CN106600167B - Ocean platform fire risk assessment method considering human errors and tissue defects - Google Patents
Ocean platform fire risk assessment method considering human errors and tissue defects Download PDFInfo
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Abstract
The invention discloses an ocean platform fire risk assessment method considering human errors and organization defects, which comprises the steps of establishing an HFACS model suitable for ocean platform human error analysis according to characteristics of ocean platform fire accidents, determining technical factors and human organization factors causing ocean platform fire, and establishing an ocean platform fire dynamic Bayesian network model; calculating the prior probability of the human factor tissue factor in the dynamic Bayesian network model; calculating the conditional probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model based on a triangular fuzzy function and a grade node distance formula; based on a Markov model, calculating the transition probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model: and calculating the probability of the fire of the ocean platform by adopting Netica software according to the calculated prior probability, the conditional probability and the transition probability.
Description
Technical Field
The invention relates to a risk assessment method for fire occurrence probability, in particular to a marine platform fire risk assessment method considering human errors and organization defects.
Background
Ocean platform environment is abominable, and equipment is highly concentrated, has a large amount of flammable and explosive materials and leaks the source with leakage such as oil gas pipeline, flange, and some artificial factor effect in addition leads to oil gas leakage easily, and then probably arouses big fire and explosion, and this not only can cause casualties and heavy economic loss, still can cause serious pollution and destruction to coastal surrounding environment and marine ecology. The high cost of ocean platforms, the severity of fire consequences, and the complex and varied sea environment determine the necessity and difficulty of fire risk assessment.
Ocean oil and gas development is one of the world's recognized industries with the greatest risk. The ocean platform is a basic facility for developing ocean oil and natural gas resources, and has the disadvantages of complex design, dense high-end equipment and high manufacturing cost. The ocean platform integrates drilling, oil testing, oil (natural gas) production, underground operation, oil and gas gathering and transportation, primary processing and storage and transportation, a large amount of flammable and explosive substances exist, and the fire load is very large. Statistically, fire accidents are the most reported types of accidents on ocean platforms. Once a fire accident occurs, it is very likely to cause great economic loss and casualties.
Statistics show that ocean platform fire accidents are mostly related to Human Organization Error (HOE). However, current ocean platform fire risk assessment is too focused on the potential failure probability of equipment and structures to ignore the effect of HOEs. HOE analysis as an important component of Quantitative Risk Assessment (QRA) has hindered the development and application of QRA due to limitations of analytical methods and lack of HOE data. The current HOE analysis method mainly has the following defects: lack of sufficient data, rely on expert's judgement more; the dynamics of the cognitive process and the dynamics of human-computer interaction cannot be simulated really; the credibility of the method is difficult to verify, etc.
Disclosure of Invention
Aiming at the defects in the prior art, the ocean platform fire risk assessment method considering the human errors and the tissue defects introduces a dynamic Bayesian network model in the risk assessment, and can describe the dynamic risk of the ocean platform fire accidents and quantitatively simulate the influence of the human errors and the tissue errors.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the method for evaluating the fire risk of the ocean platform by considering human errors and tissue defects comprises the following steps:
according to the characteristics of the fire accident of the ocean platform, establishing an HFACS model suitable for human error analysis of the ocean platform, determining technical factors and human factors and tissue factors causing the fire of the ocean platform, and establishing a dynamic Bayesian network model of the fire of the ocean platform;
obtaining the prior probability of the technical factors by adopting a mathematical statistical method according to historical empirical data, and calculating the prior probability of the human factor tissue factors in the dynamic Bayesian network model by adopting an expert scoring in the field and combining an evidence theory method;
calculating the conditional probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model based on a triangular fuzzy function and a grade node distance formula;
based on a Markov model, calculating the transition probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model:
based on a Markov state transition model, calculating the transition probability of the variable under the condition that the equipment is not in a repair state and is not in an incomplete maintenance state; calculating the human error transition probability based on a homogeneous Markov state transition model; obtaining the state transition probability of the tissue factor variable based on the introduced system repair factor;
and calculating the probability of the fire of the ocean platform by adopting Netica software according to the calculated prior probability, the conditional probability and the transition probability.
