Man-machine interaction operation risk assessment method and device in oil and gas drilling and production process

Abstract

The invention provides a man-machine interaction operation risk assessment method and device in an oil and gas drilling and production process. Firstly, influence factors between any two operation nodes in the man-machine interaction process are obtained, first influence degrees and first occurrence probabilities corresponding to the influence factors are obtained, and for each influence factor, defuzzification processing is carried out on the first influence degree corresponding to the influence factor to obtain second influence degrees of the influence factors; and performing defuzzification processing on the first occurrence probability corresponding to the influence factor to obtain a second occurrence probability corresponding to the influence factor, and aiming at each influence factor, according to the second influence degree and the second occurrence probability corresponding to the influence factor, obtaining a second occurrence probability corresponding to the influence factor. And determining the comprehensive influence degree of the influence factors, and performing risk assessment on man-machine interaction communication according to the comprehensive influence degree of each influence factor of the man-machine interaction process. By quantifying the influence factors in the man-machine interaction process, the risk levels of the influence factors can be accurately obtained.

Description

Technical Field

[0001] The present application relates to the field of human-computer interaction technology, and in particular to a method and device for assessing the risk of human-computer interaction operations in oil and gas drilling and production processes. Background Art

[0002] Intelligent well site refers to a comprehensive system that integrates a variety of intelligent equipment and systems to achieve comprehensive monitoring, automated operation and intelligent decision-making of well site operations. At present, the 7000m automated drilling rig is equipped with a full set of pipe string automation equipment and wellhead mechanized tools, which realizes the full process of automated transportation, screwing and pipe laying of the entire pipe string from the ground to the drilling platform and then to the second-floor platform, thereby improving work efficiency. At present, the development of intelligent well sites is still in its infancy, and the well sites currently used can only realize the automation of the full process of pipe string operations. There is still a certain distance to achieve a fully intelligent well site.

[0003] In the automation of the whole process, human-machine interaction is widely used. For example, the operator can accurately control the lifting speed and height of the suspension system through the control panel to ensure that the suspension system can accurately lift the drill bit to the specified position. In the above process, the risks that may arise are: failure of the suspension system, operator error, resulting in inaccurate alignment between the drill bit and the wellhead. The above risks are evaluated to determine the risk level, and then when risks occur, the risk factors with higher risk levels are prioritized according to the risk level, which helps to quickly respond to and solve problems and reduce the degree of losses.

[0004] However, risk assessment in existing technologies mainly adopts manual assessment, which is highly subjective and difficult to quantify. As a result, the determination of risk levels is too subjective and direct comparisons cannot be made between different risks. As a result, decision makers cannot formulate response measures and optimize resource allocation based on the importance and urgency of risks. Summary of the invention

[0005] The present application provides a method and device for assessing the risks of human-computer interaction operations in the oil and gas drilling and production process, which are used to quantitatively assess the risks that arise in the human-computer interaction process, and then accurately derive the risk level, so that decision makers can formulate response measures based on the accurate risk level and optimize resource allocation.

[0006] In a first aspect, the present application provides a method for risk assessment of human-machine interaction operations in oil and gas drilling and production processes, comprising:

[0007] Obtaining an influencing factor between any two operation nodes in a human-computer interaction process, and obtaining a first influence degree and a first occurrence probability corresponding to the influencing factor; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers;

[0008] For each of the influencing factors, defuzzify the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor; and defuzzify the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor;

[0009] For each of the influencing factors, determining a comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor;

[0010] According to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process, a risk assessment is performed on the human-computer interaction communication.

[0011] In a possible implementation manner, the obtaining the first influence degree and the first occurrence probability corresponding to the influencing factor includes: for each of the influencing factors, obtaining a third influence degree and a third occurrence probability set by a plurality of experts for the influencing factor;

[0012] For each of the experts, the weight of the expert is determined according to the basic information of the expert; the basic information of the expert includes at least one of the following: education level, professional position and years of work experience;

[0013] For each of the influencing factors, the third degree of influence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first degree of influence of the influencing factor, and the third probability of occurrence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first probability of occurrence of the influencing factor.

[0014] In a possible implementation manner, obtaining the third influence degree and the third occurrence probability set by the plurality of experts for the influencing factor includes:

[0015] Obtaining impact degree evaluation texts and occurrence probability evaluation texts for the impact factor settings from multiple experts;

[0016] A corresponding third impact degree is determined according to the impact degree evaluation text, and a corresponding third occurrence probability is determined according to the occurrence probability evaluation text, wherein both the third impact degree and the third occurrence probability are triangular fuzzy numbers.

