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

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...

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

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

This research effort aims in the estimation of Wood Loss Factor by employing Support Vector Machines. For this purpose experimental data for two different wood species were used. The estimation of the dielectric properties of wood was done by using various Kernel algorithms as a function of both ambient electro-thermal conditions applied during drying of wood and basic wood chemistry. Actually ...

Support Vector Machines versus Artificial Neural Networks – New Potential in Data Mining for Customer Relationship Management?

In competitive consumer markets, data mining for customer relationship management faces the challenge of systematic knowledge discovery in large data streams to achieve operational, tactical and strategic competitive advantages. Methods from computational intelligence, most prominently artificial neural networks and support vector machines, compete with established statistical methods in the do...

A Unified Approach to the Extraction of Rules from Artificial Neural Networks and Support Vector Machines

Support Vector Machines (SVM) are believed to be as powerful as Artificial Neural Networks (ANN) in modeling complex problems while avoiding some of the drawbacks of the latter such as local minimæ or reliance on architecture. However, a question that remains to be answered is whether SVM users may expect improvements in the interpretability of their models, namely by using rule extraction meth...

Separating Well Log Data to Train Support Vector Machines for Lithology Prediction in a Heterogeneous Carbonate Reservoir

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...