Open AccessLithofacies classification using Bayes theorem method : Case study Western Desert, Egypt
Author(s) -
Moataz Mohamed Gomaa Abdelrahman
Publication year - 2021
Publication title -
multidiszciplináris tudományok
Language(s) - English
Resource type - Journals
eISSN - 2786-1465
pISSN - 2062-9737
DOI - 10.35925/j.multi.2021.1.8
Subject(s) - bayesian probability , petrophysics , desert (philosophy) , workflow , geology , bayes' theorem , naive bayes classifier , artificial neural network , gaussian , pattern recognition (psychology) , artificial intelligence , data mining , computer science , support vector machine , geotechnical engineering , porosity , philosophy , physics , epistemology , quantum mechanics , database
This paper shows the availability of using the Bayesian classification method to predict class membership probabilities in one of the deep tight reservoirs in Western Desert, Egypt. The workflow of our project that using the Bayesian method used the deterministic petrophysical results of three training wells to train the data and extract the classifiers. The classified data were modeled using Gaussian distribution for each lithofacies. The used wells were acquired from a deep Jurassic gas reservoir in the Western Desert of Egypt. The fitting between actual and modeled data has been reached by minimizing the L2 norm. Besides, a cross-validation process was used for validating the resulted classifiers. Finally, the Bayesian classification method can predict the GWC with an accuracy of 4 m. To avoid probability interference caused by the compacted shale more data should be added to the initial model.