The invention has the beneficial effects that: the scheme can provide corresponding fire probability dynamic prediction aiming at different fire scenes of the ocean platform, research equipment faults, human errors and organization errors which possibly cause ocean platform fire accidents, explore the influence degree of the three on the ocean platform fire accident probability and the probability of possibly causing fire, and provide reference for ocean platform production and managers to make effective risk control measures.
In addition, the influence of the HOE on the fire risk of the ocean platform can be quantitatively analyzed, a risk control scheme is determined from the root of the occurrence of the HOE, and the fire risk of the ocean platform is reduced.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for evaluating a fire risk of an ocean platform considering human error and tissue defects.
Fig. 2 is a triangular blur function for arrival when calculating the conditional probability.
Figure 3 is a markov state transition model for arriving when computing device failover probabilities.
FIG. 4 is a HFACS model suitable for human error analysis of an ocean platform.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Referring to FIG. 1, FIG. 1 illustrates a flow diagram 100 of one embodiment of a method for ocean platform fire risk assessment that accounts for human error and organizational defects. As shown in fig. 1, the method 100 includes steps 101 to 105.
In step 101, according to the characteristics of the fire accident of the ocean platform, a HFACS model suitable for human error analysis of the ocean platform is established, technical factors and human organization factors causing the fire of the ocean platform are determined, and a dynamic Bayesian network model of the fire of the ocean platform is established.
The method for determining the technical factors and the human factors and the organization factors comprises the following steps of firstly determining main operation activities and operation units of the ocean platform with fire hazard according to a historical accident report of the ocean platform fire accident; and then analyzing to obtain the main cause of the fire accident.
For example, technical factors of the oil-gas separation unit (the following are attributed to technical factors except for the case of human error and organization error), and human factors and organization factors (human and organization error) can be subdivided into the following cases:
in the oil-gas separation process, due to the fact that personnel misoperation or accidents occur, phenomena of local high pressure, high temperature, excessive flow and the like are caused, if monitoring is not timely, overpressure, overtemperature and excessive flow of equipment are caused due to process abnormity, and further process equipment is leaked, meanwhile, a large number of process facilities, pressure containers, pipelines and valves are arranged on the separation/compression unit, and leakage and damage of the pipelines, the valves and the like are possibly caused due to accidents of corrosion, unreasonable design or falling object collision and the like. If the gas monitoring equipment fails, or personnel do not timely detect hydrocarbon leakage, or take wrong plugging measures, the hydrocarbon leakage can be diffused, and the ignition hazard is increased. Once the leaked hydrocarbons encounter a fire source, a fire accident is very likely to occur. Analyzing the fire hazard of the oil and gas processing unit of the ocean platform from the aspects of hydrocarbon leakage and ignition source:
(1) the hydrocarbon leakage source of the oil gas treatment unit is mainly applied to the damaged parts or areas of process facilities, valves, flanges and pipelines, and the leakage scene of the main hydrocarbon is as follows:
firstly, a local high temperature/high pressure/excessive flow is caused by human misoperation or an accident, and process abnormality is not monitored in time, so that process equipment is leaked;
the use of different types of valves/flanges due to manual operation errors causes hydrocarbon leakage;
hydrocarbon leakage caused by valve/flange/pipeline damage caused by internal corrosion, external corrosion and abrasion;
fourthly, equipment damage caused by vibration and impact causes leakage or natural gas release;
in the checking/maintaining process, the valve/flange is removed, and no safety mark or safety mark is pasted on the valve/flange and damaged, thus causing the leakage of the leakage valve/flange.
(2) Hydrocarbon leak diffusion
Compared with equipment and device faults, emergency response of personnel has important influence on chain reaction of accidents. If the operator fails to find out the process abnormality in time or takes wrong plugging measures, the repair of the leakage point is delayed, and the leakage is diffused, the ignition probability is increased.
