Prestack gathers feature enhancement method based on BEMD and CTKEO

The hydrocarbon detection method based on pre-stack gather data has higher accuracy and reliability than those methods based on post-stack data.This kind of method requires that pre-stack gather data have high SNR(Signal Noise Ratio)and obvious AVO(amplitude variation with offset)characteristics.Therefore,it is important to optimize the pre-stack gathers and enhance their AVO(Amplitude variation with offset)effect.This abstract proposes a gather optimization and AVO feature enhanced method for hydrocarbon detection.The gather optimization method based on the improved BEMD(bidimensional empirical mode decomposition).It starts from the original signal directly,and can be optimized as pre-stack gather steadily and quickly.Then,the characters of CTKEO(Corss Teager-Kaiser energy)algorithm which is strong sensitivity to interaction of two signals is fully utilized to enhance AVO features.The method proposed in this abstract provides a good data base for pre-stack inversion from gather optimization to AVO feature enhancement.

S-wave velocity inversion and prediction using a deep hybrid neural network

The S-wave velocity is a critical petrophysical parameter in reservoir description,prestack seismic inversion,and geomechanical analysis.However,obtaining the S-wave velocity from field measurements is difficult.When no measured Swave data are available,petrophysical modelling provides the most accurate S-wave velocity prediction.However,because of the complexity of underground geological structures and diversity of rock minerals,the prediction results of petrophysical modelling are easily affected by factors such as the cognition and experience of the modeller.Therefore,the development of novel robust and simple S-wave velocity inversion and prediction methods independent of the modeller is critical.Inspired by ensemble learning and based on the geologic sedimentation law of reservoirs and their characteristics in logging response,an Swave velocity inversion and prediction method based on deep hybrid neural network was developed by combining the classical convolution neural network(CNN)with the long short-term memory(LSTM)network.Considering the conventional logging data such as acoustic and density as the input in the proposed method,the CNN was used to establish the nonlinear mapping relationship between the input data and S-wave velocity,and the LSTM network was used to integrate the vertical variation trend of the stratum.Thus,intelligent data-driven inversion and prediction of the S-wave velocity were realised.The experimental results revealed that the proposed method exhibited a strong generalisation ability and could obtain prediction results comparable to those of petrophysical modelling with a single-well data set for training.Thus,a novel methodology for robust and convenient S-wave velocity prediction was devised.The proposed method has considerable academic and application implications.