CN112883538B - Corrosion prediction system and method for buried crude oil pipeline - Google Patents
Corrosion prediction system and method for buried crude oil pipeline Download PDFInfo
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Abstract
The invention provides a corrosion prediction system and method for a buried crude oil pipeline. The corrosion prediction method specifically comprises the following steps: acquiring pipeline data and environment data of a buried pipeline through a data acquisition system, and establishing a pipeline twinning data model according to the pipeline data and the environment data; the application module performs simulation operation by using a pipeline twinning data model to acquire operation state data and corrosion state data; and performing initial evaluation through an application module, performing residual intensity calculation and residual life prediction on the pipeline according to the initial evaluation result, and performing maintenance decision by the application module when the calculation result or the prediction result is smaller than a threshold value. The invention utilizes the digital twinning technology to simulate the pipeline running and corrosion states, so that the prediction result is closer to the reality, and the obtained maintenance scheme is more real and reliable.
Description
Technical Field
The invention relates to the technical field of digital twinning, in particular to a corrosion prediction system and method for a buried crude oil pipeline.
Background
One of the five transportation industries in China plays a very important role in national economy in China during pipeline transportation of petroleum. However, buried pipelines are gradually aged with time, protective layers are damaged with time, pipe walls are thinned due to corrosion pits formed on the surfaces of the pipelines, and when the pipe walls are thin enough that the strength of the pipe walls is insufficient to resist the stress generated by the internal pressure of the pipes and external load, structural breakage and failure of the pipelines can occur. If the damage of the pipe wall caused by corrosion cannot be solved in time, the accident of crude oil leakage is very likely to occur, and great loss is brought to economy and environment. At present, the evaluation and prediction method for pipeline corrosion is single, and has greater conservation compared with the actual situation, the prediction requirement of pipeline corrosion cannot be met, and the application standards and conditions are not uniform for different types of pipelines when the pipeline corrosion prediction is carried out.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a corrosion prediction system and method for a buried crude oil pipeline.
The purpose of the invention is realized by the following technical scheme:
a corrosion prediction method for a buried crude oil pipeline comprises the following steps:
the method comprises the steps that firstly, a data acquisition module acquires pipeline data and environment data of a buried crude oil pipeline, a database classifies the acquired pipeline data and environment data of the buried crude oil pipeline according to a data model, and the classified pipeline data and environment data are stored in an entity data unit of the database;
reading the pipeline data and the environment data by the simulation module, and constructing a pipeline digital twin model;
performing simulation calculation on the running state and the corrosion state of the buried crude oil pipeline by the application module through a pipeline digital twin model, and storing the running state data and the corrosion state data of the buried crude oil pipeline obtained through the simulation calculation in a twin data unit of a database;
step four, the application module carries out simulation calculation on the internal and external corrosion rates of the pipeline through a pipeline digital twin model and a corrosion rate algorithm, then the application module adjusts the running state data, the corrosion state data and the evaluation standard in a database of the buried crude oil pipeline in a twin data unit, carries out primary evaluation on the buried crude oil pipeline, if the internal and external corrosion rates, the running state data and the corrosion state data do not accord with the evaluation standard, the application module divides an inspection area according to an evaluation result, simulates the direct inspection process of the pipeline through the pipeline digital twin model, collects the data of the direct inspection process of the pipeline and stores the data in the evaluation data unit of the database, and executes step five; under other conditions, the pipeline is not directly inspected, the date of the next evaluation is calculated according to the internal and external corrosion rates, the evaluation result, the internal and external corrosion rate data and the date data obtained through calculation are stored in an evaluation data unit of a database, and the third step is executed when the estimated date of the direct inspection of the pipeline is reached;
fifthly, the application module calculates the residual strength of the pipeline and predicts the residual life according to the data in the evaluation data unit, the residual strength algorithm and the residual life prediction algorithm, and the obtained residual strength calculation result and the residual life prediction result are stored in the evaluation data unit;
step six, the application module calls residual strength threshold data and residual life threshold data of pipeline maintenance in the database, then the application module compares a residual strength calculation result with a residual strength threshold, meanwhile, the application module compares a residual life calculation result with a residual life threshold, if the application module detects that the residual strength calculation result is lower than the residual strength threshold or the residual life calculation result is lower than the residual life threshold, the application module calls a maintenance decision unit, and the maintenance decision unit determines a pipeline maintenance scheme according to data in the evaluation data unit; and if the application module detects that the calculation result of the residual strength is higher than the threshold value of the residual strength and the calculation result of the residual service life is higher than the threshold value of the residual service life, the pipeline continues to be used.
The pipeline is modeled by a digital twinning technology, the corrosion process of the pipeline is simulated, and the simulation process can be synchronous with the real situation, so that the result obtained by predicting the subsequent residual strength and residual life based on simulation data can be more fit with the actual situation. And before the maintenance decision is carried out, the maintenance simulation is carried out in the digital twin model, and the maintenance decision is carried out according to the data obtained in the simulation process, so that the result of the maintenance decision is more accurate and closer to reality.
