Method and system for evaluating macro resilience of offshore oil well control equipment

Patent metadata

Field	Value
Publication number	US-11922335-B2
Application number	US-201916704557-A
Country	US
Kind code	B2
Filing date	Dec 5, 2019
Priority date	May 20, 2019
Publication date	Mar 5, 2024
Grant date	Mar 5, 2024

Abstract

The present disclosure relates to a method and system for evaluating the macro resilience of offshore oil well control equipment. The method for evaluating the macro resilience of offshore oil well control equipment comprises six steps: determining the type and strength of external disaster; calculating the failure rate of components; calculating the recovery rate of the components; modeling the BN for a degradation process; modeling the BN for a maintenance process; and calculating the resilience of the offshore oil well control equipment. A system for evaluating the macro resilience of offshore oil well control equipment comprises an external disaster evaluation module, a component failure rate calculation subsystem, a reliability degradation process simulation module, a fault identification module, a component recovery rate calculation module, a reliability recovery process simulation module, a reliability change curve derivation unit and an resilience calculation unit.

First claim

The invention claimed is:

1. A method for evaluating the macro resilience of an offshore oil well control equipment, comprising six major steps: determining the type and strength of external disaster; calculating failure rates of components of the offshore oil well control equipment; calculating recovery rates of the components of the offshore oil well control equipment; modeling Bayesian networks (BNs) for degradation process of the offshore oil well control equipment; modeling BNs for maintenance process of the offshore oil well control equipment; and calculating the resilience of the offshore oil well control equipment;
step 1: determining the type and strength of the external disaster; under the same failure mechanism, a reliability degradation process of the components of the well control equipment under the external disaster is equivalent to a reliability degradation process of the components in an acceleration test, and an acceleration model is used to quantitatively describe the reliability degradation process; influence factors for failure times of the components of the well control equipment are determined after the determination of the type of the disaster; and a value range of variables in the acceleration model are determined after the determination of the strength of the disaster; relevant data of the external disaster are collected by sensors and subjected to data analysis and data processing so as to determine by which kind of factors the components of the offshore oil well control equipment are influenced;
step 2, calculating the failure rates of the components of the offshore oil well control equipment; the influence factors for the failure times of the components of the offshore oil well control equipment are divided into four types: temperature, humidity, vibration and electrical stress; under the influence of the external disaster, the components may be influenced by only one factor, that is, single-factor action, or may be influenced by multiple factors, that is, multi-factor action; after the determination of the degradation acceleration model of the components, a physical model is mapped into a BN, and failure time distribution and probability of the components are determined by the computational results of the BN;
step 3, calculating the recovery rates of the components of the offshore oil well control equipment; relevant data of the well control equipment after the disaster are collected by the sensor, the degree of damage of the well control equipment is determined, a corresponding maintenance strategy is then decided by an expert, and maintenance times of the well control equipment are obtained through the maintenance strategy, thereby calculating the recovery rates of the well control equipment;
step 4, modeling the BN for the degradation process of the offshore oil well control equipment; a static BN for simulating change in the reliability of the well control equipment is established firstly, the nodes changing with time in the static BN are determined, and the static BN is extended into a dynamic Bayesian network (DBN); in terms of parameter modeling, priori probability of the BN for the degradation process of the offshore oil well control equipment is decided by states of the components of the well control equipment, and the degradation process follows an exponential degradation law;
step 5, modeling the BN for the maintenance process of the offshore oil well control equipment; a static BN for simulating change in the reliability of the well control equipment is established firstly, the nodes changing with time in the static BN are determined, and the static BN is extended into a DBN; in terms of structure, the BNs for the degradation process and the maintenance process of the offshore oil well control equipment are the same, and the modeling is performed on the same set of offshore oil well control equipment; in terms of parameter modeling, priori probability of the BN for the maintenance process of the offshore oil well control equipment is decided by states of the components of the well control equipment, and the recovery process follows the Markov law;
the first layer nodes C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 , C 11 , C 12 , C 13 , C 14 in the static BN are parent nodes, which represent basic components of the well control equipment, the second layer nodes M 1 , M 2 , M 3 , M 4 , M 5 , M 6 are intermediate nodes, which represent series-parallel relations of the components of the well control equipment, the series-parallel relations are determined by a conditional transition probability table, the third layer nodes S 1 , S 2 , S 3 represent subsystems in the offshore oil well control equipment, and the fourth layer node R represents the reliability of the offshore oil well control equipment;
the DBN is an extension of the static BN in time, wherein the first layer nodes are time clone nodes of the basic components C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 , C 11 , C 12 , C 13 , C 14 of the well control equipment, which represent temporal change relations of the components of the offshore oil well control equipment, the second layer nodes C 1 , C 2 , C 3 , C 4 , C 5 , C 6 , C 7 , C 8 , C 9 , C 10 , C 11 , C 12 , C 13 , C 14 represent the components of the well control equipment, the third layer nodes M 1 , M 2 , M 3 , M 4 , M 5 , M 6 are intermediate nodes, which represent the series-parallel relations of the components of the well control equipment, the series-parallel relations are determined by the conditional transition probability table, the fourth layer nodes S 1 , S 2 , S 3 represent subsystems in the offshore oil well control equipment, and the fifth layer node R represents the reliability of the offshore oil well control equipment; and
step 6, calculating the resilience of the offshore oil well control equipment; the reliability versus the change in time of the offshore oil well control equipment after being subjected to disaster and maintenance measures are respectively obtained through the BN; and reliability degradation and recovery curves are drawn through the computational results of the BN, and the specific resilience value of the system is obtained by calculating an area ratio; wherein the calculation method is as follows: when the resilience value is calculated, a perpendicular line is drawn from a reliability point at the time of occurrence of the disaster to an x-axis, perpendicular lines are drawn from a reliability point at time of completion of the maintenance measures to x-axis and the y-axis respectively, an area enclosed by a curve and a straight line in the horizontal direction is called A 1 , and an area enclosed by the curve and two perpendicular lines as well as the x-axis is called A 2 , and the sum of A 1 and A 2 is the total area; and the ratio of the area of A 2 to the total area is the required resilience value.

2. The method for evaluating the macro resilience of the offshore oil well control equipment of claim 1 , wherein
when the components of the offshore oil well control equipment are subjected to a single-stress action,
S211, determining a degradation model of the offshore oil well control equipment component under the influence of the external disaster;
(1) when the well control equipment is influenced by temperature, the failure time of the component is shown in formula (1):

1
t

=

A
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e

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E

Assignees

• Univ China Petroleum East China

Inventors

• Cai Baoping
• Liu Yonghong
• Zhang Yanping
• Gao Chuntan
• Shao Xiaoyan
• Xu Hongqi
• Li Xincheng
• Chen Yandong
• Ji Renjie
• Liu Zengkai
• Liu Libing
• Zhang Rikui
• Yang Yuqian
• Liu Shitang
• Wei Xin

Classifications

• G06N7/01 Primary

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• E21B43/00

  Methods or apparatus for obtaining oil, gas, water, soluble or meltable materials or a slurry of minerals from wells (applicable only to water E03B) · CPC title

• G01V99/005

  Physics · mapped topic

• G06F30/20 Primary

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06Q10/06

  Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling · CPC title