Gathering and transportation pipeline integrity evaluation method and device, electronic equipment and storage medium

Abstract

The invention relates to a gathering and transportation pipeline integrity evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the analysis of a first data index of an undetected gathering and transportation pipeline through a pre-established risk evaluation model, and obtaining a safety risk score of the undetected gathering and transportation pipeline; analyzing the second data index and the detection data of the detected gathering and transportation pipeline by using a pre-established safety coefficient calculation model to obtain a safety coefficient of the detected gathering and transportation pipeline; and determining an integrity evaluation result of the whole gathering and transportation pipeline based on the safety risk score of the undetected gathering and transportation pipeline and the safety coefficient of the detected gathering and transportation pipeline. According to the method, different evaluation models are adopted for analysis and evaluation for different pipelines, so that the gathering and transportation pipelines in two different states, namely the detected state and the undetected state, on the whole gathering and transportation pipeline can be effectively and systematically evaluated, the safety states of the two gathering and transportation pipelines are better reflected, and the evaluation accuracy is improved.

Description

Technical Field

[0001] The invention relates to a gathering and transportation pipeline integrity evaluation method, device, electronic equipment and storage medium. Background Art

[0002] Existing integrity evaluation methods mainly include online detection, pressure test evaluation, direct evaluation and finite element simulation evaluation; for long-distance pipelines with large diameters, high pressures and equipped with pig transmitters and receivers, online detection such as magnetic leakage internal detection is widely used and has high accuracy. However, since most gathering and transportation pipelines have small diameters, many specifications, low pressures and most do not have pig transmitters and receivers, internal detection is difficult to apply; pressure test evaluation is destructive, and production needs to be stopped and pipelines need to be cleaned before the test; direct evaluation includes direct evaluation of internal corrosion and direct evaluation of external corrosion. Among them, direct evaluation of internal corrosion is aimed at dry gas transmission pipelines where liquid accumulation may occur. It identifies the pipe sections where liquid accumulation may occur through multiphase flow characteristics, and the evaluation results are difficult to fully cover pipeline defects. Direct evaluation of external corrosion uses external detection technology to find the damaged points of the anti-corrosion layer and conduct excavation inspection. The internal corrosion of gathering and transportation pipelines is serious, and the internal corrosion defects cannot be covered only by the damaged points of the anti-corrosion layer. Finite element simulation evaluation is to evaluate individual defect points. The evaluation process is cumbersome and the calculation is large, which is not suitable for large-scale pipeline integrity evaluation. Currently, existing pipeline integrity evaluation technologies are widely used in long-distance pipelines, but the existing evaluation technologies rely on mechanical characteristics such as operating pressure and cannot be applied to the integrity evaluation of gathering and transportation pipelines. Summary of the invention

[0003] In order to enrich the integrity evaluation methods in the prior art and provide a method suitable for gathering and transportation pipeline integrity evaluation, the present invention proposes a gathering and transportation pipeline integrity evaluation method, device, electronic device and storage medium. The technical solution proposed by the present invention is as follows:

[0004] In a first aspect, the present invention provides a method for evaluating the integrity of a gathering and transportation pipeline, comprising:

[0005] Obtaining a first data indicator of an undetected gathering and transportation pipeline;

[0006] Analyze the first data indicator using a pre-established risk assessment model to obtain a safety risk score for the uninspected gathering and transportation pipeline;

[0007] Acquire the second data index and detection data of the detected gathering and transportation pipeline;

[0008] Analyzing the second data indicator and the detection data using a pre-established safety factor calculation model to obtain a safety factor of the detected gathering and transportation pipeline;

[0009] Wherein, the risk assessment model is obtained by pre-training based on the first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline;

[0010] The safety factor calculation model is obtained by extracting the second key risk factor from the historical second data indicator and the historical detection data in advance to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output;

[0011] Based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline, an integrity evaluation result of the overall gathering and transportation pipeline is determined.

[0012] In one or some embodiments, the risk assessment model is trained in the following manner:

[0013] Extracting the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator;

[0014] constructing a risk factor hierarchy model using the first key risk factor, and constructing a judgment matrix according to the risk factor hierarchy model;

[0015] Determine a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis;

[0016] The risk assessment model is established according to the first indicator weight and a pre-established first risk score system.

[0017] In one or some embodiments, the use of a pre-established risk assessment model to analyze the first data indicator to obtain a safety risk score of an uninspected gathering and transportation pipeline includes:

[0018] Using the first data indicator as input of the risk assessment model, scoring the first data indicator according to the first risk scoring system to obtain a corresponding first risk assignment;

[0019] A safety risk score of the uninspected gathering and transportation pipeline is determined according to a first indicator weight and a first risk assignment corresponding to the first data indicator.

[0020] In one or some embodiments, extracting the first key risk factor that can characterize the safety status of the pipeline from the historical first data indicator includes:

[0021] Selecting a first decision indicator that can characterize the safety status of the pipeline from the historical first data indicators;

[0022] The mutual information method is used to respectively determine the mutual information value of each historical first data indicator and the first decision indicator, and the first key risk factor is extracted from the historical first data indicator according to the mutual information value.

[0023] In one or some embodiments, the first decision metric includes a first estimated maintenance factor;

[0024] The method of using the mutual information method to respectively determine the mutual information value between each historical first data indicator and the first decision indicator, and extracting the first key risk factor from the historical first data indicator according to the mutual information value, includes:

[0025] The mutual information method is used to respectively determine the mutual information value of each historical first data indicator and the first estimated maintenance coefficient, and the first key risk factor is extracted from the historical first data indicator according to the mutual information value.

[0026] In one or some embodiments, the judgment matrix is ​​constructed in the following manner:

[0027] If there are risk factors in the risk factor hierarchy model that cannot be analyzed, the relative importance of the risk factors that cannot be analyzed and other risk factors is determined according to the actual pipeline situation or expert scores to construct the judgment matrix.

[0028] In one or some embodiments, the safety factor calculation model is trained in the following manner:

[0029] Extracting the second key risk factor from the historical second data indicators and the historical detection data by using the hierarchical analysis method, and determining the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance;

[0030] Using the second indicator weight and a pre-established second risk score system, the initial safety factor is corrected to obtain the corrected safety factor;

[0031] The safety factor calculation model is obtained by taking the historical second data indicator and the historical detection data as input and taking the corrected safety factor as output for training.

[0032] In one or some embodiments, the method further comprises:

[0033] Determine a second estimated maintenance factor of a defective point on the inspected gathering and transportation pipeline according to the safety factor of the inspected gathering and transportation pipeline;

[0034] Based on the second estimated maintenance coefficient, the defect point is evaluated using a preset evaluation standard to obtain an evaluation result.

[0035] In one or some embodiments, the method further includes: calculating the residual strength of the defect point by the preset evaluation standard; and evaluating the defect point based on the second estimated maintenance coefficient by using the preset evaluation standard to obtain an evaluation result, including:

[0036] An evaluation result of the defect point is determined according to the remaining strength and the second estimated maintenance coefficient.

[0037] In one or some embodiments, obtaining the first data indicator of the undetected gathering and transportation pipeline includes:

[0038] Acquiring the original first data indicator of the undetected gathering and transportation pipeline;

[0039] The original first data index is preprocessed to obtain the first data index that can be used for analysis and calculation and includes the entity data, environmental data and operation data of the undetected gathering and transportation pipeline.

[0040] In one or some embodiments, obtaining the second data indicator and detection data of the detected gathering and transportation pipeline includes:

[0041] Acquiring the original second data index and original detection data of the detected gathering and transportation pipeline;

[0042] The original second data index and the original detection data are preprocessed to obtain the second data index that can be used for analysis and calculation and includes the entity data, environmental data and operation data of the detected gathering and transportation pipeline.

