The application discloses a kind of based on
deep learning's seismic brine reservoir response feature identification method, by fusing
induction logging data and
seismic inversion result, break through the technical limitation that brine reservoir feature response is weak to traditional acoustic
logging, improve the accuracy of reservoir identification;Improved UNET++
network structure utilizes dense skip connection and channel attention mechanism, effectively integrates multi-scale geological features, enhances the capture ability of
thin layer brine weak reflection
signal, improves the accuracy of boundary identification;Introduce bidirectional LSTM and spatial feature alignment technology, realize the depth fusion of
logging vertical feature and seismic horizontal profile, optimize
horizon matching relationship, effectively reduce the risk of misjudgment caused by velocity anomaly;Based on deep supervision mechanism and joint training strategy, generate three-dimensional reservoir
spatial distribution model, realize the description of deep pore-
fracture type brine reservoir, greatly improve exploration efficiency, provide reliable
technical support for brine resource positioning under complex geological conditions.