[0017] In a possible implementation manner, determining the weight of the expert according to the basic information of the expert includes:

[0018] Converting the basic information of the expert from text to corresponding numerical values;

[0019] Determine an intermediate weight of the expert according to at least one of the numerical values ​​of the expert, wherein the intermediate weight is positively correlated with each of the numerical values;

[0020] According to the sum of the intermediate weights of the multiple experts, the intermediate weight of each expert is normalized to obtain the weight of each expert.

[0021] In a possible implementation, performing a defuzzification process on the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor, and performing a defuzzification process on the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor includes:

[0022] The first influence degree is defuzzified according to an influence degree defuzzification formula to obtain a second influence degree of the influencing factor. The influence degree defuzzification formula is:

[0023]

[0024] Among them, X i is the second influence degree of the influencing factor, and a1, a2, and a3 are triangular fuzzy numbers of the first influence degree respectively;

[0025] The first occurrence probability is defuzzified according to the occurrence probability defuzzification formula to obtain the second occurrence probability of the influencing factor. The occurrence probability defuzzification formula is:

[0026]

[0027] Among them, Y i is the second occurrence probability of the occurrence probability, and b1, b2, and b3 are respectively the triangular fuzzy numbers of the first occurrence probability.

[0028] In a possible implementation manner, performing risk assessment on the human-computer interaction communication according to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process includes:

[0029] According to the comprehensive impact degree threshold, the impact factor whose comprehensive impact degree is greater than or equal to the comprehensive impact degree threshold is determined as a high-risk impact factor;

[0030] Determine the influencing factors whose comprehensive impact degree is less than the comprehensive impact degree threshold as low-risk influencing factors;

[0031] The high-risk influencing factors are continuously monitored, and the low-risk influencing factors are regularly monitored.

[0032] In a second aspect, the present application provides a human-machine interactive operation risk assessment device for oil and gas drilling and production processes, comprising:

[0033] An acquisition module, used to acquire an influencing factor between any two operation nodes in a human-computer interaction process, and acquire a first influence degree and a first occurrence probability corresponding to the influencing factor; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers;

[0034] a processing module, configured to, for each of the influencing factors, perform a defuzzification process on the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor; and perform a defuzzification process on the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor;

[0035] The processing module is further configured to determine, for each of the influencing factors, a comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor;

[0036] The processing module is further used to perform risk assessment on the human-computer interaction communication according to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process.

[0037] In a human-machine interactive operation risk assessment device for oil and gas drilling and production provided in this application:

[0038] A processing module, for determining the weight of each expert according to the basic information of the expert; the basic information of the expert includes at least one of the following: education level, professional position and years of work experience;

[0039] The processing module is also used to weight the third influence degree set for the influencing factor by each expert according to the weight of each expert to obtain the first influence degree of the influencing factor, and to weight the third occurrence probability set for the influencing factor by each expert according to the weight of each expert to obtain the first occurrence probability of the influencing factor.

[0040] In a human-machine interactive operation risk assessment device for oil and gas drilling and production provided in this application:

[0041] An acquisition module, used for acquiring the impact degree evaluation text and the occurrence probability evaluation text set by multiple experts for the impact factor;

[0042] A processing module is used to determine a corresponding third impact degree according to the impact degree evaluation text, and to determine a corresponding third occurrence probability according to the occurrence probability evaluation text, wherein both the third impact degree and the third occurrence probability are triangular fuzzy numbers.

[0043] In a human-machine interactive operation risk assessment device for oil and gas drilling and production provided in this application:

[0044] A processing module, used for converting the basic information of the expert from text into corresponding numerical values;

[0045] a processing module, configured to determine an intermediate weight of the expert according to at least one of the numerical values ​​of the expert, wherein the intermediate weight is positively correlated with each of the numerical values;

[0046] The processing module is further used to perform unitization processing on the intermediate weight of each expert according to the sum of the intermediate weights of multiple experts to obtain the weight of each expert.

[0047] In a human-machine interactive operation risk assessment device for oil and gas drilling and production provided in this application:

[0048] A processing module is used to perform defuzzification processing on the first influence degree according to an influence degree defuzzification formula to obtain a second influence degree of the influencing factor, and the influence degree defuzzification formula is:

[0049]

[0050] Among them, X i is the second influence degree of the influencing factor, and a1, a2, and a3 are triangular fuzzy numbers of the first influence degree respectively;

[0051] The processing module is further used to perform defuzzification processing on the first occurrence probability according to an occurrence probability defuzzification formula to obtain a second occurrence probability of the occurrence probability, and the occurrence probability defuzzification formula is:

[0052]

[0053] Among them, Y i is the second occurrence probability of the occurrence probability, and b1, b2, and b3 are respectively the triangular fuzzy numbers of the first occurrence probability.

