  Fractures are widely developed in hydrocarbon reservoirs and constitute the
accumulation spaces and transport channels of oil and gas. Fracture detection
is a fundamental task for reservoir characterization. From prestack seismic
gathers, anisotropic analysis and inversion were commonly applied to
characterize the dominant orientations and relative intensities of fractures.
However, the existing methods were mostly based on the vertical aligned facture
hypothesis, it is impossible for them to recognize fracture dip. Furthermore,
it is difficult or impractical for existing methods to attain the real fracture
densities. Based on data-driven deep learning, this paper designed a
convolutional neural network to perform prestack fracture detection.
Capitalizing on the connections between seismic responses and fracture
parameters, a suitable azimuth dataset was firstly generated through fracture
effective medium modeling and anisotropic plane wave analyzing. Then a
multi-input and multi-output convolutional neural network was constructed to
simultaneously detect fracture density, dip and strike azimuth. The application
on a practical survey validated the effectiveness of the proposed CNN model.
