Search for: sequential-simulation
0.006 seconds

    Integration of adaptive neuro-fuzzy inference system, neural networks and geostatistical methods for fracture density modeling

    , Article Oil and Gas Science and Technology ; Vol. 69, issue. 7 , 2014 , pp. 1143-1154 ; ISSN: 12944475 Jafari, A ; Kadkhodaie-Ilkhchi, A ; Sharghi, Y ; Ghaedi, M ; Sharif University of Technology
    Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural... 

    Constructing the Bayesian network for components reliability importance ranking in composite power systems

    , Article International Journal of Electrical Power and Energy Systems ; Volume 43, Issue 1 , 2012 , Pages 474-480 ; 01420615 (ISSN) Daemi, T ; Ebrahimi, A ; Fotuhi Firuzabad, M ; Sharif University of Technology
    In this paper, Bayesian Network (BN) is used for reliability assessment of composite power systems with emphasis on the importance of system components. A simple approach is presented to construct the BN associated with a given power system. The approach is based on the capability of the BN to learn from data which makes it possible to be applied to large power systems. The required training data is provided by state sampling using the Monte Carlo simulation. The constructed BN is then used to perform different probabilistic assessments such as ranking the criticality and importance of system components from reliability perspective. The BN is also used to compute the frequency and... 

    Comparison of different univariate and multivariate geostatistical methods by porosity modeling of an iranian oil field

    , Article Petroleum Science and Technology ; Volume 29, Issue 19 , 2011 , Pages 2061-2076 ; 10916466 (ISSN) Zarei, A ; Masihi, M ; Salahshoor, K ; Sharif University of Technology
    Geostatistical methods are grouped in two main divisions: univariate and multivariate. When there is adequate amount of primary data, univariate methods such as kriging and SGS give a good representation of property distribution in the reservoir, but practical difficulties appear when there is no sufficient data. In such a case it is necessary to choose multivariate geostatistical methods in which some covariables are contributed to model the primary variable. Multivariate geostatistics is a broad term that encompasses all geostatistical methods that utilize more than one variable to predict some physical property of the earth. Bivariate geostatistics is obviously the simplest subset of the... 

    Permeability modeling using ANN and collocated cokriging

    , Article 72nd European Association of Geoscientists and Engineers Conference and Exhibition 2010: A New Spring for Geoscience. Incorporating SPE EUROPEC 2010 ; Volume 5 , 2010 , Pages 3939-3943 ; 9781617386671 (ISBN) Zarei, A ; Masihi, M ; Sharif University of Technology
    Society of Petroleum Engineers  2010
    Obtaining a reliable reservoir permeability map that is consistent with all available data is of great important for reservoir engineers. However, there is not enough core from existing wells to estimate the reservoir permeability directly but Well log data are more widely available. This study aims to model permeability within the reservoir while there is no enough data. In particular, we use artificial neural networks to estimate permeability using four input logs of sonic, gravity, porosity and neutron logs in six existing wells. In order to eliminate the correlated data, we have done Principal Component Analysis on selected input logs. Collocated cokriging is considered as a valuable...