A method for quantitatively evaluating corrosion failure risk of a tubular string

Abstract

The application discloses a kind of pipe column corrosion failure risk quantitative evaluation method, belong to oil and gas field corrosion protection technical field.The method includes: first, the same work area N well pipe column corrosion condition data are collected, from which n well is extracted to its actual inspection pipe data;Second, according to the actual inspection pipe data of n well, the pipe column corrosion risk level is clear;Then, the pipe column corrosion risk value M calculation model is established;Again, the pipe column corrosion risk value M is converted into corrosion risk evaluation value Y, and the risk level evaluation result is further obtained;Finally, the coincidence rate of risk evaluation result is tested.The application quantitatively evaluates pipe column corrosion failure risk based on field oil and gas well condition data, can quickly and accurately evaluate downhole pipe column corrosion risk situation, provides guidance for reducing safety risk and reducing economic loss of oil and gas well, and has important significance for sustained and safe exploitation of oil and gas well.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas well corrosion protection, and more particularly to a method for quantitatively evaluating the corrosion failure risk of an oil and gas well pipe string. BACKGROUND

[0002] The corrosion of the pipe string of the current domestic oil and gas well is a prominent problem, and the corrosion of the pipe string under the alternating production conditions of gas injection and water injection is serious. With the increase of the gas injection and water injection amount, the problems of fouling and plugging gradually become more serious, the corrosion risk of the pipe string increases, and there are many factors affecting the corrosion risk of the pipe string, mainly including time factors, corrosion medium factors and engineering factors. Under the combined influence of various corrosion factors, if the pipe string cannot be replaced in time, the pipe string may fail, and eventually cause major losses such as the decrease of oil production efficiency and the increase of workover cost. Therefore, it is necessary to evaluate the corrosion risk degree of the downhole pipe string and perform inspection operation in time to minimize the consumption and loss of manpower and financial resources.

[0003] At present, the methods for evaluating the corrosion risk of the pipe string of the oil and gas well mostly consider the influence of the corrosion medium on the corrosion of the pipe string, such as analyzing the functional relationship between the relevant single factor such as the concentration of carbon dioxide and the concentration of hydrogen sulfide and the corrosion rate, and lack of consideration of the relevant time factors such as the production days of the oil and gas well, the gas injection days and the soak days, and do not combine the actual engineering factors. Therefore, there are problems such as incomplete consideration of corrosion factors and inaccurate prediction of the corrosion risk of the downhole pipe string. Therefore, it is of great significance to establish a quantitative evaluation method for the corrosion failure risk of the pipe string. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a quantitative evaluation method for the corrosion failure risk of a pipe string, which expands the range of influencing factors by combining the analysis of the oil and gas well site, comprehensively considers the time factors, the corrosion medium factors and the engineering factors, establishes a mathematical model, and can directly evaluate the corrosion risk of the site pipe string through real-time working condition data of the site, thereby providing guidance for the corrosion protection work of the oil and gas well pipe string.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] A quantitative evaluation method for the corrosion failure risk of a pipe string, comprising the following steps:

[0007] S1, collecting the pipe string corrosion working condition data of N wells in the same work area, including the corrosion working condition data related to the time factors, the corrosion working condition data related to the corrosion medium factors and the corrosion working condition data related to the engineering factors; and extracting n wells from the N wells for actual pipe inspection data statistics, and the number n of the extracted wells is 10% to 30% of the number N of the wells in the work area;

[0008] S2, according to the actual inspection pipe data of S1, the corrosion risk level of n well pipe column is determined;

[0009] S3, collect no less than 6 groups of corrosion monitoring data of the same injection well in different time in the work area, and build time factor corrosion risk function P1;

[0010] S4, build corrosion medium factor corrosion risk function P2;

[0011] S5, build engineering factor corrosion risk function P3;

[0012] S6, according to S3, S4, S5, build pipe column corrosion risk value M calculation model considering time factor, corrosion medium factor and engineering factor;

[0013] S7, according to S6, calculate n well pipe column corrosion risk value M, and convert pipe column corrosion risk value M into corrosion risk evaluation value Y, predict corrosion risk level according to Y value, and get corrosion risk evaluation result of n well;

[0014] S8, compare the risk level evaluation result of n well in S7 with the pipe column corrosion risk level in the actual inspection pipe data of n well in S2, record the number of wells with the same risk level as w, and test the coincidence rate R of the risk level evaluation result;

[0015] S81. If the coincidence rate R is greater than or equal to 80%, the risk level evaluation result is effective, which indicates that the pipe column corrosion risk value M and the corrosion risk evaluation value Y can reflect the corrosion degree of the pipe column;

[0016] S82. If the coincidence rate R is less than 80%, the number of corrosion monitoring data groups of the same injection well in different time in the work area is expanded, 2 groups are expanded each time, the time factor corrosion risk function P1 is corrected, and the steps S3, S6, S7 and S8 are repeated until R≥80%.

