A comparative study of heterogeneous ensemble methods for the identification of geological lithofacies

Abstract

Mudstone reservoirs demand accurate information about subsurface lithofacies for field development and production. Normally, quantitative lithofacies modeling is performed using well logs data to identify subsurface lithofacies. Well logs data, recorded from these unconventional mudstone formations, are complex in nature. Therefore, identification of lithofacies, using conventional interpretation techniques, is a challenging task. Several data-driven machine learning models have been proposed in the literature to recognize mudstone lithofacies. Recently, heterogeneous ensemble methods (HEMs) have emerged as robust, more reliable and accurate intelligent techniques for solving pattern recognition problems. In this paper, two HEMs, namely voting and stacking, ensembles have been applied for the quantitative modeling of mudstone lithofacies using Kansas oilfield data. The prediction performance of HEMs is also compared with four state-of-the-art classifiers, namely support vector machine, multilayer perceptron, gradient boosting, and random forest. Moreover, the contribution of each well logs on the prediction performance of classifiers has been analyzed using the Relief algorithm. Further, validation curve and grid search techniques have also been applied to obtain valid search ranges and optimum values for HEM parameters. The comparison of the test results confirms the superiority of stacking ensemble over all the above-mentioned paradigms applied in the paper for lithofacies modeling. This research work is specially designed to evaluate worst-to bestcase scenarios in lithofacies modeling. Prediction accuracy of individual facies has also been determined, and maximum overall prediction accuracy is obtained using stacking ensemble.

A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

“…Xie et al (2019) applied regularization on GTB and xgboosting and stacked the classifiers to improve the classification accuracy. Tewari and Dwivedi (2020) also showed that the heterogeneous ensemble methods, namely voting and stacking, could improve the prediction accuracy for mudstone lithofacies in a Kansas oil-field area. Ao et al (2019b) proposed a linear random forest (LRF) algorithm for better logging regression modeling with limited samples.…”

A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality

“…Each base classifier independently gives a class label to a given test sample during the testing phase. The final grouping of the test sample is determined by the maximum number of times a particular class label is assigned to that test sample 61 . On the other hand, soft voting methods calculate the average probability of all classes, and the final prediction is made on the basis that which class is having the highest probability 60 .…”

OAAE: Optimized adaptive anomaly detection ensemble—Base model boosting by parameter optimization

“…These dynamic methods that combine outcomes from their base models produce superior results compared to their static counterparts that, for example, only take a majority vote over their participating base models. Two combination schemes can be used to merge results from different base models: majority voting (also known as hard voting) and average confidence probability (also known as soft voting) 32 . In addition, weighted voting and voting without weights are two ways of combining not only homogeneous but also heterogeneous base algorithm outputs 33 …”