In the sixth step, the maintenance decision unit executes a maintenance decision substep when determining a pipeline maintenance scheme according to data in the evaluation data unit, wherein the maintenance decision substep specifically comprises the following steps:
1.1, the maintenance decision unit determines the corrosion type of the pipeline according to the data in the evaluation data unit, judges whether to perform primary evaluation according to the corrosion type of the pipeline, and executes the step 1.2 if the primary evaluation is performed; if the first-level evaluation is not carried out, the step 1.3 is executed;
1.2, calling pipeline defect data and pipeline calculation data, calling a primary evaluation standard in a database at the same time, performing primary evaluation according to the pipeline defect data, the pipeline calculation data and the primary evaluation standard, judging whether a primary evaluation result is received, and if the primary evaluation result is received, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a primary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the voltage reduction is not carried out, the step 1.3 is executed;
1.3, judging whether to perform secondary evaluation, and if so, executing the step 1.4; if the secondary evaluation is not carried out, directly maintaining the pipeline;
1.4 calling a primary evaluation standard in the database, performing secondary evaluation according to the pipeline defect data, the pipeline calculation data and the secondary evaluation standard, judging whether to receive a secondary evaluation result, and if so, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a secondary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the pressure reduction is not carried out, the pipeline is directly maintained.
Evaluating the data of the buried pipeline by adopting a uniform evaluation standard, and specifying a maintenance scheme according to an evaluation result; two evaluation standards are established and compared aiming at different states of the pipeline, wherein the first evaluation standard aims at the pipeline which ignores the action of the additional load under the internal pressure, and the second evaluation standard aims at the pipeline which is under the action of the internal pressure, the external pressure and the additional load. Even if the obtained data cannot meet the evaluation standard, whether the data can be adjusted through a pressure reduction means is tried at first, maintenance through a direct maintenance scheme is avoided as much as possible, and consumption of manpower and material resources is reduced.
The corrosion types comprise a uniform corrosion type, a local corrosion type and a punctiform corrosion type; the pipeline defect data is corrosion state data in the twin data unit; the pipeline calculation data are the calculation result of the internal and external corrosion rates of the evaluation data unit, the calculation result of the residual strength and the prediction result of the residual service life.
Because different corrosion types have different influences on the residual strength and the residual life of the pipeline, when a maintenance decision is made, the corrosion type needs to be determined first, the pipeline defect data and the pipeline calculation data required by a subsequent maintenance decision can be determined, and different maintenance schemes can be made according to different corrosion types.
The corrosion rate algorithm formula in step four is as follows:
CR＝nKtn-1；
wherein:CRis the rate of corrosion; t is the burying time; k is a constant and has a value range of 0.1 to 0.5; n is an index and ranges from 0.3 to 1.2.
Fifthly, calculating results of the residual strength comprise a minimum wall thickness calculating result, a maximum allowable working pressure calculating result of the pipeline and a maximum allowable defect length calculating result; the minimum wall thickness calculation formula, the pipeline maximum allowable working pressure calculation formula and the maximum allowable defect length calculation formula are specifically as follows:
1) minimum wall thickness calculation formula:
circumferential minimum wall thickness formula:
axial minimum wall thickness formula:
minimum wall thickness formula:
wherein:
the minimum wall thickness in the circumferential direction is,
at least axial wall thickness, tminIs the minimum wall thickness; p is the internal pressure of the pipeline, D0 is the external diameter of the pipeline, EWIs the welding seam coefficient, S is the allowable stress of the material, beta is twice the pipe loose poise ratio, DmIs the median diameter of the pipe, F, MxIs the additional load pressure;
2) the maximum allowable working pressure calculation formula of the pipeline is as follows:
maximum allowable working pressure of axial pipeline:
maximum allowable working pressure of circumferential pipeline:
maximum allowable working pressure formula after corrosion pipeline degradation:
MAWPt＝max(PL，PC)；
wherein: p isLMaximum allowable working pressure, P, for axial pipesCMaximum allowable working pressure, t, for circumferential direction pipes1For additional load induced wall thickness increase, FCA is future corrosion margin, t is pipe nominal wall thickness, MAWPtMaximum allowable working pressure after degradation for the corroded pipe;
3) maximum allowable defect length calculation formula:
residual intensity coefficient:
residual wall thickness ratio:
axial maximum allowable defect length:
wherein: RSF is residual strength coefficient, PF is burst pressure, Rt is residual wall thickness ratio, L is axial maximum allowable defect length, RSFaFor allowable residual intensity factor, DiIs the inner diameter of the pipe.
The residual life prediction algorithm in the step five specifically calculates the following formula:
wherein: hmaxThe allowable limit corrosion depth; h0Initial etch depth; Δ VKIs the corrosion potential; t is the residual life; xi is the soil resistivity; ρ is the pipe density.
And predicting the residual life according to different corrosion types, so that the result of maintenance decision is closer to the reality.
In the second step, the digital twin model of the pipeline is defined as follows:
Pi.code(t)＝f{Bi，Ci，Si，Gi，Oi，Mi(t)，Di(t)，Ai(t)}；
wherein: p is an information value of the pipeline data model, and a characteristic value of the pipeline data model is a certain item of information value in an expression after an equation; code represents a pipeline code; b isiThe ith basic information value of the pipeline is obtained; ciThe ith construction information value of the pipeline is obtained; siThe ith protective layer information value of the pipeline is obtained; giThe soil information value of the position where the ith item of the pipeline is located; o isiConveying a crude oil information value for the ith item of the pipeline; mi(t) is the ith maintenance information value of the pipeline at t; di(t) is the ith detection information value of the pipeline at t; a. theiAnd (t) is the ith evaluation information value of the pipeline at t.