[0017] Preferably, the pipe column corrosion condition data of n well in the same work area and the actual inspection pipe data of n well in step S1 include:

[0018] S11. Corrosion condition data related to time factor: well injection gas days, days; soak well days, days; production days, days;

[0019] S12. Corrosion condition data related to corrosion medium factor: total gas injection volume, ten thousand square meters; water injection volume, square meters; oxygen content, %;

[0020] S13. Corrosion condition data related to engineering factor:

[0021] ① Pipe column isolation state: whether the tubing has a packer;

[0022] ② Tubing string new or old degree: new tubing or old tubing;

[0023] ③ Water content, %;

[0024] ④ Whether acidizing: whether acidizing operation has been performed or not;

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

[0026] S14. Actual pipe inspection data:

[0027] ① Tubing string fracture or perforation;

[0028] ② Corrosion depth, mm.

[0029] Preferably, step S2 determines the corrosion risk level of the tubing string of the n wells, and the judgment criteria are as follows:

[0030] ① When the tubing string is fractured, or perforated, or the corrosion depth is greater than 3 mm, the corrosion risk level is determined as "extremely serious";

[0031] ② When the corrosion depth of the tubing string is greater than 2 mm and less than or equal to 3 mm, the corrosion risk level is determined as "serious";

[0032] ③ When the corrosion depth of the tubing string is greater than 1 mm and less than or equal to 2 mm, the corrosion risk level is determined as "moderate";

[0033] ④ When the corrosion depth of the tubing string is less than or equal to 1 mm, the corrosion risk level is determined as "mild".

[0034] Preferably, step S3 collects not less than 6 groups of corrosion monitoring data of the same injection well at different times in the work area, and constructs the time factor corrosion risk function P1. The specific steps are as follows:

[0035] S31. Collect not less than 6 groups of corrosion monitoring data of the same injection well at different times in the work area, which include:

[0036] ① Different injection times t i , t i take t1, t2...t m , days; interval time is not less than 4 days and not more than 90 days;

[0037] ② Corrosion rate v i at different injection times t i , v i take v1, v2...v m , mm / a;

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

[0039]

[0040] m is the number of groups of corrosion monitoring data of the same injection well in the work area at different times, m≥6 groups, t i is different injection time, day; v i is the corrosion rate at different injection time t i , mm / a; the corrosion monitoring data t i , v i of the same injection well in the work area at different times in step S31 are substituted into formula (1), formula (2) to obtain β, g;

[0041] S33. Construct the function P1 of the time factor corrosion risk:

[0042] P1=g(A β +B β ×α+C β ) (3)

[0043] In the formula, A is the injection days, days; B is the soak days, days; C is the production days, days; α is the loss coefficient, indicating the difference between dynamic corrosion and static corrosion, α is 0.85.

[0044] Preferably, step S4 constructs the corrosion medium factor corrosion risk function P2 as follows:

[0045] P2=D×log(E)×0.1[2.34ln(F)+10.621]÷10000 (4)

[0046] In the formula, D is the total gas injection volume, 104m3; E is the water injection volume, m3; F is the oxygen content, %.

[0047] Preferably, step S5 constructs the engineering factor corrosion risk function P3 as follows:

[0048] P3=a×b×c×d×e (5)

[0049] In the formula, a, b, c, d, e are assignment variables, which are assigned as follows according to the proportional relationship of the service life of the pipe string under different engineering factor control conditions in the work area: a represents the pipe string sealing state, if the tubing has a packer, a takes 1, if the tubing has no packer, a takes 1.3; b represents the degree of new and old 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, if acidification is performed, d takes 1.1, if there is no acidification, d takes 1; e represents the corrosion protection measure, if it is coating, e takes 0.5, if it is lining, e takes 0.6, if it is corrosion inhibitor, e takes 0.9, if there is no corrosion protection measure, e takes 1.

