Quantitative evaluation method for corrosion failure risk of tubular column

Abstract

The invention discloses a tubular column corrosion failure risk quantitative evaluation method, and belongs to the technical field of oil and gas field corrosion protection. The method comprises the steps that firstly, corrosion working condition data of N wells in the same work area are collected, and n wells are extracted from the data to count actual pipe detection data of the n wells; secondly, determining the corrosion risk grade of the pipe column according to actual pipe detection data of n wells; establishing a tubular column corrosion risk value M calculation model; the pipe column corrosion risk value M is converted into a corrosion risk assessment value Y, and a risk grade assessment result is further obtained; and finally checking the coincidence rate of the risk assessment result. The tubular column corrosion failure risk is quantitatively evaluated on the basis of on-site oil-gas well working condition data, the underground tubular column corrosion risk situation can be rapidly and accurately evaluated, guidance is provided for reducing safety risks and economic losses of an oil-gas well, and the method has important significance on continuous and safe exploitation of the oil-gas well.

Description

Technical Field

[0001] The present invention relates to the field of oil and gas well corrosion protection, and more specifically to a method for quantitatively evaluating the corrosion failure risk of a pipe column in an oil and gas well. Background Art

[0002] At present, the domestic oil and gas well tubing corrosion problem is prominent. The tubing is seriously corroded under the alternating production conditions of gas injection and water injection. With the increase of gas injection and water injection, the scaling and clogging problems gradually increase, and the risk of tubing corrosion increases. There are many factors that affect the risk of tubing corrosion, mainly including time factors, corrosive medium factors and engineering factors. Under the condition of the combined influence of multiple corrosion factors, if the tubing is not replaced in time, it may lead to tubing failure, and ultimately cause significant losses such as reduced oil production efficiency and increased well repair costs. Therefore, it is necessary to evaluate the corrosion risk level of the downhole tubing, conduct inspection operations in a timely manner, and minimize the consumption and loss of manpower and financial resources.

[0003] At present, most of the methods for evaluating the corrosion risk of oil and gas well tubing consider the impact of corrosive media on tubing corrosion, such as analyzing the functional relationship between relevant single factors such as carbon dioxide concentration and hydrogen sulfide concentration and corrosion rate. There is a lack of consideration of relevant time factors such as production days, gas injection days and well shut-in days of oil and gas wells, and there is no combination of actual engineering factors. There are related problems such as incomplete consideration of corrosion factors and inaccurate prediction of downhole tubing corrosion risk. Therefore, it is of great significance to establish a quantitative evaluation method for tubing corrosion failure risk. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a method for quantitatively evaluating the risk of tubular column corrosion failure, combines on-site analysis with oil and gas wells, expands the scope of influencing factors, comprehensively considers time factors, corrosive medium factors and engineering factors, and establishes a mathematical model. It can directly use real-time on-site operating data to accurately evaluate the on-site tubular column corrosion risk, and provide guidance for oil and gas well tubular column corrosion protection work.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for quantitatively evaluating the risk of pipe column corrosion failure comprises the following steps:

[0007] S1. Collect the corrosion condition data of the tubulars of N wells in the same work area, including the corrosion condition data related to time factors, the corrosion condition data related to corrosive medium factors, and the corrosion condition data related to engineering factors; and extract n wells from the N wells for actual inspection data statistics, and the number of wells extracted n is 10% to 30% of the number of wells N in the work area;

[0008] S2. Based on the actual inspection data of S1, determine the corrosion risk level of the tubing of n wells;

[0009] S3. Collect at least 6 groups of corrosion monitoring data of the same gas injection well at different times in the work area, and construct a time factor corrosion risk function P 1 ;

[0010] S4. Constructing the corrosion risk function P of the corrosive medium factor 2 ;

[0011] S5. Constructing the corrosion risk function P of engineering factors 3 ;

