Study on Artificial Neural Networks to Identify Sedimentary Microfacies

2014 ◽  
Vol 912-914 ◽  
pp. 1395-1398
Author(s):  
Wei Fu Liu ◽  
Shuang Long Liu ◽  
Li Xin Sun
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ordos Basin ◽  
Well Logging ◽  
Effective Parameters ◽  
Geological Interpretation ◽  
Artificial Neural ◽  
Sedimentary Microfacies ◽  
Local Geology ◽  
Network Pattern

Based on a number of stratigraphic sedimentary information included in log data, application of the Artificial Neural Network to identify sedimentary microfacies from well logging data can complete the series auto-interpreting. The application can improve the auto-interpreting accuracy and make us get more satisfied results. Ten parameters from the well logging curves are selected for to describing their shape characteristics when the deposition patterns of 8 in gas-bearing formation of Upper Paleozoic group, Ordos basin are studied. Effective parameters were selected on the basis of cores, and based on artificial neural network pattern recognition technique; the sedimentary microfacies of well cross section were auto-interpreted. About 300 wells and the results were interpreted by using the software. The software will be fit for the researchers who have the experiences of geological interpretation and some backgrounds of local geology.

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

2005 ◽  
Vol 34 (4) ◽  
pp. 335-341 ◽  
Author(s):  
A. Bahrami ◽  
S.H. Mousavi Anijdan ◽  
H.R. Madaah Hosseini ◽  
A. Shafyei ◽  
R. Narimani
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Effective Parameters ◽  
Artificial Neural

Experimental, modeling and optimization study on the mechanical properties of epoxy/high-impact polystyrene/multi-walled carbon nanotube ternary nanocomposite using artificial neural network and genetic algorithm

2013 ◽  
Vol 20 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Abdolhosein Fereidoon ◽  
Amin Hamed Mashhadzadeh ◽  
Yasser Rostamiyan
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Fitness Function ◽  
Optimization Method ◽  
High Impact ◽  
Effective Parameters ◽  
High Impact Polystyrene ◽  
Artificial Neural

AbstractIn spite of Epoxy resin’s good tensile strength, are brittle in nature and have poor resistance at the front of crack propagation. In enhancing simultaneously the mechanical strength and fracture toughness of epoxy-based nanocomposites, high-impact polystyrene (HIPS) as thermoplastic phase and multi-walled carbon nanotubes (MWCNT) as nanofiller phases are used incorporately to obtain ternary epoxy-based nanocomposites. Tensile, flexural, compression and impact are the four different mechanical properties. Artificial neural network was used to present models for predicting the mechanical behavior of epoxy/HIPS/MWCNT nanocomposites. Also, this model used as a fitness function of genetic algorithm as a powerful optimization method to find the optimum value of the above-mentioned mechanical properties. The effective parameters investigated were HIPS, MWCNT and hardener. From the result, it was found that the combination of HIPS and MWCNT nanofillers significantly increases tensile, compression and impact strength of neat resin by up to 52%, 43% and 334%, respectively, but flexural strength did not change positively. Also, elongation at break for tensile, flexural and compression rose to 223%, 36% and 26% of neat epoxy, respectively. The morphology of fracture surface was studied by scanning electron microscopy.

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