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NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.
Czasopismo
Rocznik
Strony
735--760
Opis fizyczny
Bibliogr. 35 poz., rys., tab. wykr.
Twórcy
autor
  • Department of Petroleum Engineering, Aligudarz Branch, Islamic Azad University, Aligudarz, Iran
  • Department of Petroleum Engineering, Aligudarz Branch, Islamic Azad University, Aligudarz, Iran
autor
  • Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran
Bibliografia
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d89bb448-fbef-4a5f-af98-a6ab26c2aa6e
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