Suitability of low‐field nuclear magnetic resonance (LF‐NMR) combining with back propagation artificial neural network (BP‐ANN) to predict printability of polysaccharide hydrogels 3D printing

2020 ◽  
Author(s):  
Chaofan Guo ◽  
Min Zhang ◽  
Huizhi Chen
Keyword(s):  
Neural Network ◽  
Nuclear Magnetic Resonance ◽  
Artificial Neural Network ◽  
Magnetic Resonance ◽  
3D Printing ◽  
Back Propagation ◽  
Low Field ◽  
Artificial Neural ◽  
Lf Nmr ◽  
Nuclear Magnetic

Estimate Permeability from Nuclear Magnetic Resonance Measurements Using Improved Artificial Neural Network Based on Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 5072-5077
Author(s):  
Yu Zhou ◽  
Guo Qi Wei ◽  
He Kun Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Nuclear Magnetic Resonance ◽  
Artificial Neural Network ◽  
Magnetic Resonance ◽  
Information Gain ◽  
Oil And Gas Industry ◽  
Intelligent Technique ◽  
Artificial Neural ◽  
Nuclear Magnetic

Knowledge of the permeability distribution is critical to a successful reservoir model. Nuclear Magnetic Resonance (NMR) measurements can be used for permeability prediction because the T2 relaxation time is proportional to pore size. Due to the conventional estimators have difficult and complex problems in simulating the relationship between permeability and NMR measurements, an intelligent technique using artificial neural network and genetic algorithm to estimate permeability from NMR measurements is developed. Neural network is used as a nonlinear regression method to develop transformation between the permeability and NMR measurements. Genetic algorithm is used for selecting the best parameters and initial value for the neural network, which solved two major problems of the network: local minima and parameter selection depend on experience. Information gain principle is introduced to select the neural network's input parameters automatically from data. The technique is demonstrated with an application to the well data in Northeast China. The results show that the refined technique make more accurate and reliable reservoir permeability estimation compared with conventional methods. This intelligent technique can be utilized a powerful tool for estimate permeability from NMR logs in oil and gas industry.

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