[0054] In a human-machine interactive operation risk assessment device for oil and gas drilling and production provided in this application:

[0055] A processing module, configured to determine, according to a comprehensive impact degree threshold, the impact factor whose comprehensive impact degree is greater than or equal to the comprehensive impact degree threshold as a high-risk impact factor;

[0056] The processing module is further used to determine the influencing factors whose comprehensive influencing degree is less than the comprehensive influencing degree threshold as low-risk influencing factors;

[0057] The processing module is also used to continuously monitor the high-risk influencing factors and regularly monitor the low-risk influencing factors.

[0058] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0059] The memory stores computer-executable instructions;

[0060] The processor executes the computer-executable instructions stored in the memory to implement a human-computer interaction operation risk assessment method as described in any one of the first aspects.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement a human-computer interaction operation risk assessment method for oil and gas drilling and production processes as described in any one of the first aspects.

[0062] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement a method for risk assessment of human-computer interaction operations in oil and gas drilling and production processes as described in any one of the first aspects.

[0063] The present application provides a method and device for risk assessment of human-computer interaction operations in the oil and gas drilling and production process, which obtains the influencing factors between any two operation nodes in the human-computer interaction process, and obtains the first influence degree and the first occurrence probability corresponding to the influencing factors, wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers. Then, for each influencing factor, the first influence degree corresponding to the influencing factor is defuzzified to obtain the second influence degree of the influencing factor; and the first occurrence probability corresponding to the influencing factor is defuzzified to obtain the second occurrence probability corresponding to the influencing factor. For each influencing factor, the comprehensive influence degree of the influencing factor is determined according to the second influence degree and the second occurrence probability corresponding to the influencing factor. According to the comprehensive influence degree of each influencing factor in the human-computer interaction process, the risk of human-computer interaction communication is assessed. Compared with the prior art that directly evaluates the risk level manually, the present application first determines the first degree of influence and the first probability of occurrence of the influencing factors based on fuzzy numbers, and uses fuzzy numbers to determine evaluation indicators with fuzzy boundaries such as the degree of influence and the probability of occurrence, so that the evaluation results are more comprehensive and objective, and then the first degree of influence and the first probability of occurrence are converted into the second degree of influence and the second probability of occurrence through a defuzzification formula, so that the fuzzified results are restored to clear grade values, making the evaluation results more intuitive and easy to understand, and finally, according to the second degree of influence and the second probability of occurrence, the comprehensive degree of influence of the influencing factors is determined, and then according to the comprehensive degree of influence of the quantified influencing factors, the risk assessment of human-computer interaction is performed. The present application quantifies the influencing factors of the human-computer interaction process, and then can accurately derive the risk level of the influencing factors, so that decision makers can perform risk assessment according to accurate risk levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0065] Figure 1 It is a schematic diagram of the operation node;

[0066] Figure 2 A flowchart of a first embodiment of a method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment;

[0067] Figure 3 A flowchart of a second embodiment of a method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment;

[0068] Figure 4 A flowchart of a third embodiment of a method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment;

[0069] Figure 5A flowchart of a fourth embodiment of a method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment;

[0070] Figure 6 A flowchart of a fifth embodiment of a method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment;

[0071] Figure 7 A human-machine interactive operation risk assessment device for oil and gas drilling and production process provided in this embodiment;

[0072] Figure 8 A schematic diagram of the structure of an electronic device embodiment 1 provided in an embodiment of the present application.

[0073] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0074] 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, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0075] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0076] In the prior art, when conducting risk assessment in the human-computer interaction process of oil and gas drilling and production, risk assessment is only performed through subjective human determination, and the risk level cannot be quantified, resulting in the determination of risk levels being too subjective and unable to make direct comparisons between different risks. As a result, decision makers are unable to formulate response measures and optimize resource allocation based on the importance and urgency of the risks.

[0077] Based on this, the inventive concept of this application is to provide a new risk assessment method for human-computer interaction operations in the oil and gas drilling and production process. The risk level of the influencing factors is first determined by fuzzy numbers, and then the precise degree of influence of the influencing factors is obtained through defuzzification, and then the risk assessment results of the influencing factors are quantified. The risk of the influencing factors is assessed based on the accurate degree of influence results.

