowable pressure calculation model based on the API RP90 and NORSOK D-010 standards; and constructing a wellhead lift height calculation model in combination with the generation conditions of the free section of the casing.

[0055] Specifically, in step S5, based on the Matlab APP Designer platform, in combination with the wellbore temperature-pressure field model, the elastoplastic mechanics model of the multi-tubular string cement sheath assembly, the pipe string buckling mode discrimination model, the pipe string triaxial strength check model, the annulus allowable pressure calculation model, and the wellhead lift height calculation model respectively, construct modules including temperature-pressure field prediction, cement sheath integrity prediction, pipe string buckling mode discrimination, pipe string integrity check, annulus allowable pressure prediction, and wellhead lift prediction, and then calculate the critical axial compression load value of the pipe string when buckling occurs, the casing-cement sheath interface contact force, micro-annulus, casing strength, annulus allowable pressure value, and wellhead lift allowable height value during the temperature loading and unloading stages through the modules respectively.

[0056] Specifically, based on the casing-cement sheath interface contact force and the size of the micro-annulus respectively, the cement sheath integrity early warning is divided into three levels; based on the critical axial compression load value of the pipe string and the casing strength respectively, the pipe string risk early warning is divided into three levels; based on the annulus pressure range value and the duration respectively, the annulus pressure warning is divided into three levels; and based on the wellhead lift allowable height value respectively, the wellhead lift early warning is divided into three levels.

[0057] Specifically, the hierarchical early warning is as follows: when the casing-cement sheath interface contact force is less than the radial bonding strength and the displacement of the outer wall of the casing is equal to the displacement of the inner wall of the cement sheath, the cement sheath integrity is a first-level early warning; when the casing-cement sheath interface contact force is greater than the radial bonding strength or there is a small micro-annulus <100μm, the cement sheath integrity is a second-level early warning; when the casing-cement sheath interface contact force is greater than the radial bonding strength or there is a large micro-annulus >100μm, the cement sheath integrity is a third-level early warning;

[0058] When the pipe string does not buckle and the external load is less than the casing strength, the pipe string failure risk is a first-level early warning; when the pipe string undergoes sinusoidal buckling or the external load is greater than the casing strength, the pipe string failure risk is a second-level early warning; when the pipe string undergoes helical buckling or the external load is greater than the casing strength, the pipe string failure risk is a third-level early warning;

[0059] When the casing pressure is lower than 0.69 MPa, the annulus pressure is at the first-level warning. When the casing pressure is greater than 0.69 MPa and less than 5 MPa, the annulus pressure is at the second-level warning. When the casing pressure is greater than 5 MPa and continuous flaring for 24 hours fails to drop to zero, the annulus pressure is at the third-level warning;

[0060] When the wellhead lifting height is lower than 50 mm, the wellhead lifting is at the first-level warning. When the wellhead lifting height is higher than 50 mm, the wellhead lifting is at the second-level warning. When the wellhead lifting height exceeds the maximum allowable wellhead lifting value and continues to rise, the wellhead lifting is at the third-level warning.

[0061] Specifically, the pipe string buckling form discrimination model includes the following calculation formulas:

[0062]

[0063] F 0 is the critical axial compression load of the pipe string, N; I is the elastic modulus of the pipe string, N; E is the moment of inertia of the pipe string, MPa; W is the weight per unit length of the pipe string, N / m; α is the well deviation angle, °; r is the annulus clearance of the pipe string, m; F is the axial compression load of the pipe string, N;

[0064] In this embodiment,

[0065] When F < F 0 , no buckling occurs;

[0066] When 2F 0 < F < 4.71F 0 , sinusoidal buckling occurs;

[0067] When F ≥ 4.71F 0 , helical buckling occurs.

[0068] Example 2:

[0069] This embodiment is similar to Embodiment 1. The difference is that in this embodiment, in step S2, the detection methods for data anomaly points include: local outlier factor algorithm, isolation forest algorithm, and box plot statistical method.

[0070] Specifically, in step S2, the detection methods for data anomaly points are specifically as follows: the local outlier factor algorithm is used for sparse data; the isolation forest algorithm is used for high-dimensional complex data; the box plot statistical method is used for data subject to a specific distribution. The detected abnormal data is replaced with the NaN character, and the missing data is marked through the Microsoft Excel formula.

[0071] Specifically, in step S3, the data cleaning is specifically as follows: the linear interpolation method is used to fill in the missing values for data with good linearity, and the BP neural network method is used to predict and fill in the missing values for complex non-linear data.

