CN110298540A - A kind of oil gas field surface duct internal corrosion risk evaluating method - Google Patents
A kind of oil gas field surface duct internal corrosion risk evaluating method Download PDFInfo
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Abstract
The invention discloses a kind of oil gas field surface duct internal corrosion risk evaluating method, can the internal corrosion risk to pipeline carry out grading evaluation, identify oil field high risk pipeline, control the operating status of high risk pipeline in real time.A possibility that by the way that the predicted value of corrosion rate occurs as risk, simultaneously by pumped (conveying) medium, pass through the consequence seriousness that environment, H2S content and the size of population etc. occur as risk, it evaluates the corrosion risk of pipeline and completely enumerates the probability that failure occurs and the seriousness that failure occurs, effectively improve the accuracy and reliability of corrosive pipeline risk assessment.
Description
[technical field]
The invention belongs to petroleum pipeline security risk evaluations technical fields, are related to a kind of oil gas field surface duct internal corrosion risk
Evaluation method.
[background technique]
Corrosion not only affects the normal production and operation of pipeline, but also causes energy waste and economic loss, while by
There is the characteristic of inflammable, explosive and easy diffusion in oil gas medium, will also cause environmental pollution and safety accident.Due to corrosive pipeline
It cannot prejudge in advance, cause to rob maintenance and pollution abatement costs is substantially increased, to oil gas field safety in production, development benefit, ecology
Environmental protection and corporate image cause serious influence.And corrosion risk prediction is carried out, it can be achieved that right to in-service pipeline or newly-built pipeline
It the identification in advance of high risk pipeline and pays close attention to, scene can be instructed to formulate targetedly risk control measure, while can also
To reduce economic loss and the wasting of resources to a greater extent, become corrosive pipeline " post-processing " as " protecting " in advance.
The most intractable etching problem of oil gas field is the leakage of pipe perforation caused by internal corrosion at present, and is directed to internal corrosion risk
Nowadays there is no systems for evaluation comprehensively, the method for reliable and economic, and existing main problem has:
(1) influence to pipeline residual intensity is lost to evaluate its security risk size according to inner wall corrosion, needed a large amount of
Interior detection or excavate detection etc. data；
(2) the direct Evaluation Method of internal corrosion thinks that the position corrosion risk of most easy hydrops is bigger, but is not suitable for wet crude
Pipeline (moisture content > 5%)；
(3) Kent point system has comprehensively considered inside and outside burn into damage from third-party, design, maloperation etc., due to being special
The method of family's marking keeps its reliability not high；
(4) various corrosive ions have been comprehensively considered to pipe according to method of the size of corrosion rate to pipeline risk stratification
The effect in road preferably reflects the actual state of pipeline corrosion environment, but not in view of leakage security context destroys risk.
Therefore, how the size and degree of accurate characterization internal corrosion risk, can oil-gas pipeline military service in accomplish effectively and
Accurate prediction and evaluation, and take into account the size of failure consequence, it appears it is particularly important.
[summary of the invention]
The present invention is to solve the reliability of oil gas field surface duct internal corrosion risk evaluation results, proposes a kind of oil gas field
Face pipeline corrosion risk evaluating method, i.e., a possibility that generation by corrosion rate predicted value as risk, pipe leakage is drawn
The environmental disruption of hair and life security characterize the consequence occurred as risk using magnitude, and the product of the two is looked into further according to matrix table
Take risk class.Present invention is mainly used for the evaluations of oil gas field surface duct internal corrosion risk size, have comprehensively considered in various
Corrosive environment destroys the Environmental security caused after the effect of pipeline and oil and gas leakage.
