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MPC for Upstream Oil & Gas Fields a Practical View

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MPC for Upstream Oil & Gas Fields a Practical View MPC for Upstream Oil & Gas Fields a practical view Yahya Al-Naumani A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Sheffield July 2017 Dedicated to my Parents and Family i ABSTRACT This work aims to improve corporate functional departments’ confidence in adopt- ing modern control approaches in new scenarios and thus presents control structure solutions based on model predictive control (MPC) for two control problems facing ex- isting upstream oil and gas production plants. These are the disturbance growth in the series connected process and the control system dependency on operators. The suggested control solution integrates MPC as a master controller for the existing clas- sical control of each subsystem, with a focus on those with high interaction phenom- ena. The proposed approach simply and inexpensively encompass MPC features such as predictions, optimizations, coordination and constraint handling as well as PID features like simplicity and ease of troubleshoot. In addition, the proposed control concept utilises the process safeguarding information and enhances the plant-wide op- timal performance. The suggested control solution supports the role of control room operators, which is shown to reduce the growth in the impact of process disturbances. Compared with some alternative control structures (centralised MPC, decentralised MPC, distributed MPC (DMPC), and hierarchical DMPC) this proposal is simple, in- expensive to implement, and critically, builds on the local team operational experience and maintenance skills. Three process models were developed that representing the common gas treatment processes in upstream oil and gas plants, gas sweetening, gas dehydration and hydro- carbon dewopointing. The models were utilised to examine different control structures and proposals. These models are not only of benefit to studies on upstream oil and gas processes, but also to Large Scale Systems (LSS) in general. The models were used to analyse the disturbance impacts on a series connected processes, therefore to ii provide answers about how process malfunctions and different disturbances affect the processing operations. The proposed control system is designed on a cascade strategy and thus provides a flexible system control almost like a decentralised structure in dealing with distur- bances and unit failures, and at the same time improves the closed loop performance and the plant-wide optimal operation. The control system contain MPC’s that are designed to regulate the critical loops only while the rest of the uncritical loops will continue to function in a decentralised fashion under PID control algorithm. This minimises any design and set up costs, reduces demand on the communication net- work and simplifies any associated real time optimisations. The improved local control reduced the need for control room operator interactions with their associated weak- nesses. The proposed control structure communicate with the process safeguarding system to enable prompt response to disturbances caused by unit failures, and shares critical information with adjacent MPC’s, which indeed works as a feed-forward, to re- duce the impact of process disturbances and enhance optimality. The control system design is simple, inexpensive to implement and significantly reduces the frequency of plant shut downs and saves on operating costs by properly controlling the disturbance growth in the process. ACKNOWLEDGMENTS First and foremost, all thanks and praise are due to ALLAH the almighty for his blessing that made this work possible. Special thanks and gratitude goes to my supervisor, Dr J. Anthony Rossiter, for his excellent supervision, help and valuable advice throughout every stage of this work. His wisdom, knowledge, and kindness were an inspiration to me. Without his construc- tive comments, excellent support, continuous encouragement and motivation I would not be able to complete this work. I would like also to thank my sponsor, the government of Oman, and my company, PDO, for their financial support without which my PhD and degree would have not been possible. It is also a pleasure to thank everyone in the ACSE department for the good time I spent with them. Thanks are also due to my course mates for their help and support. Finally, I would like to thank my parents for everything they gave me and still giving, and my small family for their almost endless support and patience throughout my studies. iv STATEMENT OF ORIGINALITY Unless otherwise stated in the text, the work described in this thesis was carried out solely by the candidate. None of this work has already been accepted for any degree, nor is it concurrently submitted in candidature for any degree. Candidate: Yahya Al-Naumani Supervisor: J. A. Rossiter v CONTENTS Abstract ii Acknowledgments iv List of Figures xiv List of Tables xx List of Acronyms xxii Chapter 1: Introduction 1 1.1 Motivation . .4 1.2 List of Contributions and Supportive Publications . .7 1.3 Thesis Overview . .9 Chapter 2: Literature Survey 12 2.1 An Overview to Model Predictive Control . 