Seismic characterization of deeply buried paleocaves based on Bayesian deep learning

Deeply buried paleocaves exist widely in the world and act as an important type of hydrocarbon reservoirs. Three-dimensional (3-D) seismic data are commonly used to detect deeply buried paleocaves, but regular interpretation methods can hardly characterize their shape and uncertainty. We propose a novel seismic interpretation method based on Bayesian deep learning to solve this problem. The proposed Bayesian encoder- decoder network employs convolution layers, residual connections, especially dropout as the approximation of Bayesian inference. The Bayesian encoder-decoder network is trained on the synthetic 3-D seismic data and paleocave models as inputs and labels respectively. The synthetic paleocave models integrate geological knowledge such as the size and shape of paleocaves acquired from outcrops and drilled wells. The synthetic seismic data are generated based on the synthetic paleocaves and the wavelet extracted from the field seismic data. The trained Bayesian encoder-decoder model is finally tested on the blind synthetic data and the field seismic data from the Tarim Basin. Results show that Bayesian encoder-decoder can characterize the shape of paleocaves more accurately than the regular encoder-decoder and seismic attribute methods. Bayesian encoder-decoder also estimates uncertainty of the identified paleocaves which helps us evaluate the reliability of the result. Paleocave edges and small paleocaves of the results always show great uncertainty. The proposed approach based on Bayesian deep learning has great potential in similar scenarios of seismic reservoir characterization.

Automated summary

The seismic interpretation method based on Bayesian deep learning can accurately describe and evaluate the shape and reliability of paleocaves, and has great potential in seismic reservoir characterization.