Abstract
We present a deep neural network application for 4D seismic inversion to changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells is insufficient for training neural networks, thus, the network is trained on synthetic data. As neural networks can extrapolate beyond the training data, it is possible to reach physically inconsistent results when applying the network to unseen data. We present a methodology to incorporate prior physics information to constrain the neural network relations to physically consistent results. This is done by including physics information into the training loss function, guiding the training process towards models that can describe the training data as well as present physically consistent solutions. Using this method, the network models reach precise results even when trained on general non-ideal synthetic datasets.
Original language | English |
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Pages | 1-5 |
Number of pages | 5 |
DOIs | |
Publication status | Published - Oct 2021 |
Event | 82nd EAGE Annual Conference & Exhibition 2021 - Amsterdam, Netherlands Duration: 18 Oct 2021 → 21 Oct 2021 |
Conference
Conference | 82nd EAGE Annual Conference & Exhibition 2021 |
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Country/Territory | Netherlands |
City | Amsterdam |
Period | 18/10/21 → 21/10/21 |