Oil and gas surface engineering risk early warning method

Abstract

The invention provides an oil and gas surface engineering risk early warning method. The method comprises the following steps: constructing a risk index system of oil and gas surface engineering; determining a first weight of a risk index in the risk index system by adopting an analytic hierarchy process; determining a second weight of a risk index in the risk index system by adopting a triangular fuzzy analytic hierarchy process based on a COWA operator; performing combined weighting on the first weight and the second weight by using a game weighting method to obtain a comprehensive weight of the risk index; constructing a risk early warning model according to the comprehensive weight of the risk index and a matter element analysis method; determining a risk level through the risk early warning model; risks of the oil and gas surface engineering can be found according to various indexes of the oil and gas surface engineering, and early warning is carried out in time.

Description

Technical Field

[0001] The present application relates to the technical field of risk early warning, and in particular to a method for early warning of risks in oil and gas surface engineering. Background Art

[0002] In recent years, the construction of oil and gas mining projects has achieved rapid development, playing a huge role in promoting natural development and meeting people's daily production and living needs. Oil and gas surface engineering is an important part of oilfield mining engineering construction. Oil and gas surface engineering is a systematic and complex project, which is easily affected by inadequate safety risk management or geological and hydrological conditions. It has never led to safety accidents. Therefore, in order to prevent the occurrence of safety accidents in the construction of oil and gas surface engineering projects, it is necessary to strengthen the management of oil and gas surface engineering and ensure that funds, construction materials, personnel, machinery and equipment are reasonably arranged. In the existing technology, the risk warning of oil and gas surface engineering is usually achieved by manually analyzing the corresponding indicators, which has a certain lag. Therefore, how to carry out risk warning for oil and gas surface engineering is an urgent problem to be solved. Summary of the invention

[0003] In response to the above-mentioned problems, this application proposes a risk warning method for oil and gas surface engineering, which solves the technical problem that risk warning of oil and gas surface engineering is usually achieved through manual analysis of corresponding indicators, which has a certain lag. It can discover the risks of oil and gas surface engineering based on various indicators of oil and gas surface engineering and provide timely warnings.

[0004] The present application provides a method for early warning of oil and gas surface engineering risks, including:

[0005] Construct a risk indicator system for oil and gas surface engineering;

[0006] Determining the first weight of the risk indicator in the risk indicator system by using the analytic hierarchy process;

[0007] A triangular fuzzy analytic hierarchy process based on the COWA operator is used to determine the second weight of the risk indicator in the risk indicator system;

[0008] Using a game weighting method to combine and weight the first weight and the second weight to obtain a comprehensive weight of the risk indicator;

[0009] Construct a risk early warning model based on the comprehensive weight of risk indicators and matter-element analysis;

[0010] The risk level is determined through the risk early warning model.

[0011] In some embodiments, including:

[0012] Analyze the accident case database of oil and gas surface engineering according to the causes of accidents and determine the risk influencing factors;

[0013] Construct a risk indicator system based on risk influencing factors.

[0014] In some embodiments, the risk indicator system includes first-level risk indicators: economic risk, environmental risk, social risk, and technical and quality risk; the economic risk includes second-level indicators: funding source risk, material price risk, project progress risk, change and claim risk, annual appropriation amount during operation, and project investment amount; the environmental risk includes second-level indicators: hydrological and meteorological risk, environmental pollution risk, geological condition risk, and environmental adaptability; the social risk includes second-level indicators: policy risk, unemployment status, major event suspension risk, social adaptability, and public satisfaction; the technical and quality risk includes second-level indicators: construction technology risk, personnel quality risk, design error risk, and material quality risk.

[0015] In some embodiments, including:

[0016] Compare the secondary risk assessment indicators pairwise, determine their relative importance, and construct a judgment matrix;

[0017] Calculate the maximum eigenvalue of the judgment matrix;

[0018] By checking the maximum characteristic root, determine whether the judgment matrix is ​​consistent;

[0019] When the judgment matrix is ​​consistent, the first evaluation index weight is calculated using the arithmetic square root.

[0020] In some embodiments, including:

[0021] Construct the original data set of secondary indicators and arrange the elements of the original data set from small to large;

[0022] Calculate the weight vector of the combination number of the original data set of the secondary index;

[0023] Based on the weighted vector of the number of combinations, calculate the absolute weight of the corresponding secondary index;

[0024] Normalize the absolute weights to get the second weight of the secondary indicator.