[0043] In a second aspect, the present invention provides a gathering and transportation pipeline integrity assessment device, comprising:

[0044] A first indicator acquisition module, used to acquire a first data indicator of an undetected gathering and transportation pipeline;

[0045] A first evaluation module is used to analyze the first data indicator using a pre-established risk assessment model to obtain a safety risk score of an uninspected gathering and transportation pipeline;

[0046] A second indicator acquisition module, used to acquire the second data indicator and detection data of the detected gathering and transportation pipeline;

[0047] The second evaluation module is used to analyze the second data indicator and the detection data using a pre-established safety factor calculation model to obtain the safety factor of the detected gathering and transportation pipeline; wherein the risk assessment model is pre-trained based on the first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline; the safety factor calculation model is pre-extracted from the historical second data indicator and the historical detection data to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output;

[0048] The calculation module is used to determine the integrity evaluation result of the entire gathering and transportation pipeline based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline.

[0049] In one or some embodiments, the first indicator acquisition module includes:

[0050] A first data acquisition unit, used to acquire an original first data indicator of the undetected gathering and transportation pipeline;

[0051] The first preprocessing unit is used to preprocess the original first data indicators to obtain the first data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the undetected gathering and transportation pipeline.

[0052] In one or some embodiments, the second indicator acquisition module includes:

[0053] A second data acquisition unit, used to acquire the original second data index and original detection data of the detected gathering and transportation pipeline;

[0054] The second preprocessing unit is used to preprocess the original second data indicators and the original detection data to obtain the second data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the detected gathering and transportation pipeline.

[0055] In one or some embodiments, the first evaluation module includes:

[0056] A risk factor extraction module, used to extract the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator;

[0057] A judgment matrix construction module, used to construct a risk factor hierarchy model using the first key risk factor, and to construct a judgment matrix according to the risk factor hierarchy model;

[0058] An indicator weight calculation module, used to determine a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis;

[0059] The first model building module is used to establish the risk assessment model according to the first indicator weight and a pre-established first risk score system.

[0060] In one or some embodiments, the second evaluation module includes:

[0061] A risk factor weight value calculation module, used to extract the second key risk factor from the historical second data indicator and the historical detection data by using the hierarchical analysis method, and determine the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance;

[0062] an initial safety factor correction module, configured to correct the initial safety factor by using the second indicator weight and a pre-established second risk score system to obtain the corrected safety factor;

[0063] The second model building module is used to take the historical second data indicator and the historical detection data as input and the corrected safety factor as output for training to obtain the safety factor calculation model.

[0064] In one or some embodiments, the device further comprises:

[0065] A second estimated maintenance factor determination module, used to determine a second estimated maintenance factor of a defect point on the detected gathering and transportation pipeline according to the safety factor of the detected gathering and transportation pipeline;

[0066] A re-evaluation module is used to evaluate the defect point based on the second estimated maintenance coefficient using a preset evaluation standard to obtain an evaluation result.

[0067] In a third aspect, the present invention provides an electronic device, comprising:

[0068] Memory for storing computer programs;

[0069] A processor is used to execute the computer program to implement the gathering and transportation pipeline integrity assessment method as described in the first aspect.

[0070] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the gathering and transportation pipeline integrity assessment method as described in the first aspect.

[0071] Based on the above technical solution, the beneficial effects of the present invention compared with the prior art are as follows:

[0072] The gathering and transportation pipeline integrity evaluation method provided by the present invention obtains the first data index of the undetected gathering and transportation pipeline, analyzes the first data index using a pre-established risk assessment model, and obtains the safety risk score of the undetected gathering and transportation pipeline; obtains the second data index and the detection data of the detected gathering and transportation pipeline, analyzes the second data index and the detection data using a pre-established safety factor calculation model, and obtains the safety factor of the detected gathering and transportation pipeline. The safety factor calculation model can be used to comprehensively analyze the second data index and the detection data of the detected gathering and transportation pipeline to obtain the safety factor of the detected gathering and transportation pipeline, and the safety factor is used as the safety evaluation of the detected gathering and transportation pipeline to more accurately reflect the safety status of the detected gathering and transportation pipeline. Different evaluation models are adopted for different pipeline data, that is, for the undetected gathering and transportation pipeline and the detected gathering and transportation pipeline to analyze and evaluate, so that the gathering and transportation pipelines in the two different states of detected and undetected on the entire gathering and transportation pipeline can be effectively and systematically evaluated, better reflecting the safety status of the two gathering and transportation pipelines, and improving the accuracy of the evaluation. The risk assessment model is pre-trained based on the first key risk factor extracted from the historical first data indicators of the undetected gathering and transportation pipelines. The data of the oilfield gathering and transportation pipelines themselves are analyzed to extract the first key risk factor related to the integrity of the pipeline, making the risk factors clearer. The safety factor calculation model is pre-extracted from the historical second data indicators and historical detection data to correct the initial safety factor. After obtaining the corrected safety factor, it is trained with the historical second data indicators and historical detection data as input and the corrected safety factor as output. The safety factor is corrected based on the second key risk factor. The corrected safety factor based on risk assessment can better reflect the safety status of the defect, thereby more accurately evaluating the integrity of the gathering and transportation pipelines.

[0073] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0074] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0076] Figure 1 It is a flow chart of a gathering and transportation pipeline integrity assessment method provided by an embodiment of the present invention;

[0077] Figure 2 is a flow chart of the risk assessment model training process provided by an embodiment of the present invention;

[0078] Figure 3 It is a flowchart of the safety factor calculation model training process provided by an embodiment of the present invention;

[0079] Figure 4 It is a structural schematic diagram of a gathering and transportation pipeline integrity assessment device provided by an embodiment of the present invention;

[0080] Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0081] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0082] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0083] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0084] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0085] The inventor found in his work that the existing integrity evaluation methods mainly include online detection, pressure test evaluation, direct evaluation and finite element simulation evaluation; for long-distance pipelines and other large-diameter, high-pressure pipelines equipped with pig transceivers, online detection such as leakage magnetic internal detection is widely used and has high accuracy, but since most gathering and transportation pipelines have small diameters, many specifications, low pressures and most do not have pig transceivers, internal detection is difficult to apply; pressure test evaluation is destructive, and production needs to be stopped and pipelines need to be cleaned before the test; direct evaluation includes direct evaluation of internal corrosion and direct evaluation of external corrosion, among which direct evaluation of internal corrosion is aimed at dry gas transmission pipelines where liquid accumulation may occur, and the pipe sections where liquid accumulation may occur are identified through multiphase flow characteristics, and the evaluation results are difficult to fully cover pipeline defects. Direct evaluation of external corrosion uses external detection technology to find out the damaged points of the anti-corrosion layer and conduct excavation inspection. The internal corrosion of the gathering and transportation pipeline is serious, and the internal corrosion defects cannot be covered only by the damaged points of the anti-corrosion layer. Finite element simulation evaluation is to evaluate individual defect points, and the evaluation process is cumbersome and computationally intensive, which is not suitable for large-scale pipeline integrity evaluation. Currently, pipeline integrity evaluation technology is widely used in long-distance pipelines, but the existing evaluation technology relies on mechanical characteristics such as operating pressure and cannot be applied to the integrity evaluation of gathering and transportation pipelines, and cannot meet the expectations of the inventors.