(3) Main ignition source of oil gas treatment unit
Firstly, the hot surface of an ethylene glycol reboiler, the hot surface of a press, the hot surface of an engine and other equipment are used for discharging waste gas discharged by a pipeline;
welding sparks and welding slag generated in the process of welding or cutting operation on site;
thirdly, electric sparks are generated when the electric equipment fails;
fourthly, sparks are caused by static electricity;
spark generated by mechanical collision between equipment and tools during operation;
sixthly, the seeds are burned and the cigarette is smoked.
(4) Description of the fire accident scenario where hydrocarbon leaks are ignited in an oil and gas processing unit:
immediately igniting: if ignited immediately after a leak, a continuous leak of the mixture will result in a jet fire.
Ignition is delayed: if the leak is delayed from igniting, a cloud of flammable vapors may form and drift downwind, in which case a flash fire, cloud of vapors may explode if the cloud of vapors encounters an ignition source.
In implementation, the HFACS model for the human error analysis of the ocean platform is constructed by combining the fire accident characteristics of the ocean platform on the basis of the HFACS model in the aviation field, and the improved HFACS model refers to fig. 4.
In step 102, based on statistical analysis of the ocean platform fire accident historical data, the prior probabilities of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model are obtained by combining an evidence theory method;
the obtained prior probability comprises the prior probability of the technical factor and the prior probability of the human factor tissue factor; the prior probability of the technical factors can be obtained by adopting a mathematical statistical method according to historical empirical data; the prior probability of the human factor tissue factor can be calculated by combining an evidence theory method after expert scoring in the field.
When expert evaluation is adopted, according to an evidence theory method, each piece of expert information can be fused into credible data information, and the evidence theory combination method is calculated through the following formula:
wherein m is1(pa) And m2(pb) Respectively represent experts m1、m2Scoring of the same event; k denotes a conflict of expert information.
When n expert scoring data need to be integrated, the following steps are performed:
in step 103, calculating the conditional probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model based on the triangular fuzzy function and the grade node distance formula;
the specific algorithm of the conditional probability is as follows:
wherein R is a result distribution index indicating all probability densities that may cause a fire to occur; j is the state of the parent node all possible; zjIs the probability of all possible states of the parent node; a is the first state of the father node; f is the last state of the father node; the father node is the cause of fire, and the child node is the father node possibly bringing consequence; e is the natural logarithm.
In one embodiment of the present invention, the calculation of the conditional probability of all variables describing the occurrence and development process of the fire accident in the dynamic bayesian network model can be subdivided into the following four steps:
(1) all state assignments are given to nodes (all nodes mentioned in the application document represent relevant variables capable of describing the fire accident occurrence and development process, and comprise parent nodes and child nodes mentioned in the application document).
For example, variables X, Y, Z share state variables (very high, high, medium, low, very low), then these 5 states are assigned with respective values: the term "high" is 1, high "2, medium" 3, low "4, and term" low "5.
(2) Determining weight omega of parent node based on triangular fuzzy functioni：
Determining the fuzzy grade of the triangular fuzzy function. Firstly, establishing fuzzy grades evaluated by experts, wherein 9 fuzzy grades are adopted in the text, and the fuzzy grades are shown in a table 1; the triangular blur function is built according to table 1, see fig. 2.
TABLE 1 fuzzy grades
Determining initial weight
The initial weight D of the parent node is calculated by equation (1):
③ defuzzification
DX1(l1,m1,μ1) And DX2(l2,m2,μ2) Is a triangular fuzzy number, DX1≥DX2Is defined by a trigonometric fuzzy function as:
if the multi-target nodes need to be fuzzified, the final weight of each node is calculated according to the following formula:
d(Xi)＝minν(DXi≥DX3，DX2,L,DXn) (3)
and finally, carrying out normalization processing on the obtained weight values to obtain the normalized weight of each father node:
(3) calculating all node states Zj
Where n is the number of parent nodes and j is all possible states of the parent nodes. Absolute values are used to indicate distance, that is, whether positive or negative, their importance to the child node is the same.