[0012] S6. Based on S3, S4 and S5, a calculation model for the pipe string corrosion risk value M is constructed which comprehensively considers time factors, corrosive medium factors and engineering factors;

[0013] S7, calculating the tubular corrosion risk value M of the n wells according to S6, and converting the tubular corrosion risk value M into a corrosion risk assessment value Y, predicting the corrosion risk level according to the Y value, and obtaining the corrosion risk assessment results of the n wells;

[0014] S8, comparing the risk level assessment results of the n wells in S7 with the pipe string corrosion risk levels in the actual pipe inspection data of the n wells in S2, recording the number of wells with the same risk level as w, and checking the coincidence rate R of the risk level assessment results;

[0015] S81. If the matching rate R ≥ 80%, the risk level assessment result is valid, indicating that the pipe string corrosion risk value M and the corrosion risk assessment value Y can reflect the degree of pipe string corrosion;

[0016] S82. If the matching rate R<80%, then expand the number of corrosion monitoring data sets of the same gas injection well at different times in the work area, expand 2 sets each time, and modify the time factor corrosion risk function P 1 , repeat step S3, step S6, step S7, and step S8 until R ≥ 80% is satisfied.

[0017] Preferably, step S1 collects the corrosion condition data of the tubular strings of N wells in the same work area and the actual inspection data of n wells, and specifically includes:

[0018] S11. Corrosion working condition data related to time factors: gas injection days, days; well shut-down days, days; production days, days;

[0019] S12. Corrosion working condition data related to corrosive media factors: total gas injection volume, 10,000 cubic meters; water injection volume, cubic meters; oxygen content percentage, %;

[0020] S13. Corrosion conditions data related to engineering factors:

[0021] ①Pipeline isolation status: whether the tubing is equipped with an isolation device;

[0022] ② The state of the tubing: whether the tubing is new or old;

[0023] ③ moisture content, %;

[0024] ④Whether acidification has been carried out: whether acidification has been carried out or not;

[0025] ⑤ Anti-corrosion measures: including coating, lining, corrosion inhibitor, or no anti-corrosion measures;

[0026] S14. Actual inspection and management data:

[0027] ① The pipe string is broken or perforated;

[0028] ②Corrosion depth, mm.

[0029] Preferably, step S2 specifies the corrosion risk level of the tubular strings of the n wells, and the judgment criteria are as follows:

[0030] ① When the pipe string is broken, perforated, or the corrosion depth is greater than 3mm, the corrosion risk level is judged to be "extremely serious";

[0031] ② When the corrosion depth of the pipe column is greater than 2mm and less than or equal to 3mm, the corrosion risk level is judged to be "serious";

[0032] ③ When the corrosion depth of the pipe column is greater than 1mm and less than or equal to 2mm, the corrosion risk level is judged to be "moderate";

[0033] ④ When the corrosion depth of the pipe column is less than or equal to 1mm, the corrosion risk level is judged to be "mild".

[0034] Preferably, step S3 collects at least 6 groups of corrosion monitoring data of the same gas injection well in the work area at different times, and constructs a time factor corrosion risk function P 1 The specific steps are as follows:

[0035] S31. Collect no less than 6 groups of corrosion monitoring data of the same gas injection well at different times in the work area, including:

[0036] ① Different gas injection time t i (t i Take t respectively 1 ,t 2 ...t m ), days; the interval shall not be less than 4 days and not more than 90 days;

[0037] ② Different gas injection time t i The corrosion rate v i (v i Take v respectively 1、v 2 ...v m ), mm / a;

[0038] S32. Calculate the aging index β and aging coefficient g:

[0039]

[0040] In formula (1) and formula (2), m is the number of statistical groups of corrosion monitoring data of the same gas injection well at different times in the work area, m≥6 groups, t i is different gas injection time, day; v i For different gas injection times t i The corrosion rate under the condition of mm / a; the corrosion monitoring data of the same gas injection well in the work area at different times in step S31 t i 、v i Substituting into equation (1) and equation (2) to obtain β and g;

[0041] S33. Constructing the function P of corrosion risk of time factor 1 :

[0042] P 1 =g(A β +B β ×α+C β ) (3)

[0043] Where A is the number of days of gas injection, days; B is the number of days of well shut-in, days; C is the number of days of production, days; α is the deterioration coefficient, which represents the difference between dynamic corrosion and static corrosion, and α is taken as 0.85.

