Contents
Foreword
Preface
Chapter 1 Development of Automatic Subsea Blowout Preventer Stack Control System Using PLC Based SCADA 1
1.1 Introduction 1
1.2 Hardware architecture 3
1.2.1 Overall system architecture 3
1.2.2 Triple redundant controllers 5
1.2.3 Dual redundant Ethernet 6
1.2.4 Redundant subsea electronic modules 7
1.3 Voting algorithm 8
1.3.1 Discrete output voting 8
1.3.2 Discrete input voting 9
1.3.3 Analog input voting 9
1.4 Software methodology 10
1.4.1 Control logic 10
1.4.2 HMI program 13
1.4.3 Redundant databases 13
1.4.4 Remote access 15
1.5 Results and discussions 15
1.6 Conclusions 20
References 21
Chapter 2 Reliability Analysis of Subsea Blowout Preventer Control Systems Subjected to Multiple Error Shocks 23
2.1 Introduction 23
2.2 System description 25
2.2.1 Architecture of subsea BOP control system 25
2.2.2 Configuration of subsea BOP control system 27
2.3 System modelling and reliability analysis 29
2.3.1 Assumptions 29
2.3.2 System modelling and reliability analysis for TMR system 30
2.3.3 System modelling and reliability analysis for DDMR system 36
2.4 Results and discussions 37
2.5 Conclusions 44
References 45
Chapter 3 Performance Evaluation of Subsea Blowout Preventer Systems with Common Cause Failures 47
3.1 Introduction 47
3.2 System description 49
3.3 Reliability modelling and analysis 52
3.3.1 Assumptions 52
3.3.2 Failure rate and repair rate calculations 53
3.3.3 Reliability modelling 55
3.3.4 Performance evaluation 58
3.4 Results and discussions 59
3.5 Conclusions 64
References 64
Chapter 4 Using Bayesian Networks in Reliability Evaluation for Subsea Blowout Preventer Control System 66
4.1 Introduction 66
4.2 System description 69
4.2.1 Subsea BOP system 69
4.2.2 Configuration of subsea BOP control system 70
4.3 Bayesian networks modelling for reliability analysis 75
4.3.1 Overview of Bayesian networks 75
4.3.2 Bayesian networks modelling for redundant systems 75
4.3.3 Bayesian networks modelling for subsea BOP control systems 78
4.4 Results and discussions 82
4.4.1 Reliability of subsea BOP control systems 82
4.4.2 Difference between posterior and prior probabilities 82
4.4.3 Effects of coverage factors on the reliability 84
4.4.4 Effects of failure rate on the reliability 85
4.5 Conclusions 86
References 87
Chapter 5 Dynamic Bayesian Networks Based Performance Evaluation of
Subsea Blowout Preventers in Presence of Imperfect Repair 90
5.1 Introduction 90
5.2 Dynamic Bayesian networks with imperfect repair 92
5.2.1 Overview of BN and DBN 92
5.2.2 DBN modeling of series and parallel systems 93
5.2.3 Imperfect repair modeling 95
5.2.4 Conditional probability table 97
5.2.5 Reliability and availability 99
5.3 Case study 100
5.3.1 Fault tree 100
5.3.2 Corresponding DBN 103
5.3.3 Evaluation and validation 107
5.3.4 Results and discussions 107
5.4 Conclusions 113
References 114
Chapter 6 Performance Evaluation of Subsea BOP Control Systems Using Dynamic Bayesian Networks with Imperfect Repair and Preventive Maintenance 116
6.1 Introduction 116
6.2 DBN modeling of series， parallel and voting systems 119
6.2.1 DBN modeling with common-cause failure 119
6.2.2 Imperfect repair and preventive maintenance modeling 122
6.2.3 Conditional probability table 125
6.2.4 Reliability and availability 127
6.3 Case study 129
6.3.1 Configuration of subsea BOP control system 129
6.3.2 DBN modeling of subsea BOP control system 130
6.3.3 Evaluation and validation 133
6.3.4 Results and discussions 134
6.4 Conclusions 141
References 142
Chapter 7 Application of Bayesian Networks in Quantitative Risk Assessment of Subsea Blowout Preventer Operations 144
7.1 Introduction 144
7.2 Proposed methodology 147
7.3 Case study 148
7.3.1 Subsea BOP operations 148
7.3.2 Modeling and analysis 151
7.3.3 Results and discussions 164
7.4 Conclusions 168
References 169
Chapter 8 Research on the Dynamic Bayesian Networks Based Real-Time Reliability Evaluation Methodology 172
8.1 Introduction 172
8.2 Proposed methodology 174
8.3 Case study 175
8.3.1 Subsea pipe ram BOP system 175
8.3.2 Modelling 177
8.3.3 Results and discussions 183
8.4 Conclusions 187
References 188
Chapter 9 A Dynamic Bayesian Networks Modelling of Human Factors on Offshore Blowouts 190
9.1 Introduction 190
9.2 Pseudo-fault tree 192
9.2.1 Human factors in offshore drilling 192
9.2.2 Pseudo-fault tree of safety barriers 193
9.3 Dynamic Bayesian networks 198
9.3.1 Introduction of dynamic Bayesian networks 198
9.3.2 Translating pseudo-fault tree into dynamic Bayesian networks 199
9.3.3 Evaluation and validation of dynamic Bayesian networks 203
9.4 Results and discussions 204
9.4.1 Quantitative analysis results 204
9.4.2 Effect of repair on the HFBF 204
9.4.3 Mutual information investigation 206
9.4.4 Validation of the model 208
9.5 Conclusions 208
References 209
Chapter 10 Application of Bayesian Networks to Reliability Evaluation of Software System for Subsea Blowout Preventers 211
10.1 Introduction 211
10.2 Software development 212
10.2.1 Subsea BOP c
內容試閱：
