Predicting gas-bearing distribution using DNN based on multi-component seismic data: Quality evaluation using structural and fracture factors

The tight-fractured gas reservoir of the Upper Triassic Xujiahe Formation in the Western Sichuan Depression has low porosity and permeability. This study presents a DNN-based method for identifying gas-bearing strata in tight sandstone. First, multi-component composite seismic attributes are obtained.The strong nonlinear relationships between multi-component composite attributes and gas-bearing reservoirs can be constrained through a DNN. Therefore, we identify and predict the gas-bearing strata using a DNN. Then, sample data are fed into the DNN for training and testing. After optimized network parameters are determined by the performance curves and empirical formulas, the best deep learning gas-bearing prediction model is determined. The composite seismic attributes can then be fed into the model to extrapolate the hydrocarbon-bearing characteristics from known drilling areas to the entire region for predicting the gas reservoir distribution. Finally, we assess the proposed method in terms of the structure and fracture characteristics and predict favorable exploration areas for identifying gas reservoirs.

Reservoir prediction using multi-wave seismic attributes

The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the ＂curse of dimension＂. Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis （PCA）, independent component analysis （ICA） for attribute optimization and support vector machine （SVM） for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy.

Stretching correction for amplitude-preserving vector wavefield reverse-time migration

The migration of multi-wave seismic data is aimed at obtaining the P- and S-wave imaging results of the amplitude preserving. But the P- and S-wave stretching effect produced by the reverse time migration of the elastic wave equation will not only reduce the vertical resolution of the migration results and the amplitude preserving of the large reflection angle. In this paper, the reverse time migration technique of amplitude preserving vector wave-field separating is used. Based on the analysis of the stretch mechanism and the influencing factors of stretch magnitude, the paper gave the stretch correcting factors. Then, realize the stretch correction method at the time that after the reverse extrapolation and before the imaging by solving the problem which is how to calculate the P-wave and Ps-wave propagation directions of imaging points at different times. The stretch correction method can improve the vertical resolution and amplitude fidelity of the imaging results and provide high fidelity input data for seismic data interpretation and inversion.