[0086] Since the medium transported by the gathering and transportation pipeline contains high water and hydrogen sulfide, serious internal corrosion will occur in the pipeline with a long service time. At the same time, the gathering and transportation pipeline is also affected by the atmospheric environment and soil corrosion, which leads to external corrosion of the pipeline. The characteristics of the gathering and transportation pipeline such as small diameter, many branches, various specifications, low pipe grade, low pressure grade, and complex operating conditions make it difficult to apply the integrity evaluation model of the long-distance pipeline to the gathering and transportation pipeline, and it is impossible to identify the potential threats to the pipeline. For this reason, it is necessary to propose an evaluation method that can more comprehensively evaluate the integrity of the gathering and transportation pipeline. After further research and development, the inventor made the present invention.

[0087] Embodiment 1

[0088] There are many and complex gathering and transportation pipelines in oil fields, and it is difficult to detect all of them at one time. Therefore, usually only part of the pipelines are inspected, and some pipelines are in an uninspected state. Therefore, the inspected pipelines and the uninspected pipelines can be divided into uninspected gathering and transportation pipelines and inspected gathering and transportation pipelines. The inspected gathering and transportation pipelines will have more inspection-related inspection data than the uninspected gathering and transportation pipelines. Therefore, in order to accurately conduct safety evaluation of uninspected gathering and transportation pipelines and inspected gathering and transportation pipelines, different evaluation models can be used for safety evaluation.

[0089] The embodiment of the present invention provides a method for evaluating the integrity of a gathering and transportation pipeline, referring to Figure 1 As shown, including:

[0090] S101, obtaining a first data indicator of an undetected gathering and transportation pipeline;

[0091] Specifically, there is no inspection data related to the uninspected gathering and transportation pipeline. Therefore, the first data indicator corresponding to the uninspected gathering and transportation pipeline that can be obtained without inspection can be directly received. The first data indicator can include data indicators such as the entity data, environmental data, and operation data of the uninspected gathering and transportation pipeline. Among them, the entity data can include data such as pipeline burial depth, elevation, turning angle, slope, defect point attributes (whether it is a turning point); the environmental data can include data such as soil type around the pipeline, soil resistivity, ground topography, and penetration and spanning conditions; the operation data can include data such as operating temperature, operating pressure, oil viscosity, medium complexity, and service life.

[0092] S102, analyzing the first data indicator using a pre-established risk assessment model to obtain a safety risk score of the undetected gathering and transportation pipeline; wherein the risk assessment model is pre-trained based on a first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline;

[0093] The first key risk factor is extracted from the historical first data indicator. Therefore, after receiving the first data indicator, the risk assessment model can extract the corresponding first key risk factor from the first data indicator according to the training results, and analyze the safety risk score of the undetected gathering and transportation pipeline based on the first key risk factor of the current undetected gathering and transportation pipeline. For example, the first data indicator includes the turning angle and slope of the entity data, the soil resistivity and ground topography of the environmental data, and the operating temperature and operating pressure of the operating data. According to the training results, the first key risk factors such as turning angle, ground topography, operating temperature and operating pressure can be extracted from the first data indicator, and the safety risk score of the undetected gathering and transportation pipeline can be analyzed based on these key indicators. The training process of the risk assessment model used in the present invention is as follows: ① after performing correlation analysis on the historical first data indicators of the undetected gathering and transportation pipeline, a first key risk factor (for example, 6 out of 10) is obtained; ② the first key risk factor obtained is analyzed by using the hierarchical analysis method to obtain the first indicator weight of each first key risk factor (the sum of all weight values ​​is 1); ③ the risk score of each first key risk factor is determined by using the risk assessment model, and the first indicator weight and risk score of each first key risk factor are weighted and summed to obtain the safety risk score of the undetected gathering and transportation pipeline; ④ the risk size of the undetected gathering and transportation pipeline is evaluated by the risk score (for example: low risk, medium risk or high risk).

[0094] S103, obtaining the second data index and detection data of the detected gathering and transportation pipeline;

[0095] The inspected gathering and transportation pipeline may also include a second data indicator of the same type as the uninspected gathering and transportation pipeline. The second data indicator may include entity data, environmental data and operation data, wherein the entity data may also include data such as pipeline burial depth, elevation, turning angle, slope, defect point attributes (whether it is an inflection point), etc.; the environmental data may also include data such as soil type around the pipeline, soil resistivity, ground topography, and penetration and spanning conditions; the operation data may also include data such as operating temperature, operating pressure, oil viscosity, medium complexity, and service life. The second data indicator of the inspected gathering and transportation pipeline may be the same as the first indicator data of the uninspected gathering and transportation pipeline in terms of data type and type, except that the inspected gathering and transportation pipeline additionally includes inspection data.

[0096] Specifically, in the inspection of gathering and transportation pipelines, according to conditions such as pipe diameter, excavation inspection, internal inspection, and non-contact magnetic stress inspection can be used to obtain inspection data; excavation inspection and internal inspection can obtain the metal loss type, defect depth, length, width, clock direction, defect mileage, defect point located on the inner / outer wall of the pipeline, and the distance of the defect from the front ring weld and the distance from the rear ring weld. Non-contact magnetic stress inspection can obtain inspection data such as the mileage of the pipeline defect area, the comprehensive index of magnetic anomalies, the clock direction, the starting point and end point of the defect area. Although different inspection methods obtain different inspection data, the same method can be used to train the corresponding safety factor calculation model or the safety factor calculation model that can integrate the above-mentioned various inspection data.

[0097] S104, using a pre-established safety factor calculation model to analyze the second data indicator and the detection data to obtain a safety factor of the detected gathering and transportation pipeline; wherein the safety factor calculation model is obtained by extracting a second key risk factor from the historical second data indicator and the historical detection data in advance to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output;

[0098] Since the inspected gathering and transportation pipelines have an additional item of inspection data, the safety factor can be calculated for the inspected gathering and transportation pipelines. Therefore, the safety factor calculation model extracts the second key risk factor from the historical second data indicators and historical inspection data in advance to correct the initial safety factor to obtain a corrected safety factor, which is obtained by training with the historical second data indicators and historical inspection data as input and the corrected safety factor as output. The safety factor calculation model can be used to conduct a comprehensive analysis of the second data indicators and inspection data of the inspected gathering and transportation pipelines to obtain the safety factor of the inspected gathering and transportation pipelines. The safety factor is used as the safety evaluation result of the inspected gathering and transportation pipelines to more accurately reflect the safety status of the inspected gathering and transportation pipelines.

[0099] The training process of the safety factor calculation model used in the present invention is as follows: ① after correlation analysis is performed on the historical second data indicators of the detected gathering and transportation pipeline, the second key risk factor is obtained; ② the obtained second key risk factor is hierarchically analyzed by using the hierarchical analysis method to obtain the second indicator weight of each second key risk factor, and the risk score of each second key risk factor is determined; ③ the initial safety factor in the original specification is a single safety factor value, which is only related to the regional level and the design factor. The second key risk factor is taken into account to correct the initial safety factor, and then a corrected safety factor can be obtained; ④ with the historical second data indicators and historical detection data as input and the corrected safety factor as output, the model is trained using the algorithm of the support vector machine to achieve ideal accuracy; ⑤ the safety factor calculation model can be used to comprehensively analyze the second data indicators and detection data of the detected gathering and transportation pipeline to obtain the safety factor of the detected gathering and transportation pipeline, and the safety factor is used as the safety evaluation result of the detected gathering and transportation pipeline.

[0100] S105. Determine the integrity evaluation result of the entire gathering and transportation pipeline based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline.

[0101] The overall gathering and transportation pipeline is composed of undetected gathering and transportation pipelines and detected gathering and transportation pipelines. The embodiment of the present application adopts different evaluation models to analyze and evaluate the undetected gathering and transportation pipelines and the detected gathering and transportation pipelines according to different data, so as to effectively and systematically evaluate the gathering and transportation pipelines in the two different states of detected and undetected on the entire gathering and transportation pipeline, better reflect the safety status of the two gathering and transportation pipelines, and improve the accuracy of the evaluation. The safety risk score of the undetected gathering and transportation pipeline and the safety factor of the detected gathering and transportation pipeline are combined to obtain the integrity evaluation result of the overall gathering and transportation pipeline.