[0078] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0079] In the process of full-process operation automation of intelligent well sites, professionals sort out the operation process of on-site operators in the intelligent well site during the automation process through on-site investigation and data query, and divide the overall operation process into multiple operation nodes, such as Figure 1 The figure shows a schematic diagram of an operation node. Each operation node constructs a hexagonal structure. Each corner of the hexagon represents a different function: I (Input), O (Output), P (Prerequisite), T (Time), R (Resource) and C (Control). The lines between each hexagon reflect the relationship between different operation nodes.

[0080] Figure 2 The flowchart of the first embodiment of the method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided by this embodiment is as follows: Figure 2 As shown, the method comprises the following steps:

[0081] S201: Obtain influencing factors between any two operation nodes in the human-computer interaction process, and obtain a first influence degree and a first occurrence probability corresponding to the influencing factors; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers.

[0082] In this embodiment, the influencing factors between any two operation nodes in the human-computer interaction process are first obtained, where any two operation nodes are related to each other, that is, Figure 1 There is a line connecting the two operation nodes shown. The first influence degree and the first occurrence probability of all influencing factors are obtained, where the first influence degree and the first occurrence probability are triangular fuzzy numbers, which are usually taken as (s, m, u), s and u are the lower and upper limits of the fuzzy number, respectively, and m is the most likely value. Describing the first influence degree and the first occurrence probability by triangular fuzzy numbers can make the first influence degree and the first occurrence probability more comprehensive and objective.

[0083] S202: For each influencing factor, defuzzify the first influence degree corresponding to the influencing factor to obtain the second influence degree of the influencing factor; and defuzzify the first occurrence probability corresponding to the influencing factor to obtain the second occurrence probability corresponding to the influencing factor.

[0084] In this embodiment, the first influence degree and the first occurrence probability of each influencing factor are defuzzified to obtain the second influence degree and the second occurrence probability of each influencing factor. By defuzzifying the triangular fuzzy numbers of the first influence degree and the first occurrence probability, the fuzzified result can be restored to a clear level value, that is, the result values ​​of the second influence degree and the second occurrence probability are definite values.

[0085] S203: For each influencing factor, determine the comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor.

[0086] In this embodiment, the second degree of influence and the second probability of occurrence of each influencing factor are comprehensively evaluated. For example, the second degree of influence and the second probability of occurrence corresponding to the influencing factor can be multiplied to obtain the comprehensive degree of influence of the influencing factor, or the second degree of influence and the second probability of occurrence corresponding to the influencing factor can be added to obtain the comprehensive degree of influence of the influencing factor, or weights can be assigned to the second degree of influence and the second probability of occurrence of the influencing factor, and then addition or multiplication operations are performed. According to actual needs, a variety of methods can be selected to obtain the comprehensive degree of influence.

[0087] S204: Conduct risk assessment on human-computer interaction based on the comprehensive impact of various influencing factors of the human-computer interaction process.

[0088] In this embodiment, all influencing factors appearing in the human-computer interaction process are ranked by obtaining the comprehensive influence degree of each influencing factor, and the risk assessment of the human-computer interaction communication is performed based on the ranking results.

[0089] In this embodiment, the influencing factors between any two operation nodes in the human-computer interaction process are obtained, and the first influence degree and the first occurrence probability corresponding to the influencing factors are obtained, wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers. Then, for each influencing factor, the first influence degree corresponding to the influencing factor is defuzzified to obtain the second influence degree of the influencing factor; and the first occurrence probability corresponding to the influencing factor is defuzzified to obtain the second occurrence probability corresponding to the influencing factor. For each influencing factor, the comprehensive influence degree of the influencing factor is determined according to the second influence degree and the second occurrence probability corresponding to the influencing factor. According to the comprehensive influence degree of each influencing factor in the human-computer interaction process, the risk assessment of the human-computer interaction communication is performed. Compared with the prior art that directly evaluates the risk level manually, the present application first determines the first degree of influence and the first probability of occurrence of the influencing factors based on fuzzy numbers, and uses fuzzy numbers to determine evaluation indicators with fuzzy boundaries such as the degree of influence and the probability of occurrence, so that the evaluation results are more comprehensive and objective, and then the first degree of influence and the first probability of occurrence are converted into the second degree of influence and the second probability of occurrence through a defuzzification formula, so that the fuzzified results are restored to clear grade values, making the evaluation results more intuitive and easy to understand, and finally, according to the second degree of influence and the second probability of occurrence, the comprehensive degree of influence of the influencing factors is determined, and then according to the comprehensive degree of influence of the quantified influencing factors, the risk assessment of human-computer interaction is performed. The present application quantifies the influencing factors of the human-computer interaction process, and then can accurately derive the risk level of the influencing factors, so that decision makers can perform risk assessment according to accurate risk levels.