[0044] Preferably, step S4 constructs a corrosion risk function P of the corrosive medium factor 2 as follows:

[0045] P 2 =D×log(E)×0.1[2.34ln(F)+10.621]÷10000 (4)

[0046] Where D is the total gas injection volume, cubic meters; E is the water injection volume, cubic meters; F is the oxygen content percentage, %.

[0047] Preferably, step S5 constructs the engineering factor corrosion risk function P 3 as follows:

[0048] P 3 =a×b×c×d×e (5)

[0049] Where a, b, c, d, and e are assigned variables. According to the proportional relationship of the service life of the tubing under the control of different engineering factors in the oil and gas wells in this work area, their values ​​are assigned as follows: a represents the isolation state of the tubing. If the tubing is equipped with a packer, a is 1, and if the tubing is not equipped with a packer, a is 1.3; b represents the age of the tubing. If it is a new tubing, b is 1, and if it is an old tubing, b is 1.2; c represents the water content. If the water content is <50%, c is 1, and if the water content is ≥50%, c is 1.5; d represents whether it is acidified. If it has been acidified, d is 1.1, and if it has not been acidified, d is 1; e represents the anti-corrosion measures. If it is a coating, e is 0.5, if it is a lining, e is 0.6, if it is a corrosion inhibitor, e is 0.9, and if there is no anti-corrosion measure, e is 1.

[0050] Preferably, step S6 constructs a calculation model for the pipe column corrosion risk value M as formula (6):

[0051] M=P 1 ×P 2 ×P 3 (6)

[0052] Substituting equations (3), (4) and (5) into equation (6), we can obtain the specific calculation equation (7) for the column corrosion risk value M:

[0053] M=g(A β +B β ×α+C β )×D×log(E)×0.1[2.34ln(F)+10.621]×a×b×c×d×e÷10000 (7)

[0054] Where M is the risk value of column corrosion.

[0055] Preferably, the specific steps of step S7 for converting the corrosion risk value M of the tubular string of n wells into the corrosion risk assessment value Y of n wells are as follows:

[0056] S71. According to step S5, the corrosion condition data related to the engineering factors in the corrosion condition data of the n wells are assigned values, and the processed corrosion condition data are substituted into formula (7) to calculate the corrosion risk value M of the tubular string of the n wells;

[0057] S72. Construct a corrosion risk level assessment model, substitute the corrosion risk value M into formula (8) to obtain the corrosion risk assessment value Y;

[0058]

[0059] Where Y is the corrosion risk assessment value, and M is the string corrosion risk value;

[0060] S73. Predicting the corrosion risk level according to the Y value, obtaining the corrosion risk assessment results of n wells;

[0061] If 100% > Y ≥ 90%, the predicted corrosion risk level is "extremely severe";

[0062] If 90% > Y ≥ 80%, the predicted corrosion risk level is "severe";

[0063] If 80% > Y ≥ 50%, the predicted corrosion risk level is "moderate";

[0064] If 50% < Y, the predicted corrosion risk level is "mild".

[0065] Preferably, the calculation formula for the coincidence rate R in step S8 is:

[0066]

[0067] Where R is the coincidence rate, %; w is the number of wells with the same risk level in step S8, wells; n is the number of wells extracted in step S1, wells. Brief Description of the Drawings

[0068] Figure 1 It is a flowchart of a quantitative evaluation method for the risk of tubing corrosion failure. Detailed Embodiments

[0069] The present invention will be described in detail below in conjunction with the drawings and specific embodiments of the present invention.