[0050] Preferably, step S6 builds the tubing string corrosion risk value M calculation model as formula (6):

[0051] M=P1×P2×P3 (6)

[0052] Substitute formula (3), formula (4) and formula (5) into formula (6) to obtain the specific calculation formula (7) of the tubing string 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] In formula (7), M is the tubing string corrosion risk value.

[0055] Preferably, step S7 converts the tubing string corrosion risk values M of n wells into corrosion risk assessment values Y of the n wells, and the specific steps are as follows:

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

[0057] S72. A corrosion risk grade assessment model is built, the corrosion risk value M is substituted into formula (8) to obtain the corrosion risk assessment value Y;

[0058]

[0059] In formula (8), Y is the corrosion risk assessment value, and M is the tubing string corrosion risk value.

[0060] S73. According to the Y value, the corrosion risk grade is predicted, and the corrosion risk assessment results of the n wells are obtained;

[0061] If 100%>Y≥90%, the corrosion risk grade is predicted as “extremely severe”;

[0062] If 90%>Y≥80%, the corrosion risk grade is predicted as “severe”;

[0063] If 80%>Y≥50%, the corrosion risk grade is predicted as “moderate”;

[0064] If 50%<Y, the corrosion risk grade is predicted as “mild”.

[0065] Preferably, the calculation formula of the coincidence rate R is as follows:

[0066]

[0067] Wherein R is the matching rate, %; w is the number of wells with the same risk level in step S8, well; n is the number of wells drawn in step S1, well. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 A flow chart of a quantitative evaluation method for pipe string corrosion failure risk. DETAILED DESCRIPTION

[0069] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments of the present application.

[0070] The embodiment of the present application discloses a quantitative evaluation method for pipe string corrosion failure risk, comprising the following steps:

[0071] S1, collect pipe string corrosion condition data of N wells in the same work area, including corrosion condition data related to time factors: gas injection days, days; soak days, days; production days, days; corrosion condition data related to corrosion medium factors: total gas injection volume, ten thousand square meters; water injection volume, square meters; oxygen content percentage, % (wherein the wells with water injection volume of 0 or oxygen content percentage of 0 are not involved in the statistics); corrosion condition data related to engineering factors: pipe string sealing state, pipe string newness degree, water cut, whether acidification, corrosion prevention measures; and draw n wells from the N wells for actual pipe inspection data statistics, and the number of wells n is 10% to 30% of the number of wells N in the work area.

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

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

[0074] Table 1: Pipe string corrosion condition data statistics table

[0075]

[0076]

[0077]

[0078] The actual pipe inspection data of the 10 wells drawn is shown in Table 2 as follows.

[0079] Table 2: Actual pipe inspection data table

[0080] Well No. Actual Inspected Tubular Data 1 Tubular Perforation 2 Tubular Corrosion Depth 3.3 mm 3 Tubular Rupture 4 Tubular Corrosion Depth 2.5 mm. 5 Tubular Corrosion Depth 2.3 mm. 6 Tubular Corrosion Depth 1.7 mm. 7 Tubular Corrosion Depth 1.3 mm. 8 Tubular Corrosion Depth 1.6 mm. 9 Tubular Corrosion Depth 1.3 mm. 10 Tubular Corrosion Depth 0.8 mm.

[0081] S2, according to the actual pipe inspection data in step S1, determine the pipe string corrosion risk level of the 10 wells, and the judgment criteria are as follows:

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

[0083] ③ When the pipe string corrosion depth is greater than 1mm and less than or equal to 2mm, the corrosion risk level is determined as "moderate";

[0084] ④ When the pipe string corrosion depth is less than or equal to 1mm, the corrosion risk level is determined as "mild";

[0085] According to the judgment standard, the actual pipe string corrosion risk level of 10 wells is shown in Table 3 as follows.

[0086] Table 3 Pipe string corrosion risk level table

[0087] Well No. Actual Inspected Tubular Data Actual Tubular Corrosion Risk Level 1 Tubular Perforation Extremely Severe 2 Tubular Corrosion Depth 3.3 mm Extremely Severe 3 Tubular Rupture Extremely Severe 4 Tubular Corrosion Depth 2.5 mm. Severe 5 Tubular Corrosion Depth 2.3 mm. Severe 6 Tubular Corrosion Depth 2.2 mm. Severe 7 Tubular Corrosion Depth 1.3 mm. Moderate 8 Tubular Corrosion Depth 1.6 mm. Moderate 9 Tubular Corrosion Depth 1.3 mm. Moderate 10 Tubular Corrosion Depth 0.8 mm. Mild

[0088] S3, collect not less than 6 groups of corrosion monitoring data of the same gas injection well in the work area at different times to construct a time factor corrosion risk function P1;

[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.

