Electrofacies classification using Supervised learning algorithms

Abstract

Summary The aim of this paper is to present a simple but effective workflow to classify depositional facies using conventional well logging data and supervised learning algorithms. Facies recognition is a time-consuming task and economically expensive. Therefore, depositional facies provide essential information regarding the distribution of rock properties like porosity and permeability. Using seismic attributes is possible to define depositional facies using different attributes extracted from 3D seismic and well logs to construct a detailed distribution map ( Bagheri & Riahi, 2015 ). However, this method is limited by seismic resolution and the available 3D seismic data. Core analysis greatly supports facies recognition within depositional environments, yet it is time-consuming and expensive making it hard to apply for every well. Finally, there are some other works related to facies classification using conventional well logging data and Machine learning algorithms, ( Puskarczyk, 2019 ) uses Artificial neural network (ANN) for pattern recognition and identification of electrofacies. ( Al-Mudhafa & (Basrah Oil Company - Iraq), 2020 ) predict facies classification using two non-parametric supervised machine learning approaches: K-Nearest Neighbours (KNN) and Random Forest (RF) in a carbonate reservoir. Despite all the studies already explored using machine learning algorithms for several authors, there are still issues that most of the works have not been covered totally concerning data structure, algorithms limitations, and hyperparameters management, making most of those in other conditions not replicable. This paper may drive all these points, given and important emphasis on the different issues that imply the use of machine learning algorithms as a predictor of depositional facies in a transitional environment. Results show a much better performance of SVM over KNN to predict depositional facies.