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Support Vector Machines Framework for Predicting the PVT Properties of Crude-Oil Systems
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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The Nature of Statistical Learning Theory
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Text categorization with Support Vector Machines: Learning with many relevant features
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Connectionist learning procedures
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Use of production data to estimate volume factor, density and compressibility of reservoir fluids
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Evaluation of empirical correlations for bubblepoint oil formation volume factor
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Assessment of the PVT correlations for predicting the properties of Kuwaiti crude oils
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Handwritten digit recognition: benchmarking of state-of-the-art techniques
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Application of support vector machines in financial time series forecasting
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Evaluation of empirically derived PVT properties for Pakistani crude oils
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Training support vector machines: an application to face detection
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Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Information criteria for support vector machines
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Learning in Artificial Neural Networks: A Statistical Perspective
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Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
inferredcites →https://doi.org/10.1627/jpi1958.36.472openalex-snapshot
Derivation of PVT Correlations for the Gulf of Suez Crude Oils.
inferredcites →https://doi.org/10.2118/13172-paopenalex-snapshot
Evaluation of Empirically Derived PVT Properties for Gulf of Mexico Crude Oils
inferredcites →https://doi.org/10.2118/13718-paopenalex-snapshot
PVT Correlations for Middle East Crude Oils
inferredcites →https://doi.org/10.2118/18571-paopenalex-snapshot
Reservoir-Fluid Property Correlations—State of the Art
inferredcites →https://doi.org/10.2118/20989-paopenalex-snapshot
Correlation of PVT Properties for UAE Crudes
inferredcites →https://doi.org/10.2118/26644-msopenalex-snapshot
Pressure-Volume-Temperature Correlations for Gulf of Mexico Crude Oils
inferredcites →https://doi.org/10.2118/27561-msopenalex-snapshot
Neural Networks: A New Tool for the Petroleum Industry?
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Higher-Order Neural Networks in Petroleum Engineering
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Neural Network: What It Can Do for Petroleum Engineers
inferredcites →https://doi.org/10.2118/37295-msopenalex-snapshot
Empirical PVT Correlations Applied to Egyptian Crude Oils Exemplify Significance of Using Regional Correlations
inferredcites →https://doi.org/10.2118/37691-msopenalex-snapshot
Improved PVT Correlations For UAE Crude Oils
inferredcites →https://doi.org/10.2118/37695-msopenalex-snapshot
Neural Network Model for Estimating The PVT Properties of Middle East Crude Oils
inferredcites →https://doi.org/10.2118/38099-msopenalex-snapshot
Universal Neural Network Based Model for Estimating The PVT Properties of Crude Oil Systems
inferredcites →https://doi.org/10.2118/49961-msopenalex-snapshot
Modeling the Properties of Crude Oil and Gas Systems Using RBF Network
inferredcites →https://doi.org/10.2118/53185-msopenalex-snapshot
Bubble Point Pressure and Oil Formation Volume Factor Correlations
inferredcites →https://doi.org/10.2118/56745-msopenalex-snapshot
A Novel Non-Iterative Method for the Prediction of the PVT Behavior of Reservoir Fluids
inferredcites →https://doi.org/10.2118/58046-jptopenalex-snapshot
Virtual-Intelligence Applications in Petroleum Engineering: Part 1—Artificial Neural Networks
inferredcites →https://doi.org/10.2118/6719-paopenalex-snapshot
Correlations for Fluid Physical Property Prediction
inferredcites →https://doi.org/10.2118/68233-msopenalex-snapshot
Prediction of Oil PVT Properties Using Neural Networks
inferredcites →https://doi.org/10.2118/71302-paopenalex-snapshot
A Review of Bubblepoint Pressure and Oil Formation Volume Factor Correlations
inferredcites →https://doi.org/10.2118/77882-msopenalex-snapshot
Abductive Networks: A New Modeling Tool for the Oil and Gas Industry
inferredcites →https://doi.org/10.2118/8016-paopenalex-snapshot
Generalized Pressure-Volume-Temperature Correlations
inferredcites →https://doi.org/10.2118/85650-msopenalex-snapshot
Prediction of the PVT Data using Neural Network Computing Theory
inferredcites →https://doi.org/10.2118/93765-msopenalex-snapshot
Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines
inferredcites →https://doi.org/10.2333/jbhmk.26.46openalex-snapshot
Data Mining and Knowledge Discovery
inferredcites →https://doi.org/10.2523/15721-msopenalex-snapshot
Evaluation of Empirically Derived PVT Properties for Egyptians Oils
inferredcites →https://doi.org/10.2523/25338-msopenalex-snapshot
Development of New Modified Black Oil Correlations for Malaysian Crudes
inferredcites →https://doi.org/10.2523/27561-msopenalex-snapshot
Neural Networks: A New Tool for the Petroleum Industry?
inferredcites →https://doi.org/10.2523/51086-msopenalex-snapshot
Correlation of Bubblepoint Pressures for Reservoir Oils--A Comparative Study
inferredcites →https://doi.org/10.2523/78592-msopenalex-snapshot
Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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