The project is focused within exploration and production of hydrocarbons, and by combining geophysical and mathematical methods we will develop new methods and insight. We aim to get added value by i) using statistical machine learning techniques to improve the way geophysical data are integrated in subsurface models, ii) realistically constraining spatio-temporal statistical models by rock physics relations. This project proposal focuses on innovative methods for exploration and safe production of hydrocarbon resources offshore Norway, developing geophysical and statistical methodologies that are also applicable to petroleum resources elsewhere, as well as other applications relying on spatio-temporal prediction and monitoring. PhD candidates from the current project will use basic disciplines (mathematical sciences and geophysics) to develop insight that is directly relevant to petroleum-related industries. The generic form of knowledge is also applicable to other domains of earth sciences (mining, oceanography, meteorology, etc.), as well as other industries related to energy or new digital-type companies. Key areas that will be investigated are: - geophysical analysis of uplift - new geophysical monitoring methods - elastic seismic migration and inversion Six PhDs and one postdoc will be employed by the project. Seven companies have the intent to support the project both financially and by sharing data and hosting students. Four international highly reputed academic institutions have stated their willingness and interest to participate in the project.
Project leader: Martin Landrø
Institution: NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET NTNU