[0025] In some embodiments, including:

[0026] According to the first weight and the second weight, setting a linear combination coefficient;

[0027] Linearly combine the linear combination coefficient with the first weight and the second weight to obtain a combined weight;

[0028] Taking the deviation and minimization of the combination weight from the first weight and the second weight as the goal, optimizing the linear combination coefficient to obtain an optimization objective function;

[0029] According to the matrix differential properties, the optimization objective function is converted into a linear equation system in the corresponding matrix form to obtain the optimal combination coefficient;

[0030] Normalizing the optimal combination coefficient to obtain an optimal weight coefficient;

[0031] Based on the optimal weight coefficient, the first weight and the second weight are combined to obtain the comprehensive weight.

[0032] In some embodiments, including:

[0033] Collect metadata of the object to be evaluated based on the comprehensive weight of risk indicators;

[0034] Determine the classical domain and the section domain according to the metadata of the object to be evaluated and the interval where its value lies;

[0035] Calculate the correlation degree based on the risk index and the matter-element to be evaluated;

[0036] Determine the risk level corresponding to each risk indicator based on the correlation.

[0037] In some embodiments, the risk levels include: safe, relatively safe, basically safe, relatively dangerous, and dangerous.

[0038] An embodiment of the present application provides an electronic device, including:

[0039] A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, any one of the methods described above is executed.

[0040] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the methods described above.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] A risk indicator system for oil and gas surface engineering is constructed; a hierarchical analysis method is used to determine the first weight of the risk indicator in the risk indicator system; a triangular fuzzy hierarchical analysis method based on the COWA operator is used to determine the second weight of the risk indicator in the risk indicator system; the first weight and the second weight are combined and weighted using a game weighting method to obtain a comprehensive weight of the risk indicator; a risk warning model is constructed based on the comprehensive weight of the risk indicator and the matter-element analysis method; the risk level is determined through the risk warning model; the risks of the oil and gas surface engineering can be discovered based on various indicators of the oil and gas surface engineering, and timely warnings can be issued. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The embodiments of the present invention are further described below with reference to the accompanying drawings:

[0044] Figure 1 A schematic diagram of the implementation process of an oil and gas surface engineering risk early warning method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0047] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0048] If similar descriptions of "first\second\third" appear in the application documents, the following instructions will be added. In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0050] Based on the problems existing in the related art, the embodiment of the present application provides a risk warning method for oil and gas surface engineering, and the execution subject of the risk warning method can be an electronic device. The electronic device can be various types of terminals such as a laptop, a tablet computer, a desktop computer, a set-top box, a mobile device (for example, a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, a portable gaming device), and can also be implemented as a server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (Content De l ivery Network, CDN), and big data and artificial intelligence platforms.

[0051] In some embodiments, the functions implemented by the risk warning method provided in the embodiments of the present application can be implemented by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0052] The present application provides a method for early warning of oil and gas surface engineering risks. Figure 1 A schematic diagram of the implementation process of an oil and gas surface engineering risk early warning method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, including:

[0053] Step S1: constructing a risk indicator system for oil and gas surface engineering;

[0054] In some embodiments, including:

[0055] Step S11: Analyze the accident case library of oil and gas surface engineering according to the causes of the accidents and determine the risk influencing factors;

[0056] Step S12: Construct a risk indicator system based on risk influencing factors.

[0057] In some embodiments, the risk indicator system includes first-level risk indicators: economic risk, environmental risk, social risk, and technical and quality risk; the economic risk includes second-level indicators: funding source risk, material price risk, project progress risk, change and claim risk, annual appropriation amount during operation, and project investment amount; the environmental risk includes second-level indicators: hydrological and meteorological risk, environmental pollution risk, geological condition risk, and environmental adaptability; the social risk includes second-level indicators: policy risk, unemployment status, major event suspension risk, social adaptability, and public satisfaction; the technical and quality risk includes second-level indicators: construction technology risk, personnel quality risk, design error risk, and material quality risk.

[0058] In the embodiment of the present application, the accident case library of oil and gas surface engineering can be analyzed by accident cause analysis, the risk influencing factors corresponding to the oil and gas surface engineering can be determined, and then a risk indicator system can be constructed based on the risk influencing factors.

[0059] Step S2: using the analytic hierarchy process to determine the first weight of the risk indicator in the risk indicator system;

[0060] In some embodiments, including:

[0061] Step S21: compare the secondary risk assessment indicators in pairs, determine the relative importance, and construct a judgment matrix;

[0062] Step S22: Calculate the maximum eigenvalue of the judgment matrix;

[0063] Step S23: Determine whether the judgment matrix is ​​consistent by checking the maximum characteristic root;

[0064] Step S24: When the judgment matrix is ​​consistent, the first evaluation index weight is calculated using the arithmetic square root.