[0072] In this embodiment, for data with a relatively sparse distribution, the Local Outlier Factor (LOF) algorithm is used to screen out abnormal data: the neighborhood size is defined according to the data size and characteristics; the reachable distance is calculated; the local reachability density is calculated; the local outlier factor LOF is calculated, and if the LOF value of the data is significantly greater than 1, the point is determined as an outlier.

[0073] In this embodiment, the formula for calculating the reachable distance is:

[0074] r d (p, o) = max{d(o, p), k - d(o)}

[0075] Where: p is the data point to be screened; o is any point in the set of the nearest neighbor points of p; r d (p, o) is the reachable distance; d(o, p) is the distance between o and p; k - d(o) is the distance from o to its k nearest neighbor points.

[0076] In this embodiment, the formula for calculating the reachability density is:

[0077]

[0078] Where: lrd(p) is the local reachability density of p; N k (p) is the sum of the reachable distances of all k nearest neighbor points of p.

[0079] In this embodiment, the formula for calculating the local outlier factor LOF is:

[0080]

[0081] Where: LOF(p) is the local outlier factor of p; lrd(o) is the local reachability density of o; |N k (p)| is the number of points in the neighborhood of p, i.e., k.

[0082] In this embodiment, for large-scale and high-dimensional data, the Isolation Forest algorithm is used to screen out abnormal data: randomly select sample subsets and feature subsets to construct multiple decision trees; calculate the average path length of each data point; calculate the anomaly score according to the average path length; set a threshold according to the percentile of the anomaly score distribution; compare the anomaly score with the threshold, and the data points with scores lower than the threshold are determined as outliers.

[0083] In this embodiment, for data that follows a certain distribution, the box plot statistical method is used to screen out abnormal data: the data is arranged in ascending order; the first quartile Q1, the median Q2, and the third quartile Q3 are calculated; the interquartile range, that is, the difference L between Q3 and Q1, is calculated; the upper and lower bounds of abnormal data are determined based on Q1, Q2, Q3, and L, and the data points that fall outside the lower or upper bounds are outliers.

[0084] In this embodiment, the on-site production data is detected, the outliers are judged as abnormal data and the corresponding position data is replaced with NaN characters, and the rest of the data is retained: for discontinuous data such as shut-in pressure build-up test, acoustic logging data, resistivity logging data, etc., the local outlier factor algorithm is used; for data with a large number of dimensions and complex correlations such as the production, pressure, temperature, and wellbore stress of multiple wells, the isolation forest algorithm is used; for data such as wellhead pressure and downhole temperature that follows a certain distribution, the box plot statistical method is used.

[0085] In this embodiment, the processed data marks the missing data positions through the Microsoft Excel formula method, and the data replaced with NAN characters and the rest of the data are retained: use conditional formatting to highlight the missing data; use the ISBLANK() formula to find the missing data; record the corresponding positions of the missing data and mark them with NaN characters.

[0086] In this embodiment, according to the type of processed data, linear interpolation and BP neural network methods are used for data cleaning. Among them, the linear interpolation method is used to clean data with good linearity, and the BP neural network method is used to clean complex non-linear data to ensure the accuracy, reliability, and readability of the data.

[0087] In this embodiment, for data with good linearity such as temperature and pressure in the data type, the linear interpolation method is used for cleaning: determine the range of positions of the data to be filled; perform linear interpolation on it using the built-in interp1 function in Matlab according to the data position range to obtain predicted values; replace the NaN characters in the data with the predicted values, thereby completing data cleaning to obtain accurate, reliable, and readable data.

[0088] In this embodiment, for data showing complex non-linear trends such as oil and gas production, cementing quality, and annulus pressure, the BP neural network method is used for cleaning: randomly initialize the weights and biases of the neural network; use historical data as the training set, and divide the data into input data (all variables except the target variable) and target data (the variable to be predicted, i.e., abnormal and missing data); pass the input data through each layer of the network and apply the activation function to calculate the output of each neuron; compare the output of the network with the target value and calculate the error; according to the error, starting from the output layer, calculate the contribution of each neuron to the error and update the weights and biases; repeat the above steps, through multiple epochs, continuously adjust the weights and biases until the predetermined number of training times or error convergence is reached; use the trained network to predict the positions of abnormal and missing data; replace the NaN characters in the data with the predicted values, thereby completing the data cleaning to obtain accurate, reliable, and readable data.