[0102] The present invention relates to the field of production safety supervision, and specifically to a gathering and transportation pipeline integrity evaluation method, which obtains a first data indicator of an undetected gathering and transportation pipeline, analyzes the first data indicator using a pre-established risk assessment model, and obtains a safety risk score of the undetected gathering and transportation pipeline; obtains a second data indicator and detection data of a detected gathering and transportation pipeline, analyzes the second data indicator and detection data using a pre-established safety factor calculation model, and obtains a safety factor of the detected gathering and transportation pipeline. The safety factor calculation model can be used to comprehensively analyze the second data indicator and detection data of the detected gathering and transportation pipeline to obtain the safety factor of the detected gathering and transportation pipeline, and the safety factor is used as a safety evaluation of the detected gathering and transportation pipeline to more accurately reflect the safety status of the detected gathering and transportation pipeline. Different evaluation models are adopted for different pipeline data, that is, for undetected gathering and transportation pipelines and detected gathering and transportation pipelines to analyze and evaluate, so that the gathering and transportation pipelines in two different states of detected and undetected on the entire gathering and transportation pipeline can be effectively and systematically evaluated, better reflecting the safety status of the two gathering and transportation pipelines, and improving the accuracy of the evaluation. The risk assessment model is pre-trained based on the first key risk factor extracted from the historical first data indicators of the undetected gathering and transportation pipelines. The data of the oilfield gathering and transportation pipelines themselves are analyzed to extract the first key risk factor related to the integrity of the pipeline, making the risk factors clearer. The safety factor calculation model is pre-extracted from the historical second data indicators and historical detection data to correct the initial safety factor. After obtaining the corrected safety factor, it is trained with the historical second data indicators and historical detection data as input and the corrected safety factor as output. The safety factor is corrected based on the second key risk factor. The corrected safety factor based on risk assessment can better reflect the safety status of the defect, thereby more accurately evaluating the integrity of the gathering and transportation pipelines.

[0103] The embodiment of the present application discloses a specific gathering and transportation pipeline integrity evaluation method. Compared with the previous embodiment, this embodiment further illustrates and optimizes the technical solution.

[0104] In some embodiments, the step of obtaining the first data indicator of the undetected gathering and transportation pipeline described in S101 above includes:

[0105] S1011, obtaining the original first data index of the undetected gathering and transportation pipeline;

[0106] S1012: Preprocess the original first data indicators to obtain the first data indicators that can be used for analysis and calculation, including the entity data, environmental data, and operation data of the undetected gathering and transportation pipeline.

[0107] In some embodiments, the step of obtaining the second data indicator and the detection data of the detected gathering and transportation pipeline described in S103 above includes:

[0108] S1031, obtaining the original second data index and original detection data of the detected gathering and transportation pipeline;

[0109] S1032: Preprocess the original second data indicator and the original detection data to obtain the second data indicator that can be used for analysis and calculation and includes the entity data, environmental data, and operation data of the detected gathering and transportation pipeline.

[0110] The preprocessing principles of the data indicators and detection data for undetected and detected gathering and transportation pipelines are the same, and can include five processes: indicator selection, missing value filling, noise data processing, data conversion, and attribute domain discretization.

[0111] Among them, indicator selection: take as many types of field data as possible, which can include pipeline entity data, environmental data, operation data, detection data, historical failure data, etc. In the indicator selection process, select by checking the distribution of data in the indicator. If there is a single-value indicator, it can be deleted, and the usable data indicators are selected from it.

[0112] Missing value filling: Choose the appropriate filling method based on the attributes of the missing values ​​and the data distribution. For example, the missing values ​​of the pipeline depth can be filled by using the adjacent point interpolation method, the missing values ​​of the soil type can be filled by the nearest neighbor method, and the crossing situation can be judged based on the crossing situation statistics table of the pipeline and the mileage of the defect point. There are many ways to fill missing values, such as ignoring tuples, filling with global constants, filling with the nearest neighbor method, filling with attribute means or attribute means of similar samples, etc.

[0113] Noise data processing: You can view the basic distribution of data through drawing to determine whether there are outliers in the data. Outliers can be deleted or focused on according to the indicator attributes.

[0114] Data conversion: Since some of the collected indicator data exist in character type, they need to be converted into numerical data according to the type of indicator data for data analysis. For example, if the defect exists on the inner / outer wall, if it exists on the inner wall, it is recorded as 1, and if it exists on the outer wall, it is recorded as 2.

[0115] Attribute domain discretization: When performing correlation analysis, it is necessary to discretize the attribute domain when using information theory to calculate mutual information in the correlation analysis. Attribute domain discretization is to divide the distance between the maximum and minimum values ​​in the attribute domain into sub-intervals with equal distances, and then put the value of each attribute into the corresponding interval to obtain the number of values ​​in each sub-interval. In the embodiment of the present application, the K-Means algorithm can be used to discretize the attribute domain for non-continuous variables, and the BWP indicator can be used to calculate the optimal number of clusters in the K-Means algorithm, thereby converting each indicator data into data that can be used for mutual information calculation.

[0116] In some embodiments, reference Figure 2 As shown, the risk assessment model in S102 is trained in the following way:

[0117] S1021. Extracting the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator;

[0118] Specifically, an indicator that can characterize the safety status of the pipeline can be selected from the historical first data indicators as a decision indicator, and the correlation between each historical first data indicator and the decision indicator can be calculated using the mutual information method to extract the first key risk factor. The decision indicator refers to an indicator that can characterize the safety status of the pipeline, such as ERF (estimated maintenance factor). When ERF>1, the pipeline defect is unacceptable; when ERF<1, the pipeline defect is acceptable.

[0119] Specifically, after preprocessing the collected multi-source data of the gathering and transportation pipeline, the mutual information value between each historical first data indicator and the decision indicator is calculated as the basis for judging the size of the correlation. The larger the mutual information value, the higher the correlation between the risk factor and the safety status of the defect. For the example in this study, the internal detection data such as defect length, depth, width, the external detection data such as the anti-corrosion layer condition and cathodic protection effectiveness, the body data such as burial depth and elevation, the environmental data such as soil type and soil resistivity, the operating data such as operating temperature and pressure are collected and aligned, the mutual information between each indicator and the decision indicator is calculated and sorted, and the mutual information value is sorted from large to small as the correlation sorting of the risk factors, from which the risk factors whose correlation sorting is in the front preset position are obtained or the risk factors whose mutual information value is greater than the preset value are obtained, thereby determining the first key risk factor. Specifically, it is necessary to pre-construct the first risk score system in the embodiment of the present application. For examples, see the risk score system in the following embodiment.

[0120] S1022. Construct a risk factor hierarchy model using the first key risk factor, and construct a judgment matrix according to the risk factor hierarchy model;

[0121] Constructing a risk factor hierarchical model means determining the target layer as the safety risk score, the criterion layer as the hierarchical structure of the first key risk factor, and using each first key risk factor as an evaluation indicator to measure the safety risk score. The importance of each first key risk factor at the same level relative to the safety risk score at the previous level is compared pairwise, and a pairwise comparison matrix, i.e., a judgment matrix, is constructed. For the specific structural form of the risk factor hierarchical model and the judgment matrix, those skilled in the art can refer to the description of the hierarchical analysis method in the prior art.

