Support Vector Machines versus Artificial Neural Networks for Wood Dielectric Loss Factor Estimation

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...

Estimation of Iron concentration by using a support vector machineand an artificial neural network - the case study of the Choghart deposit southeast of Yazd, Yazd, Iran

Estimation of the metal value in the metallic deposits is one of the important factors to evaluate the deposits in exploration studies andmineral processing. Therefore, one accurate estimator is essential to obtain a fine insight into the accumulation of the ore body. Thereare geostatistical methods for the estimation of the concentration of iron which performance of these models is complexity ...

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

Permeability estimation from the joint use of stoneley wave velocity and support vector machine neural networks: a case study of the Cheshmeh Khush Field, South Iran

Accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure andrelative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.Acoustic method can be used as analternative and effective tool for permeability determination. In this study, a four-step approach is proposed for permeability estimati...

Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine

Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...