When the digital twin model of the pipeline is built, the multidimensional static and dynamic characteristics of the environment, medium, maintenance data and the like of the buried pipeline are utilized, and the fact that the digital twin model can be close to the maximum degree during simulation is guaranteed.
The pipeline digital twin model is constructed by means of real data of the pipeline, so that the simulation can be synchronized with the real situation when the simulation is carried out through the pipeline digital twin model, and the simulation data acquired through the pipeline digital twin model is more real and effective.
A corrosion prediction system of a buried crude oil pipeline comprises a data acquisition module, a database, a simulation module and an application module, wherein the data acquisition module, the simulation module and the application module are all connected with the database, the data acquisition module is used for acquiring pipeline data and environment data of the buried crude oil pipeline in real time, the simulation module is used for constructing a pipeline simulation model, the simulation module further comprises a data model unit and an algorithm model unit, the data model unit is connected with the database, the data model unit is used for providing a data model for the database, the algorithm model unit is connected with the application module, and the algorithm model unit is used for providing an algorithm required by calculation for the application module; the application module is used for analyzing the corrosion of the pipeline and making a maintenance decision, the maintenance decision is made through a maintenance decision unit in the application module, and the maintenance decision unit is connected with the database; the database is used for storing the data collected by the data collection module and also used for storing the data generated in the simulation module and the application module.
The database can classify the data acquired by the data acquisition module according to the data model provided by the data model unit, and can call the data as required during subsequent calculation without calculating all the data, so that the calculation workload is reduced.
The database comprises an entity data unit, a twin data unit, an evaluation data unit and an evaluation standard model unit, wherein the entity data unit is connected with a data acquisition module and is used for storing data acquired by the data acquisition module; the twin data unit is connected with the simulation module and is used for storing data obtained by simulation calculation of the simulation module; the evaluation data unit is connected with the application module and is used for storing data obtained by analysis and calculation of the application module; the evaluation standard model unit is connected with the application module and comprises an evaluation standard definition data model, an outer anticorrosive layer evaluation standard model, a soil corrosion evaluation standard model, an impressed current cathodic protection evaluation standard model, a corrosion damage evaluation standard model, a comprehensive evaluation standard model and a residual strength evaluation model.
The evaluation standard model unit has various evaluation standards, and can perform accurate evaluation during initial evaluation, primary evaluation and secondary evaluation, so that the reliability of evaluation results is improved.
The data model unit comprises a pipeline data model and an environment data model, the pipeline data model and the environment data model are both connected with a database, the environment data model comprises a soil model and a crude oil model, the soil model comprises a soil definition data model and a soil detection data model, the crude oil model comprises a crude oil definition model and a crude oil detection data model, and the pipeline data model comprises pipeline definition data, pipeline foundation data, pipeline construction data, pipeline protection data, pipeline detection definition data, pipeline external detection data and pipeline internal monitoring data.
The invention has the beneficial effects that:
the pipeline model is established by adopting a digital twinning technology, the model established by the digital twinning technology can simulate the running state and the corrosion state of a real pipeline, the established pipeline digital twinning model can be synchronous with the real condition under the support of real data, and the data obtained by simulation is more real and reliable. And a unified evaluation standard is formulated, so that the method is suitable for corrosion prediction of different buried pipelines, and application scenes are more changeable and richer. And the existing data of the pipeline is evaluated twice during maintenance decision, and different evaluation standards are adopted, so that the reliability of evaluation is improved. And when the maintenance scheme is customized, the scheme of directly maintaining excavated soil is avoided to the greatest extent, and the waste of manpower and material resources caused by the fact that the direct maintenance scheme is adopted due to small problems is avoided.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a functional block diagram of the present invention;
FIG. 3 is a schematic view of a maintenance decision flow according to the present invention;
wherein: 1. the system comprises a data acquisition system, 2, a database, 2-1, an entity data unit, 2-2, a twin data unit, 2-3, an evaluation data unit, 2-4, an evaluation standard model unit, 3, a simulation module, 3-1, a data model unit, 3-2, an algorithm model unit, 4, an application module, 4-1 and a maintenance decision unit.
Detailed Description
The invention is further described below with reference to the figures and examples.
