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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we present a method based on the attribute reduction of rough set and support vector machine regression and the new method can be used to predict three important petroleum reservoir parameters which are porosity, permeability and saturation. First, we use rough set theory to reduce the attributes of sampling dataset in order to select the decision-making attributes constituting a new sampling dataset. Second, we use the theory of Support Vector Regression (SVR) for training data and establish the predicting model. After that, the test data will be predicted. The experimental results show that the method can get a better fitting result and reduce the computational complexity of SVR in training dataset and it can also improve the accuracy of reservoir physical parameters. The implementation of the method can provide the foundation of decision making for reservoir development.