[0089] Embodiment 3:

[0090] As Figure 1 shown, this embodiment provides an emergency disposal method for the wellbore integrity of an offshore well. Based on the wellbore integrity emergency warning method of Embodiment 1, it includes:

[0091] When a first-level warning of cement sheath integrity occurs, regularly detect the micro-annulus. When a second-level warning of cement sheath integrity occurs, regularly detect the micro-annulus and inject a plugging agent to repair the cement sheath. When a third-level warning of cement sheath integrity occurs, perform acoustic logging or gamma-ray logging and analyze and judge the cause of cement sheath integrity failure;

[0092] When a first-level warning of tubing failure risk occurs, regularly detect the tubing buckling condition and stress distribution. When a second-level warning of tubing risk occurs, regularly detect the tubing buckling condition and stress distribution and reinforce the tubing or reduce the load. When a third-level warning of tubing risk occurs, perform downhole vibration monitoring or acoustic logging and analyze and judge the cause of tubing stability failure;

[0093] When a first-level warning of annulus pressure occurs, regularly monitor the casing pressure. When a second-level warning of annulus pressure occurs, regularly monitor the casing pressure and install an automatic pressure relief needle valve to vent until the pressure is lower than the maximum allowable annulus pressure value at the wellhead. When a third-level warning of annulus pressure occurs, perform a pressure build-up test or leak point detection and analyze and judge the wellbore leakage condition and cause;

[0094] When a first-level warning of wellhead lift occurs, regularly monitor the wellhead lift height. When a second-level warning of wellhead lift occurs, regularly monitor the casing pressure and install an automatic production control valve to reduce production until the lift height is lower than the maximum allowable lift value at the wellhead. When a third-level warning of wellhead lift occurs, shut in the well and perform a wellhead lift height recovery test to analyze and judge the cause of wellhead lift.

[0095] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0096] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention and are not limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or alterations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. An offshore gas wellbore integrity emergency warning and disposal method, characterized in that: The following steps are involved: S1: Real-time collection of wellbore integrity-related data through downhole sensors and IoT devices, including downhole temperature, pressure, stress, casing pressure, and wellhead lift height; S2: Detect outliers in the data and mark missing data; S3: Clean the missing data; S4: Based on the data, a wellbore temperature and pressure field model, a multi-string cement sheath assembly elastic-plastic mechanical model, a string buckling morphology discrimination model, a string triaxial strength verification model, an annulus allowable pressure calculation model, and a wellhead lift height calculation model are established; S5: Combined with the wellbore temperature and pressure field model, the multi-string cement sheath assembly elastoplastic mechanics model, the string buckling morphology discrimination model, the string triaxial strength verification model, the annulus allowable pressure calculation model and the wellhead lift height calculation model, analysis is performed to provide graded warnings for cement sheath integrity failure, string buckling deformation, annulus pressure and wellhead lift; S6: Provide corresponding emergency response measures according to the warning level.

2. The offshore gas well bore integrity emergency warning and disposal method according to claim 1, characterized in that: In step S2, the data outlier detection methods include: local outlier factor algorithm, isolation forest algorithm and box plot statistics method.

3. The offshore gas well bore integrity emergency warning and disposal method according to claim 1, characterized in that: In step S2, the method for detecting data anomalies is specifically as follows: a local outlier factor algorithm is used for sparsely distributed data; an isolation forest algorithm is used for high-dimensional complex data; a box plot statistical method is used for data that obeys a specific distribution, the detected abnormal data is replaced with NaN characters, and missing data is marked by a Microsoft Excel formula.

4. The offshore gas well bore integrity emergency warning and disposal method according to claim 1, characterized in that: In step S3, the data cleaning specifically includes: using linear interpolation method to fill missing values ​​for data with good linearity, and using BP neural network method to predict and fill missing values ​​for complex nonlinear data.

5. The offshore gas well bore integrity emergency warning and disposal method according to claim 1, characterized in that: Step S4 specifically includes: constructing a wellbore temperature and pressure field model based on the laws of thermodynamics and the theorem of conservation of energy; constructing an elastic-plastic mechanical model of a multi-string cement sheath assembly based on the elastic-plastic theory and the Mohr-Coulomb criterion; constructing a string buckling morphology discrimination model based on the string axial compression critical load discrimination formula; constructing a string triaxial strength verification model based on the thick-walled cylinder theory and the Von-Mises strength criterion; constructing an annulus allowable pressure calculation model based on the API RP90 and NORSOK D-010 standards; and constructing a wellhead lift height calculation model in combination with the casing free section generation conditions.

6. The offshore gas well bore integrity emergency warning and disposal method according to claim 1, characterized in that: In step S5, based on the Matlab APP Designer platform, the wellbore temperature and pressure field model, the multi-string cement ring assembly elastoplastic mechanics model, the string buckling morphology discrimination model, the string triaxial strength verification model, the annulus allowable pressure calculation model and the wellhead lift height calculation model are respectively combined to construct modules including temperature and pressure field prediction, cement ring integrity prediction, string buckling morphology discrimination, string integrity verification, annulus allowable pressure prediction and wellhead lift prediction. Then, the module is used to calculate the critical load value of the axial compression of the string where the string buckles, the casing-cement ring interface contact force in the temperature loading and unloading stages, the micro-annulus, the casing strength, the annulus allowable pressure value, and the wellhead lift allowable height value.