[0090] Figure 3 The flowchart of the second embodiment of the method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment is as follows: Figure 3 As shown in the above Figure 2 Based on the illustrated embodiment, the specific expression of obtaining the first influence degree and the first occurrence probability corresponding to the influence factor in step S201 is:

[0091] S301: For each expert, determine the expert's weight according to the expert's basic information; the expert's basic information includes at least one of the following: education level, professional position, and years of work experience.

[0092] In this embodiment, since each expert has different professional knowledge and experience levels, the credibility of the expert's judgment is also different, so before conducting expert evaluation, it is necessary to determine the expert's weight. First, the education level, professional position or years of work of each expert is obtained, and the expert's weight is determined according to one or more of the expert's education level, professional position or years of work.

[0093] After obtaining the expert weights, the third influence degree and the third occurrence probability of each expert's setting of the influencing factors are obtained.

[0094] Obtain the impact degree evaluation text and occurrence probability evaluation text of the influencing factor settings from multiple experts.

[0095] In this embodiment, firstly, the experts' evaluation text of the degree of influence of the influencing factors and the evaluation text of the probability of occurrence are obtained. For example, as shown in Table 1, there are the evaluation text of the degree of influence of the influencing factors and the corresponding fuzzy numerical values ​​of the degree of influence. As shown in Table 2, there are the evaluation text of the probability of occurrence and the corresponding fuzzy numerical values ​​of the probability of occurrence.

[0096] Table 1

[0097]

[0098] Table 2

[0099]

[0100]

[0101] The corresponding third impact degree is determined according to the impact degree evaluation text, and the corresponding third occurrence probability is determined according to the occurrence probability evaluation text, and both the third impact degree and the third occurrence probability are triangular fuzzy numbers.

[0102] In this embodiment, each expert will give an impact degree evaluation text and an occurrence probability evaluation text for the impact factor. For example, for the impact factor: the operator's trust in the intelligent system, that is, the operator can trust the information collected by the system and use it. Expert A's impact degree evaluation text for the impact factor is none, and the occurrence probability evaluation text is very low; Expert B's impact degree evaluation text for the impact factor is slight, and the occurrence probability evaluation text is low; Expert C's impact degree evaluation text for the impact factor is slight, and the occurrence probability evaluation text is low.

[0103] According to Table 1 and Table 2, it can be concluded that the third degree of influence of this influencing factor is: Expert A evaluates it as (0, 1, 2), Expert B evaluates it as (1, 2, 3), and Expert C evaluates it as (1, 2, 3); the third probability of occurrence of this influencing factor is: Expert A evaluates it as (0, 1, 2), Expert B evaluates it as (1, 2, 3), and Expert C evaluates it as (1, 2, 3).

[0104] S302: For each influencing factor, weight the third degree of influence set by each expert for the influencing factor according to the weight of each expert to obtain the first degree of influence of the influencing factor, and weight the third probability of occurrence set by each expert for the influencing factor according to the weight of each expert to obtain the first probability of occurrence of the influencing factor.

[0105] In this embodiment, the first degree of influence of the influencing factor is obtained by weighting the weights of each expert and the third degree of influence of each expert on the influencing factor. For example, when the weight of expert A is 0.412, the weight of expert B is 0.294, and the weight of expert C is 0.294, the third degree of influence of the influencing factor being the trust of the operator in the intelligent system is: expert A evaluates as (0, 1, 2), expert B evaluates as (1, 2, 3), and expert C evaluates as (1, 2, 3). After weighting, (0, 1, 2)*0.412+(1, 2, 3)*0.294+(1, 2, 3)*0.294=(0.588, 1.588, 2.59), then the third degree of influence of the influencing factor being the trust of the operator in the intelligent system is: expert A evaluates as (0, 1, 2), expert B evaluates as (1, 2, 3), and expert C evaluates as (1, 2, 3). After weighting, (0, 1, 2)*0.412+(1, 2, 3)*0.294+(1, 2, 3)*0.294=(0.588, 1.588, 2.59). The first degree of influence is (0.588, 1.588, 2.59); for the third probability of occurrence whose influencing factor is the operator's trust in the intelligent system, it is: expert A's evaluation is (0, 1, 2), expert B's evaluation is (1, 2, 3), and expert C's evaluation is (1, 2, 3). After weighting, (0, 1, 2)*0.412+(1, 2, 3)*0.294+(1, 2, 3)*0.294=(0.588, 1.588, 2.59), then the first probability of occurrence is (0.588, 1.588, 2.59).