In order to achieve the above objectives, the present invention is achieved by the following scheme:
A kind of oil gas field surface duct internal corrosion risk evaluating method, comprising the following steps:
Step 1: oil gas field block divides
A. high to contain H2S block is H in pipeline medium2S content >=5000mg/m3And CO2Content < 3.0mol% block；
B. high to contain CO2Block, CO in pipeline medium2Content >=3.0mol% and H2S content < 5000mg/m3Block；
C. low in acidity block, H in pipeline medium2S content < 5000mg/m3And CO2Content < 3.0mol% block；
D. peracidity block, H in pipeline medium2S content >=5000mg/m3And CO2Content >=3.0mol% block；
Step 2: corrosion rate prediction
The corrosion influence factors in each block pipeline are collected, the factor of factor and operating condition including fluid media (medium), with
And the corrosion rate v of on-site test corresponding thereto；Corroded according to the corrosion influence factors of each block pipeline of collection
Rate prediction establishes the Prediction of Pipeline Corrosion Rate model of different blocks；
Step 3: leakage consequence classification
To the environmental disruption risk and life security risk progress magnitude characterization after pipeline failure, respectively to the shadow of medium
Sound, the influence of hydrogen sulfide and the influence of the size of population take coefficient, obtain failure consequence Ψ are as follows:
Ψ=ξ medium ξ hydrogen sulfide ξ population (1)
Wherein, ξMediumFor medium influence coefficient, ξ in pipelineMediumCoefficient, ξ are influenced for pipeline mediumMediumFor hydrogen sulfide in pipeline
Influence coefficient, ξMediumCoefficient is influenced for the size of population of pipeline resident；
When crude oil pipeline passes through sandy soil environment, ξMediumIt is 1, sandy soil environment is gobi or desert；When crude oil pipeline pass through it is quick
When feeling environment, ξMediumIt is 1.5, sensitive environment is waters or the woods；When crude oil pipeline passes through farmland or pasture environment, ξMediumIt is 2；
The ξ of natural gas lineMediumIt is 1；
As hydrogen sulfide content≤500mg/m in pipeline3When, ξHydrogen sulfideIt is 1；When in pipeline hydrogen sulfide content 500~
5000mg/m3Between when, ξHydrogen sulfideIt is 1.5；As hydrogen sulfide content >=5000mg/m in pipeline3When, ξHydrogen sulfideIt is 2；
When the size of population≤15 family, ξPopulationIt is 1；When the size of population is between 15~100 families, ξPopulationIt is 1.5；Work as people
When mouth quantity >=100 family, ξPopulationIt is 2；
Step 4: corrosion risk evaluation
Corrosion risk degree R is evaluated according to formula (2):
Corrosion risk degree R=possibility L × seriousness S (2)
A possibility that corrosion risk occurs L is obtained according to corrosive pipeline rate v, according to standard NACE RP0775-2005
" preparation, installation, analysis and the explanation of test data of corrosion coupon in petroleum-gas fiedl production ":
It is 1 grade as v < 0.0254mm/a；
It is 2 grades as 0.0254mm/a≤v < 0.125mm/a；
It is 3 grades as 0.125mm/a≤v < 0.254mm/a；
It is 4 grades as v > 0.254mm/a；
The seriousness S of failure consequence is according to pumped (conveying) medium, H2S content and the size of population obtain:
It is 1 grade as Ψ≤1.5；
It is 2 grades as 1.5 < Ψ≤3；
It is 3 grades as 3 < Ψ≤4.5；
It is 4 grades as 4.5 < Ψ≤6；
It is 5 grades as the ψ=8 of Ψ=8.
A further improvement of the present invention lies in that:
In step 2, the factor of fluid media (medium) includes H2S content, CO2Content and Cl-Content；The factor packet of operating condition
Include temperature, pressure and flow velocity.