Chapter 1 Development of Automatic Subsea Blowout Preventer Stack Control System Using PLC Based SCADA
An extremely reliable remotely control system for subsea BOP stack is developed based on the off-the-shelf triple modular redundancy system. To meet a high reliability requirement， various redundancy techniques such as controller redundancy， bus redundancy and network redundancy are used to design the system hardware architecture. The control logic， human machine interface graphical design and redundant databases are developed by using off-the-shelf software. A series of experiments were performed in laboratory to test the subsea BOP stack control system. The results showed that the tested subsea BOP functions could be executed successfully. For the faults of programmable logic controllers， discrete input groups and analog input groups， the control system could give correct alarms in the human machine interface.
1.1 Introduction
Subsea BOP stack plays an extremely important role in providing safe working conditions for the drilling activities in 10000ft ultra-deepwater region. Failures of subsea BOP stack could cause a catastrophic accident， for example， the deep-sea petroleum drilling rig Deepwater Horizon exploded and oil spill off the coast of Louisiana on April 20， 2010. It was considered that on the Deepwater Horizon rig， the subsea BOP did not isolate the well before and after the explosions. The subsea BOP stack might have been faulty before the blowout or it might have been damaged due to the accident.
The BOP stack is between the lower marine riser package LMRP connector and wellhead connector in the seafloor. The traditional electro-hydraulic control system transmits electrical command signals using pairs of wires. The large quantity of wires makes the armored umbilical cables and electrical interface heavy and bulky with increasing depth of water. The multiplex electro-hydraulic control systems have been developed in recent years， which employ multi-conductor armored subsea umbilical cables to transmit coded commands that activate solenoid operated pilot valves in the subsea pods. An intelligent supervisory control and data acquisition SCADA platform， which provides economical and user-friendly solutions to subsea BOP stack management， is the kernel of the multiplex electro-hydraulic control system.
In recent years， various SCADA systems have been developed. Large numbers of programmable logic controller PLC based SCADA systems are used in wastewater treatment plant， cryogenic pumping facility， water pumping control system， fuzzy proportion integration differentiation controller and petroleum industry. Chaudhuri et al. developed a PLC based automation system to control the water flow in the secondary cooling zones of the strand. The automation system configuration was also given. Aydogmus presented a SCADA control via PLC for a fluid level control system with fuzzy controller. Bayindir and Cetinceviz described the water pumping control system that was designed for production plants and implemented in an experimental setup in a laboratory by using PLC and industrial wireless local area network technologies. Some new approaches are used for PLC software design. Kandare et al. presented a model-based approach to PLC software development by introducing a new procedural modeling language called ProcGraph. In addition， PC-based SCADA systems are used in electric power system， desalination plant and laboratory testing system. And Web-based and mobile-phone-based SCADA systems are also developed.
However， most of the SCADA systems described above are non-redundant， which can not provide high reliability. The redundancy systems are usually developed based on the chip-level processors， such as filed programmable gate array FPGA or single chip microcomputer SCM， but not the system-level processors， such as PLC or PC. The redundancy systems based on chip-level processors are difficult to develop， which require professionals to develop the control hardware and software.
For the subsea BOP system， both of the high reliability and easy development are need， therefore， PLC based triple modular redundancy system GE Fanuc Genius modular redundancy GMR is chosen to provide supervisory control and data acquisition due to the fact that the system can provide the tolerance against single hardware component failures. The subsea BOP system can be developed easily based on the off-the-shelf GMR system， and the potential errors can be corrected easily and rapidly by using the off-the-shelf software.
This work focuses on the extremely high reliability of subsea BOP stack which is designed by using hardware redundancy techniques， software redundancy techniques and a series of voting algorithm. The chapter is structured as follows: Section 1.2 describes the hardware architecture of subsea BOP control system. Section 1.3 describes the triple modular redundancy input and output voting algorithm. Section 1.4 d
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