Abstract
The degree of success of many oil and gas drilling, completion, and production activities depends on the accuracy of the models used in the reservoir lateral prediction and description. In this paper, a hybrid MPSO-BP-RBFN model for predicting reservoir from seismic attributes is proposed. The model in which every particle consists of binary and real parts is able to simultaneously search for optimal network topology (the number of hidden nodes) and parameters, as it proceeds. The model has been used to reservoir lateral prediction of a reservoir zone and proved the model’s applicability.
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Yu, S., Zhu, K., Guo, X., Wang, J. (2009). A Hybrid MPSO-BP-RBFN Model for Reservoir Lateral Prediction. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5551. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01507-6_69
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DOI: https://doi.org/10.1007/978-3-642-01507-6_69
Publisher Name: Springer, Berlin, Heidelberg
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