In step 2, corrosion rate prediction technique is specific as follows:
Prediction algorithm includes input layer, hidden layer and output layer, and each interlayer is connected by weight；
Wherein, input vector X=[x1,x2,x3,...,xn], xiIndicate the input of i-th group of pipeline data；xi-lIndicate i-th
First of corrosion influence factors of group pipeline data；Output vector Y=[y1,y2,y3,...,ym], yiIndicate i-th of corrosion rate
Output；ojIndicate hidden layer threshold value, okIndicate output layer threshold value；
Step 2-1: initialization algorithm
W is setijAnd wjkInitial connection weight, initial connection weight is the nonzero value randomly selected in (- 1,1) section,
Given computational accuracy value ε=10 simultaneously5；wijIndicate weight of the input layer to hidden layer, wjkPower of the expression hidden layer to output layer
Value；
Step 2-2: specified input data and output data calculate output
Work as q=1, when 2,3 ..., l, if q group data input xq=[x1q,x2q,x3q,...,xnq], desired output dq=
[d1q,d2q,d3q,...,dmq], then node i q group data input when reality output yiqAre as follows:
In formula, wijIt (t) is weight of the input layer to hidden layer through t adjustment；IjqIt is the node i in the input of q group sample
J-th input；
Step 2-3: calculating target function
The objective function of network is E when being located at the input of q group sampleq, then
In formula, yq(t) be q group data input when after t weighed value adjusting algorithm output；K is k-th of output layer
Node；
Step 2-4: catalogue scalar functions J (t)
As the evaluation to calculating process, if:
J (t)=≤ ε (4)
Terminate, otherwise carries out in next step；
Step 2-5: backpropagation calculates
Weight by gradient descent method retrospectively calculate and is successively adjusted by output layer according to J, step-length η takes constant value, byFormula obtains node j to the weight of node i adjusted through t+1 times:
The specific method is as follows:
If
If δ in formulaiqThe state x of i-th of node when being the input of q group dataiqTo EqSensitivity；
It can be obtained by formula (5) and formula (6):
As i=k, i.e. i is output node, is obtained by formula (3-2) and formula (3-6):
Formula (7) are substituted into formula (6), then
As i ≠ k, i.e. i is not output node, this up-to-date style (3-7) is
Wherein:
In formula, m1It is lower layer of node i of j-th of node；I*jqNode m when being the input of q group data1J-th it is defeated
Enter；
As i=j, yjq=I*jqWhen, formula (11) and formula (12) are substituted into formula (6), then had:
Compared with prior art, the invention has the following advantages:
The present invention can internal corrosion risk to pipeline carry out grading evaluation, identify oil field high risk pipeline, control in real time
The operating status of high risk pipeline.The method that the present invention predicts corrosion rate accurately has found between corrosion rate and influence factor
Hiding relationship, and the corrosive pipeline rate under the conditions of reasonable prediction varying environment.Simultaneously by pumped (conveying) medium, pass through environment, H2S
Content and the size of population etc. carry out classification quantitative processing, evaluate the corrosion risk of pipeline and completely enumerate the general of failure generation
The seriousness that rate and failure occur, effectively improves the accuracy and reliability of corrosive pipeline risk assessment.
[Detailed description of the invention]
Fig. 1 is oil gas field surface duct internal corrosion risk assessment flow chart；
Fig. 2 is the illustraton of model of corrosion rate of the present invention prediction.
[specific embodiment]
The invention will be described in further detail with reference to the accompanying drawing:
Referring to Fig. 1, oil gas field surface duct internal corrosion risk evaluating method of the present invention, comprising the following steps:
(1) oil gas field block divides
According to H in pipeline medium2S content >=5000mg/m3And CO2Content < 3.0mol% points contain H to be high2S block, CO2Contain
Amount >=3.0mol% and H2S content < 5000mg/m3It is divided into high containing CO2Block, H2S content < 5000mg/m3And CO2Content <
3.0mol% points are low in acidity block, H2S content >=5000mg/m3And CO2Content >=3.0mol% points are peracidity block.
(2) corrosion rate is predicted
Collect corrosion influence factors common in each block pipeline, factor (such as H including fluid media (medium)2S content, CO2Contain
Amount, Cl-Content etc.) and operating condition factor (such as temperature, pressure, flow velocity), and corresponding thereto corrosion rate (come
From live corrosion monitoring result).Corrosion rate prediction is carried out according to each block pipeline data of collection, establishes the pipe of different blocks
Road corrosion rate prediction model.
Above-mentioned corrosion rate prediction steps are as follows:
As shown in Fig. 2, corrosion rate prediction algorithm is made of input layer, hidden layer and output layer, each interlayer passes through weight
It is connected.Wherein, X=[x1,x2,x3,...,xn] be algorithm input vector, xiIndicate the input of i-th group of pipeline data；xi-l
Indicate first of corrosion influence factors of i-th group of pipeline data；Y=[y1,y2,y3,...,ym] be algorithm output vector, yiTable
Show the output of i-th of corrosion rate；ojIndicate hidden layer threshold value, okIndicate output layer threshold value；wijIndicate input layer to hidden layer
Weight, wjkWeight of the expression hidden layer to output layer.
A) initialization algorithm.
W is setijAnd wjkInitial connection weight, initial connection weight is the nonzero value randomly selected in (- 1,1) section,
Given computational accuracy value ε=10 simultaneously5(ε>0)。
B input data and output data, the output of computational algorithm) are specified.