[0106] In this embodiment, the weights of the experts are determined based on the basic information of the experts, and then for each influencing factor, the third degree of influence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first degree of influence of the influencing factor, and the third probability of occurrence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first probability of occurrence of the influencing factor. The education level, years of work experience and professional position of the experts directly reflect the depth, breadth and practical experience of their knowledge in the field. By giving higher weights to experts who perform well in these aspects, the accuracy and reliability of the evaluation results can be ensured.

[0107] Figure 4 The flowchart of the third embodiment of the method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment is as follows: Figure 4 As shown in the above Figure 3 Based on the illustrated embodiment, a specific implementation of step S301 is as follows:

[0108] S401: Convert the expert's basic information from text to corresponding numerical values.

[0109] In this embodiment, the basic information is converted from text to corresponding values ​​according to the given basic information evaluation correspondence. For example, the education level and working years of three experts are given, and the weights of the three experts are determined. Table 3 shows the evaluation indicators and scores of education level and working years. Table 4 shows the information of the three experts.

[0110] Table 3

[0111]

[0112] Table 4

[0113] expert Education level Years of working experience First PhD 26 Second master 12 C College 37

[0114] According to Table 1, we can conclude that Expert A’s education level score is 4 and his years of work experience score is 3; Expert B’s education level score is 3 and his years of work experience score is 2; Expert C’s education level score is 1 and his years of work experience score is 4.

[0115] S402: Determine an intermediate weight of the expert according to at least one numerical value of the expert, where the intermediate weight is positively correlated with each numerical value.

[0116] In this embodiment, the intermediate weight of the expert is determined based on the numerical value corresponding to the obtained expert basic information, and the intermediate weight of experts A, B, and C is the sum of the education level score and the working experience score, that is, the intermediate weight of expert A is 7, the sum of the intermediate weights of expert B is 5, and the sum of the intermediate weights of expert C is 5.

[0117] S403: According to the sum of the intermediate weights of multiple experts, the intermediate weight of each expert is normalized to obtain the weight of each expert.

[0118] In this embodiment, the intermediate weight of each expert is normalized according to the sum of the intermediate weights of the experts to obtain the weight of each expert. For example, the intermediate weight of expert A is 7, the sum of the intermediate weights of expert B is 5, and the sum of the intermediate weights of expert C is 5. The intermediate weights of the three experts are normalized to obtain the weight of expert A is 0.412, the weight of expert B is 0.294, and the weight of expert C is 0.294.

[0119] In this embodiment, since each expert has different professional knowledge and experience levels, the credibility of their judgments on influencing factors is also different. Therefore, before the experts evaluate the influencing factors, it is necessary to first determine the weight of each expert based on the expert's education level, years of work experience, and professional position, so as to ensure the accuracy and reliability of the evaluation results.

[0120] Figure 5 The flowchart of the fourth embodiment of the method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided by this embodiment is as follows: Figure 5 As shown in the above Figure 2 Based on the illustrated embodiment, a specific implementation of step S202 is as follows:

[0121] S501: Defuzzify the first influence degree according to the influence degree defuzzification formula to obtain the second influence degree of the influencing factor. The influence degree defuzzification formula is:

[0122]

[0123] Among them, X i is the second influence degree of the influencing factor, a1, a2, a3 are the triangular fuzzy numbers of the first influence degree respectively.

[0124] In this embodiment, the first influence degree of the influencing factor is defuzzified to obtain the second influence degree of the influencing factor. For example, the first influence degree of the influencing factor being the operator's trust in the intelligent system is defuzzified, that is, (0.588, 1.588, 2.59) is defuzzified according to the influence degree defuzzification formula, that is, a1=0.588, a2=1.588, a3=2.59, and the second influence degree of the operator's trust in the intelligent system is 1.588.

[0125] S502: Defuzzify the first occurrence probability according to the occurrence probability defuzzification formula to obtain the second occurrence probability of the influencing factor. The occurrence probability defuzzification formula is:

[0126]

[0127] Among them, Y i is the second occurrence probability of the occurrence probability, b1, b2, b3 are the triangular fuzzy numbers of the first occurrence probability respectively.

