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dc.contributor.authorKadkhodaie, Ali
dc.contributor.authorGolsanami, N.
dc.contributor.authorSharghi, Y.
dc.date.accessioned2017-01-30T10:29:25Z
dc.date.available2017-01-30T10:29:25Z
dc.date.created2016-02-01T00:47:13Z
dc.date.issued2014
dc.date.submitted2016-02-01
dc.identifier.citationKadkhodaie, A. and Golsanami, N. and Sharghi, Y. 2014. Estimating NMR T2 distribution data from well log data with the use of a committee machine approach: A case study from the Asmari formation in the Zagros Basin, Iran. Journal of Petroleum Science and Engineering. 114: pp. 38-51.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/3211
dc.identifier.doi10.1016/j.petrol.2013.12.003
dc.description.abstract

The Nuclear Magnetic Resonance (NMR) log is one of the most valuable logs in petroleum exploration which is used to precisely evaluate the reservoir and non-reservoir horizons. Along with porosity logs (neutron, density, sonic), NMR log is used to estimate the porosity and permeability of the hydrocarbon bearing intervals. The current study focuses on estimating NMR T2 distribution data from conventional well log data with the use of artificial intelligent systems. The eight bin porosities of the combinable magnetic resonance (CMR) T2 distribution alongside with the T2 logarithmic mean (T2LM) values are predicted using the intelligent models developed in this study. The methodology applied here combines the results of the individual models in a committee machine with intelligent systems (CMIS) for estimating the NMR T2 distribution and T2 logarithmic mean data. The Fuzzy logic (FL), the adaptive neuro fuzzy system (ANFIS) and artificial neural networks (ANNs) are utilized as intelligent experts of the CMIS. The NN models are developed with four different training algorithms (Levenberg–Marquardt (LM), scaled conjugate gradient (SCG), one step secant (OSS) and resilient back-propagation (RP)) and the best one is chosen as the optimal NN expert of the CMIS. The CMIS assigns a weight factor to each individual expert by the simple averaging and weighted averaging methods. A genetic algorithm (GA) optimization technique is used to derive the weighted averaging coefficients. The results indicate that the GA optimized CMIS performs better than the individual experts acting alone for synthesizing the NMR T2 curve and T2LM data from one specific set of conventional well logs.

dc.titleEstimating NMR T2 distribution data from well log data with the use of a committee machine approach: A case study from the Asmari formation in the Zagros Basin, Iran
dc.typeJournal Article
dcterms.dateSubmitted2016-02-01
dcterms.source.volume114
dcterms.source.startPage38
dcterms.source.endPage51
dcterms.source.titleJournal of Petroleum Science and Engineering
curtin.digitool.pid237411
curtin.departmentDepartment of Petroleum Engineering
curtin.identifier.elementsidELEMENTS-119811
curtin.accessStatusFulltext not available


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