[0065] In the embodiments of the present application, the analytic hierarchy process is mainly based on the evaluator's understanding of the nature and elements of the evaluation problem, and emphasizes more qualitative analysis and judgment than general quantitative methods. The analytic hierarchy process is an evaluation method that organically combines qualitative methods with quantitative methods. It neither unilaterally pursues advanced mathematical logic nor simply focuses on subjective behavior and conscious judgment.

[0066] Step S3: using a triangular fuzzy analytic hierarchy process based on the COWA operator to determine the second weight of the risk indicator in the risk indicator system;

[0067] In some embodiments, including:

[0068] Step S31: constructing the original data set of the secondary indicators, and arranging the elements of the original data set from small to large;

[0069] Step S32: Calculate the weighted vector of the combination number of the original data set of the secondary index;

[0070] Step S33: Calculate the absolute weight of the corresponding secondary index based on the combination number weighting vector;

[0071] Step S34: Normalize the absolute weight to obtain the second weight of the secondary indicator.

[0072] In the embodiment of the present application, the triangular fuzzy analytic hierarchy process of the COWA operator can process and merge uncertain information in a fuzzy decision environment, and is a solution for multi-attribute decision making, mainly for interval decision making. The fundamental feature of this operator is: re-sorting the attribute values ​​in order from large to small and weighting them by the position of the attribute values ​​before aggregation.

[0073] Step S4: Combining and weighting the first weight and the second weight using a game weighting method to obtain a comprehensive weight of the risk indicator;

[0074] In some embodiments, including:

[0075] Step S41: setting a linear combination coefficient according to the first weight and the second weight;

[0076] Step S42: linearly combining the linear combination coefficient with the first weight and the second weight to obtain a combined weight;

[0077] Step S43: optimizing the linear combination coefficients with the goal of minimizing the deviation between the combination weight and the first weight and the second weight, and obtaining an optimization objective function;

[0078] Step S44: according to the matrix differential property, convert the optimization objective function into a linear equation system in the corresponding matrix form to obtain the optimal combination coefficient;

[0079] Step S45: normalizing the optimal combination coefficient to obtain an optimal weight coefficient;

[0080] Step S46: Based on the optimal weight coefficient, the first weight and the second weight are combined to obtain the comprehensive weight.

[0081] In the embodiment of the present application, the first weight of the subjective judgment of the hierarchical analysis method and the second weight of the objective data of the triangular fuzzy hierarchical analysis method of the COWA operator are combined, so that the distribution of weights is more dynamic and flexible, adapting to changing environmental conditions, and being able to balance the advantages of intuitive and data-driven decision-making, thereby improving the comprehensiveness and accuracy of decision-making.

[0082] Step S5: constructing a risk early warning model based on the comprehensive weight of risk indicators and matter-element analysis method;

[0083] In some embodiments, including:

[0084] Step S51: Collect metadata of the object to be evaluated according to the comprehensive weight of the risk index;

[0085] Step S52: determining the classical domain and the section domain according to the metadata of the object to be evaluated and the interval where its value is located;

[0086] Step S53: Calculate the correlation degree according to the risk index and the matter-element to be evaluated;

[0087] Step S54: Determine the risk level corresponding to each risk indicator according to the correlation degree.

[0088] In the embodiment of the present application, the metadata of the object to be evaluated is collected according to the comprehensive weight of the risk index, and the classical domain and the section domain are determined according to the metadata of the object to be evaluated and the interval in which its value is located. For the calculation of the correlation degree, the index value of the required risk index is calculated according to the correlation degree calculation formula and the evaluation value of the object element to be determined, and the correlation degree, the first-level and second-level index risk level association membership, and the comprehensive risk level association membership are obtained, which correspond to the risk level corresponding to each risk indicator.

[0089] Step S6: Determine the risk level through the risk warning model.

[0090] In some embodiments, the risk levels include: safe, relatively safe, basically safe, relatively dangerous, and dangerous.

[0091] In the embodiment of the present application, the purpose of risk assessment and early warning is achieved through the risk early warning model, which can detect the risks of oil and gas surface engineering according to various indicators of oil and gas surface engineering and give early warning in time.

