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Intelligent operation and maintenance for subsea production systems
Livre
Edité par China University of Petroleum Press ; Springer : Qingdao - 2025
Description : 1 vol. (XXII-510 p.) ; couv. ill. en coul., ill. en coul. ; 24 cm
Mots-clés : pétrole ; exploitation pétrolière offshore ; maintenance des installations ;
Cote : 627.98 CAI
Type de monographie : Livre
Langue du texte : anglais
Part I Fault Diagnosis
1 Fault Diagnosis for Composite Faults and Minor Faults
1.1 Introduction
1.2 Digital Twin-Driven Fault Diagnosis Model for Composite Faults
1.2.1 Dynamically Updated Digital Twin Model
1.2.2 Fault Diagnosis Models for Composite Faults
1.2.3 Dynamic Error Calculation1.3 Cross-Validation Enhanced Fault Diagnosis Model for Minor Faults
1.3.1 Digital Twin Model Integrating Control, Loss and Fault Parameters
1.3.2 Fault Diagnosis Model Combined Feedback Data from Digital Twin
1.3.3 Interactive Promotion Algorithm Between Digital Twin and Fault Diagnosis Models1.4 Cases of Subsea Production System
1.4.1 Introduction of Subsea Production System in South China Sea
1.4.2 Composite Faults
1.4.3 Minor Faults
2 Digital Twin-Assisted Intelligent Fault Diagnosis
2.1 Introduction
2.2 Proposed IFD Method
2.2.1 Virtual Model Parameter Transfer Based on Modelica
2.2.2 Virtual Real Data Fusion Using Bidirectional Data Consistency Evaluation Framework
2.2.3 Construction of Fault Diagnosis Model Based on MCIDCNN-GRU
2.3 Case Study of Subsea BOP System
2.3.1 Description of Hydraulic Control System for Subsea BOP
2.3.2 Verification of Virtual Models
2.3.3 Collection of Virtual and Real Data.
2.3.4 Ragu2DSW Algorithm Optimization Process for Virtual Data.
2.3.5 Optimization and Hyperparameter Adjustment of MCIDCNN-GRU Model
2.4 Results and Discussion
2.4.1 Results of Virtual and Real Data
2.4.2 The Performance of DT.
2.4.3 The Impact of the Ratio of Input Virtual to Real Data on the Model
2.4.4 The Performance of the MIDNIGRU Diagnostic Model.
3 Optimal Sensor Placement for Fault Diagnosis
3.1 Introduction
3.2 Sensor Placement Methodology
3.2.1 Fault Propagation and Sensor Response Time Model
3.2.2 Sensor Placement Optimization Model
3.2.3 Sensor Placement Optimization Based on Discrete PSO
3.3 Case of Common Multi-circuit Hydraulic Control Systems
3.3.1 Description of the System
3.3.2 Multi-circuit Hydraulic Control System with Single Loop
3.3.3 Multi-circuit Hydraulic Control System with Double Parallel Loops
3.3.4 Results and Discussions
3.4 Case of Actual Subsea BOP Hydraulic Control System
3.4.1 Description of Hydraulic Control System of Subsea BOP
3.4.2 Sensor Placement Optimization for Hydraulic Control System of Subsea BOP
3.4.3 Results and Discussions
4 Fault Diagnosis for Subsea Control System
4.1 Introduction
4.2Fault Diagnosis for Complex Hydraulic Control System
4.2.1 Energy Flow Model
4.2.2 Fluid Flow Model
4.2.3 Information Flow Model
4.2.4 Three-Mode-Driven Hydraulic System Fault Diagnosis Framework
4.2.5 Fault Identification Rules
4.2.6 Verification and Validation
4.3 Fault Diagnosis for Closed-Loop Feedback Control System
4.3.1 Proposed Fault Diagnosis Methodology
4.3.2 Structure Modeling of DBNs
4.3.3 Parameter Modeling of DBNs
4.4 Cases of Subsea BOP Control System
4.4.1 Description of Subsea BOP Control Systems
4.4.2 Case of Complex Subsea Hydraulic Control System
4.4.3 Case of Subsea Closed-Loop Feedback Control System
5 Concurrent Fault Diagnosis for Electric-Hydraulic System
5.1 Introduction
5.2 Concurrent Fault Diagnosis Model
5.2.1 Preliminary Diagnosis and Reasoning Sub-models Based on OOBNs