[0128] In this embodiment, the first occurrence probability of the influencing factor is defuzzified to obtain the second occurrence probability of the influencing factor. For example, the first occurrence probability of the influencing factor being the trust degree of the operator in the intelligent system is defuzzified, that is, (0.588, 1.588, 2.59) is defuzzified according to the occurrence probability defuzzification formula, that is, b1=0.588, b2=1.588, b3=2.59, and the second occurrence probability of the trust degree of the operator in the intelligent system is 1.588, then the comprehensive influence degree X i Y i That is 1.588*1.588=2.5217.

[0129] In this embodiment, the first influence degree and the first occurrence probability of the influencing factor are defuzzified according to the defuzzification formula to obtain the second influence degree and the second occurrence probability of the influencing factor, so that the second influence degree and the second occurrence probability are clear values, and then the comprehensive influence degree of the final influencing factor is obtained by multiplying or adding the clear second influence degree and the second occurrence probability of the influencing factor. Therefore, the risk of the influencing factor of the operation node can be quantitatively evaluated, so that decision makers can perform risk assessment according to accurate risk levels.

[0130] Figure 6 The flowchart of Embodiment 5 of the method for risk assessment of human-machine interaction operation in oil and gas drilling and production provided in this embodiment is as follows: Figure 6 As shown in the above Figure 2 Based on the illustrated embodiment, a specific implementation of step S204 is as follows:

[0131] S601: According to the comprehensive impact degree threshold, an impact factor whose comprehensive impact degree is greater than or equal to the comprehensive impact degree threshold is determined as a high-risk impact factor.

[0132] In this embodiment, when the comprehensive impact degree of all influencing factors is obtained, the comprehensive impact degree threshold can be determined by experts. When the calculated comprehensive impact degree of the influencing factors is greater than or equal to the comprehensive impact degree threshold, it is determined as a high-risk influencing factor.

[0133] S602: Determine the influencing factors whose comprehensive impact degree is less than the comprehensive impact degree threshold as low-risk influencing factors;

[0134] In this embodiment, when the calculated comprehensive impact degree of the impact factor is less than the comprehensive impact degree threshold, it is determined to be a low-risk impact factor.

[0135] S603: Conduct continuous monitoring of high-risk influencing factors and regular monitoring of low-risk influencing factors.

[0136] In this embodiment, long-term continuous monitoring should be implemented for the defined high-risk influencing factors. Through continuous monitoring, the status of the high-risk influencing factors can be grasped in real time. Once an abnormal situation is found, measures can be taken quickly to intervene to prevent accidents or mitigate the consequences of accidents. Compared with high-risk influencing factors, the defined low-risk influencing factors have a relatively low degree of harm. Therefore, regular monitoring can be adopted to optimize resource allocation and avoid waste of resources caused by excessive monitoring.

[0137] Figure 7The present embodiment provides a human-machine interactive operation risk assessment device for oil and gas drilling and production. The present application provides a human-machine interactive operation risk assessment device for oil and gas drilling and production, including: an acquisition module 71 and a processing module 72; wherein:

[0138] The acquisition module 71 is used to obtain the influencing factors between any two operation nodes in the human-computer interaction process, and to obtain the first influence degree and the first occurrence probability corresponding to the influencing factors; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers; the processing module 72 is used to defuzzify the first influence degree corresponding to each influencing factor to obtain the second influence degree of the influencing factor; and to defuzzify the first occurrence probability corresponding to the influencing factor to obtain the second occurrence probability corresponding to the influencing factor; the processing module 72 is also used to determine the comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor for each influencing factor; the processing module 72 is also used to perform risk assessment on the human-computer interaction communication according to the comprehensive influence degree of each influencing factor in the human-computer interaction process.

[0139] Figure 8 This is a structural diagram of an electronic device embodiment 1 provided in the present application. Figure 8 The server provided in this embodiment includes: at least one processor 81 and a memory 82. The processor 81 and the memory 82 are connected via a bus 83.

[0140] The specific implementation process of the processor 81 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0141] In the above Figure 8 In the illustrated embodiment, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0142] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0143] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0144] The present application also provides a computer-readable storage medium having computer-executable instructions stored thereon. When a processor executes the computer-executable instructions, a human-computer interactive operation risk assessment method for oil and gas drilling and production processes as described above is implemented.

[0145] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The computer-readable storage medium may be any available medium that can be accessed by a general or special purpose computer.

[0146] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0147] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0148] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, 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 solution of this embodiment.