[0090] Table 4 On-site gas injection well monitoring and detection corrosion rate and gas injection time statistics table

[0091] Group No. t i / day 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 the 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 in the work area at different times, m=6 groups, t i is different gas injection time, day; v i is the corrosion rate at different gas injection time t i , mm / a; the corrosion monitoring data t i , v i of the same gas injection well in the work area at different times in Table 4 are substituted into formula (1), formula (2) to obtain lng=0, g=1, β=0.35.

[0095] S33. Construct the function P1 of the time factor corrosion risk:

[0096] P1=g(A β +B β ×α+C β ) (3)

[0097] In the formula, A is the number of gas injection days, days; B is the number of soak days, days; C is the number of production days, days; a is the loss coefficient, indicating the difference between dynamic corrosion and static corrosion, a is 0.85, g=1, β=0.35, a=0.85 are brought into formula (3) to obtain formula (4):

[0098] P1=A 0.35 +B 0.35 ×0.85+C 0.35 (4)

[0099] S4, build a corrosion medium factor corrosion risk function P2:

[0100] P2=D×log(E)×0.1[2.34ln(F)+10.621]÷10000 (5)

[0101] In the formula, D is the total gas injection volume, ten thousand square meters; E is the water injection volume, square meters; F is the oxygen content percentage, %.

[0102] S5, build an engineering factor corrosion risk function P3:

[0103] P3=a×b×c×d×e (6)

[0104] In the formula, a, b, c, d, e are value assignment variables, according to the proportional relationship of the service life of the pipe string under different engineering factor control conditions in the work area, they are assigned as follows: a represents the pipe string sealing state, if the tubing has a packer, a is 1, if the tubing has no packer, a is 1.3; b represents the degree of new and old of the pipe string, if it is a new tubing, b is 1, if it is an old tubing, b is 1.2; c represents the water cut, if the water cut is <50%, c is 1, if the water cut is ≥50%, c is 1.5; d represents whether acidification, if acidification is performed, d is 1.1, if there is no acidification, d is 1; e represents the corrosion prevention measure, if it is coating, e is 0.5, if it is lining, e is 0.6, if it is corrosion inhibitor, e is 0.9, if there is no corrosion prevention measure, e is 1.

[0105] S6, build a pipe string corrosion risk value M calculation model as formula (7):

[0106] M=P1×P2×P3 (7)

[0107] Substitute formula (4), formula (5), formula (6) into formula (7) to obtain the specific calculation formula (8) of the pipe string 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] wherein M is the string corrosion risk value.

[0110] S7. Converting the string corrosion risk value M of the 10 wells into the corrosion risk assessment value Y according to step S6, the specific steps are as follows:

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

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

[0113]

[0114]

[0115] The data in Table 5 is substituted into formula (8), and the string corrosion risk value M of the 10 wells is calculated as shown in Table 6 below.

[0116] Table 6 String corrosion risk value M calculation result table

[0117] Well No. 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. Constructing a corrosion risk grade assessment model, substituting the corrosion risk value M in Table 6 into formula (9) to obtain the corrosion risk assessment value Y, as shown in Table 7.

[0119]

[0120] Table 7 Corrosion risk assessment value Y calculation result table

[0121] Well No. 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. According to the Y value, the corrosion risk grade is predicted, and the corrosion risk assessment result of the 10 wells is obtained:

[0123] If 100%>Y≥90%, the corrosion risk grade is predicted to be "extremely severe";

[0124] If 90%>Y≥80%, the corrosion risk grade is predicted to be "severe";

[0125] If 80%>Y≥50%, the corrosion risk grade is predicted to be "moderate";

[0126] If 50%<Y, the corrosion risk grade is predicted to be "mild".

[0127] The risk grade assessment result is shown in Table 8 below.

[0128] Table 8 risk grade evaluation result table

[0129] Well No. M Y Predicted Corrosion Risk Level 1 17.72 92.62％ Extremely Severe 2 16.86 91.91％ Extremely Severe 3 16.09 91.19％ Extremely Severe 4 11.74 84.64％ Severe 5 12.03 85.27％ Severe 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 grade evaluation result;

[0131] The risk grade evaluation result in table 8 in step S7 is compared with the actual inspection data in table 3 in step S2, and the comparison result is shown in table 9, and the number of wells with the same grade is w.