[0122] S1023, determining a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis;

[0123] In the process of constructing the judgment matrix, the relative importance between each first key risk factor is mainly based on the size of the correlation as the judgment basis, and the correlation refers to the correlation between the first key risk factor and the safety risk score. The elements in the above judgment matrix are the relative importance between different first key risk factors. The relative importance between the first key risk factors obtained in advance is obtained, and the elements corresponding to each first key risk factor in the judgment matrix are assigned as the relative importance between the first key risk factors. According to the assigned judgment matrix, the first indicator weight of each key risk factor is determined. The specific calculation method can refer to the description of the hierarchical analysis method in the prior art. For example, it can be that for each first key risk factor, the following operation is performed on the first key risk factor of each column in the judgment matrix: the relative importance between the first key risk factors corresponding to the first key risk factor of the column is divided by the sum of the correlations of the first risk factors in the column to obtain the initial weight of the first key risk factor. Then the initial weights of each column corresponding to the first key risk factor are arithmetic averaged to obtain the first indicator weight corresponding to the first key risk factor. In some embodiments, the judgment matrix is ​​constructed in the following way:

[0124] If there are risk factors in the risk factor hierarchy model that cannot be correlated, the relative importance of the risk factors that cannot be correlated and other risk factors is determined based on the actual pipeline situation or expert scoring, so as to determine the indicator weights and construct the judgment matrix. For example, in the embodiment of the present application, since the service life of the target pipeline is a single value, it is not included in the correlation analysis, but this important parameter is considered in the construction of the risk factor hierarchy model, so its relative importance in constructing the judgment matrix can be determined in advance through the expert's experience scoring, thereby obtaining the corresponding first indicator weight.

[0125] S1024: Establish the risk assessment model according to the first indicator weight and the pre-established first risk score system.

[0126] For different pipelines, data of each first key risk factor in the risk assessment model is collected as input, and each first key risk factor is scored according to a pre-established first risk score system. The first indicator weight and risk assignment of each first key risk factor are weighted and summed to obtain a safety risk score as output. The safety status of the pipeline is judged according to the safety risk score. The classification standard of the safety risk score and the risk level in the embodiment of the present application can be: 0-20 points for low risk, 20-40 points for medium-low risk, 40-60 points for medium risk, 60-80 points for medium-high risk, and 80-100 points for high risk.

[0127] In some embodiments, the above S102 uses a pre-established risk assessment model to analyze the first data indicator to obtain a safety risk score of the uninspected gathering and transportation pipeline, including:

[0128] The first data indicator is used as the input of the risk assessment model, and the first data indicator is scored according to the first risk scoring system to obtain a corresponding first risk assignment; the safety risk score of the uninspected gathering and transportation pipeline is determined according to the first indicator weight and the first risk assignment corresponding to the first data indicator.

[0129] The first indicator weight and the first risk assignment of the first key risk factor are weighted and summed to obtain the safety risk score.

[0130] According to the type of the first key risk factor extracted from the first historical data indicator when establishing the risk assessment model, the corresponding first key risk factor is extracted from the first data indicator. The corresponding first key risk factor extracted from the first data indicator is then input into the risk assessment model, and the first key risk factor extracted from the first data indicator is analyzed to obtain the corresponding safety risk score.

[0131] In some embodiments, the step S1021 of extracting the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator includes:

[0132] S10211. Selecting a first decision indicator that can characterize the safety status of the pipeline from the historical first data indicators;

[0133] S10212. Determine the mutual information value between each historical first data indicator and the first decision indicator respectively by using the mutual information method, and extract the first key risk factor from the historical first data indicator according to the mutual information value.

[0134] In some embodiments, the first decision metric includes a first estimated maintenance factor;

[0135] The method of using the mutual information method to respectively determine the mutual information value between each historical first data indicator and the first decision indicator, and extracting the first key risk factor from the historical first data indicator according to the mutual information value, includes:

[0136] The mutual information method is used to respectively determine the mutual information value of each historical first data indicator and the first estimated maintenance coefficient, and the first key risk factor is extracted from the historical first data indicator according to the mutual information value.

[0137] In some embodiments, reference Figure 3 As shown, the safety factor calculation model in the above S104 is trained in the following way:

[0138] S1041. Extract the second key risk factor from the historical second data indicator and the historical detection data using the analytic hierarchy process, and determine the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance;

[0139] The implementation principle of extracting the second key risk factor from the historical second data indicators and historical detection data is similar to the above-mentioned extraction of the first key risk factor from the historical first data indicators, and will not be repeated here. The mutual information value between each historical second data indicator and the historical detection data and the decision indicator is calculated as the basis for judging the size of the correlation. The larger the mutual information value, the higher the correlation between the risk factor and the defect safety state, that is, the safety factor. The mutual information between each indicator and the decision indicator is calculated and sorted, and the mutual information value is sorted from large to small as the correlation sorting of the risk factors, from which the risk factors with the correlation sorting in the front preset position or the risk factors with the mutual information value greater than the preset value are obtained, thereby determining the second key risk factor.

[0140] Specifically, when the safety factor calculation model is constructed by the correlation data analysis method, the risk factor hierarchical model can be constructed with the second key risk factor of the detected gathering and transportation pipeline, and the correlation analysis results between the second key risk factor and the safety factor are used as the basis for judging the relative importance of each second key risk factor to construct a judgment matrix, and the second indicator weight of each second key risk factor is calculated by the hierarchical analysis method. Specifically, in the process of constructing the judgment matrix, the relative importance of each second key risk factor is mainly based on the size of the correlation as the basis for judgment. If there are risk factors that have not been analyzed in the risk factor hierarchical model, the relative importance of each risk factor can be determined according to the actual pipeline situation or according to the expert scoring. For example, in this example, since the service life of the target pipeline is a single value, it is not included in the correlation analysis, but this important parameter is considered in the construction of the risk factor hierarchical model, and its relative importance in constructing the judgment matrix is ​​determined by the expert scoring. The risk factor hierarchical model, the construction process of the judgment matrix, and the method for determining the second indicator weight of the second key risk factor are similar to those of the first key risk factor, and the description of the risk factor hierarchical model, the judgment matrix, and the process of determining the first indicator weight of the first key risk factor can be referred to.

[0141] S1042: using the second indicator weight and a pre-established second risk score system, modifying the initial safety factor to obtain the modified safety factor;

[0142] According to the second indicator weight, the types of each risk factor are divided and scored to form a second risk score system. The maximum risk score of each first key risk factor is determined according to the second indicator weight. For example, if the second indicator weight of a second key risk factor is 0.08, then the maximum risk score of the second key risk factor is 8 points; if the second indicator weight is 0.12, the maximum risk score is 12 points. Taking the service years as an example, the second indicator weight of the service years is 0.15, then the maximum risk score is 15 points, which is divided into four grades, namely: more than 10 years of service, 15 points; 5 to 10 years of service, 10 points; 3 to 5 years of service, 5 points; less than 3 years of service, 2 points. The second risk score system in the embodiment of the present application can refer to the risk score system in the following embodiment. For different pipelines, the data of each second key risk factor in the safety factor calculation model is collected as input, and each second key risk factor is scored according to the second risk score system. The score of each second key risk factor and the weight of the second indicator are weighted and summed to obtain the safety factor as output. The safety status of the pipeline is judged according to the safety factor. The classification standard of the safety factor and risk level in this example is: 0-20 points for low risk, 20-40 points for medium-low risk, 40-60 points for medium risk, 60-80 points for medium-high risk, and 80-100 points for high risk.