The embodiment is as follows:
a method for predicting corrosion of a buried crude oil pipeline, as shown in fig. 1, comprises the following steps:
firstly, a data acquisition module 1 acquires pipeline data and environment data of a buried crude oil pipeline, a database 2 classifies the acquired pipeline data and environment data of the buried crude oil pipeline according to a data model, and the classified pipeline data and environment data are stored in an entity data unit 2-1 of the database 2;
step two, the simulation module 3 reads the pipeline data and the environment data to construct a pipeline digital twin model;
step three, the application module 4 carries out simulation calculation on the running state and the corrosion state of the buried crude oil pipeline through a pipeline digital twin model, and the running state data and the corrosion state data of the buried crude oil pipeline obtained through the simulation calculation are stored in a twin data unit 2-2 of the database 2;
fourthly, the application module 4 carries out simulation calculation on the internal and external corrosion rates of the pipeline through a pipeline digital twin model and a corrosion rate algorithm, then the application module 4 adjusts the running state data and the corrosion state data of the buried crude oil pipeline in the twin data unit 2-2 and the evaluation standard in the database 2, carries out primary evaluation on the buried crude oil pipeline, if the internal and external corrosion rates, the running state data and the corrosion state data do not accord with the evaluation standard, the application module 4 divides an inspection area according to the evaluation result, simulates the direct inspection process of the pipeline through the pipeline digital twin model, collects the data of the direct inspection process of the pipeline and stores the data in the evaluation data unit 2-3 of the database 2, and executes the fifth step; under other conditions, the direct pipeline inspection is not carried out, the date of the next evaluation is calculated according to the internal and external corrosion rates, the evaluation result, the internal and external corrosion rate data and the date data obtained through calculation are stored in an evaluation data unit 2-3 of the database 2, and the third step is executed when the date of the direct pipeline inspection is estimated;
fifthly, the application module 4 calculates the residual strength of the pipeline and predicts the residual life according to the data in the evaluation data unit 2-3, the residual strength algorithm and the residual life prediction algorithm, and the obtained residual strength calculation result and the residual life prediction result are stored in the evaluation data unit 2-3;
step six, the application module 4 calls the remaining strength threshold data and the remaining life threshold data of the pipeline maintenance in the database 2, then the application module 4 compares the calculation result of the remaining strength with the remaining strength threshold, meanwhile, the application module 4 compares the calculation result of the remaining life with the remaining life threshold, if the application module 4 detects that the calculation result of the remaining strength is lower than the remaining strength threshold or the calculation result of the remaining life is lower than the remaining life threshold, the application module 4 calls a maintenance decision unit 4-1, and the maintenance decision unit 4-1 determines a pipeline maintenance scheme according to the data in the evaluation data unit 2-3; if the application module 4 detects that the calculation result of the remaining strength is higher than the threshold value of the remaining strength and the calculation result of the remaining life is higher than the threshold value of the remaining life, the pipeline continues to be used.
In the sixth step, when the maintenance decision unit 4-1 determines the pipeline maintenance scheme according to the data in the evaluation data unit 2-3, a maintenance decision substep is executed, and as shown in fig. 3, the maintenance decision substep specifically includes the following steps:
1.1, a maintenance decision unit 4-1 determines the corrosion type of the pipeline according to the data in the evaluation data unit 2-3, judges whether to perform primary evaluation according to the corrosion type of the pipeline, and executes a step 1.2 if the primary evaluation is performed; if the first-level evaluation is not carried out, executing the step 1.3;
1.2, calling pipeline defect data and pipeline calculation data, calling a primary evaluation standard in the database 2, performing primary evaluation according to the pipeline defect data, the pipeline calculation data and the primary evaluation standard, judging whether a primary evaluation result is received, and if the primary evaluation result is received, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a primary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the voltage reduction is not carried out, the step 1.3 is executed;
1.3, judging whether to perform secondary evaluation, and if so, executing the step 1.4; if the secondary evaluation is not carried out, directly maintaining the pipeline;
1.4 calling a primary evaluation standard in the database 2, performing secondary evaluation according to the pipeline defect data, the pipeline calculation data and the secondary evaluation standard, judging whether to receive a secondary evaluation result, and if so, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a secondary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the pressure reduction is not carried out, the pipeline is directly maintained.
The corrosion type comprises a uniform corrosion type, a local corrosion type and a pitting corrosion type; the pipeline defect data is the data of the corrosion state in the twin data unit 2-2; the pipeline calculation data are the calculation results of the internal and external corrosion rates, the calculation results of the residual strength and the prediction results of the residual service life in the evaluation data unit 2-3.
The corrosion rate algorithm formula in the fourth step is as follows:
CR＝nKtn-1；
wherein: cRIs the corrosion rate; t is the burying time; k is a constant and has a value range of 0.1 to 0.5; n is an index and ranges from 0.3 to 1.2.
The calculation result of the residual strength in the step five comprises a calculation result of the minimum wall thickness, a calculation result of the maximum allowable working pressure of the pipeline and a calculation result of the maximum allowable defect length; the minimum wall thickness calculation formula, the pipeline maximum allowable working pressure calculation formula and the maximum allowable defect length calculation formula are specifically as follows:
1) minimum wall thickness calculation formula:
circumferential minimum wall thickness formula:
axial minimum wall thickness formula:
minimum wall thickness formula:
wherein:
the minimum wall thickness in the circumferential direction is,
axial minimum wall thickness, tminIs the minimum wall thickness; p is the internal pressure of pipeline, D0 is the external diameter of pipeline, EWIs the welding seam coefficient, S is the allowable stress of the material, beta is twice the pipe loose poise ratio, DmIs the middle diameter of the pipe, F, MxIs the additional load pressure;
2) the maximum allowable working pressure calculation formula of the pipeline is as follows:
maximum allowable working pressure of axial pipeline:
maximum allowable working pressure of circumferential pipeline:
maximum allowable working pressure formula after corrosion pipeline degradation:
MAWPt＝max(PL，PC)；
wherein: p isLMaximum allowable working pressure, P, for axial pipesCMaximum allowable working pressure, t, for circumferential direction of the pipeslFor the increase in wall thickness caused by additional load, FCA isFuture corrosion allowance, t is the nominal wall thickness of the pipeline, MAWPtMaximum allowable working pressure after degradation of the corroded pipeline;
3) maximum allowable defect length calculation formula:
residual intensity coefficient:
residual wall thickness ratio:
axial maximum allowable defect length:
wherein: RSF is residual strength coefficient, PF is burst pressure, Rt is residual wall thickness ratio, L is axial maximum allowable defect length, RSFaFor allowable residual intensity factor, DiIs the inner diameter of the pipe.