Work as q=1, when 2,3 ..., l, if q group data input xq=[x1q,x2q,x3q,...,xnq], desired output dq=
[d1q,d2q,d3q,...,dmq], then node i q group data input when reality output yiqAre as follows:
In formula, wijIt (t) is weight of the input layer to hidden layer through t adjustment；IjqIt is the node i in the input of q group sample
J-th input.
C) calculating target function.
The objective function of network is E when being located at the input of q group sampleq, then
In formula, yq(t) be q group data input when after t weighed value adjusting algorithm output；K is k-th of output layer
Node.
D) the catalogue scalar functions of algorithm
As the evaluation to algorithm calculating process, if
J(t)≤ε (4)
Algorithm terminates, and otherwise carries out in next step.
E) backpropagation calculates, and by gradient descent method retrospectively calculate and successively adjusts weight, step-length η by output layer according to J
Constant value is taken, byFormula obtains node j to the weight of node i adjusted through t+1 times:
Specific algorithm is as follows:
If
If δ in formulaiqThe state x of i-th of node when being the input of q group dataiqTo EqSensitivity.
It can be obtained by formula (5) and formula (6):
As i=k, i.e. i is output node, can be obtained by formula (3-2) and formula (3-6):
Formula (7) are substituted into formula (6), then
As i ≠ k, i.e. i is not output node, this up-to-date style (3-7) is
Wherein:
In formula, m1It is lower layer of node i of j-th of node；I*jqNode m when being the input of q group data1J-th it is defeated
Enter.
As i=j, yjq=I*jqWhen, formula (11) and formula (12) are substituted into formula (6), then had:
(3) Consequence calculation is leaked
To the environmental disruption risk and life security risk progress magnitude characterization after pipeline failure, respectively to the shadow of medium
Sound, the influence of hydrogen sulfide and the influence of the size of population take coefficient, obtain failure consequence Ψ are as follows:
Ψ=ζMedium `ζHydrogen sulfide `ζPopulation (14)
Wherein, (1) environmental disruption risk can pollute surrounding after mainly considering crude oil leakage according to actual fed medium situation
Environment, i.e. crude oil pipeline pass through the sandy soil environment ζ such as gobi or desertMediumFor 1, the sensitive environment ζ such as pass through waters, the woodsMediumFor
1.5, the agro-farmings such as farmland, pasture environment ζ is passed throughMediumIt is 2, natural gas line ζMediumIt is 1；(2) life security risk mainly considers H2S
Toxicity and surrounding resident quantity, wherein the toxicity of hydrogen sulfide is mainly from the point of view of medical research and Release and dispersion, i.e. sulphur
Change hydrogen content≤500mg/m3When ζHydrogen sulfideFor 1,500~5000mg/m3When ζHydrogen sulfideFor 1.5, >=5000mg/m3When ζHydrogen sulfideIt is 2；Ginseng
It examines in GB50251-2003 " Gas Pipeline Project design specification " and 3 grades, the size of population≤15 families is divided into the size of population
When ζHydrogen sulfideζ when for 1,15~100 familyHydrogen sulfideζ when for 1.5, >=100 familyHydrogen sulfideIt is 2.According to formula 14, the multiplication taken is obtained
Consequence seriousness size ψ.
(4) corrosion risk is evaluated
A possibility that corrosion risk occurs L considers the size of corrosive pipeline rate, reference standard NACE RP 0775-2005
" preparation, installation, analysis and the explanation of test data of corrosion coupon in petroleum-gas fiedl production ", is divided into 4 grades for corrosion rate,
That is corrosion rate v < 0.0254mm/a is 1 grade, 0.0254mm/a≤v < 0.125mm/a is 2 grades, 0.125mm/a≤v <
0.254mm/a is 3 grades, v > 0.254mm/a is 4 grades；The seriousness S of failure consequence includes pumped (conveying) medium, H2S content, population
Amount etc., i.e. ψ≤1.5 are 1 grade, 1.5 < ψ≤3 are 2 grades, 3 < ψ≤4.5 are 3 grades, 4.5 < ψ≤6 are 4 grades, ψ=8 are 5 grades.According to public affairs
Formula:
Corrosion risk degree R=possibility L × seriousness S (15)
Finally obtained corrosion risk evaluations matrix, as shown in table 1.
