Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir

Sebtosheikh, Mohammad Ali; Salehi, Ali

doi:10.1016/j.petrol.2015.08.001

Cited by 82 publications

(19 citation statements)

References 22 publications

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“…Results from Fig. 15 suggest that the performance of SVM classifier depends on the size of the training samples for lithofacies classification, which confirms the findings of Sebtosheikh and Salehi (2015), suggesting increase in classification accuracy with increasing training sample size. These results are also similar to other studies (Pal and Foody, 2010;Pal and Mather, 2005).…”

Section: Actualsupporting

confidence: 76%

Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Bhattacharya

Carr

Pal

2016

Journal of Natural Gas Science and Engineering

146

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Quantitative lithofacies modeling is important to understand the depositional and diagenetic history, and hydrocarbon potential of unconventional resources at a regional scale. The complex heterogeneous nature and large data dimensionality of unconventional mudstone reservoirs increase the challenge of lithofacies interpretation by conventional qualitative methods. Quantitative shale lithofacies, which are meaningful, mappable, and predictable at core, well log, and regional scales, can be defined based on mineralogy and Total Organic Carbon (TOC) derived from core analysis and advanced geochemical spectroscopy logs (e.g. Pulsed Neutron Spectroscopy, PNS). However, access to numerous and widespread core samples and geochemical log responses is typically limited by cost and time. We apply different mathematical techniques to ubiquitous conventional well log suites calibrated to rock types, defined by the limited number of wells with high-quality core and geochemical logs. The documented interrelationships between lithofacies and conventional logs are propagated with a quantified degree of accuracy in wells without advanced log or core data. Our study addresses issues of different approaches of quantitative lithofacies classification and

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Section: Actualsupporting

confidence: 76%

Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Bhattacharya

Carr

Pal

2016

Journal of Natural Gas Science and Engineering

146

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“…By expanding Ω(h(x i )), the optimal solution is to minimize objective in equation 4, which can be rewritten to equation (5) with a second-order approximation [23]. en, the optimal leaf weight w * j of leaf j can be computed by equation (6). In this case study, we applied L1 regularization with subsampling on the GTB model and L2 regularization on the XGBoost model: [27].…”

Section: Regularizationmentioning

confidence: 99%

“…eir result showed that the performance of SVM is better than neural network in lithology identification. Sebtosheikh and Salehi concluded that the prediction accuracy of an SVM classifier with normalized polynomial kernel function could be improved by using the tuning approach to obtain the optimum parameter set for the kernel function [6]. Besides SVM, several studies have used random forest models to classify lithology.…”

Section: Introductionmentioning

confidence: 99%

Towards Optimization of Boosting Models for Formation Lithology Identification

Xie

Zhu

et al. 2019

Mathematical Problems in Engineering

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Lithology identification is an indispensable part in geological research and petroleum engineering study. In recent years, several mathematical approaches have been used to improve the accuracy of lithology classification. Based on our earlier work that assessed machine learning models on formation lithology classification, we optimize the boosting approaches to improve the classification ability of our boosting models with the data collected from the Daniudi gas field and Hangjinqi gas field. Three boosting models, namely, AdaBoost, Gradient Tree Boosting, and eXtreme Gradient Boosting, are evaluated with 5-fold cross validation. Regularization is applied to the Gradient Tree Boosting and eXtreme Gradient Boosting to avoid overfitting. After adapting the hyperparameter tuning approach on each boosting model to optimize the parameter set, we use stacking to combine the three optimized models to improve the classification accuracy. Results suggest that the optimized stacked boosting model has better performance concerning the evaluation matrix such as precision, recall, and f1 score compared with the single optimized boosting model. Confusion matrix also shows that the stacked model has better performance in distinguishing sandstone classes.

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“…Horrocks et al [2] explores different machine learning algorithms and architectures for classifying lithologies using wireline data for coal exploration. Other approaches include multivariate statistical analysis [11], neural networks with probabilistic neurons [12] or radial basis function kernel [13], random forests [14,15], combination of classification and regression methods [16] and collaborative learning agents [7]. ELM networks may need a higher number of hidden neurons due to the random determination of the input weights and hidden biases.…”

Section: Introductionmentioning

confidence: 99%

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Saporetti

Duarte

Fonseca

et al. 2018

RITA

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Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an activation function recently proposed in the literature is implemented and tested. The computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.

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Lithology prediction by support vector classifiers using inverted seismic attributes data and petrophysical logs as a new approach and investigation of training data set size effect on its performance in a heterogeneous carbonate reservoir

Cited by 82 publications

References 22 publications

Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Comparison of supervised and unsupervised approaches for mudstone lithofacies classification: Case studies from the Bakken and Mahantango-Marcellus Shale, USA

Towards Optimization of Boosting Models for Formation Lithology Identification

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

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