Gas-bearing prediction of deep reservoir based on DNN embeddings

Abstract

The extraction of gas-bearing information from the deeply underground reservoir is extremely difficult due to the weak seismic response and complicated gas distribution characteristics. To predict gas-bearing reservoirs efficiently, we developed a deep neural network (DNN) embedding-based gas-bearing prediction scheme. First, the cepstrum coefficient that is sensitive to hydrocarbons is computed using the raw seismic data. A DNN model inspired by the x-vector in speech recognition is designed, comprising the long short-term memory (LSTM) networks and two fully connected (FC) networks, stacked from the bottom to the top layer. Then, the cepstrum features are fed into the DNN for training and testing, and DNN embedding is extracted from the top layers after optimized network parameters are determined. Finally, the gas-bearing probability of the reservoir is predicted by calculating the cosine distance between pairs of DNN embeddings. When applied to synthetic seismic data, the proposed method offers greater than 90% accuracy at SNR > 3 dB. Besides, the predicted result applied in deep carbonate reservoirs in China’s Sichuan Basin is in basic agreement with the actual situation, demonstrating the certain feasibility of the proposed scheme.