5.2.2 D-S Reason Modeling of Concurrent Fault Diagnosis
5.2.3 Fault Identification Rules Based on Belief Degree
5.3 Case of Subsea BOP System
5.3.1 Analysis of the Cause of Concurrent Fault
5.3.2 Results and Discussion
6 Intelligent Fault Diagnosis for Subsea Production System
6.1 Introduction
6.2 Intelligent Full-Stage Stable Fault Diagnosis Method
6.2.1 The Method of Establishing a Digital Twin Model
6.2.2 Model-Based Fault Diagnosis Method ..........
6.2.3 Data Driven Fault Diagnosis Method
6.3 Case of a Subsea Production System
6.3.1 A Subsea Production System in the South China Sea
6.3.2
6.3.3 Output of the Dynamic Digital Twin Model Performance of the Fault Diagnosis Model
Part I Concluding Remarks
Part II Fault Prognosis
7 RUL Prediction with Small Sample Data and Multi-sample Data
7.1 Introduction
7.2 Data-Model-Linked RUL Prediction Model
7.2.1 BN-Based HI Assessment ..
7.2.2 Data Augmentation Based on GMM
7.2.3 RUL Assessment...
7.3 Hybrid DBN-KF-Based RUL Prediction Model
7.3.1 Hybrid DBN-KF-Based Method
7.3.2 RUL Calculation
7.4 Cases of Subsea Valves
7.4.1 Case with Small Sample Data
7.4.2 Case with Multi-sample Data
8 RUL Prediction with Multiple Causes
8.1 Introduction
8.2 RUL Estimation Model Based on Multiple-Cause Integration
8.2.1 Modeling Methodology
8.2.2 Structure Modeling of DBNs
8.2.3 Parameter Modeling of DBNs
8.2.4 Estimation of RUL
8.3 Case of Subsea Pipelines
8.3.1 Influence of Multiple Causes on Subsea Pipelines
8.3.2 RUL Modeling for Subsea Pipelines
8.3.3 Results and Discussions
9 RUL Prediction with Hybrid Multi-stage
9.1 Introduction
9.2 RUL Prediction Model Based on DUKF
9.2.1 Modeling of the Degradation
9.2.2 Proposed DUKF Methodology
9.2.3 RUL Prediction
9.3 Case Study of Subsea Christmas Tree System
9.3.1 Introduction of Subsea Christmas Tree Control System
9.3.2 RUL Prediction of Subsea Christmas Tree Control System Based on DUKF
9.3.3 Results and Discussions
10 RUL Prediction with Failure Dependence
10.1 Introduction
10.2 RUL Estimation Model Considering Degradation Interactions.
10.2.1 Degradation Models of Components ..
10.2.2 Degradation Models Considering Interactions Influence
10.2.3 Structure Modeling and Parameter Modeling of DBNs .....
10.2.4 RUL Estimation Based on Performance
10.3 RUL Estimation Model Considering Cascading Failure
10.3.1 Modeling Methodology
10.3.2 Mathematical Basis of DBNs
10.3.3 Structure Modeling and Parameter Modeling of DBNs.
10.3.4 Estimation of RUL
10.4 Case of Subsea Christmas Tree
10.4.1 Degradation Modeling of Subsea Christmas Tree
10.4.2 Results and Discussions
10.5 Case of Subsea Transportation Systems .......
10.5.1 Influence of Cascading Failure of Subsea Transportation Systems
10.5.2 DBNs Modeling of Subsea Oil and Gas Transportation Systems
10.5.3 Results and Discussions
11 RUL Prediction with Degradation-Shock Dependency
11.1 Introduction
11.2 RUL Estimation Model with Degradation-shock Dependence ...
- 11.2.1 Dependency Modeling ....
- 11.2.2 Performance Evaluation Based on DBNs
- 11.2.3 RUL Estimation Based on Performance
11.3 Case of Subsea Hydraulic Control System
11.3.1 Control System Modeling
11.3.2 Hydraulic System Modeling
11.3.3 Modeling of the Hydraulic Control System on the Basis of DBNs Results and Discussion
11.3.4 RUL Re-prediction
12.1 Introduction
12.2 RUL Prediction and Re-prediction Model
- 12.2.1 Wiener Process
- 12.2.2 RUL Prediction Method Based on Wiener Process
12.3 Case of a Subsea Christmas Tree System
- 12.3.1 Introduction of Subsea Christmas Tree System
- 12.3.2 Modeling Process ...
