Method for assessing safety integrity level of offshore oil well control equipment

Patent metadata

Field	Value
Publication number	US-11243509-B2
Application number	US-201916704664-A
Country	US
Kind code	B2
Filing date	Dec 5, 2019
Priority date	May 21, 2019
Publication date	Feb 8, 2022
Grant date	Feb 8, 2022

Abstract

The present disclosure belongs to the field of offshore oil, and in particular relates to a method for assessing the safety integrity level of offshore oil well control equipment. The method for assessing the safety integrity level of the offshore oil well control equipment comprises three major steps: creating a safety instrumented function evaluation module and dividing the related devices for performing the safety instrumented functions into a sensor subsystem; a controller subsystem and an actuator subsystem, establishing a dynamic Bayesian network model for respective subsystems for calculation; and integrating, analyzing and optimizing the safety integrity data of the subsystems.

First claim

The invention claimed is:

1. A method for assessing the safety integrity level of an offshore oil well control equipment, comprising three major steps: creating a safety instrument function evaluation module and dividing system devices into subsystems, establishing a Bayesian network model for calculation, and performing integrated calculation and optimization analysis;
the creating the safety instrument function evaluation module and dividing the system devices into subsystems specifically comprises:
S101: according to assessment requirements, creating the safety instrumented function module;
S102: dividing a plurality of the system devices into a sensor subsystem, a controller subsystem, and an actuator subsystem; wherein the division of the sensor subsystem, the controller subsystem, and the actuator subsystem is as follows:
(1) the sensor subsystem includes at least one device capable of detecting and predicting blowout parameters and kick parameters, and at least one device for transmitting detected information;
(2) the controller subsystem consists of a ground control part, an underwater control module and an operator as the operating subject, wherein the ground control part consists of a main panel, a drillers panel, a toolpushers panel, and a hydraulic power system; the underwater control module consists of a blue pod underwater control module, a yellow pod underwater control module, an underwater accumulator bottle group, and an emergency battery DC power supply; and
(3) the actuator subsystem includes underwater solenoid valves, hydraulic control valves, and hydraulic valves;
the establishing a Bayesian network model for calculation specially comprises:
S201: establishing a dynamic Bayesian network model for configuration characteristics of the controller subsystem of the offshore oil well control equipment; wherein the dynamic Bayesian network for the controller subsystem consists of N static Bayesian network models of the same structure; the number N of the static Bayesian networks is calculated by the following formula:
N=TS/Δt

wherein TS is the running time of system, and Δt is a self-inspection time interval; and
the system performs the self-inspection each time after one self-inspection time interval Δt is elapsed, the inspection test is performed on the system, and the detected failure is repaired after the inspection test interval (TI) is elapsed; and the process of establishing the dynamic Bayesian network for the controller subsystem is as follows:
(1) determining the static Bayesian network model structure of the controller subsystem according to structural configuration characteristics of the controller subsystem and a fault tree model of the controller subsystem; wherein the static Bayesian network model of the controller subsystem has four layers of nodes in total; the first layer is a failure factor node layer, the type of nodes includes single-channel independent failure nodes and common cause failure nodes each of which has five states including normal state (NS), detected safe failure state (SD), undetected safe failure state (SU), detected dangerous failure state (DD) and undetected dangerous failure state (DU), respectively; the second layer is a single-channel state node layer, the node represents the state of each channel in unit, and each node has five states including normal state (NS), detected safe failure state (SD), undetected safe failure state (SU), detected dangerous failure state (DD) and undetected dangerous failure state (DU), respectively; the third layer is a unit state node layer, the node represents the state of each unit, and each node has four states including normal state (NS), safe failure state (SF), detected dangerous failure state (DD) and undetected dangerous failure state (DU), and the unit has a safe failure when the unit is in the safe failure state (SF); and the fourth layer is a system state node layer, and the node represents the state of the controller subsystem and has three states including normal state (NS), safe failure state (SF) and dangerous failure state (DF), respectively;
(2) determining a plurality of conditional probability tables within a single static Bayesian network; wherein the probability at which respective nodes of the failure factor node layer within a first static Bayesian network is in the normal state (NS) is 100%; the conditional probability table of the second layer nodes is determined according to the effect of failure factors on the single-channel state; the conditional probability table of the third layer nodes is determined according to the failure criterion of a redundant structure; and the conditional probability table of the fourth layer nodes is determined according to the syntagmatic relations among various units and the fault tree model;
(3) determining at least one transition conditional probability of the dynamic Bayesian network at self-inspection; wherein the probability of the single-channel failure factor nodes of a next static Bayesian network is affected by the single-channel failure factor nodes and the unit state nodes of the previous static Bayesian network, and the probability of the common cause failure factor nodes of the next static Bayesian network is only affected by the common cause failure factor nodes of the previous static Bayesian network; and the probability of the failure factor nodes of the next static Bayesian network is determined according to the device degradation law and the self-inspection capability of the system;
(4) determining at least one transition conditional probability of the dynamic Bayesian network at inspection test; wherein the probability of the failure factor nodes of the next static Bayesian network is affected by the failure factor nodes of the previous static Bayesian network; and the probability of the failure factor nodes of the next static Bayesian network is determined according to the inspection coverage rate and repair parameters of device;
S202: determining failure probability parameters of each unit device in the controller subsystem;
S203: determining time parameters of the controller subsystem device; wherein the time parameters includes mean time to repair (MTTR), mean time to system restoration (MTSR), inspection test interval period (TI), running time of a system (TS), and self-inspection time interval;
S204: determining a structurally constraint type of the controller subsystem; wherein the structurally constraint type is divided into A type and B type, the A type subsystem includes instrument device with a simple structure such as switch, valve and relay, and the B type subsystem includes device with a complicated structure such as microprocessor and intelligent transducer;
S205: determining inspection test parameters of devices in the controller subsystem;
S206: performing a calculation by the established dynamic Bayesian network model to obtain safety integrity parameters of the controller subsystem; wherein the safety integrity parameters include a safe failure fraction (SFF) of the controller subsystem, an allowable maximum safety integrity level of the controller subsystem, the safety integrity level (SIL) of the controller subsystem, the probability of dangerous failure on demand (PFD) at respective time points in the controller subsystem operation, the probability of safe failure on demand (PFS) at respective time points in the system operation, and the average probability of dangerous failure on system demand PFDavg and the average probability of safe failure on system demand PFSavg are obtained by the following formula:

PFD
avg

=

∑

t
=

Assignees

• Univ China Petroleum East China

Inventors

• Cai Baoping
• Liu Yonghong
• Li Wenchao
• Kong Xiangdi
• 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

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

• G05B9/02 Primary

  electric · CPC title

• G05B2219/24008

  Safety integrity level, safety integrated systems SIL SIS · CPC title

• G05B19/406 Primary

  characterised by monitoring or safety (G05B19/19 takes precedence) · CPC title

• G05B23/024

  Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks · CPC title