1 corrosion risk evaluations matrix of table
Wherein, result be 1 or 2 indicate low-risks, result 3,4 or 5 indicate medium to low-risk, result 6,8 or 9 indicate in
Risk, result are 12 or 10 expression medium or high risks, and result 15,16 or 20 indicates high risk.
Oil gas field surface duct internal corrosion risk evaluating method proposed by the present invention is specifically for oil-gas pipeline internal corrosion wind
Dangerous and progress evaluation, its advantage is that having divided block node and having covered most corrosion influence factors, and is reasonably examined
The consequence of corrosion leakage generation, i.e. environmental disruption risk and life security risk are considered, finally by corrosion rate and leakage consequence
Correspondingly be divided into it is low, in it is low, in, middle high, high five risk.By the anticipation to pipeline risk size, live system can be instructed
Fixed targetedly risk control measure, the risk level of pipeline is controlled in reasonable, acceptable range.
Embodiment:
(1) oil gas field block divides
The reservoirs in one oilfield in western China of selection, corrosive environment is complex, and medium contains H2S、CO2Equal sour gas, according in pipeline
H2S and CO2Content oil field is divided into 4 blocks, be high respectively containing H2S block, Gao Han CO2Block, low in acidity block and height
Acid block.
(2) corrosion rate is predicted
The numerical values recited and corresponding corrosion rate value of 4 main influence factors of block pipeline, Mei Gequ are collected respectively
Block is collected into the data of 5 pipelines, and the corrosion rate of prediction is obtained by calculation, the corrosion rate error with live actual monitoring
Less than 10%, as shown in table 2-1~table 2-3.
Table 2-1 corrosion risk evaluation result
Table 2-2 corrosion risk evaluation result
Table 2-3 corrosion risk evaluation result
(3) Consequence calculation is leaked
According to the media type of pipeline, pass through environment, H2S content and the size of population etc. calculate the leakage consequence value of pipeline,
As shown in table 1.
(4) corrosion risk is evaluated
According to the corrosion rate of pipeline and leakage consequence, corrosion risk evaluation is carried out according to the risk Metrics of table 1.Amount to 20
1 high risk pipeline, 1 medium or high risk pipeline, 4 risk pipelines, 5 medium to low-risk pipelines and 9 are evaluated in pipeline
Low-risk pipeline.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press
According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention
Protection scope within.
Claims (3)
1. a kind of oil gas field surface duct internal corrosion risk evaluating method, which comprises the following steps:
Step 1: oil gas field block divides
A. high to contain H2S block is H in pipeline medium2S content >=5000mg/m3And CO2Content < 3.0mol% block；
B. high to contain CO2Block, CO in pipeline medium2Content >=3.0mol% and H2S content < 5000mg/m3Block；
C. low in acidity block, H in pipeline medium2S content < 5000mg/m3And CO2Content < 3.0mol% block；
D. peracidity block, H in pipeline medium2S content >=5000mg/m3And CO2Content >=3.0mol% block；
Step 2: corrosion rate prediction
The corrosion influence factors in each block pipeline are collected, the factor of factor and operating condition including fluid media (medium), Yi Jiyu
The corrosion rate v of its corresponding on-site test；Corrosion rate is carried out according to the corrosion influence factors of each block pipeline of collection
Prediction, establishes the Prediction of Pipeline Corrosion Rate model of different blocks；
Step 3: leakage consequence classification
To the environmental disruption risk and life security risk progress magnitude characterization after pipeline failure, respectively to the influence of medium, sulphur
The influence of the influence and the size of population of changing hydrogen takes coefficient, obtains failure consequence Ψ are as follows:
Ψ=ξMedium·ξHydrogen sulfide·ξPopulation (1)
Wherein, ξMediumFor medium influence coefficient, ξ in pipelineMediumCoefficient, ξ are influenced for pipeline mediumMediumIt is influenced for hydrogen sulfide in pipeline