(4) Calculating a sub-node conditional probability Pj
R is the result distribution index, representing the probability density of all possible results. If the R index value is large, the state of the father node is very similar to that of the son node. The determination of the R-index distribution can be directly determined by expert judgment.
In step 104, based on the markov model, the transition probabilities of all variables describing the fire accident occurrence and development process in the dynamic bayesian network model are calculated:
calculating the transition probability of the variable of the equipment in a non-repair state and an incomplete maintenance state based on a Markov state transition model; calculating the human error transition probability based on a homogeneous Markov state transition model; obtaining the state transition probability of the tissue factor variable based on the introduced system repair factor;
parent nodes of a dynamic bayesian network have multiple states, and the state values of the parent nodes transition over time. The device has three hierarchical states: low, uncompellet, high. low represents that the equipment is in a fault state and cannot meet the system requirement; incomplete indicates that the functional part of the equipment is damaged and works with diseases; high indicates that the device is functioning properly and has no faults. When the equipment is detected to be in a low state, performing repair operation, and if complete repair is performed, recovering the equipment from the low state to a high state; if incomplete maintenance is performed, the device may revert from the low state to the high state or may revert to the incomplete state. Assuming that the device failure rate λ and the repair rate μ obey exponential distribution, a markov state transition model of the device is established, as shown in fig. 3.
In an embodiment of the present invention, a specific algorithm for calculating the transition probability of the variable in the non-repair state and incomplete-maintenance state of the device by using the established markov state transition model is as follows:
the probability of the equipment failure at the time t and the next time t + delta t is as follows:
P(Nt+1＝yes|Nt＝yes)＝e-μΔt
the probability that the equipment fails at the time t and the equipment fails at the next time t + delta t is as follows:
P(Nt+1＝no|Nt＝yes)＝1-e-μΔt
the probability that the equipment does not have a fault at the time t and the equipment has a fault at the next time t + delta t is as follows:
P(Nt+1＝yes|Nt＝no)＝1-e-λΔt
the probability that the equipment does not have fault at the moment t and the next moment t + delta t is as follows:
P(Nt+1＝no|Nt＝no)＝e-λΔt
wherein, lambda is equipment failure rate, mu is repair rate; e is a natural logarithm; Δ t is the time interval between the current moment and the next moment; yes represents equipment failure; no indicates that the device is not malfunctioning.
When the repair rate of the equipment is not considered, a specific algorithm for calculating the transition probability of the variable under the non-repair state and the incomplete repair state of the equipment is as follows:
the probability of the equipment failure at the time t and the next time t plus delta t is as follows:
P(Nt+1＝yes|Nt＝yes)＝1
the probability that the equipment fails at the moment t and the equipment does not fail at the next moment t plus delta t is as follows:
P(Nt+1＝no|Nt＝yes)＝0
the probability that the equipment does not have a fault at the time t and the equipment has a fault at the next time t plus delta t is as follows:
P(Nt+1＝yes|Nt＝no)＝1-e-λΔt
the probability that the equipment does not have faults at the time t and the next time t plus delta t is as follows:
P(Nt+1＝no|Nt＝no)＝e-λΔt
as the operator performs the task operation, each operation has two states of success and failure. The human-caused error is a random event, and in the process of operation, the number of times of the human-caused error is a random independent variable, and the human-caused error random variable is a counting process and meets Poisson distribution.
