The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Identification of lithofacies using Kohonen self-organizing maps
Computers & Geosciences
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Comparison of four approaches to a rock facies classification problem
Computers & Geosciences
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Multi-class supervised classification of electrical borehole wall images using texture features
Computers & Geosciences
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Unconventional shale reservoirs as the result of extremely low matrix permeability, higher potential gas productivity requires not only sufficient gas-in-place, but also a high concentration of brittle minerals (silica and/or carbonate) that is amenable to hydraulic fracturing. Shale lithofacies is primarily defined by mineral composition and organic matter richness, and its representation as a 3-D model has advantages in recognizing productive zones of shale-gas reservoirs, designing horizontal wells and stimulation strategy, and aiding in understanding depositional process of organic-rich shale. A challenging and key step is to effectively recognize shale lithofacies from well conventional logs, where the relationship is very complex and nonlinear. In the recognition of shale lithofacies, the application of support vector machine (SVM), which underlies statistical learning theory and structural risk minimization principle, is superior to the traditional empirical risk minimization principle employed by artificial neural network (ANN). We propose SVM classifier combined with learning algorithms, such as grid searching, genetic algorithm and particle swarm optimization, and various kernel functions the approach to identify Marcellus Shale lithofacies. Compared with ANN classifiers, the experimental results of SVM classifiers showed higher cross-validation accuracy, better stability and less computational time cost. The SVM classifier with radius basis function as kernel worked best as it is trained by particle swarm optimization. The lithofacies predicted using the SVM classifier are used to build a 3-D Marcellus Shale lithofacies model, which assists in identifying higher productive zones, especially with thermal maturity and natural fractures.