[0070] The embodiment of the present invention discloses a quantitative evaluation method for the risk of tubing corrosion failure, including the following steps:

[0071] S1. Collect the tubing corrosion condition data of N wells in the same work area, including the corrosion condition data related to time factors: injection days, days; shut-in days, days; production days, days; the corrosion condition data related to corrosion medium factors: total injection volume, 10,000 m³; injection water volume, m³; oxygen content percentage, % (where the wells with injection water volume of 0 or oxygen content percentage of 0 are not included in the statistics); the corrosion condition data related to engineering factors: tubing isolation state, tubing newness, water cut, whether acidified, anti-corrosion measures; and extract n wells from the N wells for actual tubing inspection data statistics, and the number of extracted wells n is 10% to 30% of the number of wells N in the work area.

[0072] In this embodiment, N = 40, n = 10.

[0073] Collect the tubing corrosion condition data of 40 wells in a certain work area as shown in Table 1.

[0074] Table 1 Tubing Corrosion Condition Data Statistical Table

[0075]

[0076]

[0077]

[0078] The actual inspection data of the 10 wells selected are shown in Table 2 below.

[0079] Table 2 Actual inspection and management data table

[0080] hashtag Actual inspection data 1 Pipeline perforation 2 The corrosion depth of the pipe column is 3.3mm 3 The pipe string breaks 4 The corrosion depth of the pipe column is 2.5 mm. 5 The corrosion depth of the pipe column is 2.3 mm. 6 The corrosion depth of the pipe column is 1.7 mm. 7 The corrosion depth of the pipe column is 1.3 mm. 8 The corrosion depth of the pipe column is 1.6 mm. 9 The corrosion depth of the pipe column is 1.3 mm. 10 The corrosion depth of the pipe column is 0.8mm.

[0081] S2. Based on the actual inspection data of step S1, the corrosion risk levels of the tubulars of the 10 wells are determined. The judgment criteria are as follows:

[0082] ① When the pipe string is broken, perforated, or the corrosion depth is greater than 3mm, the corrosion risk level is judged to be "extremely serious"; ② When the corrosion depth of the pipe string is greater than 2mm and less than or equal to 3mm, the corrosion risk level is judged to be "serious";

[0083] ③ When the corrosion depth of the pipe column is greater than 1mm and less than or equal to 2mm, the corrosion risk level is judged to be "moderate";

[0084] ④ When the corrosion depth of the pipe column is less than or equal to 1mm, the corrosion risk level is judged to be "mild";

[0085] According to the judgment criteria, the actual tubing corrosion risk levels of the 10 wells are shown in Table 3 below.

[0086] Table 3 Pipeline corrosion risk level table

[0087] hashtag Actual inspection data Actual pipe string corrosion risk level 1 Pipeline perforation Very severe 2 The corrosion depth of the pipe column is 3.3mm Very severe 3 The pipe string breaks Very severe 4 The corrosion depth of the pipe column is 2.5 mm. serious 5 The corrosion depth of the pipe column is 2.3 mm. serious 6 The corrosion depth of the pipe column is 2.2 mm. serious 7 The corrosion depth of the pipe column is 1.3 mm. Moderate 8 The corrosion depth of the pipe column is 1.6 mm. Moderate 9 The corrosion depth of the pipe column is 1.3 mm. Moderate 10 The corrosion depth of the pipe column is 0.8mm. Mild

[0088] S3. Collect at least 6 groups of corrosion monitoring data of the same gas injection well at different times in the work area, and construct a time factor corrosion risk function P 1 ;

[0089] S31. In this embodiment, 6 groups of corrosion monitoring data of the same gas injection well in the work area at different times are collected, as shown in Table 4 below.

