Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Practical application of fuzzy logic and neural networks to fractured reservoir characterization
Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Evolutionary extreme learning machine – based on particle swarm optimization
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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In reservoir engineering, the knowledge of Pressure-Volume-Temperature (PVT) properties is of great importance for many uses, such as well test analyses, reserve estimation, material balance calculations, inflow performance calculations, fluid flow in porous media and the evaluation of new formations for the potential development and enhancement oil recovery projects. The determination of these properties is a complex problem because laboratory-measured properties of rock samples (''cores'') are only available from limited and isolated well locations and/or intervals. Several correlation models have been developed to relate these properties to other measures which are relatively abundant. These models include empirical correlations, statistical regression and artificial neural networks (ANNs). In this paper, a comprehensive study is conducted on the prediction of the bubble point pressure and oil formation volume factor using two hybrid of soft computing techniques; a genetically optimised neural network and a genetically enhanced subtractive clustering for the parameter identification of an adaptive neuro-fuzzy inference system. Simulation experiments are provided, showing the performance of the proposed techniques as compared with commonly used regression correlations, including standard artificial neural networks.