The residual life prediction algorithm in the fifth step specifically calculates the formula as follows:
wherein: hmaxThe allowable limit corrosion depth; h0Initial etch depth; Δ VKIs the corrosion potential; t is the remaining life; xi is the soil resistivity; ρ is the pipe density.
In the second step, the digital twin model of the pipeline is defined as follows:
Pi.code(t)＝f{Bi，Ci，Si，Gi，Oi，Mi(t)，Di(t)，Ai(t)}；
wherein: p is an information value of the pipeline data model, and a characteristic value of the pipeline data model is a certain item of information value in an expression after an equation; code represents a pipeline code; biThe ith basic information value of the pipeline is obtained; ciThe ith construction information value of the pipeline is obtained; siThe ith protective layer information value of the pipeline is obtained; giThe soil information value of the ith position of the pipeline is obtained; o isiConveying the crude oil information value for the ith item of the pipeline; m is a group ofi(t) is the ith maintenance information value of the pipeline at t; di(t) is the ith detection information value of the pipeline at t; a. theiAnd (t) is the ith evaluation information value of the pipeline at t.
A corrosion prediction system of a buried crude oil pipeline is shown in figure 2 and comprises a data acquisition module 1, a database 2, a simulation module 3 and an application module 4, the data acquisition module 1, the simulation module 3 and the application module 4 are all connected with the database 2, the data acquisition module 1 is used for acquiring pipeline data and environmental data of buried crude oil pipelines in real time, the simulation module 3 is used for constructing a pipeline simulation model, the simulation module 3 further comprises a data model unit 3-1 and an algorithm model unit 3-2, the data model unit 3-1 is connected with the database 2, the data model unit 3-1 is used for providing a data model for the database 2, the algorithm model unit 3-2 is connected with the application module 4, and the algorithm model unit 3-2 is used for providing an algorithm required by calculation for the application module 4; the application module 4 is used for analyzing pipeline corrosion and making maintenance decisions, the maintenance decisions are made through a maintenance decision unit 4-1 in the application module 4, and the maintenance decision unit 4-1 is connected with the database 2; the database 2 is used for storing the data collected by the data collection module 1, and the database 2 is also used for storing the data generated in the simulation module 3 and the application module 4.
The database 2 comprises an entity data unit 2-1, a twin data unit 2-2, an evaluation data unit 2-3 and an evaluation standard model unit 2-4, the entity data unit 2-1 is connected with the data acquisition module 1, and the entity data unit 2-1 is used for storing data acquired by the data acquisition module 1; the twin data unit 2-2 is connected with the simulation module 3, and the twin data unit 2-2 is used for storing data obtained by simulation calculation of the simulation module 3; the evaluation data unit 2-3 is connected with the application module 4, and the evaluation data unit 2-3 is used for storing data obtained by analysis and calculation of the application module 4; the evaluation standard model unit 2-4 is connected with the application module 4, and the evaluation standard model unit 2-4 comprises an evaluation standard definition data model, an outer anticorrosive layer evaluation standard model, a soil corrosion evaluation standard model, an impressed current cathodic protection evaluation standard model, a corrosion damage evaluation standard model, a comprehensive evaluation standard model and a residual strength evaluation model.
The data model unit 3-1 comprises a pipeline data model and an environment data model, the pipeline data model and the environment data model are both connected with the database 2, the environment data model comprises a soil model and a crude oil model, the soil model comprises a soil definition data model and a soil detection data model, the crude oil model comprises a crude oil definition model and a crude oil detection data model, and the pipeline data model comprises pipeline definition data, pipeline foundation data, pipeline construction data, pipeline protection data, pipeline detection definition data, pipeline external detection data and pipeline internal monitoring data.
Because the digital twin model of the pipeline is established by depending on the real data of the pipeline, the provided pipeline detection definition data and the pipeline basic data need to be detailed as much as possible, for example, the pipeline detection definition data specifically comprises a series of data generated during detection, such as a pipeline external detection method, a pipeline detection starting point, a pipeline detection stopping point, a pipeline detection length, the number of corrosion points, a pipeline operation temperature, a pipeline operation pressure, a pipeline operation flow rate, a detection starting time, a detection ending time, a detector, a creator, a modification time and the like, and the pipeline basic data comprises data such as pipeline material, nominal diameter, wall thickness, low temperature allowed to work, high temperature allowed to work, pressure level, design life, crude oil identification, operation pressure, operation temperature, thickness measuring points, a commissioning date, construction time, a creator, modification time and the like, the detailed data ensures that the data generated when the established pipeline digital twin model is used for simulation is real and reliable.
