Automatic Lithofacies Classification with t-SNE and K-Nearest Neighbors Algorithm

Abstract

One of the critical processes in the exploration of hydrocarbons is the identification and prediction of lithofacies that constitute the reservoir. One of the cheapest and most efficient ways to carry out that process is from the interpretation of well log data, which are often obtained continuously and in the majority of drilled wells. The main methodologies used to correlate log data to data obtained in well cores are based on statistical analyses, machine learning models and artificial neural networks. This study aims to test an algorithm of dimension reduction of data together with an unsupervised classification method of predicting lithofacies automatically. The performance of the methodology presented was compared to predictions made with artificial neural networks. We used the t-Distributed Stochastic Neighbor Embedding (t-SNE) as an algorithm for mapping the wells logging data in a smaller feature space. Then, the predictions of facies are performed using a KNN algorithm. The method is assessed in the public dataset of the Hugoton and Panoma fields. Prediction of facies through traditional artificial neural networks obtained an accuracy of 69%, where facies predicted through the t-SNE + K-NN algorithm obtained an accuracy of 79%. Considering the nature of the data, which have high dimensionality and are not linearly correlated, the efficiency of t SNE+KNN can be explained by the ability of the algorithm to identify hidden patterns in a fuzzy boundary in data set. It is important to stress that the application of machine learning algorithms offers relevant benefits to the hydrocarbon exploration sector, such as identifying hidden patterns in high-dimensional datasets, searching for complex and non-linear relationships, and avoiding the need for a preliminary definition of mathematic relations among the model’s input data.

Study of a high-precision complex 3D geological modelling method based on a fine KNN and kriging coupling algorithm: a case study for Jiangsu, China

“…According to previous studies, there are many studies on the accuracy of the algorithm to prove the effect of the algorithm (Bacal et al, 2019;Potratz et al, 2021;Ren et al, 2022). There are few studies on modeling the data obtained by machine learning.…”

“…Supervised learning for solving classification problems in machine learning is more suitable for the above work. The application of supervised learning algorithms in geological modeling mainly includes the following categories; 1) k-nearest neighbor algorithm (Pratama, 2019;Potratz et al, 2021;Bullejos et al, 2022); 2) bayesian algorithm (Olierook et al, 2021;Zhang et al, 2021); 3) decision tree algorithm (Bacal et al, 2019;Zhou et al, 2020); 4) support vector machine algorithm (Wang et al, 2019;Ghezelbash et al, 2021;Hu et al, 2022); 5) neural network algorithm (Bai and Tahmasebi, 2020;Hillier et al, 2021). The above algorithms have their own advantages, as shown in Table 1 below.…”

An Overview of Content-Based Image Retrieval Methods and Techniques

“…) is recognized as one of the most effective dimensionality reduction and data visualization technologies [59].It is a probabilistic strategy for visualizing high-dimensional data in 2D or 3D space, seeking to retain the local structure of the data as much as possible in the low-dimensional area. Despite the initial introduction of data visualization, the cluster high-dimensional data at any Euclidean distance has been expanded [60].…”