Then, when implemented, the preferred specific algorithm for calculating the human error transition probability in the scheme is as follows:
the person has no fault at the time t, and the probability of the fault at the next time t + Deltat is as follows:
P(Xt+1＝yes|Xt＝no)＝ke-k
the probability that no mistake occurs to the personnel at the time t and the next time t +. DELTA.t is as follows:
P(Xt+1＝no|Xt＝no)＝1-ke-k
the probability that the person fails at the moment t and the probability that no failure occurs at the next moment t +. DELTA.t is as follows:
P(Xt+1＝no|Xt＝yes)＝e-k
the probability of the personnel having faults at the time t and the next time t plus delta t is as follows:
P(Xt+1＝yes|Xt＝yes)＝1-e-k
wherein k is the average value of the human factor error times in unit time; e is a natural logarithm; yes represents personnel error; no indicates that the person has not made a mistake.
Organization factors in a dynamic bayesian network have three states: low, incomplete, high, low indicates that the node variable is in a dangerous state, and at this time, a fire accident is likely to occur; incomplete indicates that the variable has dangerous hidden dangers, such as insufficient programs, insufficient communication, low safety awareness and the like; high indicates that the variable is in a good state and the safe state of the system can be maintained.
The organization factor layer introduces a system recovery factor c, which is P (system recovery | failure occurrence). In the ocean platform fire probability prediction model, the organization factor state is (low, incomplete, high), and then in implementation, the organization factor variable state transition probability obtained based on the introduced system repair factor is:
the probability of the t moment and the next moment t plus delta t being in a dangerous state and the probability of the t moment and the next moment t plus delta t being in a dangerous hidden danger state are 1-c; the time t is in a dangerous state, the next time t plus delta t is in a dangerous hidden danger state and the time t is in a dangerous hidden danger state, the probability that the next time t plus delta t is in a safe state is c, and the probability that the time t plus delta t and the next time t plus delta t are in the safe state is 1; the probabilities of the other states are all zero;
wherein c is a system repair factor; the time t and the next time t plus delta t have three states which are respectively a dangerous state, a dangerous hidden danger state and a safe state.
In step 105, calculating the probability of the fire of the ocean platform by adopting Netica software according to the calculated prior probability, the conditional probability and the transition probability.
In one embodiment of the present invention, the dynamic bayesian network model of the present scheme is a dynamic bayesian network with 10 time slices, and the time interval Δ t between any two time slices is 1 year.
In conclusion, the evaluation method adopts the dynamic Bayesian network model to dynamically predict equipment faults, human errors and organization errors which may cause the ocean platform fire accidents, and explores the probability that the three possibly cause the ocean platform fire accidents.
Claims (4)
1. The ocean platform fire risk assessment method considering human errors and tissue defects is characterized by comprising the following steps of:
according to the characteristics of the fire accident of the ocean platform, establishing an HFACS model suitable for human error analysis of the ocean platform, determining technical factors and human factors and tissue factors causing the fire of the ocean platform, and establishing a dynamic Bayesian network model of the fire of the ocean platform;
obtaining the prior probability of the technical factors by adopting a mathematical statistical method according to historical empirical data, and calculating the prior probability of the human factor tissue factors in the dynamic Bayesian network model by adopting an expert scoring in the field and combining an evidence theory method;
based on a triangular fuzzy function and a grade node distance formula, calculating the conditional probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model:
wherein R is a result distribution index indicating all probability densities that may cause a fire to occur; j is the state of the parent node all possible; zjIs the probability of all possible states of the parent node; a is the first state of the father node; f is the last state of the father node; the father node is the cause of fire, and the child node is the father node possibly bringing consequence; e is a natural logarithm;
based on a Markov model, calculating the transition probability of all variables describing the fire accident occurrence and development process in the dynamic Bayesian network model;