Coefficient, ξMediumCoefficient is influenced for the size of population of pipeline resident；
When crude oil pipeline passes through sandy soil environment, ξMediumIt is 1, sandy soil environment is gobi or desert；When crude oil pipeline passes through sensing ring
When border, ξMediumIt is 1.5, sensitive environment is waters or the woods；When crude oil pipeline passes through farmland or pasture environment, ξMediumIt is 2；Naturally
The ξ of feed channelMediumIt is 1；
As hydrogen sulfide content≤500mg/m in pipeline3When, ξHydrogen sulfideIt is 1；When in pipeline hydrogen sulfide content in 500~5000mg/m3
Between when, ξHydrogen sulfideIt is 1.5；As hydrogen sulfide content >=5000mg/m in pipeline3When, ξHydrogen sulfideIt is 2；
When the size of population≤15 family, ξPopulationIt is 1；When the size of population is between 15~100 families, ξPopulationIt is 1.5；Work as population
When measuring >=100 family, ξPopulationIt is 2；
Step 4: corrosion risk evaluation
Corrosion risk degree R is evaluated according to formula (2):
Corrosion risk degree R=possibility L × seriousness S (2)
Corrosion risk occur a possibility that L obtained according to corrosive pipeline rate v, according to standard NACE RP 0775-2005 " oil,
Preparation, installation, analysis and the explanation of test data of corrosion coupon in the production of gas field ":
It is 1 grade as v < 0.0254mm/a；
It is 2 grades as 0.0254mm/a≤v < 0.125mm/a；
It is 3 grades as 0.125mm/a≤v < 0.254mm/a；
It is 4 grades as v > 0.254mm/a；
The seriousness S of failure consequence is according to pumped (conveying) medium, H2S content and the size of population obtain:
It is 1 grade as Ψ≤1.5；
It is 2 grades as 1.5 < Ψ≤3；
It is 3 grades as 3 < Ψ≤4.5；
It is 4 grades as 4.5 < Ψ≤6；
It is 5 grades as the ψ=8 of Ψ=8.
2. oil gas field surface duct internal corrosion risk evaluating method according to claim 1, which is characterized in that in step 2,
The factor of fluid media (medium) includes H2S content, CO2Content and Cl-Content；The factor of operating condition includes temperature, pressure and stream
Speed.
3. oil gas field surface duct internal corrosion risk evaluating method according to claim 1, which is characterized in that in step 2,
Corrosion rate prediction technique is specific as follows:
Prediction algorithm includes input layer, hidden layer and output layer, and each interlayer is connected by weight；
Wherein, input vector X=[x1,x2,x3,...,xn], xiIndicate the input of i-th group of pipeline data；xi-lIndicate i-th group of pipe
First of corrosion influence factors of track data；Output vector Y=[y1,y2,y3,...,ym], yiIndicate the defeated of i-th of corrosion rate
Out；ojIndicate hidden layer threshold value, okIndicate output layer threshold value；
Step 2-1: initialization algorithm
W is setijAnd wjkInitial connection weight, initial connection weight is the nonzero value randomly selected in (- 1,1) section, simultaneously
Given computational accuracy value ε=105；wijIndicate weight of the input layer to hidden layer, wjkWeight of the expression hidden layer to output layer；
Step 2-2: specified input data and output data calculate output
Work as q=1, when 2,3 ..., l, if q group data input xq=[x1q,x2q,x3q,...,xnq], desired output dq=[d1q,
d2q,d3q,...,dmq], then node i q group data input when reality output yiqAre as follows:
In formula, wijIt (t) is weight of the input layer to hidden layer through t adjustment；IjqIt is the jth of the node i in the input of q group sample
A input；
Step 2-3: calculating target function
The objective function of network is E when being located at the input of q group sampleq, then
In formula, yq(t) be q group data input when after t weighed value adjusting algorithm output；K is k-th of node of output layer；
Step 2-4: catalogue scalar functions J (t)
As the evaluation to calculating process, if:
J (t)=≤ ε (4)
Terminate, otherwise carries out in next step；
Step 2-5: backpropagation calculates
Weight by gradient descent method retrospectively calculate and is successively adjusted by output layer according to J, step-length η takes constant value, byFormula obtains node j to the weight of node i adjusted through t+1 times:
