Theory-guided data science-based reservoir prediction of a North Sea oil field

Data science-based methods, such as supervised neural networks, provide powerful techniques to predict reservoir properties from seismic and well data without the aid of a theoretical model. In these supervised learning approaches, the seismic to rock property relationship is learned from the data. One of the major factors limiting the success of these methods is whether there exists enough labelled data, sampled over the expected geology, to train the neural network adequately. To overcome these issues, this paper explores hybrid theory-guided data science (TGDS)-based methods.