based on a Markov state transition model, calculating the transition probability of the variable under the condition that the equipment is not in a repair state and is not in an incomplete maintenance state; calculating the human error transition probability based on a homogeneous Markov state transition model; obtaining the state transition probability of the tissue factor variable based on the introduced system repair factor;
calculating the probability of the fire of the ocean platform by adopting Netica software according to the calculated prior probability, the conditional probability and the transition probability;
the specific algorithm for calculating the transition probability of the variable under the non-repair state and the incomplete maintenance state of the equipment is as follows:
the probability of the equipment failure at the time t and the next time t + delta t is as follows:
P(Nt+1＝yes|Nt＝yes)＝e-μΔt
the probability that the equipment fails at the time t and the equipment fails at the next time t + delta t is as follows:
P(Nt+1＝no|Nt＝yes)＝1-e-μΔt
the probability that the equipment does not have a fault at the time t and the equipment has a fault at the next time t + delta t is as follows:
P(Nt+1＝yes|Nt＝no)＝1-e-λΔt
the probability that the equipment does not have fault at the moment t and the next moment t + delta t is as follows:
P(Nt+1＝no|Nt＝no)＝e-λΔt
wherein, lambda is equipment failure rate, mu is repair rate; e is a natural logarithm; Δ t is the time interval between the current moment and the next moment; yes represents equipment failure; no indicates that the device is not malfunctioning;
when the repair rate of the equipment is not considered, the specific algorithm for calculating the transition probability of the variable in the non-repair state and the incomplete repair state of the equipment is as follows:
the probability of the equipment failure at the time t and the next time t plus delta t is as follows:
P(Nt+1＝yes|Nt＝yes)＝1
the probability that the equipment fails at the moment t and the equipment does not fail at the next moment t plus delta t is as follows:
P(Nt+1＝no|Nt＝yes)＝0
the probability that the equipment does not have a fault at the time t and the equipment has a fault at the next time t plus delta t is as follows:
P(Nt+1＝yes|Nt＝no)＝1-e-λΔt
the probability that the equipment does not have faults at the time t and the next time t plus delta t is as follows:
P(Nt+1＝no|Nt＝no)＝e-λΔt。
2. the method for evaluating fire risk of ocean platform according to claim 1, wherein the specific algorithm for calculating the probability of human error transfer is:
the person has no fault at the time t, and the probability of the fault at the next time t + Deltat is as follows:
P(Xt+1＝yes|Xt＝no)＝ke-k
the probability that no mistake occurs to the personnel at the time t and the next time t +. DELTA.t is as follows:
P(Xt+1＝no|Xt＝no)＝1-ke-k
the probability that the person fails at the moment t and the probability that no failure occurs at the next moment t +. DELTA.t is as follows:
P(Xt+1＝no|Xt＝yes)＝e-k
the probability of the personnel having faults at the time t and the next time t plus delta t is as follows:
P(Xt+1＝yes|Xt＝yes)＝1-e-k
wherein k is the average value of the human factor error times in unit time; e is a natural logarithm; yes represents personnel error; no indicates that the person has not made a mistake.
3. The method of claim 1, wherein the state transition probability of the tissue factor variable based on the introduced system repair factor is:
the probability of the t moment and the next moment t plus delta t being in a dangerous state and the probability of the t moment and the next moment t plus delta t being in a dangerous hidden danger state are 1-c; the time t is in a dangerous state, the next time t plus delta t is in a dangerous hidden danger state and the time t is in a dangerous hidden danger state, the probability that the next time t plus delta t is in a safe state is c, and the probability that the time t plus delta t and the next time t plus delta t are in the safe state is 1; the probabilities of the other states are all zero;
wherein c is a system repair factor; the time t and the next time t plus delta t have three states which are respectively a dangerous state, a dangerous hidden danger state and a safe state.
4. The method according to any one of claims 1 to 3, wherein the HFACS model for marine platform human error analysis is constructed based on an HFACS model in the field of aviation by combining the characteristics of the marine platform fire accident.
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| CN104899664A (en) * | 2015-06-17 | 2015-09-09 | 西南石油大学 | Drilling well risk prediction method based on Markov chain and Bayesian network |
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| CN103745080A (en) * | 2013-12-02 | 2014-04-23 | 国家电网公司 | Power grid operation human factor reliability assessment method and device based on Bayesian model |
| CN104899664A (en) * | 2015-06-17 | 2015-09-09 | 西南石油大学 | Drilling well risk prediction method based on Markov chain and Bayesian network |
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