Here, we present the R&D project Real-Time Digital Twin for Boosting Performance of Seismic Operations, which aims at increasing the overall operational efficiency of seismic vessels through digitisation and automation. The cornerstone in this project is the development of a real-time digital twin (RTDT) — a sophisticated mathematical model and state estimator of all the in-sea seismic equipment, augmented with real-time measurements from the actual equipment. This provides users and systems on-board the vessel with a live digital representation of the state of the equipment during operations. By combining the RTDT with state-of-the-art methods in machine learning and control theory, the project will develop new advisory and automation systems that improve the efficiency of seismic survey operations, reduce the risk of equipment damage, improve health monitoring and fault detection systems, and improve the quality of the seismic data. This will lead to less unproductive time, reduced costs, reduced fuel consumption and reduced emissions for a given operational scope.

The main focus in this paper is the presentation of today’s challenges in offshore seismic surveys, and how state-of-the-art technology can be adopted to improve various operations. We discuss how simulation technology, machine learning and live sensor measurements can be integrated in on-board decision support and automation systems, and highlight the importance of such systems for designing the complex, autonomous offshore vessels of the future. Finally, we present some early results from the project in the form of two brief case studies.

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