Integrating Bayesian classification and ANN for lithofacies classification using well and seismic data: Bahregansar case study

This paper integrates supervised Bayesian classification with a conjugate-based artificial neural network (ANN) to classify seismic data into four lithofacies defined at well locations. This study focuses on the Miocene Ghar Formation, which serve as a highly productive reservoir in the northwest Persian Gulf and the Mesopotamian Basin. Following well log corrections and shear wave velocity (Vs) prediction through rock physics modeling, an adaptive rock physics template (AI vs. SQp), was generated to enhance the accuracy of facies discrimination in well log data. Based on the observed lithology and petrophysical evaluation, four facies were identified: shale, carbonate, high-porosity sand, and low-porosity sand. Following pre-stack simultaneous seismic inversion, an unsupervised facies classification was conducted to validate the capability of the available seismic data, along with its quantitative interpretation results, in effectively classifying facies. AI and SQp were utilized as the primary inputs to facies discrimination in the Ghar Reservoir, while the Probability density function (PDF) results served as auxiliary inputs in the conjugate-based artificial neural network analysis. This study presents a novel integration of Bayesian classification and artificial neural networks (ANN), resulting in a 3D lithofacies distribution model along with associated probabilities, offering new insights into reservoir quality and spatial distribution. This combined approach not only enhances the accuracy of predictions by leveraging the strengths of both methods but also addresses challenges such as uncertainty quantification and the interpretability of ANN models. The results indicate that high-porosity sand facies are concentrated at the crest of the structure, with the probability data and geological interpretation supporting this finding.