[0090] Table 4 Statistics of on-site gas injection well monitoring, detection corrosion rate and gas injection time

[0091] Number of groups <![CDATA[t i / day]]> <![CDATA[v i / mm*a -1 ]]> 1 3 0.4896 2 7 0.2823 3 15 0.1720 4 30 0.1096 5 60 0.0698 6 90 0.0537

[0092] S32. Calculate the aging index β and aging coefficient g:

[0093]

[0094] In formula (1) and formula (2), m is the number of statistical groups of corrosion monitoring data of the same gas injection well at different times in the work area, m = 6 groups, t iis different gas injection time, day; v i For different gas injection times t i Corrosion rate under the condition of mm / a; Corrosion monitoring data of the same gas injection well at different times in Table 4 are t i 、v i Substituting into equation (1) and equation (2), we obtain lng=0, g=1, β=0.35.

[0095] S33. Constructing the function P of corrosion risk of time factor 1 :

[0096] P 1 =g(A β +B β ×α+C β ) (3)

[0097] Where A is the number of days of gas injection, days; B is the number of days of well shut-in, days; C is the number of days of production, days; α is the depreciation coefficient, which represents the difference between dynamic corrosion and static corrosion. α is taken as 0.85. Substituting g = 1, β = 0.35, α = 0.85 into formula (3), we get formula (4):

[0098] P 1 =A 0.35 +B 0.35 ×0.85+C 0.35 (4)

[0099] S4. Constructing the corrosion risk function P of the corrosive medium factor 2 :

[0100] P 2 =D×log(E)×0.1[2.34ln(F)+10.621]÷10000 (5)

[0101] Where D is the total gas injection volume, cubic meters; E is the water injection volume, cubic meters; F is the oxygen content percentage, %.

[0102] S5. Constructing the corrosion risk function P of engineering factors 3 :

[0103] P 3 =a×b×c×d×e (6)

[0104] Where a, b, c, d, and e are assigned variables. According to the proportional relationship of the service life of the tubing under the control of different engineering factors in the oil and gas wells in this work area, their values ​​are assigned as follows: a represents the isolation state of the tubing. If the tubing is equipped with a packer, a is 1, and if the tubing is not equipped with a packer, a is 1.3; b represents the age of the tubing. If it is a new tubing, b is 1, and if it is an old tubing, b is 1.2; c represents the water content. If the water content is <50%, c is 1, and if the water content is ≥50%, c is 1.5; d represents whether it is acidified. If it has been acidified, d is 1.1, and if it has not been acidified, d is 1; e represents the anti-corrosion measures. If it is a coating, e is 0.5, if it is a lining, e is 0.6, if it is a corrosion inhibitor, e is 0.9, and if there is no anti-corrosion measure, e is 1.

[0105] S6. Construct the calculation model of pipe column corrosion risk value M as formula (7):

[0106] M=P 1 ×P 2 ×P 3 (7)

[0107] Substituting equations (4), (5) and (6) into equation (7), we can obtain the specific calculation equation (8) for the column corrosion risk value M:

[0108] M=(A 0.35 +B 0.35 ×0.85+C 0.35 )×D×log(E)×0.1[2.34ln(F)+10.621]×a×b×c×d×e÷10000 (8)

[0109] Where M is the risk value of column corrosion.

[0110] S7. According to step S6, the corrosion risk value M of the tubulars of the 10 wells is converted into the corrosion risk assessment value Y of the 10 wells. The specific steps are as follows:

[0111] S71. According to step S5, the corrosion condition data related to the engineering factors in the corrosion condition data of the 10 wells are assigned values, as shown in Table 5 below. Substituting the data in Table 5 into formula (8), the corrosion risk values ​​M of the tubulars of the 10 wells are calculated;

[0112] Table 5 Corrosion condition data table after variable assignment

[0113]

[0114]

[0115] Substituting the data in Table 5 into formula (8), the tubing corrosion risk values ​​M of the 10 wells are calculated as shown in Table 6 below.

