A deep-learning framework with seismic-frequency-band constraints for thin-reservoir characterization

ABSTRACT The characterization of the spatial structures of thin-layer sand bodies is the foundation for detailed reservoir description and physical property estimation. This requires using the geophysical inversion technique to make full use of the subsurface sedimentation information found in well-logging and seismic data for comprehensive evaluation. However, the traditional model-based inversion method is limited by the frequency bandwidth of seismic data, and the resolution of the inversion results cannot meet the accuracy requirements of thin-layer reservoir characterization. In this paper, we introduce a convolutional model and seismic-frequency-band constraints into a deep-learning framework to create a high-resolution inversion framework under physical constraints and then test our technical scheme in the model and on field data. We construct a 3D thin-layer sand body model with specific geologic implications and carry out inversion tests based on sparse spike inversion, geostatistical inversion, and a deep-learning framework. The experimental results demonstrate the feasibility of the deep-learning framework and reveal its limitations in characterizing the spatial distribution of sand bodies. After introducing the convolutional model and seismic-frequency-band constraints, the inversion results more clearly distinguish the spatial distribution and superposition relationships of different sand bodies. The results obtained based on the field data fully confirm the effectiveness and applicability of our method.