[0143] Specifically, the support vector machine safety factor calculation model is used as an example to illustrate that pipeline inspection data, ontology data, environmental data, and operation data are collected, and multi-source heterogeneous data are preprocessed. The estimated maintenance factor ERF is used as the decision attribute, and the mutual information between each indicator and the decision indicator is calculated and sorted to extract the second key risk factor. A risk factor hierarchical model is constructed based on the second key risk factors of the gathering and transportation pipeline, and a judgment matrix is ​​constructed based on the relative importance of the second key risk factors. The hierarchical analysis method is used to calculate the second indicator weight of each second key risk factor. A second risk score system is constructed based on the type of each second key risk factor, and the risk score of the pipeline defect is linearly mapped to the range of the safety factor to correct the safety factor.

[0144] S1043. Take the historical second data indicator and the historical detection data as input and the corrected safety factor as output for training to obtain the safety factor calculation model.

[0145] The second key risk factor extracted by correlation analysis is used as input, and the corrected safety factor is used as the label, i.e., the actual safety factor, to construct a support vector machine safety factor calculation model and train and test the model. The second key risk factor is divided into a training set and a test set, and the support vector machine safety factor calculation model is trained based on the training set, and the trained support vector machine safety factor calculation model is tested using the test set. If the accuracy of the trained support vector machine safety factor calculation model reaches the preset accuracy, the safety factor calculation model is obtained. If the preset accuracy is not reached, the training set is used to continue to train the support vector machine safety factor calculation model, and the test set is used to test the support vector machine safety factor calculation model, until the accuracy of the trained support vector machine safety factor calculation model reaches the preset accuracy, and the final safety factor calculation model is obtained. The training process is specifically: constructing a loss function of the output safety factor and the actual safety factor, inputting the training set into the support vector machine safety factor calculation model to obtain the corresponding output safety factor, determining the loss function value according to the output safety factor and the actual safety factor, and adjusting the model parameters of the support vector machine safety factor calculation model based on the loss function value until the number of iterations reaches the preset number. In actual application scenarios, relevant indicator data of the pipeline defects to be evaluated are collected according to the input of the support vector machine safety factor calculation model, and the safety factor of the pipeline defect is predicted by the support vector machine safety factor calculation model.

[0146] In some embodiments, after the safety factor is obtained, the safety factor may be further used for evaluation.

[0147] Specifically, the method further includes:

[0148] S106, determining a second estimated maintenance factor of a defect point on the inspected gathering and transportation pipeline according to the safety factor of the inspected gathering and transportation pipeline;

[0149] S107: Based on the second estimated maintenance coefficient, the defect point is evaluated using a preset evaluation standard to obtain an evaluation result.

[0150] The method further comprises: calculating the residual strength of the defect point by the preset evaluation standard;

[0151] Specifically, based on the second estimated maintenance coefficient, the defect point is evaluated using a preset evaluation standard to obtain an evaluation result, including:

[0152] An evaluation result of the defect point is determined according to the remaining strength and the second estimated maintenance coefficient.

[0153] The above preset evaluation standard is the ASME B31G evaluation standard. The ASME B31G evaluation standard is used to calculate the residual strength of the defect and the estimated repair factor ERF. ERF is the ratio of the maximum allowable operating pressure of the pipeline to the safe operating pressure of the pipeline. If the defect ERF value is greater than 1, the defect is unacceptable and requires immediate repair; if the defect ERF value is greater than 0.9 and less than 1, the defect needs to be monitored or used at reduced pressure; if the defect ERF value is less than 0.9, the defect is considered acceptable, and the defect needs to be dynamically compared in subsequent inspections and evaluations. The calculation method of residual strength is clearly defined in the ASME B31G standard: when the pipe diameter, wall thickness, minimum yield strength, defect depth, defect length and other parameters of the pipeline are known, the failure pressure of the pipeline can be calculated to obtain the residual strength, and the safety status of the pipeline defect point can be judged in combination with the ERF value.

[0154] The present invention proposes a pipeline integrity evaluation method based on multi-source data fusion, uses correlation analysis to clarify the second key risk factor related to the defect, and uses support vector machine to predict the safety factor at the defect. It can take into account the influence of pipeline factors, environmental factors, operation factors, etc. on the safety status of the defect, so as to correct the shortcomings of the original evaluation method, provide new ideas for the integrity evaluation of the gathering and transportation pipeline, and provide decision-making support for the maintenance and repair of the gathering and transportation pipeline. The present invention uses a correlation analysis method to analyze the data of the oil field gathering and transportation pipeline itself, so as to extract the risk factors related to the integrity of the pipeline, making the risk factors clearer, and the safety factor correction based on the risk evaluation of the defect can better reflect the safety status of the defect, so as to make the integrity evaluation of the gathering and transportation pipeline more accurate.

[0155] The embodiment of the present invention further discloses a specific risk score system, which includes the first risk score system and the second risk score system, for example:

[0156] 1. Operational factors (64 points in total)

[0157] 1. Medium complexity (20 points)

[0158] The medium contains a lot of hydrogen sulfide and CO2, which is highly corrosive and gets 20 points;

[0159] The medium contains more hydrogen sulfide and CO2, which is more corrosive, 14 points;

[0160] Contains less hydrogen sulfide and CO2 medium, weak corrosiveness, 7 points;

[0161] No points will be awarded for media that do not contain hydrogen sulfide and CO2.

[0162] 2. Years of service (15 points)

[0163] More than 10 years, 15 points;

[0164] 5 to 10 years, 10 points;

[0165] 3 to 5 years, 5 points;

[0166] Less than 3 years: 2 points.

[0167] 3. Anti-corrosion layer performance (12 points)

[0168] The resistivity of the anti-corrosion layer does not meet the requirements, there are a large number of leaks during the test, and the epoxy adhesion does not meet the requirements, so 12 points are awarded;

[0169] The resistivity of the anti-corrosion layer is high, there are leaks when detected, and the epoxy adhesion basically meets the requirements, so 6 points are awarded.

[0170] The anti-corrosion layer has high resistivity, no leaks, and epoxy adhesion meets the requirements, so no points will be awarded.

[0171] 4. Cathodic protection operation (10 points)

[0172] No cathodic protection, 10 points;

[0173] If the cathodic protection operation rate is less than 50% throughout the year, or corrosion failure occurs, 7 points will be awarded;

[0174] If the annual cathodic protection rate is below 95%, and there is over-protection and under-protection of the cathodic protection potential, 4 points will be awarded;

[0175] If the cathodic protection rate reaches 95% or above throughout the year, 1 point will be awarded.

[0176] 5. Number of corrosion perforations (7 points)

[0177] 3 or more times, 7 points;

[0178] 1 to 3 times, 4 points;

[0179] None, score 1 point.

[0180] II. Environmental Factors (26 points in total)

[0181] 1. Soil corrosivity (8 points)

[0182] High: resistivity <20; salt content >0.75; water content >12; current density 0.3; corrosion level: extra strong; 8 points;

[0183] Medium: Resistivity: 20-50; Salt content: 0.75-0.05; Water content: 5-12; Current density: 0.3-0.025; Corrosion level: Enhanced level, 5 points;

[0184] Low: resistivity>50; salt content<0.05; water content<5; current density<0.025; corrosion level: ordinary level, 2 points;

[0185] 2. Stress environment (6 points)

[0186] 6 points for obvious external stress environment, complex topography, and significant subsidence area characteristics;

[0187] 3 points if the external stress environment is obvious, the topography is complex, and there are subsidence area characteristics;

[0188] If the external stress environment is not obvious, the terrain is flat, and there are no subsidence area characteristics, 1 point will be awarded.

[0189] 3. High consequence areas, external interference and occupation and crushing (5 points)

[0190] Located in a high consequence area with a lot of external interference and construction work, 5 points;

[0191] Located at the junction of urban and rural areas, with frequent personnel movements and chaotic public security, 4 points;

[0192] There are accumulations near mining and ore dressing, and heavy vehicles frequently run over pipelines; 3 points will be awarded.