Since the external causes causing the corrosion of the pipeline are mostly factors such as the water content, the salt content and the resistivity of soil, when the evaluation standards are compared, a very important reference factor is a soil corrosion evaluation standard, and the soil corrosion evaluation standard specifically comprises an evaluation standard definition mark, a soil corrosion index type, a corrosion high-level evaluation, a corrosion medium-level evaluation, a corrosion low-level evaluation and a corrosion low-level evaluation.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (10)
1. A corrosion prediction method for a buried crude oil pipeline is characterized by comprising the following steps:
the method comprises the steps that firstly, a data acquisition module (1) acquires pipeline data and environment data of a buried crude oil pipeline, a database (2) classifies the acquired pipeline data and environment data of the buried crude oil pipeline according to a data model, and the classified pipeline data and environment data are stored in an entity data unit (2-1) of the database (2);
reading the pipeline data and the environment data by the simulation module (3) and constructing a pipeline digital twin model;
performing simulation calculation on the running state and the corrosion state of the buried crude oil pipeline by the application module (4) through a pipeline digital twin model, and storing the running state data and the corrosion state data of the buried crude oil pipeline obtained through the simulation calculation in a twin data unit (2-2) of the database (2);
fourthly, the application module (4) carries out simulation calculation on the internal and external corrosion rates of the pipeline through a pipeline digital twinning model and a corrosion rate algorithm, then the application module (4) adjusts the running state data and the corrosion state data of the buried crude oil pipeline in the twinning data unit (2-2) and the evaluation standard in the database (2), carries out primary evaluation on the buried crude oil pipeline, if the internal and external corrosion rates, the running state data and the corrosion state data do not accord with the evaluation standard, the application module (4) divides an inspection area according to the evaluation result, simulates the direct inspection process of the pipeline through the pipeline digital twinning model, collects the data of the direct inspection process of the pipeline and stores the data in the evaluation data unit (2-3) of the database (2), and executes the fifth step; under other conditions, the direct pipeline inspection is not carried out, the date of the next evaluation is calculated according to the internal and external corrosion rates, the evaluation result, the internal and external corrosion rate data and the date data obtained through calculation are stored in an evaluation data unit (2-3) of the database (2), and the third step is executed when the date of the estimated direct pipeline inspection is reached;
fifthly, the application module (4) calculates the residual strength of the pipeline and predicts the residual life according to the data in the evaluation data unit (2-3), the residual strength algorithm and the residual life prediction algorithm, and the obtained residual strength calculation result and the residual life prediction result are stored in the evaluation data unit (2-3);
step six, the application module (4) calls the residual intensity threshold value data and the residual life threshold value data of the pipeline maintenance in the database (2), then the application module (4) compares the residual intensity calculation result with the residual intensity threshold value, meanwhile, the application module (4) compares the residual life calculation result with the residual life threshold value, if the application module (4) detects that the residual intensity calculation result is lower than the residual intensity threshold value or the residual life calculation result is lower than the residual life threshold value, the application module (4) calls a maintenance decision unit (4-1), and the maintenance decision unit (4-1) decides a pipeline maintenance scheme according to the data in the evaluation data unit (2-3); if the application module (4) detects that the calculation result of the residual strength is higher than the threshold value of the residual strength and the calculation result of the residual service life is higher than the threshold value of the residual service life, the pipeline continues to be used.
2. The method for predicting corrosion of a buried crude oil pipeline according to claim 1, wherein in the sixth step, the maintenance decision unit (4-1) executes a maintenance decision sub-step when deciding a pipeline maintenance scheme according to data in the evaluation data unit (2-3), and the maintenance decision sub-step specifically comprises the following steps:
1.1, a maintenance decision unit (4-1) determines the corrosion type of the pipeline according to the data in the evaluation data unit (2-3), judges whether to perform primary evaluation according to the corrosion type of the pipeline, and executes the step 1.2 if the primary evaluation is performed; if the first-level evaluation is not carried out, executing the step 1.3;
1.2, calling pipeline defect data and pipeline calculation data, calling a primary evaluation standard in a database (2), performing primary evaluation according to the pipeline defect data, the pipeline calculation data and the primary evaluation standard, judging whether a primary evaluation result is received, and if the primary evaluation result is received, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a primary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the voltage reduction is not carried out, the step 1.3 is executed;
1.3, judging whether secondary evaluation is carried out or not, and if the secondary evaluation is carried out, executing a step 1.4; if the secondary evaluation is not carried out, directly maintaining the pipeline;
1.4 calling a primary evaluation standard in the database (2), performing secondary evaluation according to the pipeline defect data, the pipeline calculation data and the secondary evaluation standard, judging whether to receive a secondary evaluation result, and if so, continuing to use the pipeline; if not, further judging whether the pressure reduction is carried out, if so, calculating the maximum allowable working pressure after the corrosion pipeline is degraded according to a secondary evaluation criterion, and carrying out pressure reduction treatment according to the calculated maximum allowable working pressure after the corrosion pipeline is degraded; if the pressure reduction is not carried out, the pipeline is directly maintained.
3. The method of predicting corrosion of a buried crude oil pipeline according to claim 2, wherein the corrosion types include a homogeneous corrosion type, a localized corrosion type, and a pitting corrosion type; the pipeline defect data is corrosion state data in the twin data unit (2-2); the pipeline calculation data are the calculation result of the internal and external corrosion rates in the evaluation data unit (2-3), the calculation result of the residual strength and the prediction result of the residual life.
4. A method of predicting corrosion of a buried crude oil pipeline according to claim 1, wherein in step four the corrosion rate algorithm is as follows:
CR＝nKtn-1；
wherein: cRIs the corrosion rate; t is the burying time; k is a constant and has a value range of 0.1 to 0.5; n is an index and ranges from 0.3 to 1.2.