[0116] Table 6 Calculation results of pipe column corrosion risk value M

[0117] hashtag M 1 17.72 2 16.86 3 16.09 4 11.74 5 12.03 6 6.53 7 6.86 8 6.27 9 7.13 10 4.14

[0118] S72. Construct a corrosion risk level assessment model, substitute the corrosion risk value M in Table 6 into Equation (9) to obtain the corrosion risk assessment value Y, as shown in Table 7.

[0119]

[0120] Table 7 Calculation Results Table of Corrosion Risk Assessment Value Y

[0121] hashtag M Y 1 17.72 92.62％ 2 16.86 91.91％ 3 16.09 91.19％ 4 11.74 84.64％ 5 12.03 85.27％ 6 6.53 63.07％ 7 6.86 65.33％ 8 6.27 61.16％ 9 7.13 67.05％ 10 4.14 40.72％

[0122] S73. Predict the corrosion risk level according to the Y value to obtain the corrosion risk assessment results of 10 wells:

[0123] If 100% > Y ≥ 90%, predict the corrosion risk level as "extremely severe";

[0124] If 90% > Y ≥ 80%, predict the corrosion risk level as "severe";

[0125] If 80% > Y ≥ 50%, predict the corrosion risk level as "moderate";

[0126] If 50% < Y, predict the corrosion risk level as "mild".

[0127] The risk level assessment results are shown in Table 8 below.

[0128] Table 8 Risk Level Assessment Results Table

[0129] hashtag M Y Predicting corrosion risk level 1 17.72 92.62％ Very severe 2 16.86 91.91％ Very severe 3 16.09 91.19％ Very severe 4 11.74 84.64％ serious 5 12.03 85.27％ serious 6 6.53 63.07％ Moderate 7 6.86 65.33％ Moderate 8 6.27 61.16％ Moderate 9 7.13 67.05％ Moderate 10 4.14 40.72％ Mild

[0130] S8. Check the coincidence rate R of the risk level assessment results;

[0131] Compare the risk level assessment results in Table 8 of Step S7 with the pipe string corrosion risk levels in the actual inspection data in Table 3 of Step S2. The comparison results are shown in Table 9 below. Denote the number of wells with the same level as w.

[0132] Table 9 Comparison Table of Pipe String Corrosion Risk Levels

[0133] hashtag Predicting corrosion risk level Actual corrosion risk level Comparison results 1 Very severe Very severe Match 2 Very severe Very severe Match 3 Very severe Very severe Match 4 serious serious Match 5 serious serious Match 6 Moderate serious abnormal 7 Moderate Moderate Match 8 Moderate Moderate Match 9 Moderate Moderate Match 10 Mild Mild Match

[0134] According to the comparison results in Table 9: The number of wells with the same level is 9, that is, w = 9;

[0135] Then the calculation result of the coincidence rate R is as follows:

[0136]

[0137] Wherein n is the number of wells extracted in step S1, and in this embodiment, n=10.

[0138] The above test results show that: when the matching rate R≥80%, the risk level assessment result is valid, indicating that the tubing corrosion risk value M and the corrosion risk assessment value Y can reflect the degree of tubing corrosion, and can make an accurate assessment of the downhole tubing corrosion risk situation, which is convenient for on-site staff to make relevant decisions for inspection operations and avoid serious losses caused by tubing failure.

[0139] Therefore, according to the method involved in the present invention, the corrosion failure risk of the remaining 30 wells in the work area can be quantitatively evaluated, and the risk level assessment results are shown in Table 10 below.

[0140] Table 10 Risk level assessment results of remaining well strings in the work area

[0141]

[0142]

[0143] The above is a detailed description of the specific implementation methods and implementation steps of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention. Moreover, the prediction of the risk of corrosion failure of the pipe column by the present invention is not limited to the above implementation methods. Ordinary technicians in this field can also change the factors affecting the corrosion risk and adjust the assigned variables in combination with the actual situation on site without departing from the purpose of the present invention, so as to achieve the best prediction effect.

