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

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  • Runhai Feng, Delft Univ Technol, Delft University of Technology, Dept Geosci & Engn
  • ,
  • Thomas Mejer Hansen
  • Dario Grana, Univ Wyoming, University of Wyoming, Dept Geol & Geophys
  • ,
  • Niels Balling

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.

Sider (fra-til)M97-M105
Antal sider9
StatusUdgivet - nov. 2020

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