[0132] Table 9 comparison table of column corrosion risk grade

[0133] Well No. Predicted Corrosion Risk Level Actual Corrosion Risk Level Comparison Result 1 Extremely Severe Extremely Severe Match 2 Extremely Severe Extremely Severe Match 3 Extremely Severe Extremely Severe Match 4 Severe Severe Match 5 Severe Severe Match 6 Moderate Severe Abnormal 7 Moderate Moderate Match 8 Moderate Moderate Match 9 Moderate Moderate Match 10 Mild Mild Match

[0134] According to the comparison result in table 9, the number of wells with the same grade is 9, that is, w=9;

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

[0136]

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

[0138] The above test result shows that the coincidence rate R is greater than or equal to 80%, so the risk grade evaluation result is effective, which shows that the column corrosion risk value M and the corrosion risk evaluation value Y can reflect the column corrosion degree, and can accurately judge the column corrosion risk situation, so that the on-site staff can make relevant decisions for inspection operation, and serious losses caused by column failure can be avoided.

[0139] Therefore, according to the method of the present application, the remaining 30 wells in the work area can be quantitatively evaluated for column corrosion failure risk, and the risk grade evaluation result is shown in table 10.

[0140] Table 10 risk grade evaluation result table of column in work area

[0141]

[0142]

[0143] The above is a detailed description of the specific embodiments and implementation steps of the present application. Obviously, the described embodiments are only part of the embodiments of the present application. Moreover, the prediction of column corrosion failure risk by the present application is not limited to the above-mentioned embodiments, and those skilled in the art can also change the corrosion risk influencing factors and adjust the assigned variables according to the actual situation on site without departing from the purpose of the present application, so as to achieve the optimal prediction effect.

Claims

1. A method for quantitatively evaluating the risk of corrosion failure of a tubular string, characterized by, Comprising the following steps: S1, collecting corrosion working condition data of N wells in the same work area, including corrosion working condition data related to time factor, corrosion working condition data related to corrosion medium factor, and corrosion working condition data related to engineering factor; and extracting n wells from N wells for actual pipe inspection data statistics, and the number n of extracted wells is 10% to 30% of the number N of wells in the work area; S2, according to the actual pipe inspection data of S1, determining the corrosion risk level of the n wells; S3, collecting not less than 6 groups of corrosion monitoring data of the same gas injection well at different times in the work area, and constructing a time factor corrosion risk function P1; S4, constructing a corrosion medium factor corrosion risk function P2; S5, constructing an engineering factor corrosion risk function P3; S6, according to S3, S4, and S5, constructing a pipe corrosion risk value M calculation model considering time factor, corrosion medium factor, and engineering factor; S7, according to S6, calculating the pipe corrosion risk value M of the n wells, and converting the pipe corrosion risk value M into a corrosion risk evaluation value Y, predicting the corrosion risk level according to the value Y, and obtaining the corrosion risk evaluation result of the n wells; S8, comparing the risk level evaluation result of the n wells in S7 with the pipe corrosion risk level 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 testing the coincidence rate R of the risk level evaluation result; S81. If the coincidence rate R is greater than or equal to 80%, the risk level evaluation result is valid, indicating that the pipe corrosion risk value M and the corrosion risk evaluation value Y can reflect the corrosion degree of the pipe; S82. If the coincidence rate R is less than 80%, the number of corrosion monitoring data groups of the same gas injection well at different times in the work area is expanded, 2 groups are expanded each time, the time factor corrosion risk function P1 is corrected, and steps S3, S6, S7, and S8 are repeated until R is greater than or equal to 80%.

2. The method according to claim 1, wherein, The specific content of the step S1 collecting corrosion working condition data of N wells in the same work area and actual pipe inspection data of n wells includes: S11. Corrosion working condition data related to time factor: well gas injection days, days; soak well days, days; production days, days; S12. Corrosion working condition data related to corrosion medium factor: total gas injection volume, ten thousand square meters; water injection volume, square meters; oxygen content percentage, %; S13. Corrosion working condition data related to engineering factor: ① Pipe isolation state: whether the tubing has a packer; ② Pipe new or old degree: whether the tubing is new or old; ③ Water content percentage, %; ④ Whether acidification: whether acidification operation is performed or not; ⑤ Anti-corrosion measures: including coating, lining, corrosion inhibitor, or no anti-corrosion measures; S14. Actual pipe inspection data: ① Pipe rupture or perforation; ② Corrosion depth, mm.