5. The method of claim 1, wherein the calculation of residual strength in step five includes a minimum wall thickness calculation, a maximum allowable working pressure calculation for the pipeline, and a maximum allowable defect length calculation; the minimum wall thickness calculation formula, the pipeline maximum allowable working pressure calculation formula and the maximum allowable defect length calculation formula are specifically as follows:
1) minimum wall thickness calculation formula:
circumferential minimum wall thickness formula:
axial minimum wall thickness formula:
minimum wall thickness formula:
wherein:
is the circumferential minimum wall thickness,
at least axial wall thickness, tminIs the minimum wall thickness; p is the internal pressure of pipeline，D0Is the outside diameter of the pipe, EWIs the welding seam coefficient, S is the allowable stress of the material, beta is twice the pipe loose poise ratio, DmIs the middle diameter of the pipeline, MxIs the additional load pressure;
2) the maximum allowable working pressure calculation formula of the pipeline is as follows:
axial conduit maximum allowable working pressure:
maximum allowable working pressure of the circumferential pipeline:
maximum allowable working pressure formula after corrosion pipeline degradation:
MAWPt＝max(PL，PC)；
wherein: pLMaximum allowable working pressure, P, for axial conduitsCMaximum allowable working pressure, t, for circumferential pipes1For additional load induced wall thickness increase, FCA is future corrosion margin, t is pipe nominal wall thickness, MAWPtMaximum allowable working pressure after degradation for the corroded pipe;
3) maximum allowable defect length calculation formula:
residual intensity coefficient:
residual wall thickness ratio:
axial maximum allowable defect length:
wherein: RSF is the residual intensity factor, PFTo the burst pressure, RtIs the residual wall thickness ratio, L is the axially allowable maximum defect length, RSFaFor allowable residual intensity factor, DiIs the inner diameter of the pipe, tminIs the minimum wall thickness, n is an index, and the value range is 0.3 to 1.2, and K is a constant, and the value range is 0.1 to 0.5.
6. The method for predicting corrosion of a buried crude oil pipeline according to claim 1, wherein the residual life prediction algorithm in the fifth step specifically calculates the formula as follows:
wherein: hmaxThe allowable limit corrosion depth; h0Initial etch depth; Δ VKIs the corrosion potential; t is the remaining life; xi is the soil resistivity; ρ is the pipe density.
7. A method of predicting corrosion of a buried crude oil pipeline according to claim 1, wherein in step two the pipeline digital twin model is defined as:
Pi，code(t)＝f{Bi，Ci，Si，Gi，Oi，Mi(t)，Di(t)，Ai(t)}；
wherein: p is an information value of the pipeline data model, and a characteristic value of the pipeline data model is a certain item of information value in an expression after an equation; code represents a pipeline code; b isiThe ith basic information value of the pipeline is obtained;
Cithe ith construction information value of the pipeline is obtained; siThe ith protective layer information value of the pipeline is obtained; giThe soil information value of the ith position of the pipeline is obtained; o isiConveying the crude oil information value for the ith item of the pipeline; mi(t) is the ith maintenance information value of the pipeline at t, and t is the buried time; di(t) the ith item of detection information of the pipeline at tInformation value; a. theiAnd (t) is the ith evaluation information value of the pipeline at t, and i is any information value number in the pipeline data model.
8. The corrosion prediction system for the buried crude oil pipeline is characterized by comprising a data acquisition module (1), a database (2), a simulation module (3) and an application module (4), wherein the data acquisition module (1), the simulation module (3) and the application module (4) are all connected with the database (2), the data acquisition module (1) is used for acquiring pipeline data and environment data of the buried crude oil pipeline in real time and storing the pipeline data and the environment data into the database, the simulation module (3) is used for constructing a pipeline digital twin model according to the pipeline data and the environment data acquired by the data acquisition module (1) stored in the database (2), the simulation module (3) further comprises a data model unit (3-1) and an algorithm model unit (3-2), the data model unit (3-1) is connected with the database (2), the data model unit (3-1) is used for providing a data model for the database (2), the algorithm model unit (3-2) is connected with the application module (4), and the algorithm model unit (3-2) is used for providing a needed algorithm for the application module (4) when simulation calculation of the internal and external corrosion rates of the pipeline is carried out; the application module (4) is used for performing pipeline corrosion analysis and maintenance decision, the application module (4) is also used for performing pipeline residual strength calculation and residual life analysis, the maintenance decision is performed through a maintenance decision unit (4-1) in the application module (4), the maintenance decision unit (4-1) is connected with the database (2), and the maintenance decision unit (4-1) determines a pipeline maintenance scheme according to data generated when the application module (4) stored in the database (2) evaluates the buried crude oil pipeline; the database (2) is used for storing pipeline data and environment data collected by the data collection module (1), the database (2) is also used for storing running state data and corrosion state data generated by the simulation module (3), and data generated by the application module (4) when the buried crude oil pipeline is evaluated.
