  Rapid development of big data and high-performance computing have encouraged
explosive studies of deep learning in geoscience. However, most studies only
take single-type data as input, frittering away invaluable multisource,
multi-scale information. We develop a general architecture of hybrid deep
neural networks (HDNNs) to support mixed inputs. Regarding as a combination of
feature learning and target learning, the new proposed networks provide great
capacity in high-hierarchy feature extraction and in-depth data mining.
Furthermore, the hybrid architecture is an aggregation of multiple networks,
demonstrating good flexibility and wide applicability. The configuration of
multiple networks depends on application tasks and varies with inputs and
targets. Concentrating on reservoir production prediction, a specific HDNN
model is configured and applied to an oil development block. Considering their
contributions to hydrocarbon production, core photos, logging images and
curves, geologic and engineering parameters can all be taken as inputs. After
preprocessing, the mixed inputs are prepared as regular-sampled structural and
numerical data. For feature learning, convolutional neural networks (CNN) and
multilayer perceptron (MLP) network are configured to separately process
structural and numerical inputs. Learned features are then concatenated and fed
to subsequent networks for target learning. Comparison with typical MLP model
and CNN model highlights the superiority of proposed HDNN model with high
accuracy and good generalization.
