{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/132801"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/132801","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"A system-theoretic approach to oil and gas assurance programs","abstract":"Chevron, one of the world's leading integrated energy companies, faces new challenges as it aggressively pursues digital innovation and acceleration. Oil and gas well construction, in particular, will continue to incorporate automation to enhance capabilities and gain a competitive advantage. These changes to the technology landscape will fundamentally alter the nature of well construction and the interactions pertaining to well design, operation, and maintenance. WellSafe, Chevron's well control assurance program, was created to ensure process safety hazards are controlled and to prevent large-scale incidents. Since its inception in 2015, WellSafe has brought incremental improvements. To continuously adapt and keep pace with the ongoing digital transformation, WellSafe must use systems engineering principles, methods, and tools to improve in the face of a changing environment. System-Theoretic Accident Models and Processes (STAMP) and System-Theoretic Process Analysis (STPA) developed by MIT's Nancy Leveson help assess WellSafe and uncover opportunities to improve. This thesis analyzes the WellSafe assurance program and generates system requirements based on causal factors that impact the efficacy of the program. This, in turn, helps identify safe system boundaries and constraints that must be enforced to achieve system safety. This thesis demonstrates the value of STPA as an integrated analysis method and offers specific recommendations to improve the WellSafe program.","abstract_html":"Chevron, one of the world&#x27;s leading integrated energy companies, faces new challenges as it aggressively pursues digital innovation and acceleration. Oil and gas well construction, in particular, will continue to incorporate automation to enhance capabilities and gain a competitive advantage. These changes to the technology landscape will fundamentally alter the nature of well construction and the interactions pertaining to well design, operation, and maintenance. WellSafe, Chevron&#x27;s well control assurance program, was created to ensure process safety hazards are controlled and to prevent large-scale incidents. Since its inception in 2015, WellSafe has brought incremental improvements. To continuously adapt and keep pace with the ongoing digital transformation, WellSafe must use systems engineering principles, methods, and tools to improve in the face of a changing environment. System-Theoretic Accident Models and Processes (STAMP) and System-Theoretic Process Analysis (STPA) developed by MIT&#x27;s Nancy Leveson help assess WellSafe and uncover opportunities to improve. This thesis analyzes the WellSafe assurance program and generates system requirements based on causal factors that impact the efficacy of the program. This, in turn, helps identify safe system boundaries and constraints that must be enforced to achieve system safety. This thesis demonstrates the value of STPA as an integrated analysis method and offers specific recommendations to improve the WellSafe program.","abstract_has_math":false,"creators":["Baylor, Brandon S. 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WellSafe, Chevron's well control assurance program, was created to ensure process safety hazards are controlled and to prevent large-scale incidents. Since its inception in 2015, WellSafe has brought incremental improvements. To continuously adapt and keep pace with the ongoing digital transformation, WellSafe must use systems engineering principles, methods, and tools to improve in the face of a changing environment. System-Theoretic Accident Models and Processes (STAMP) and System-Theoretic Process Analysis (STPA) developed by MIT's Nancy Leveson help assess WellSafe and uncover opportunities to improve. This thesis analyzes the WellSafe assurance program and generates system requirements based on causal factors that impact the efficacy of the program. This, in turn, helps identify safe system boundaries and constraints that must be enforced to achieve system safety. 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