Deep learning

Geology

Porosity

Artificial intelligence

Management

Economics

Seismology

Geotechnical engineering

Computer science

Unsupervised learning

Estimation

Geophysics

We propose to invert reservoir porosity from poststack seismic data using an innovative approach based on deep-learning methods. We develop an unsupervised approach to circumvent the requirement of large volumes of labeled data sets for a conventional learning process. We apply convolutional neural networks (CNN) on seismic data to predict the relative porosity that is to be added to a low-frequency prior component. We then apply a forward model to synthesize seismic data based on a source wavelet and an acoustic impedance converted from the network-determined porosity. The parameters in the CNN are iteratively updated to minimize the error between recorded and simulated seismic data. We test the capability of our deep-learning approach to estimate reservoir porosity using a synthetic rock-physics model with two different signal-to-noise ratios. We also apply the proposed method to a real case study of seismic data acquired for hydrocarbon exploration of clastic reservoirs in the Vienna Basin. Instead of randomly assigning neural parameters, we use pretrained weights and biases at a previous location as initialization values for the next location, to preserve the geologically lateral continuity of the layers' physical properties. As shown by these analyses, the unsupervised CNN-based scheme provides more or equally accurate results than standard methods for porosity estimation from seismically inverted acoustic impedance, which makes it a promising tool in seismic reservoir characterization with less user intervention.

An unsupervised deep-learning method for porosity estimation based on poststack seismic data