Claims

1. A method for quantitatively evaluating the risk of pipe column corrosion failure, characterized in that: The following steps are involved: S1. Collect the corrosion condition data of the tubulars of N wells in the same work area, including the corrosion condition data related to time factors, the corrosion condition data related to corrosive medium factors, and the corrosion condition data related to engineering factors; and extract n wells from the N wells for actual inspection data statistics, and the number of wells extracted n is 10% to 30% of the number of wells N in the work area; S2. Based on the actual inspection data of S1, determine the corrosion risk level of the tubing of n wells; S3, collect no less than 6 groups of corrosion monitoring data of the same gas injection well in the work area at different times, and construct a time factor corrosion risk function P1; S4, constructing the corrosion risk function P2 of the corrosive medium factor; S5. Constructing the engineering factor corrosion risk function P3; S6. Based on S3, S4 and S5, a calculation model for the pipe string corrosion risk value M is constructed which comprehensively considers time factors, corrosive medium factors and engineering factors; S7, calculating the tubular corrosion risk value M of the n wells according to S6, and converting the tubular corrosion risk value M into a corrosion risk assessment value Y, predicting the corrosion risk level according to the Y value, and obtaining the corrosion risk assessment results of the n wells; S8, comparing the risk level assessment results of the n wells in S7 with the pipe string corrosion risk levels in the actual pipe inspection data of the n wells in S2, recording the number of wells with the same risk level as w, and checking the coincidence rate R of the risk level assessment results; S81. If the matching rate R ≥ 80%, the risk level assessment result is valid, indicating that the pipe string corrosion risk value M and the corrosion risk assessment value Y can reflect the degree of pipe string corrosion; S82. If the matching rate R<80%, then expand the number of corrosion monitoring data sets of the same gas injection well at different times in the work area, expand 2 sets each time, correct the time factor corrosion risk function P1, and repeat steps S3, S6, S7, and S8 until R≥80% is satisfied.

2. A method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The specific contents of step S1 of collecting the corrosion condition data of the pipe strings of N wells in the same work area and the actual inspection data of n wells include: S11. Corrosion working condition data related to time factors: gas injection days, days; well shut-down days, days; production days, days; S12. Corrosion working condition data related to corrosive media factors: total gas injection volume, 10,000 cubic meters; water injection volume, cubic meters; oxygen content percentage, %; S13. Corrosion conditions data related to engineering factors: ①Pipeline isolation status: whether the tubing is equipped with an isolation device; ② The state of the tubing: whether the tubing is new or old; ③ Moisture content, %; ④Whether acidification has been carried out: whether acidification has been carried out or not; ⑤ Anti-corrosion measures: including coating, lining, corrosion inhibitor, or no anti-corrosion measures; S14. Actual inspection and management data: ① The pipe string is broken or perforated; ②Corrosion depth, mm.

3. The method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The step S2 defines the corrosion risk level of the tubular strings of the n wells, and the judgment criteria are as follows: ① When the pipe string is broken, perforated, or the corrosion depth is greater than 3mm, the corrosion risk level is judged to be "extremely serious"; ② When the corrosion depth of the pipe column is greater than 2mm and less than or equal to 3mm, the corrosion risk level is judged to be "serious"; ③ When the corrosion depth of the pipe column is greater than 1mm and less than or equal to 2mm, the corrosion risk level is judged to be "moderate"; ④When the corrosion depth of the pipe string is less than or equal to 1 mm, the corrosion risk level is determined to be "mild".