3. The method according to claim 1, wherein, The step S2 determines the pipe corrosion risk level of the n wells, and the judgment criteria are as follows: ① When the pipe is ruptured, perforated, or the corrosion depth is greater than 3mm, the corrosion risk level is determined to be "extremely serious"; ② When the pipe corrosion depth is greater than 2mm and less than or equal to 3mm, the corrosion risk level is determined to be "serious"; ③ When the pipe corrosion depth is greater than 1mm and less than or equal to 2mm, the corrosion risk level is determined to be "moderate"; ④When the pipe column corrosion depth is less than or equal to 1mm, the corrosion risk level is determined as "mild".

4. The method according to claim 1, wherein, The step S3 collects not less than 6 groups of corrosion monitoring data of the same injection well at different time in the work area, and constructs a time factor corrosion risk function P1, the specific steps are as follows: S31. Collect not less than 6 groups of corrosion monitoring data of the same injection well at different time in the work area, which includes: ① Different injection time t i , t i , respectively, t1, t2...t m , days; interval time is not less than 4 days and not more than 90 days; The corrosion rate v under different injection time t i The corrosion rate v under different injection time t i The corrosion rate v under different injection time t i The corrosion rate v under different injection time t m mm / a; S32. Calculate the aging index β and the aging coefficient g: m is the number of corrosion monitoring data statistics of the same injection well in the work area at different times in formula (1) and formula (2), m≥6 groups, t i is different injection time, day; v i is the corrosion rate at different injection time t i , mm / a; the corrosion monitoring data t i , v i of the same injection well in the work area at different times in step S31 are substituted into formula (1), formula (2) to solve β, g; S33. Construct the time factor corrosion risk function P1: P1 = g(A β + B β x a + C β ) (3) In the formula, A is the injection days, days; B is the soak days, days; C is the production days, days; α is the loss coefficient, which represents the difference between dynamic corrosion and static corrosion, α is 0.

85.

5. The method according to claim 1, wherein, The step S4 constructs the corrosion medium factor corrosion risk function P2 as follows: P2=D×log(E)×0.1[2.34ln(F)+10.621]÷10000 (4) In the formula, D is the total injection volume, 104m3; E is the water injection volume, m3; F is the oxygen content, %.

6. The method according to claim 1, wherein, The step S5 constructs the engineering factor corrosion risk function P3 as follows: P3=a×b×c×d×e (5) In the formula, a, b, c, d, e are assignment variables, according to the proportion relationship of the service life of the pipe column under different engineering factor control conditions in the work area, they are assigned as follows: a represents the pipe column sealing state, if the tubing has a packer, a is 1, if the tubing has no packer, a is 1.3; b represents the pipe column new or old degree, if it is a new tubing, b is 1, if it is an old tubing, b is 1.2; c represents the water cut, if the water cut is less than 50%, c is 1, if the water cut is greater than or equal to 50%, c is 1.5; d represents whether acidification, if acidification is performed, d is 1.1, if no acidification, d is 1; e represents the corrosion prevention measures, if it is coating, e is 0.5, if it is lining, e is 0.6, if it is corrosion inhibitor, e is 0.9, if there is no corrosion prevention measure, e is 1.

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

8. The method according to claim 1, wherein, The step S7 converts the pipe column corrosion risk value M of n wells into the corrosion risk assessment value Y of n wells, the specific steps are as follows: S71. According to the step S5, the corrosion working condition data related to the engineering factor in the corrosion working condition data of n wells are assigned, the processed corrosion working condition data are substituted into formula (7), and the pipe column corrosion risk value M of n wells is calculated; S72. Construct the corrosion risk level assessment model, 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, M is the pipe column corrosion risk value; S73. According to the Y value, the corrosion risk level is predicted, and the corrosion risk assessment result of n wells is obtained; If 100%>Y≥90%, the corrosion risk level is predicted as "extremely serious"; If 90%>Y≥80%, the corrosion risk level is predicted as "serious"; If 80%>Y≥50%, the corrosion risk level is predicted as "moderate"; If 50%<Y, the corrosion risk level is predicted as "mild".

9. The method according to claim 1, wherein, The formula for calculating the coincidence rate R in step S8 is: In the formula, R is the coincidence rate, %; w is the number of wells with the same risk grade in step S8, well; and n is the number of wells drawn in step S1, well.

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