[0193] 4. Pipeline passing situation (4 points)

[0194] 4 points for sections passing through ponds, saline-alkali land, strong earthquakes, faults and fault zones;

[0195] 3 points for route crossing sections, excavation construction, blasting, and directional drilling crossing sections;

[0196] 2 points for flooded riverbanks, flooded catchment areas, rocky areas, slopes, and hydraulic protection areas

[0197] 5. Stray current interference (3 points)

[0198] 3 points if the positive deviation of the pipe-to-ground potential from the natural potential is greater than 200 mV or the soil potential gradient is greater than 5 mV / m;

[0199] 2 points if the positive deviation of the pipe-to-ground relative to the natural potential is less than 200mV but greater than 20mV or the soil potential gradient is greater than 0.5mV / m but less than 5V / m;

[0200] 1 point will be awarded if the positive deviation of the pipe-to-ground potential from the natural potential is less than 20 mV or the soil potential gradient is less than 0.5 mV / m.

[0201] 3. Design and construction factors (10 points in total)

[0202] 1. Engineering construction welding (6 points)

[0203] The weld film re-examination failure rate is high and the manual welding joints are often repaired, which is 6 points;

[0204] The weld film re-inspection failure rate is high, and the semi-automatic welding joints are repaired frequently, which will be awarded 3 points;

[0205] The failure rate of weld film review is low and the number of automatic weld repairs is small, so 1 point is awarded.

[0206] 2. Slope (4 points)

[0207] The pipeline has large undulations and a large slope change, which is worth 4 points;

[0208] The pipeline has small undulations and a small slope change, so 2 points are awarded.

[0209] Embodiment 2

[0210] The embodiment of the present invention provides a gathering and transportation pipeline integrity assessment device, referring to Figure 4 As shown, including:

[0211] The first indicator acquisition module 301 is used to acquire the first data indicator of the undetected gathering and transportation pipeline;

[0212] A first evaluation module 302 is used to analyze the first data indicator using a pre-established risk assessment model to obtain a safety risk score of an uninspected gathering and transportation pipeline;

[0213] The second indicator acquisition module 303 is used to acquire the second data indicator and the detection data of the detected gathering and transportation pipeline;

[0214] The second evaluation module 304 is used to analyze the second data indicator and the detection data using a pre-established safety factor calculation model to obtain the safety factor of the detected gathering and transportation pipeline; wherein the risk assessment model is pre-trained based on the first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline; the safety factor calculation model is pre-trained based on the second key risk factor extracted from the historical second data indicator and the historical detection data to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output;

[0215] The calculation module 305 is used to determine the integrity evaluation result of the entire gathering and transportation pipeline based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline.

[0216] In some embodiments, the first indicator acquisition module 301 includes:

[0217] A first data acquisition unit, used to acquire an original first data indicator of the undetected gathering and transportation pipeline;

[0218] The first preprocessing unit is used to preprocess the original first data indicators to obtain the first data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the undetected gathering and transportation pipeline.

[0219] In some embodiments, the second indicator acquisition module 303 includes:

[0220] A second data acquisition unit, used to acquire the original second data index and original detection data of the detected gathering and transportation pipeline;

[0221] The second preprocessing unit is used to preprocess the original second data indicators and the original detection data to obtain the second data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the detected gathering and transportation pipeline.

[0222] In some embodiments, the first evaluation module 302 includes:

[0223] A risk factor extraction module, used to extract the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator;

[0224] A judgment matrix construction module, used to construct a risk factor hierarchy model using the first key risk factor, and to construct a judgment matrix according to the risk factor hierarchy model;

[0225] An indicator weight calculation module, used to determine a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis;

[0226] The first model building module is used to establish the risk assessment model according to the first indicator weight and a pre-established first risk score system.

[0227] In some embodiments, the second evaluation module 304 includes:

[0228] A risk factor weight value calculation module, used to extract the second key risk factor from the historical second data indicator and the historical detection data by using the hierarchical analysis method, and determine the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance;

[0229] an initial safety factor correction module, configured to correct the initial safety factor by using the second indicator weight and a pre-established second risk score system to obtain the corrected safety factor;

[0230] The second model building module is used to take the historical second data indicator and the historical detection data as input and the corrected safety factor as output for training to obtain the safety factor calculation model.

[0231] In some embodiments, the apparatus further comprises:

[0232] A second estimated maintenance factor determination module 306 is used to determine a second estimated maintenance factor of a defect point on the inspected gathering and transportation pipeline according to the safety factor of the inspected gathering and transportation pipeline;

[0233] The re-evaluation module 307 is used to evaluate the defect point based on the second estimated maintenance coefficient using a preset evaluation standard to obtain an evaluation result.

[0234] The implementation principle and technical effect of the gathering and transportation pipeline integrity assessment device provided in the embodiment of the present invention are similar to those of any of the aforementioned method embodiments, and will not be repeated here.

[0235] Embodiment 3

[0236] An embodiment of the present invention provides an electronic device, referring to Figure 5 As shown, including:

[0237] Memory for storing computer programs;

[0238] A processor is used to execute the computer program to implement the aforementioned gathering and transportation pipeline integrity assessment method.

[0239] A memory 20, used for storing computer programs;

[0240] The processor 21 is used to implement the aforementioned gathering and transportation pipeline integrity assessment method when executing the computer program.

[0241] The electronic device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer or a desktop computer.

[0242] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0243] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the method for determining the text answer disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. Data 203 may include but is not limited to a set offset, etc.

[0244] In some embodiments, the electronic device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power source 25 , and a communication bus 26 .

[0245] Those skilled in the art will understand that Figure 5 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown in the figure.

[0246] It is understandable that if the method for determining the text answer in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium to execute all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), electrically erasable programmable ROM, register, hard disk, removable disk, CD-ROM, magnetic disk or optical disk and other media that can store program codes.

[0247] Embodiment 4

[0248] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the aforementioned gathering and transportation pipeline integrity evaluation method is implemented.

[0249] The above is a detailed introduction to the gathering and transportation pipeline integrity evaluation method, device, storage medium and electronic device provided in the embodiments of the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0250] In this document, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. The orientation or position relationship indicated by the terms "upper", "lower", etc. is based on the orientation or position relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0251] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict. The present invention is not limited to any single aspect, any single embodiment, or any combination and / or permutation of these aspects and / or embodiments. Each aspect and / or embodiment of the present invention can be used alone, or in combination with one or more other aspects and / or other embodiments.

[0252] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating the integrity of a gathering and transportation pipeline, characterized in that: include: Obtaining a first data indicator of an undetected gathering and transportation pipeline; Analyze the first data indicator using a pre-established risk assessment model to obtain a safety risk score for the uninspected gathering and transportation pipeline; Acquire the second data index and detection data of the detected gathering and transportation pipeline; Analyzing the second data indicator and the detection data using a pre-established safety factor calculation model to obtain a safety factor of the detected gathering and transportation pipeline; Wherein, the risk assessment model is obtained by pre-training based on the first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline; The safety factor calculation model is obtained by extracting the second key risk factor from the historical second data indicator and the historical detection data in advance to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output; Based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline, an integrity evaluation result of the overall gathering and transportation pipeline is determined.

2. The gathering and transportation pipeline integrity evaluation method according to claim 1 is characterized in that: The risk assessment model is trained in the following way: Extracting the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator; constructing a risk factor hierarchy model using the first key risk factor, and constructing a judgment matrix according to the risk factor hierarchy model; Determine a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis; The risk assessment model is established according to the first indicator weight and a pre-established first risk score system.

