Lithofacies classification based on a hybrid system of artificial neural networks and hidden Markov models

Abstract

SUMMARY Lithofacies is one of the most important reservoir parameters, which could provide a qualitative description for hydrocarbon and geothermal reservoirs. Various techniques, such as artificial neural networks (ANN) and hidden Markov models (HMM), have been applied to extract this information, with the well log suites as inputs. However, both of these methods have their own limitations, such as no geological priors in ANN, since log samples along the depth direction are treated independently. While in HMM, a simple Gaussian assumption is usually adopted, which may not be sufficient for complex data distributions. In order to address these problems, a new method is proposed, which combines ANN and HMM into a hybrid system. This new technique allows for a more flexible approach to the probability distributions of rock properties, without any Gaussian assumption being made. At the same time, the geological dependence between adjacent samples is introduced by a representative transition matrix of lithofacies. The output probability from ANN must be reformulated to an emission function before it can be fed into the HMM, which is achieved via the Bayes’ rule. Then the Viterbi algorithm in HMM is applied for the decoding of sequential patterns in the subsurface. In this fashion, the classification process can be proceeded statistically and geologically. Better performance of the proposed approach, compared with other classification methods, is demonstrated in two case studies.

Characterization of hydrothermal alteration along geothermal wells using unsupervised machine-learning analysis of X-ray powder diffraction data

“…Machine learning is a suitable approach for automated zonation and characterization of multi-dimensional data, with several examples of its successful application to geophysical logs, such as seismic velocity, resistivity, gamma ray, and average neutron density porosity (e.g., Raeesi et al 2012;Grana et al 2017;Caté et al 2017;He Communicated by: H. Babaie Fukishima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology, Koriyama, Fukushima, Japan et al 2020;Feng 2020). Machine-learning classification of geophysical data can help with objectively classifying the subsurface's physical properties and has also been used to identify natural-resource reservoirs (e.g., oil/gas).…”

Characterization of Lithfacies Properties of Carbonate Reservoir rocks using Machine Learning Techniques

“…In the field of spatial modelling and classification based on log data. Novel hybrid inferential system called ANN-HMM models for lithofacies classification [7]. Approaches to model the rock lithology was developed by using recurrent neural networks were used [2,15].…”

Lithofacies prediction driven by logging‐based Bayesian‐optimized ensemble learning: A case study of lacustrine carbonate reservoirs

“…Tang et al (2011) discussed the possible application of PNN on the prediction of lithofacies, and accordingly based on several validations confirmed that such an attempt is successful. Feng (2020) invented a hybrid artificial neural network to realize a classification for sandy-mud lithofacies, and demonstrated that a satisfactory classification can be obtained under an extra integration of hidden Markov models. Being different from the former two models, SVM employs a new recognizing concept that all raw samples will be projected into a high-dimensional space via a kernel function, and subsequently a mapping between variables and independent variables will be constructed to implement recognition (Burges, 1998).…”