A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids
Nanofluids are broadly applied in energy systems such as solar collector, heat exchanger
and heat pipes. Dynamic viscosity of the nanofluids is among the most important features …
and heat pipes. Dynamic viscosity of the nanofluids is among the most important features …
Applications of Machine Learning in Subsurface Reservoir Simulation—A Review—Part II
A Samnioti, V Gaganis - Energies, 2023 - mdpi.com
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry,
with numerous applications which guide engineers in better decision making. The most …
with numerous applications which guide engineers in better decision making. The most …
A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm
W Deng, R Yao, H Zhao, X Yang, G Li - Soft computing, 2019 - Springer
Aiming at the problem that the most existing fault diagnosis methods could not effectively
recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy …
recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy …
Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition
The aim of this research is to develop new intelligent prediction models for estimating the
tunnel boring machine performance (TBM) by means of the rate pf penetration (PR). To …
tunnel boring machine performance (TBM) by means of the rate pf penetration (PR). To …
[PDF][PDF] Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber
Soil shear strength parameters play a remarkable role in designing geotechnical structures
such as retaining wall and dam. This study puts an effort to propose two accurate and …
such as retaining wall and dam. This study puts an effort to propose two accurate and …
[HTML][HTML] Comparison of machine learning methods for estimating permeability and porosity of oil reservoirs via petro-physical logs
This paper deals with the comparison of models for predicting porosity and permeability of
oil reservoirs by coupling a machine learning concept and petrophysical logs. Different …
oil reservoirs by coupling a machine learning concept and petrophysical logs. Different …
[HTML][HTML] Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems
AH Fath, F Madanifar, M Abbasi - Petroleum, 2020 - Elsevier
Exact determination of pressure-volume-temperature (PVT) properties of the reservoir oils is
necessary for reservoir calculations, reservoir performance prediction, and the design of …
necessary for reservoir calculations, reservoir performance prediction, and the design of …
Modeling of simultaneous adsorption of dye and metal ion by sawdust from aqueous solution using of ANN and ANFIS
The current work deals with the investigation of Simultaneous of Basic Red46 (BR46) and
Cu (dye and heavy metal) removal efficiency from aqueous solution through the adsorption …
Cu (dye and heavy metal) removal efficiency from aqueous solution through the adsorption …
Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir
Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in
numerous numbers of oil production applications like those in remote or unmanned …
numerous numbers of oil production applications like those in remote or unmanned …
Integrated workflow in 3D geological model construction for evaluation of CO2 storage capacity of a fractured basement reservoir in Cuu Long Basin, Vietnam
Abstract Carbon dioxide (CO 2) capture, utilization, and storage (CCUS) have been
proposed as a possible technique to mitigate climate change. In this vein, CO 2 storage …
proposed as a possible technique to mitigate climate change. In this vein, CO 2 storage …