14 2.2 MPC Core Components . 16 2.3 Evolution of MPC Algorithms . 19 2.3.1 LQG . 19 vi 2.3.2 Model Predictive Heuristic Control MPHC / IDCOM . 20 2.3.3 DMC . 21 2.3.4 QDMC . 22 2.3.5 GPC . 23 2.3.6 Implicit MPC . 24 2.3.7 Explicit MPC . 24 2.4 Basic Concepts of Prediction . 25 2.4.1 Prediction with state space models . 26 2.4.2 Prediction with transfer function model (CARIMA model) . 27 2.5 Model Predictive Control Basic Algorithms . 30 2.5.1 Generalised Predictive Control . 32 2.5.2 GPC with State Space model . 35 2.5.3 Independent Model GPC . 37 2.5.3.1 IM predictions based on transfer function model . 37 2.6 Constraints Handling . 38 2.6.1 Input Increment Constraints . 40 2.6.2 Input Constraints . 40 2.6.3 Output Constraints . 41 2.6.4 Constraints compact form . 42 2.7 Summary . 43 vii Chapter 3: Upstream Oil and Gas Fields Challenges and Opportunities 44 3.1 Insight to upstream production plants . 45 3.2 Research Problems . 46 3.2.1 Control system dependency on operators . 47 3.2.1.1 Related Control issues . 47 3.2.1.2 Related Process Safety issues . 49 3.2.2 Disturbance growth in a series connected process . 50 3.3 Summary . 54 Chapter 4: Upstream Oil and Gas Fields - Control Concept and Feasi- ble Solution 55 4.1 Currently available solutions . 55 4.1.1 Centralised MPC . 56 4.1.2 Decentralised MPC . 57 4.1.3 Distributed MPC . 59 4.1.4 Hierarchical Distributed MPC . 60 4.2 A feasible solution for existing oil and gas fields . 62 4.3 Summary . 64 Chapter 5: Model Development for Gas Phase Train in Upstream Oil and Gas Fields 66 5.1 Process Description . 68 5.1.1 Gas Sweetening Process . 69 viii 5.1.2 Gas Dehydration Process . 72 5.1.3 Hydrocarbon Dew Pointing Process . 72 5.2 Process Model . 73 5.2.1 Gas Sweetening Unit (GSU) Dynamical Model . 74 5.2.2 Gas Dehydration Unit (GDU) Dynamical Model . 76 5.2.3 Hydrocarbon Dew-pointing Unit (HCDP) Dynamical Model . 79 5.3 Model Validation . 81 5.3.1 Model Validation by Dynamical Process Description . 81 5.3.1.1 Gas Sweetening Unit (GSU) Model Validation for Distur- bances in Sulfinol . 82 5.3.1.2 Gas Dehydration Unit (GDU) Model Validation for Dis- turbances in Glycol . 82 5.3.1.3 Hydrocarbon Dew pointing Unit (HCDPU) Model Valida- tion . 83 5.3.2 Model Validation by Comparisons with a Real Industrial Process 84 5.3.2.1 Gas Sweetening Unit (GSU) Model Validation . 85 5.3.2.2 Gas Dehydration Unit (GDU) Model Validation . 86 5.3.2.3 Hydrocarbon Dew pointing Unit (HCDPU) Model Valida- tion . 88 5.4 Summary . 90 Chapter 6: Disturbances Impact Analysis 92 ix 6.1 Analysis of Process Disturbances . 94 6.1.1 Feed Disturbance . 94 6.1.2 Process Unit Malfunction . 97 6.1.3 Real Process Disturbance Data . 99 6.2 Summary . 99 Chapter 7: Control System Design 101 7.1 Proposed Control System . 103 7.2 Challenges and Solutions of MIMO Loops . 104 7.3 PID Controllers Setting . 105 7.4 Inner Loops PID Controllers Design . 106 7.4.1 GSU . 106 7.4.2 GDU . 110 7.4.3 HCDP . 111 7.5 MPC Algorithm . 113 7.5.1 Feed forward loops (Dn)........................ 114 7.5.2 Process constraints . 116 7.6 Control Algorithm Flow Chart . 117 7.7 Control Analysis and Simulation Plan . 119 7.8 Wide Process Control Performance Analysis . 119 7.8.1 Feed Disturbance . 120 x 7.8.1.1 Feed disturbance . 121 7.8.1.2 Impact of feed disturbance on GSU . 121 7.8.1.3 Impact of feed disturbance on GDU . 125 7.8.1.4 Impact of feed disturbance on HCDP . 126 7.8.1.5 Summary of feed disturbance impact on gas train . 126 7.8.2 Process Unit Malfunction . 127 7.8.2.1 Process disturbance . 128 7.8.2.2 Impact of process disturbance on GSU . 129 7.8.2.3 Impact of process disturbance on GDU . 129 7.8.2.4 Impact of process disturbance on HCDP . 133 7.8.2.5 Summary of process disturbance impact on gas train . 134 7.8.3 Discussion . 134 7.9 Wide Process Control with Constraints . 137 7.9.1 Major Process Unit Malfunction . 138 7.9.1.1 Process disturbance . 139 7.9.1.2 Impact of process disturbance on GSU . 139 7.9.1.3 Impact of process disturbance on GDU . 143 7.9.1.4 Impact of process disturbance on HCDP . 143 7.9.1.5 Summary of major process disturbance impact on gas train144 7.9.2 Major Feed Disturbance .
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