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Forecasting PVT Properties of Crude Oil Systems Based on Support Vector Machines Modeling Scheme
 Journal of Petroleum Science and Engineering
, 2008
"... a b s t r a c t a r t i c l e i n f o PVT properties are very important in the reservoir engineering computations. There are numerous approaches for predicting various PVT properties, namely, empirical correlations and computational intelligence schemes. The achievements of neural networks open the ..."
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a b s t r a c t a r t i c l e i n f o PVT properties are very important in the reservoir engineering computations. There are numerous approaches for predicting various PVT properties, namely, empirical correlations and computational intelligence schemes. The achievements of neural networks open the door to data mining modeling techniques to play a major role in petroleum industry. Unfortunately, the developed neural networks modeling schemes have many drawbacks and limitations as they were originally developed for certain ranges of reservoir fluid characteristics. This article proposes support vector machines a new intelligence framework for predicting the PVT properties of crude oil systems and solve most of the existing neural networks drawbacks. Both steps and training algorithms are briefly illustrated. A comparative study is carried out to compare support vector machines regression performance with the one of the neural networks, nonlinear regression, and different empirical correlation techniques. Results show that the performance of support vector machines is accurate, reliable, and outperforms most of the published correlations. This leads to a bright light of support vector machines modeling and we recommended for solving other oil and gas industry problems, such as, permeability and porosity prediction, identify liquidholdup flow regimes, and other reservoir characterization.
© Science and Education Publishing DOI:10.12691/ajams343 Of Students Academic Performance Rates Using Artificial Neural Networks (ANNs)
, 2015
"... Abstract A model based on the multilayer perception algorithm was programmed. The result from the test data evaluation showed that the programmed Artificial Neural Network model was able to correctly predict and classify the performance of students with Mean Correct Classification Rate CCR of 97.07% ..."
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Abstract A model based on the multilayer perception algorithm was programmed. The result from the test data evaluation showed that the programmed Artificial Neural Network model was able to correctly predict and classify the performance of students with Mean Correct Classification Rate CCR of 97.07%.
DYNAMIC PROGRAMMING ALGORITHM FOR TRAINING FUNCTIONAL NETWORKS
"... Abstract — The paper proposes a dynamic programming algorithm for training of functional networks. The algorithm considers each node as a state. The problem is formulated as finding the sequence of states which minimizes the sum of the squared errors approximation. Each node is optimized with regar ..."
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Abstract — The paper proposes a dynamic programming algorithm for training of functional networks. The algorithm considers each node as a state. The problem is formulated as finding the sequence of states which minimizes the sum of the squared errors approximation. Each node is optimized with regard to its corresponding neural functions and its estimated neuron functions. The dynamic programming algorithm tries to find the best path from the final layer nodes to the input layer which minimizes an optimization criterion. Finally, in the pruning stage, the unused nodes are deleted. The output layer can be taken as a summation node using some linearly independent families, such as, polynomial, exponential, Fourier,...etc. The algorithm is demonstrated by two examples and compared with other common algorithms in both computer science and statistics communities.