Neural Processing Letters
Using artificial intelligence to predict permeability from petrographic data
Computers & Geosciences - Special issue on applications of virtual intelligence to petroleum engineering
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Optimal Transformations in Multiple Linear Regression Using Functional Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques (Massive Computing)
Creating a quality map of a slate deposit using support vector machines
Journal of Computational and Applied Mathematics
Modeling intrusion detection system using hybrid intelligent systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Type-2 fuzzy logic-based classifier fusion for support vector machines
Applied Soft Computing
Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science)
Intelligent Decision Making: An AI-Based Approach
Intelligent Decision Making: An AI-Based Approach
Data Mining with Computational Intelligence
Data Mining with Computational Intelligence
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Hi-index | 12.05 |
The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than the individual components when used alone. Hybrid models are extremely useful for reservoir characterization in petroleum engineering, which requires high-accuracy predictions for efficient exploration and management of oil and gas resources. In this paper, we have utilized the capabilities of data mining and computational intelligence in the prediction of porosity and permeability, two important petroleum reservoir characteristics, based on the hybridization of Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-logs. Two hybrid models have been built. In both, Functional Networks were used to select the best of the predictor variables for training directly from input data by using its functional approximation capability with least square fitting algorithm. In the first model (FFS), the selected predictor variables were passed to Type-2 Fuzzy Logic System to handle uncertainties and extract inference rules, while Support Vector Machines made the final predictions. In the second, the selected predictor variables were passed to Support Vector Machines for training by transforming them to a higher dimensional space, and then to Type-2 Fuzzy Logic to handle uncertainties, extract inference rules and make final predictions. The simulation results show that the hybrid models perform better than the individual techniques when used alone for the same datasets with their higher correlation coefficients. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The hybrid models also performed better than a combination of two of the individual components, Type-2 Fuzzy Logic and Support Vector Machines, in terms of higher correlation coefficients as well as lower execution times. This is due to the effective role of Functional Networks, as a best-variable selector in the hybrid models.