期刊
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
卷 114, 期 -, 页码 38-51出版社
ELSEVIER
DOI: 10.1016/j.petrol.2013.12.003
关键词
NMR T-2 data; Asmari formation; well logs; intelligent systems; committee machine; genetic algorithm
资金
- Iranian Central Oil Fields Company (ICOFC)
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 T-2 distribution data from conventional well log data with the use of artificial intelligent systems. The eight bin porosities of the combinable magnetic resonance (CMR) T-2 distribution alongside with the T-2 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 T-2 distribution and T-2 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 T-2 curve and T2LM data from one specific set of conventional well logs. (C) 2013 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据