[0149] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0150] If the function 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 this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0151] The present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it is used to implement the above-mentioned human-computer interactive operation risk assessment method for oil and gas drilling and production processes.

[0152] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0153] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0154] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0155] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0156] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. If not specifically stated, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. If not specifically stated, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc.

[0157] If the integrated unit / module is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0158] In the above embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0160] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for risk assessment of human-computer interaction operation in oil and gas drilling and production process, characterized in that: include: Obtaining an influencing factor between any two operation nodes in a human-computer interaction process, and obtaining a first influence degree and a first occurrence probability corresponding to the influencing factor; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers; For each of the influencing factors, defuzzify the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor; and defuzzify the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor; For each of the influencing factors, determining a comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor; According to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process, a risk assessment is performed on the human-computer interaction communication.

2. The method according to claim 1, characterized in that The obtaining of the first influence degree and the first occurrence probability corresponding to the influencing factor includes: for each of the influencing factors, obtaining a third influence degree and a third occurrence probability set by a plurality of experts for the influencing factor; For each of the experts, the weight of the expert is determined according to the basic information of the expert; the basic information of the expert includes at least one of the following: education level, professional position and years of work experience; For each of the influencing factors, the third degree of influence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first degree of influence of the influencing factor, and the third probability of occurrence set by each expert for the influencing factor is weighted according to the weight of each expert to obtain the first probability of occurrence of the influencing factor.

3. The method according to claim 2, characterized in that The obtaining of the third influence degree and the third occurrence probability set by the plurality of experts for the influencing factor comprises: Obtaining impact degree evaluation texts and occurrence probability evaluation texts for the impact factor settings from multiple experts; A corresponding third impact degree is determined according to the impact degree evaluation text, and a corresponding third occurrence probability is determined according to the occurrence probability evaluation text, wherein both the third impact degree and the third occurrence probability are triangular fuzzy numbers.

4. The method according to claim 3, characterized in that Determining the weight of the expert according to the basic information of the expert includes: Converting the basic information of the expert from text to corresponding numerical values; Determine an intermediate weight of the expert according to at least one of the numerical values ​​of the expert, wherein the intermediate weight is positively correlated with each of the numerical values; According to the sum of the intermediate weights of the multiple experts, the intermediate weight of each expert is normalized to obtain the weight of each expert.

5. The method according to any one of claims 1 to 4, characterized in that Performing a defuzzification process on the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor, and performing a defuzzification process on the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor includes: The first influence degree is defuzzified according to an influence degree defuzzification formula to obtain a second influence degree of the influencing factor. The influence degree defuzzification formula is: Among them, X i is the second influence degree of the influencing factor, and a1, a2, and a3 are triangular fuzzy numbers of the first influence degree respectively; The first occurrence probability is defuzzified according to the occurrence probability defuzzification formula to obtain the second occurrence probability of the influencing factor. The occurrence probability defuzzification formula is: Among them, Y i is the second occurrence probability of the occurrence probability, and b1, b2, and b3 are respectively the triangular fuzzy numbers of the first occurrence probability.

6. The method according to claim 1, characterized in that According to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process, the risk assessment of the human-computer interaction communication includes: According to the comprehensive impact degree threshold, the impact factor whose comprehensive impact degree is greater than or equal to the comprehensive impact degree threshold is determined as a high-risk impact factor; Determine the influencing factors whose comprehensive impact degree is less than the comprehensive impact degree threshold as low-risk influencing factors; The high-risk influencing factors are continuously monitored, and the low-risk influencing factors are regularly monitored.

7. A human-machine interactive operation risk assessment device for oil and gas drilling and production, characterized in that: include: An acquisition module, used to acquire an influencing factor between any two operation nodes in a human-computer interaction process, and acquire a first influence degree and a first occurrence probability corresponding to the influencing factor; wherein the first influence degree and the first occurrence probability are both triangular fuzzy numbers; a processing module, configured to, for each of the influencing factors, perform a defuzzification process on the first influence degree corresponding to the influencing factor to obtain a second influence degree of the influencing factor; and perform a defuzzification process on the first occurrence probability corresponding to the influencing factor to obtain a second occurrence probability corresponding to the influencing factor; The processing module is further configured to determine, for each of the influencing factors, a comprehensive influence degree of the influencing factor according to the second influence degree and the second occurrence probability corresponding to the influencing factor; The processing module is further used to perform risk assessment on the human-computer interaction communication according to the comprehensive influence degree of each of the influencing factors of the human-computer interaction process.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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