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

Czasopismo : Acta Geophysica
Tytuł artykułu : NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Autorzy :
Stanisławska, I.
Space Research Center Polish Academy of Sciences,
Popielawska, B.
Space Research Center Polish Academy of Sciences,
Vashisth, A. K.
Department of Mathematics, Kurukshetra University, Kurukshetra, India, akvashishth@kuk.ac.in,
Rani, K.
Department of Mathematics, Government Post Graduate College, Hisar, India, karya4@gmail.com,
Singh, K.
Department of Mathematics, Guru Jambheshwar University of Science and Technology, Hisar, India, profkbgju@gmail.com,
Kotyrba, A.
Central Mining Institute (GIG), Katowice, Poland, a.kotyrba@gig.eu,
Kortas, Ł.
Central Mining Institute (GIG), Katowice, Poland, l.kortas@gig.eu,
Stańczyk, K
Central Mining Institute (GIG), Katowice, Poland, k.stanczyk@gig.eu,
Mitrofanov, G.
Trofimuk Institute of Petroleum Geology and Geophysics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia, georgymitrofanov@rambler.ru,
Priimenko, V.
Laboratory of Petroleum Engineering and Exploration, North Fluminense State University Darcy Ribeiro, Macaé, RJ, Brazil, slava@lenep.uenf.br,
Fan, Y.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China, Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
Du, X.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China, Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
An, Z.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China, Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
Liu, J.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China, Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
Tan, D.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
Chen, J.
Lanzhou Base of Institute of Earthquake Prediction, China Earthquake Administration, Lanzhou City, China Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou City, China,
Polkowski, M.
Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland, marcin.polkowski@igf.fuw.edu.pl,
Grad, M.
Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland,
Yan, H.
Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China , yanhongyong@163.com,
Yang, L.
Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China University of Chinese Academy of Sciences, Beijing, China,
Liu, H.
University of Chinese Academy of Sciences, Beijing, China,
Asoodeh, M.
Department of Petroleum Engineering, Aligudarz Branch, Islamic Azad University, Aligudarz, Iran, asoodeh.mojtaba@gmail.com,
Bagheripour, P
Department of Petroleum Engineering, Aligudarz Branch, Islamic Azad University, Aligudarz, Iran,
Gholami, A.
Abadan Faculty of Petroleum Engineering, Petroleum University of Technology, Abadan, Iran,
Abstrakty : 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.

Słowa kluczowe : rock physics, petrophysics, integrated intelligent systems,
Wydawnictwo : Instytut Geofizyki PAN
Rocznik : 2015
Numer : Vol. 63, no. 3
Strony : 735 – 760
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DOI :
Cytuj : Stanisławska, I. ,Popielawska, B. ,Vashisth, A. K. ,Rani, K. ,Singh, K. ,Kotyrba, A. ,Kortas, Ł. ,Stańczyk, K ,Mitrofanov, G. ,Priimenko, V. ,Fan, Y. ,Du, X. ,An, Z. ,Liu, J. ,Tan, D. ,Chen, J. ,Polkowski, M. ,Grad, M. ,Yan, H. ,Yang, L. ,Liu, H. ,Asoodeh, M. ,Bagheripour, P ,Gholami, A. , NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network. Acta Geophysica Vol. 63, no. 3/2015
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