- 12.3.3 Results and Discussions
13 Fault Prediction with Industrial Incomplete Information
13.1 Introduction
13.2 Proposed Fault Prediction Method
- 13.2.1 Industrial Information Preprocessing
- 13.2.2 Data Enhancement with Incomplete Information
- 13.2.3 Failure and RUL Estimation........
13.3 Case Study: Corrosion of Subsea Pipelines
13.3.1 Incomplete Information of Monitoring Data
13.3.2 Data Enhancement of Monitoring Data...
- 13.3.3 CO2 Corrosion Prediction with Enhanced Data
- 13.3.4 Sensitivity Analysis of Degradation Parameters
Part II Concluding Remarks
Part II Maintenance
14 CBM with Spare Parts Management and Imperfect
Maintenance
14.1 Introduction
14.2Imperfect CBM Model with Spare Part Strategy
- 14.2.1 RUL Prediction Model
- 14.2.2 Imperfect Maintenance Model
14.2.3 Spare Part Models of Multi-component System
14.2.4 Maintenance Optimization Model
14.3 Case of Subsea Tree System
- 14.3.1 Introduction of the Subsea Tree System
- 14.3.2 Results and Discussions
15 CBM with Maintenance Delay
15.1 Introduction
15.2 CBM Model Based on Maintenance Delay
- 15.2.1 Component Degradation Model
- 15.2.2 RUL Prediction Model
- 15.2.3 Maintenance Model
15.3 Case of Subsea Tree System
15.3.1 Results of Three Maintenance Strategies
15.3.2 Influence of Safety Time Threshold on Maintenance Cost
15.3.3 Influence of Basic Inspection Period on Maintenance Cost
15.3.4 Influence of Maintenance Threshold on Maintenance Cost
15.3.5 Discussion
16 CBM with Discrete States
16.1 Introduction
16.2 CBM Optimization Model Under Discrete-State Condition
16.2.1 Establishment of the Model List
16.2.2 Markov Chain
16.2.3 Establishment of the Degradation State Transition Matrix
16.2.4 Establishment of the Recovery State Transition Matrix
16.2.5 Optimization Goals for Maintenance Plans
16.3 Case of Subsea Production System
- 16.3.1 Introduction of Subsea Production System
- 16.3.2 Results and Discussion
17 CBM with Heterogeneous Failure Dependence
17.1 Introduction
17.2 Dependent Multi-component Degradation Model
- 17.2.1 Independent General Degradation Model
- 17.2.2 Failure Dependence Model
- 17.2.3 Dependent Multi-Component Degradation Model
17.3 CBM Model Based on Failure Dependence
- 17.3.1 Inspections and Maintenances
- 17.3.2 System Availability Analysis
17.3.3 Maintenance Cost
17.4 Case of Subsea Transmission System
17.4.1 Failure Probabilities
17.4.2 Maintenance Strategies with Various Failure Dependences
17.4.3 Maintenance Strategies for Various Initial Costs Input
18 Joint Optimization for Emergency Maintenance and CBM
- 18.1 Introduction
- 18.2 Joint Optimization Model of Emergency Maintenance and CBM
- 18.2.1 Cumulative Degradation Value Prediction Model
- 18.2.2 Joint Optimization Method of EM and CBM ..
18.3 Case of Subsea Production Control System
18.3.1 Introduction of the Subsea Production Control System
18.3.2 Performance Degradation Prediction of Subsea Control System
18.3.3 Joint Optimization for the Subsea Control System
18.3.4 Results and Discussions
19 Maintenance Strategies for Sustainability Development
19.1 Introduction
19.2 Integrated Framework with Failure Dependence and Maintenance ....
19.3 Degradation-Maintenance Model
19.4 Sustainability Evaluation Model
19.4.1 Evaluation of Impacts of Component Performance on Sustainability.
19.4.2 Evaluation of Impacts of Maintenance Activities on Sustainability ...•
19.4.3 Process of Overall Sustainability Evaluation
19.5 Case of Subsea Transmission System.
19.5.1 Numerical Analysis
19.5.2 Results and Discussion
Part III Concluding Remarks