3. The gathering and transportation pipeline integrity evaluation method according to claim 2 is characterized in that: The first data indicator is analyzed by using a pre-established risk assessment model to obtain a safety risk score of the uninspected gathering and transportation pipeline, including: Using the first data indicator as input of the risk assessment model, scoring the first data indicator according to the first risk scoring system to obtain a corresponding first risk assignment; A safety risk score of the uninspected gathering and transportation pipeline is determined according to a first indicator weight and a first risk assignment corresponding to the first data indicator.

4. The gathering and transportation pipeline integrity evaluation method according to claim 2 is characterized in that: The extracting the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator includes: Selecting a first decision indicator that can characterize the safety status of the pipeline from the historical first data indicators; The mutual information method is used to respectively determine the mutual information value of each historical first data indicator and the first decision indicator, and the first key risk factor is extracted from the historical first data indicator according to the mutual information value.

5. The gathering and transportation pipeline integrity evaluation method according to claim 4 is characterized in that: The first decision indicator includes a first estimated maintenance factor; The method of using the mutual information method to respectively determine the mutual information value of each historical first data indicator and the first decision indicator, and extracting the first key risk factor from the historical first data indicator according to the mutual information value, includes: The mutual information method is used to respectively determine the mutual information value of each historical first data indicator and the first estimated maintenance coefficient, and the first key risk factor is extracted from the historical first data indicator according to the mutual information value.

6. The gathering and transportation pipeline integrity evaluation method according to claim 2 is characterized in that: The judgment matrix is ​​constructed in the following way: If there are risk factors in the risk factor hierarchy model that cannot be analyzed, the relative importance of the risk factors that cannot be analyzed and other risk factors is determined according to the actual pipeline situation or expert scores to construct the judgment matrix.

7. The gathering and transportation pipeline integrity evaluation method according to claim 1 is characterized in that: The safety factor calculation model is trained in the following way: Extracting the second key risk factor from the historical second data indicators and the historical detection data by using the hierarchical analysis method, and determining the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance; Using the second indicator weight and a pre-established second risk score system, the initial safety factor is corrected to obtain the corrected safety factor; The safety factor calculation model is obtained by taking the historical second data indicator and the historical detection data as input and taking the corrected safety factor as output for training.

8. The gathering and transportation pipeline integrity evaluation method according to claim 1, characterized in that: The method further comprises: Determining a second estimated maintenance factor of a defective point on the inspected gathering and transportation pipeline according to the safety factor of the inspected gathering and transportation pipeline; Based on the second estimated maintenance coefficient, the defect point is evaluated using a preset evaluation standard to obtain an evaluation result.

9. The gathering and transportation pipeline integrity assessment method according to claim 8, characterized in that: The method further comprises: Calculate the residual strength of the defect point by using the preset evaluation standard; The step of evaluating the defect point based on the second estimated maintenance coefficient using a preset evaluation standard to obtain an evaluation result includes: An evaluation result of the defect point is determined according to the remaining strength and the second estimated maintenance coefficient.

10. The gathering and transportation pipeline integrity evaluation method according to claim 1, characterized in that: The obtaining of the first data indicator of the undetected gathering and transportation pipeline includes: Acquiring the original first data indicator of the undetected gathering and transportation pipeline; The original first data index is preprocessed to obtain the first data index that can be used for analysis and calculation and includes the entity data, environmental data and operation data of the undetected gathering and transportation pipeline.

11. The gathering and transportation pipeline integrity evaluation method according to claim 1, characterized in that: The obtaining of the second data index and the detection data of the detected gathering and transportation pipeline includes: Acquiring the original second data index and original detection data of the detected gathering and transportation pipeline; The original second data index and the original detection data are preprocessed to obtain the second data index that can be used for analysis and calculation and includes the entity data, environmental data and operation data of the detected gathering and transportation pipeline.

12. A gathering and transportation pipeline integrity assessment device, characterized in that: include: A first indicator acquisition module, used to acquire a first data indicator of an undetected gathering and transportation pipeline; A first evaluation module is used to analyze the first data indicator using a pre-established risk assessment model to obtain a safety risk score of an uninspected gathering and transportation pipeline; A second indicator acquisition module, used to acquire the second data indicator and detection data of the detected gathering and transportation pipeline; The second evaluation module is used to analyze the second data indicator and the detection data using a pre-established safety factor calculation model to obtain the safety factor of the detected gathering and transportation pipeline; wherein the risk assessment model is pre-trained based on the first key risk factor extracted from the historical first data indicator of the undetected gathering and transportation pipeline; the safety factor calculation model is pre-extracted from the historical second data indicator and the historical detection data to correct the initial safety factor to obtain a corrected safety factor, and is trained with the historical second data indicator and the historical detection data as input and the corrected safety factor as output; The calculation module is used to determine the integrity evaluation result of the entire gathering and transportation pipeline based on the safety risk score of the uninspected gathering and transportation pipeline and the safety factor of the inspected gathering and transportation pipeline.

13. The gathering and transportation pipeline integrity assessment device according to claim 12, characterized in that: The first indicator acquisition module includes: A first data acquisition unit, used to acquire an original first data indicator of the undetected gathering and transportation pipeline; The first preprocessing unit is used to preprocess the original first data indicators to obtain the first data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the undetected gathering and transportation pipeline.

14. The gathering and transportation pipeline integrity assessment device according to claim 12, characterized in that: The second indicator acquisition module includes: A second data acquisition unit, used to acquire the original second data index and original detection data of the detected gathering and transportation pipeline; The second preprocessing unit is used to preprocess the original second data indicators and the original detection data to obtain the second data indicators that can be used for analysis and calculation, including the entity data, environmental data and operation data of the detected gathering and transportation pipeline.

15. The gathering and transportation pipeline integrity assessment device according to claim 12, characterized in that: The first evaluation module comprises: A risk factor extraction module, used to extract the first key risk factor that can characterize the safety status of the pipeline from the first historical data indicator; A judgment matrix construction module, used to construct a risk factor hierarchy model using the first key risk factor, and to construct a judgment matrix according to the risk factor hierarchy model; An indicator weight calculation module, used to determine a first indicator weight of the first key risk factor by using the judgment matrix and the relative importance of the first key risk factor obtained according to the pre-analysis; The first model building module is used to establish the risk assessment model according to the first indicator weight and a pre-established first risk score system.

16. The gathering and transportation pipeline integrity evaluation device according to claim 12, characterized in that: The second evaluation module comprises: A risk factor weight value calculation module, used to extract the second key risk factor from the historical second data indicator and the historical detection data by using the hierarchical analysis method, and determine the second indicator weight of the second key risk factor according to the second risk factor correlation between the second key risk factor and the second decision indicator obtained in advance; an initial safety factor correction module, configured to correct the initial safety factor by using the second indicator weight and a pre-established second risk score system to obtain the corrected safety factor; The second model building module is used to take the historical second data indicator and the historical detection data as input and the corrected safety factor as output for training to obtain the safety factor calculation model.

17. The gathering and transportation pipeline integrity evaluation device according to claim 12, characterized in that: The device also includes: A second estimated maintenance factor determination module, used to determine a second estimated maintenance factor of a defect point on the detected gathering and transportation pipeline according to the safety factor of the detected gathering and transportation pipeline; A re-evaluation module is used to evaluate the defect point based on the second estimated maintenance coefficient using a preset evaluation standard to obtain an evaluation result.

18. An electronic device, characterized in that: include: Memory for storing computer programs; A processor is used to execute the computer program to implement the gathering and transportation pipeline integrity assessment method according to any one of claims 1 to 11.

19. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the gathering and transportation pipeline integrity evaluation method as described in any one of claims 1 to 11 is implemented.

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