Use and development of quantitative models for maintenance decisions in the oil and gas industry on the Norwegian Continental Shelf

This Ph.D. project belongs to the BRU21 program. BRU21 stands for Better Resource Utilization in the 21st century and is NTNU’s research and innovation program in digital and automation solutions for the Oil and Gas (O&G) industry. The basis for the BRU21 research program is a series of facts findings meeting between NTNU and companies related to the Norwegian O&G industry conducted in 2016. In these meetings, the industry expressed a belief that digitalization is vitally important to secure the industry’s competitiveness and that the O&G industry is lagging behind other industry sectors, such as manufacturing. The most prominent concept for performance improvement in the manufacturing industry has in recent years been Industry 4.0. The main economic potential of Industry 4.0 lies in the ability to make faster and better decisions. This ability is facilitated by the recent development in sensor technology, combined with improvements in systems for collecting, storing, and analyzing large amounts of data. This technological development facilitates the introduction of digital twins, i.e., digital representations of physical assets, processes, or systems. Having digital representations of physical assets that are not developed for specific needs but instead can act as a single source of the truth, for all business area and use cases, help reduce the time and effort needed for collecting the necessary data for making high-quality data-driven decisions. A large stream of papers proposing quantitative models for data-driven decision making in maintenance has been published in the last 50 years, but there is little empirical evidence of these models being used in the industry. Availability of the necessary data has traditionally been a challenge, but introducing concepts such as Industry 4.0 and digital twins may change this. Six articles have been written in this Ph.D. project. The first three articles aimed to gain insight into the potential and current use of digitalization of maintenance in the O&G industry in the Norwegian Continental Shelf (NCS). In Article I, financial data from an example O&G production platform was analyzed to assess the economic value of improving maintenance. Articles II and III found indications that some Norwegian O&G companies have entered a virtuous circle of data collection and model development, increasing the benefits of data-driven decision making in maintenance. The remaining three articles use the insights gained in the previous papers to propose how the industry can move forward with data-driven decision making in maintenance. Article IV proposes a framework for implementing Smart Maintenance that builds on elements from system engineering and lean production. Article V develops a CBM optimization model that accounts for the decision maker’s risk aversion. Article VI presents a CBM optimizing model for a system subject to hard failure, imperfect repair, maintenance windows, and maintenance delay