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Development of a hybrid learning system based on SVM, ANFIS and domain knowledge: DKFIS
The classification results have been further fine-tuned applying expert knowledge based on the relationship among predictor variables i. e. well logs and target variable - oil saturation.
A novel multiclassSVM based framework to classify lithology from well logs: a real-world application
Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one or one-against-all strategies.
A Novel Framework based on SVDD to Classify Water Saturation from Seismic Attributes
Thus, satisfactory classification of water saturation from seismic attributes is beneficial for reservoir characterization.
A One class Classifier based Framework using SVDD : Application to an Imbalanced Geological Dataset
Evaluation of hydrocarbon reservoir requires classification of petrophysical properties from available dataset.
A Novel Pre-processing Scheme to Improve the Prediction of Sand Fraction from Seismic Attributes using Neural Networks
The network yielding satisfactory performance in the validation stage is used to predict lithological properties from seismic attributes throughout a given volume.
Quantification of sand fraction from seismic attributes using Neuro-Fuzzy approach
Though seismic data is helpful in extrapolation of reservoir properties away from boreholes; yet, it could be challenging to delineate thin sand and shale reservoirs using seismic data due to its limited resolvability.
Well Tops Guided Prediction of Reservoir Properties using Modular Neural Network Concept A Case Study from Western Onshore, India
The data set used in this study comprising three seismic attributes and well log data from eight wells, is acquired from a western onshore hydrocarbon field of India.
