Complex systems occur everywhere in the oil and gas industry. Once such a system is implemented, it becomes very difficult to verify that it always performs exactly as it should and that it does so in an efficient manner. This research program employs lightweight simulation and formal methods in order to analyze the properties of such systems.
The digital transformation of the industry depends on rich information models that are intelligible to both computers and humans. Such a model should represent how domain experts view their domain in order to enable them to view and explore the data they require.
Data within the oil and gas domain typically resides in several different sources and can have vastly different forms and access methods. In order to ensure optimal decision making, all of this data must be taken into account; an end-user needs to be able to view and understand all data.
Industry-relevant data comes in many forms, from structured sources (e.g., databases) and unstructured sources (e.g., natural language documents intended to be read by humans). Having access to all this data is only as useful as the methods one has to evaluate and use this data to make decisions.
In order to ensure that industry gains a long-term benefit from the tools and methods developed in SIRIUS one must ensure the scalability of these solutions. This research program focusses on research in high-performance computing coupled with scalable cloud computing to support scalable big-data application processing.
When adopting new digital technologies, the central challenge industrial companies face is to identify and cultivate the organizational pre-conditions necessary for realizing the potential of these new technologies. Consequently, the realization of their potential falls significantly short of expectations.