Comparison of Recursive Neural Network and Markov Chain Models in Facies Inversion

Abstract

Predicting the spatial configuration of geological facies is a key step in the reservoir modeling process. The productivity of a reservoir depends not only on the facies proportions but also on the spatial patterns of the facies sequence. The recent developments in seismic to facies inversion techniques use $$1\mathrm{st}$$ -order Markov models to improve the geological realism of the inferred facies profiles. However, the emergence of deep learning techniques such as recursive neural networks shows promising results in predictive modeling of event sequences as shown by the successful applications in complex modeling problems, such as natural language processing. In this work, a comparison between hidden Markov models and recursive neural networks is presented to highlight their advantages and disadvantages. The results are discussed according to the prior assumptions related to facies proportions and sequence patterns. Then, an innovative approach integrating recursive neural networks and the state-of-the-art seismic to facies inversion, known as the convolutional hidden Markov model, is proposed in order to predict geologically more realistic facies sequences based on seismic data. The proposed inversion technique is validated using synthetic seismic data in the context of a complex geological environment.