Reservoir characterization

Applications of Artificial Neural Networks for Seismic Facies Classification

The work and the provided methodology provide a significant improvement in facies classification.

Blue and green pattern abstract

Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique.


The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows.

Restricted Content
We're sorry, but this content is reserved for SPE Members. If you are a member, please sign in for access. If you are not a member and you find JPT content valuable, we encourage you to become a part of the SPE member community.