7. An offshore gas well bore integrity emergency warning and disposal method according to claim 6, characterized in that: The graded warning is specifically as follows: based on the casing-cement ring interface contact force and the size of the micro-annulus, the cement ring integrity warning is divided into three levels; based on the critical load value of the tubing axial compression and the casing strength, the tubing risk warning is divided into three levels; based on the annulus allowable pressure range value and duration, the annulus pressure warning is divided into three levels; based on the wellhead lifting allowable height value, the wellhead lifting warning is divided into three levels.

8. An offshore gas well bore integrity emergency warning and disposal method according to claim 7, characterized in that: When the casing-cement sheath interface contact force is less than the radial bonding strength and the casing outer wall displacement is equal to the cement sheath inner wall displacement, the cement sheath integrity is a first-level warning; when the casing-cement sheath interface contact force is greater than the radial bonding strength or a small micro-annulus <100μm appears, the cement sheath integrity is a second-level warning; when the casing-cement sheath interface contact force is greater than the radial bonding strength or a large micro-annulus >100μm appears, the cement sheath integrity is a third-level warning; When the tubing string does not buckle and the external load is less than the casing strength, the tubing string failure risk is a first-level warning. When the tubing string buckles sinusoidally or the external load is greater than the casing strength, the tubing string failure risk is a second-level warning. When the tubing string buckles helically or the external load is greater than the casing strength, the tubing string failure risk is a third-level warning. When the casing pressure is lower than 0.69MPa, the annular pressure is a first-level warning; when the casing pressure is greater than 0.69MPa and less than 5MPa, the annular pressure is a second-level warning; when the casing pressure is greater than 5MPa and cannot be reduced to zero after continuous venting for 24 hours, the annular pressure is a third-level warning; When the wellhead lifting height is lower than 50mm, the wellhead lifting is a first-level warning. When the wellhead lifting height is higher than 50mm, the wellhead lifting is a second-level warning. When the wellhead lifting height exceeds the maximum allowable lifting value of the wellhead and continues to rise, the wellhead lifting is a third-level warning.

9. An offshore gas well bore integrity emergency warning and disposal method according to claim 8, characterized in that: In step S6, the corresponding emergency response method is given according to the warning level: When the cement sheath integrity is at the first level warning, the micro-annulus is regularly checked; when the cement sheath integrity is at the second level warning, the micro-annulus is regularly checked and plugging agent is injected to repair the cement sheath; when the cement sheath integrity is at the third level warning, sonic logging or gamma ray logging is used to analyze and determine the cause of cement sheath integrity failure; For the first-level early warning of the risk of tubing string failure, regularly detect the buckling condition and stress distribution of the tubing string. For the second-level early warning of tubing string risk, regularly detect the buckling condition and stress distribution of the tubing string and reinforce the tubing string or reduce the load. For the third-level early warning of tubing string risk, conduct downhole vibration monitoring or acoustic logging and analyze and judge the cause of tubing string stability failure; For the first-level early warning of annulus pressure, regularly monitor the casing pressure. For the second-level early warning of annulus pressure, regularly monitor the casing pressure and install an automatic pressure relief needle valve, and vent until the pressure is lower than the maximum allowable annulus pressure value at the wellhead. For the third-level early warning of annulus pressure, conduct a pressure build-up test or leak point detection and analyze and judge the wellbore leakage condition and cause; For the first-level early warning of wellhead lift, regularly monitor the wellhead lift height. For the second-level early warning of wellhead lift, regularly monitor the casing pressure and install an automatic production control valve to reduce production until the lift height is lower than the maximum allowable lift value at the wellhead. For the third-level early warning of wellhead lift, shut in the well and conduct a wellhead lift height recovery test, and analyze and judge the cause of wellhead lift.

10. An offshore gas well bore integrity emergency warning and disposal method according to claim 7, characterized in that: The tubing string buckling form discrimination model includes the following calculation formulas: F0 is the critical axial compression load of the tubing string, N; I is the elastic modulus of the tubing string, N; E is the moment of inertia of the tubing string, MPa; W is the weight per unit length of the tubing string, N / m; α is the well deviation angle, °; r is the annulus clearance of the tubing string, m; F is the axial compression load of the tubing string, N; When F < F0, no buckling occurs; When 2F0 < F < 4.71F0, sinusoidal buckling occurs; When F ≥ 4.71F0, helical buckling occurs.

Images