[0092] It should be noted that in the embodiment of the present application, if the above-mentioned risk warning method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, ReadOnly Memory), a disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0093] Accordingly, an embodiment of the present application provides a storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps in the risk warning method provided in the above embodiment are implemented.

[0094] An embodiment of the present application provides an electronic device; Figure 2 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 2As shown, the electronic device 400 includes: a processor 401, at least one communication bus 402, a user interface 403, at least one external communication interface 404, and a memory 405. The communication bus 402 is configured to realize the connection and communication between these components. The user interface 403 may include a display screen, and the external communication interface 404 may include a standard wired interface and a wireless interface. The processor 401 is configured to execute the program of the risk warning method stored in the memory to implement the steps in the risk warning method provided in the above embodiment.

[0095] It should be noted here that the description of the above storage medium and electronic device embodiments is similar to the description of the above method embodiments, and has similar beneficial effects as the method embodiments. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0096] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the size of the sequence number of the above-mentioned processes does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned sequence numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0097] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, object or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, object or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, object or device including the element.

[0098] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0099] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0100] In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be a separate unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0101] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read On ly Memory), disks or optical disks, etc. Various media that can store program codes.

[0102] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application can essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0103] The above is only an implementation method of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A risk early warning method for oil and gas surface engineering, characterized in that: include: Construct a risk indicator system for oil and gas surface engineering; Determining the first weight of the risk indicator in the risk indicator system by using the analytic hierarchy process; A triangular fuzzy analytic hierarchy process based on the COWA operator is used to determine the second weight of the risk indicator in the risk indicator system; Using a game weighting method to combine and weight the first weight and the second weight to obtain a comprehensive weight of the risk indicator; Construct a risk early warning model based on the comprehensive weight of risk indicators and matter-element analysis; The risk level is determined through the risk early warning model.

2. The method according to claim 1, characterized in that include: Analyze the accident case database of oil and gas surface engineering according to the causes of accidents and determine the risk influencing factors; Construct a risk indicator system based on risk influencing factors.

3. The method according to claim 1, characterized in that The risk indicator system includes first-level risk indicators: economic risk, environmental risk, social risk, and technical and quality risk; the economic risk includes second-level indicators: funding source risk, material price risk, project progress risk, change and claim risk, annual appropriation amount during operation, and project investment amount; the environmental risk includes second-level indicators: hydrological and meteorological risk, environmental pollution risk, geological condition risk, and environmental adaptability; the social risk includes second-level indicators: policy risk, unemployment status, major event suspension risk, social adaptability, and public satisfaction; the technical and quality risk includes second-level indicators: construction technology risk, personnel quality risk, design error risk, and material quality risk.

4. The method according to claim 3, characterized in that include: Compare the secondary risk assessment indicators pairwise, determine their relative importance, and construct a judgment matrix; Calculate the maximum eigenvalue of the judgment matrix; By checking the maximum characteristic root, determine whether the judgment matrix is ​​consistent; When the judgment matrix is ​​consistent, the first evaluation index weight is calculated using the arithmetic square root.

5. The method according to claim 3, characterized in that: include: Construct the original data set of secondary indicators and arrange the elements of the original data set from small to large; Calculate the weight vector of the combination number of the original data set of the secondary index; Based on the weighted vector of the number of combinations, calculate the absolute weight of the corresponding secondary index; Normalize the absolute weights to get the second weight of the secondary indicator.

6. The method according to claim 1, characterized in that include: According to the first weight and the second weight, setting a linear combination coefficient; Linearly combine the linear combination coefficient with the first weight and the second weight to obtain a combined weight; Taking the deviation and minimization of the combination weight from the first weight and the second weight as the goal, optimizing the linear combination coefficient to obtain an optimization objective function; According to the matrix differential properties, the optimization objective function is converted into a linear equation system in the corresponding matrix form to obtain the optimal combination coefficient; Normalizing the optimal combination coefficient to obtain an optimal weight coefficient; Based on the optimal weight coefficient, the first weight and the second weight are combined to obtain the comprehensive weight.

7. The method according to claim 1, characterized in that include: Collect metadata of the object to be evaluated based on the comprehensive weight of risk indicators; Determine the classical domain and the section domain according to the metadata of the object to be evaluated and the interval where its value lies; Calculate the correlation degree based on the risk index and the matter-element to be evaluated; Determine the risk level corresponding to each risk indicator based on the correlation.

8. The method according to claim 1, characterized in that The risk levels include: safe, relatively safe, basically safe, relatively dangerous, and dangerous.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method as claimed in any one of claims 1 to 8.

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