9. The corrosion prediction system of a buried crude oil pipeline according to claim 8, characterized in that the database (2) comprises an entity data unit (2-1), a twin data unit (2-2), an evaluation data unit (2-3) and an evaluation standard model unit (2-4), the entity data unit (2-1) is connected with the data acquisition module (1), and the entity data unit (2-1) is used for storing the data acquired by the data acquisition module (1); the twin data unit (2-2) is connected with the simulation module (3), and the twin data unit (2-2) is used for storing data obtained by simulation calculation of the simulation module (3); the evaluation data unit (2-3) is connected with an application module (4), and the evaluation data unit (2-3) is used for storing data obtained by analysis and calculation of the application module (4); the evaluation standard model unit (2-4) is connected with the application module (4), and the evaluation standard model unit (2-4) comprises an evaluation standard definition data model, an outer anticorrosive layer evaluation standard model, a soil corrosion evaluation standard model, an impressed current cathodic protection evaluation standard model, a corrosion damage evaluation standard model, a comprehensive evaluation standard model and a residual strength evaluation model.
10. The corrosion prediction system for a buried crude oil pipeline according to claim 8, characterized in that the data model unit (3-1) comprises a pipeline data model and an environment data model, both of which are connected to the database (2), the environment data model comprises a soil model and a crude oil model, the soil model comprises a soil definition data model and a soil detection data model, the crude oil model comprises a crude oil definition model and a crude oil detection data model, and the pipeline data model comprises pipeline definition data, pipeline foundation data, pipeline construction data, pipeline protection data, pipeline detection definition data, pipeline outside detection data and pipeline inside monitoring data.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101915728A (en) * | 2010-07-27 | 2010-12-15 | 沈阳建筑大学 | Testing method for predicting residual service life of buried metal water supply pipeline |
| CN106338006A (en) * | 2016-10-17 | 2017-01-18 | 浙江中控软件技术有限公司 | Monitor method and system for steam pipe system |
| CN106779152A (en) * | 2016-11-21 | 2017-05-31 | 天津赛文海工科技有限公司 | Oil-gas pipeline Integrity Management platform on line |
| CN107798392A (en) * | 2016-08-31 | 2018-03-13 | 中国石油化工股份有限公司 | The determination method and apparatus in the working service time limit of pipeline corrosion default |
| CN108345707A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | The pipeline corrosion default plan response time based on probability determines method and device |
| CN109308556A (en) * | 2017-07-28 | 2019-02-05 | 中国石油化工股份有限公司 | A kind of pipeline evaluation method and device based on interior detection data |
| CN110298540A (en) * | 2019-05-22 | 2019-10-01 | 中国石油天然气集团有限公司 | A kind of oil gas field surface duct internal corrosion risk evaluating method |
| CN110389209A (en) * | 2018-04-19 | 2019-10-29 | 中国石油化工股份有限公司 | A kind of evaluation method of buried pipeline soil corrosion characteristic |
| JP2020143970A (en) * | 2019-03-05 | 2020-09-10 | 株式会社日立製作所 | Corrosion diagnostic system |
| CN111667132A (en) * | 2019-03-07 | 2020-09-15 | 中国石油化工股份有限公司 | Intelligent analysis decision-making system for pipeline integrity |
| CN112001104A (en) * | 2020-08-12 | 2020-11-27 | 郑州大学 | Buried pipeline service performance evaluation method, computer readable medium and equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9933353B2 (en) * | 2014-02-13 | 2018-04-03 | Infosys Limited | Method for assessing corroded pipeline defect growth from partial inspection data and devices thereof |
-
2020
- 2020-12-29 CN CN202011589886.XA patent/CN112883538B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101915728A (en) * | 2010-07-27 | 2010-12-15 | 沈阳建筑大学 | Testing method for predicting residual service life of buried metal water supply pipeline |
| CN107798392A (en) * | 2016-08-31 | 2018-03-13 | 中国石油化工股份有限公司 | The determination method and apparatus in the working service time limit of pipeline corrosion default |
| CN106338006A (en) * | 2016-10-17 | 2017-01-18 | 浙江中控软件技术有限公司 | Monitor method and system for steam pipe system |
| CN106779152A (en) * | 2016-11-21 | 2017-05-31 | 天津赛文海工科技有限公司 | Oil-gas pipeline Integrity Management platform on line |
| CN108345707A (en) * | 2017-01-24 | 2018-07-31 | 中国石油化工股份有限公司 | The pipeline corrosion default plan response time based on probability determines method and device |
| CN109308556A (en) * | 2017-07-28 | 2019-02-05 | 中国石油化工股份有限公司 | A kind of pipeline evaluation method and device based on interior detection data |
| CN110389209A (en) * | 2018-04-19 | 2019-10-29 | 中国石油化工股份有限公司 | A kind of evaluation method of buried pipeline soil corrosion characteristic |
| JP2020143970A (en) * | 2019-03-05 | 2020-09-10 | 株式会社日立製作所 | Corrosion diagnostic system |
| CN111667132A (en) * | 2019-03-07 | 2020-09-15 | 中国石油化工股份有限公司 | Intelligent analysis decision-making system for pipeline integrity |
| CN110298540A (en) * | 2019-05-22 | 2019-10-01 | 中国石油天然气集团有限公司 | A kind of oil gas field surface duct internal corrosion risk evaluating method |
| CN112001104A (en) * | 2020-08-12 | 2020-11-27 | 郑州大学 | Buried pipeline service performance evaluation method, computer readable medium and equipment |
Non-Patent Citations (1)
| Title |
|---|
| "油气输送腐蚀管道剩余寿命预测研究";曹乃宁;《中国优秀硕士学位论文全文数据库 工程科技Ⅰ辑》;20161215(第12期);第B019-274页 * |
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