The specific method is as follows:
If
If δ in formulaiqThe state x of i-th of node when being the input of q group dataiqTo EqSensitivity；
It can be obtained by formula (5) and formula (6):
As i=k, i.e. i is output node, is obtained by formula (3-2) and formula (3-6):
Formula (7) are substituted into formula (6), then
As i ≠ k, i.e. i is not output node, this up-to-date style (3-7) is
Wherein:
In formula, m1It is lower layer of node i of j-th of node；I*jqNode m when being the input of q group data1J-th input；
As i=j, yjq=I*jqWhen, formula (11) and formula (12) are substituted into formula (6), then had:
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111222281A (en) * | 2020-02-06 | 2020-06-02 | 中国石油天然气集团有限公司 | Gas reservoir type gas storage injection-production string erosion failure risk determination method |
| CN111260207A (en) * | 2020-01-14 | 2020-06-09 | 南智（重庆）能源技术有限公司 | Intelligent diagnosis and evaluation method for corrosion of high-sulfur underground pipe column and gas transmission pipeline |
| CN112819262A (en) * | 2019-10-30 | 2021-05-18 | 中国石油化工股份有限公司 | Memory, process pipeline inspection and maintenance decision method, device and equipment |
| CN112883538A (en) * | 2020-12-29 | 2021-06-01 | 浙江中控技术股份有限公司 | Corrosion prediction system and method for buried crude oil pipeline |
| CN113128803A (en) * | 2019-12-30 | 2021-07-16 | 中国石油天然气股份有限公司 | Oil and gas pipeline risk determination method and device and computer equipment |
| CN113252547A (en) * | 2021-03-31 | 2021-08-13 | 中车青岛四方机车车辆股份有限公司 | Aluminum alloy corrosion fatigue risk grade evaluation method based on environmental threshold |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636585A (en) * | 2013-11-15 | 2015-05-20 | 中国石油天然气集团公司 | Environment risk quantitative management method of oil gas long-distance pipeline |
| CN205749233U (en) * | 2016-04-19 | 2016-11-30 | 中国石油天然气集团公司 | For evaluating oil and gas pipes assay device of erosion corrosion under high flow rate |
| CN109034546A (en) * | 2018-06-06 | 2018-12-18 | 北京市燃气集团有限责任公司 | A kind of intelligent Forecasting of city gas Buried Pipeline risk |
-
2019
- 2019-05-22 CN CN201910430422.5A patent/CN110298540B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104636585A (en) * | 2013-11-15 | 2015-05-20 | 中国石油天然气集团公司 | Environment risk quantitative management method of oil gas long-distance pipeline |
| CN205749233U (en) * | 2016-04-19 | 2016-11-30 | 中国石油天然气集团公司 | For evaluating oil and gas pipes assay device of erosion corrosion under high flow rate |
| CN109034546A (en) * | 2018-06-06 | 2018-12-18 | 北京市燃气集团有限责任公司 | A kind of intelligent Forecasting of city gas Buried Pipeline risk |
Non-Patent Citations (1)
| Title |
|---|
| 羊东明等: "塔河油田苛刻环境下集输管线腐蚀防治技术应用", 《表面技术》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112819262A (en) * | 2019-10-30 | 2021-05-18 | 中国石油化工股份有限公司 | Memory, process pipeline inspection and maintenance decision method, device and equipment |
| CN113128803A (en) * | 2019-12-30 | 2021-07-16 | 中国石油天然气股份有限公司 | Oil and gas pipeline risk determination method and device and computer equipment |
| CN113128803B (en) * | 2019-12-30 | 2024-03-29 | 中国石油天然气股份有限公司 | Oil and gas pipeline risk determination method and device and computer equipment |
| CN111260207A (en) * | 2020-01-14 | 2020-06-09 | 南智（重庆）能源技术有限公司 | Intelligent diagnosis and evaluation method for corrosion of high-sulfur underground pipe column and gas transmission pipeline |
| CN111222281A (en) * | 2020-02-06 | 2020-06-02 | 中国石油天然气集团有限公司 | Gas reservoir type gas storage injection-production string erosion failure risk determination method |
| CN112883538A (en) * | 2020-12-29 | 2021-06-01 | 浙江中控技术股份有限公司 | Corrosion prediction system and method for buried crude oil pipeline |
| CN112883538B (en) * | 2020-12-29 | 2022-07-22 | 浙江中控技术股份有限公司 | Corrosion prediction system and method for buried crude oil pipeline |
| CN113252547A (en) * | 2021-03-31 | 2021-08-13 | 中车青岛四方机车车辆股份有限公司 | Aluminum alloy corrosion fatigue risk grade evaluation method based on environmental threshold |
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