Porosity is a crucial index in reservoir evaluation. In tight reservoirs, the porosity is low, resulting in weak seismic responses to changes in porosity. Moreover, the relationship between porosity and seismic response is complex, making accurate porosity inversion prediction challenging. This paper proposes a Transformer-based seismic multi-attribute inversion prediction method for tight reservoir porosity to address this issue. The proposed method takes multiple seismic attributes as input data and porosity as output data. The Transformer mapping transformation network consists of an encoder, a multi-head attention layer, and a decoder and is optimized for training with a gating mechanism and a variable selection module. Applying this method to actual data from a tight sandstone gas exploration area in the Sichuan Basin yielded a porosity prediction coincidence rate of 95% with the well data.

Seismic waves produce anomalies when they pass through hydrocarbons; these anomalies, which are commonly used to detect hydrocarbons, are manifested differently in different domains. Here, we propose a novel hydrocarbon detection method that combines Empirical Mode Decomposition (EMD), the Teager-Kaiser energy operator (TKEO), and the cepstrum. This method utilizes EMD’s ability to adaptively decompose signals, benefits from the TKEO’s superior performance regarding the focusing of instantaneous energy, and uses the sensitivity of cepstrum domain parameters to hydrocarbons. Here, applying the developed EMD-TKE-Cepstrum method to the Marmousi2 example revealed that it could describe the position and extent of hydrocarbons more clearly than the synchronous compression wavelet transform (SCWT) method. Applying the EMD-TKE-Cepstrum algorithm to field data further confirmed its potential regarding the identification of anomalies associated with hydrocarbon reservoirs.

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.