4. The method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The specific steps for step S3 to collect corrosion monitoring data of the same gas injection well in the work area at different times for no less than 6 groups and construct the corrosion risk function P1 of the time factor are as follows: S31. Collect corrosion monitoring data of the same gas injection well in the work area at different times for no less than 6 groups, including: ① Different gas injection time t i (t i Take t1, t2, ...t m ), days; the interval shall not be less than 4 days and not more than 90 days; ② Different gas injection time t i The corrosion rate v i (v i Take v1, v2...v respectively m ), mm / a; S32. Calculate the aging index β and the aging coefficient g: In formula (1) and formula (2), m is the number of statistical groups of corrosion monitoring data of the same gas injection well at different times in the work area, m ≥ 6 groups, t i is different gas injection time, day; v i For different gas injection times t i Corrosion rate under, mm / a; Corrosion monitoring data t of the same gas injection well at different times in the work area in step S31 i 、v i Substituting into equation (1) and equation (2) to obtain β and g; S33. Construct the function P1 of the corrosion risk of the time factor: P1=g(A β +B β ×α+C β ) (3) In the formula, A is the number of gas injection days, in days; B is the shut-in days, in days; C is the production days, in days; α is the depreciation coefficient, indicating the difference between dynamic corrosion and static corrosion, and α takes 0.

85.

5. The method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The following is the construction of the corrosion risk function P2 of the corrosion medium factor in step S4: P2 = D × log(E) × 0.1[2.34ln(F) + 10.621] ÷ 10000 (4) In the formula, D is the total gas injection volume, in 10,000 m³; E is the water injection volume, in m³; F is the oxygen content percentage, in %.

6. A method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The following is the construction of the corrosion risk function P3 of the engineering factor in step S5: P3 = a × b × c × d × e (5) In the formula, a, b, c, d, and e are assignment variables. According to the proportional relationship of the service life of the pipe string under different engineering factor control conditions in this work area, the assignments are as follows: a represents the pipe string isolation state. If the tubing is equipped with a packer, a takes 1; if the tubing has no packer, a takes 1.3; b represents the newness of the pipe string. If it is a new tubing, b takes 1; if it is an old tubing, b takes 1.2; c represents the water cut. If the water cut < 50%, c takes 1; if the water cut ≥ 50%, c takes 1.5; d represents whether acidification has been carried out. If acidification has been carried out, d takes 1.1; if there is no acidification, d takes 1; e represents the anti-corrosion measure. If it is a coating, e takes 0.5; if it is a lining, e takes 0.6; if it is a corrosion inhibitor, e takes 0.9; if there is no anti-corrosion measure, e takes 1.

7. The method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The following is the construction of the calculation model of the pipe string corrosion risk value M in step S6 as formula (6): M = P1 × P2 × P3 (6) Substitute formula (3), formula (4), and formula (5) into formula (6) to obtain the calculation formula (7) of the pipe string corrosion risk value M: M=g(A β +B β ×α+C β )×D×log(E)×0.1[2.34ln(F)+10.621]×a×b×c×d×e÷10000(7) In the formula, M is the pipe string corrosion risk value.

8. The method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The specific steps for step S7 to convert the pipe string corrosion risk value M of n wells into the corrosion risk assessment value Y of n wells are as follows: S71. According to step S5, perform assignment processing on the corrosion condition data related to the engineering factor in the corrosion condition data of n wells, substitute the processed corrosion condition data into formula (7), and calculate the pipe string corrosion risk value M of n wells; S72. Construct a corrosion risk level assessment model, and substitute the corrosion risk value M into formula (8) to obtain the corrosion risk assessment value Y; In the formula, Y is the corrosion risk assessment value, and M is the pipe string corrosion risk value; S73. Predict the corrosion risk level according to the Y value to obtain the corrosion risk assessment results of n wells; If 100% > Y ≥ 90%, the predicted corrosion risk level is "extremely severe"; If 90% > Y ≥ 80%, the predicted corrosion risk level is "severe"; If 80% > Y ≥ 50%, the predicted corrosion risk level is "moderate"; If 50% < Y, the predicted corrosion risk level is "mild".

9. A method for quantitatively evaluating the risk of pipe column corrosion failure according to claim 1, characterized in that: The calculation formula of the matching rate R in step S8 is: Wherein R is the matching rate, %; w is the number of wells with the same risk level in step S8, mouth; and n is the number of wells extracted in step S1, mouth.

Images