Search for: geostatistics
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    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... 

    A sensitivity study of FILTERSIM algorithm when applied to DFN modeling

    , Article Journal of Petroleum Exploration and Production Technology ; Vol. 4, issue. 2 , June , 2014 , p. 153-174 ; ISSN: 21900558 Ahmadi, R ; Masihi, M ; Rasaei, M. R ; Eskandaridalvand, K ; Shahalipour, R ; Sharif University of Technology
    Realistic description of fractured reservoirs demands primarily for a comprehensive understanding of fracture networks and their geometry including various individual fracture parameters as well as network connectivities. Newly developed multiple-point geostatistical simulation methods like SIMPAT and FILTERSIM are able to model connectivity and complexity of fracture networks more effectively than traditional variogrambased methods. This approach is therefore adopted to be used in this paper. Among the multiple-point statistics algorithms, FILTERSIM has the priority of less computational effort than does SIMPAT by applying filters and modern dimensionality reduction techniques to the... 

    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... 

    Investigating the effect of heterogeneity on infill wells

    , Article Journal of Petroleum Exploration and Production Technology ; Volume 6, Issue 3 , 2016 , Pages 451-463 ; 21900558 (ISSN) Bagheri, M ; Masihi, M ; Sharif University of Technology
    Springer Verlag  2016
    In recent years, improving oil recovery (IOR) has become an important subject for the petroleum industry. One IOR method is infill drilling, which improves hydrocarbon recovery from virgin zones of the reservoir. Determining the appropriate location for the infill wells is very challenging and greatly depends on different factors such as the reservoir heterogeneity. This study aims to investigate the effect of reservoir heterogeneity on the location of infill well. In order to characterize the effect of heterogeneity on infill well locations, some geostatistical methods, e.g., sequential gaussian simulation, have been applied to generate various heterogeneity models. In particular, different... 

    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... 

    Analysis of cross correlations between well logs of hydrocarbon reservoirs

    , Article Transport in Porous Media ; Volume 90, Issue 2 , 2011 , Pages 445-464 ; 01693913 (ISSN) Dashtian, H ; Jafari, G. R ; Lai, Z. K ; Masihi, M ; Sahimi, M ; Sharif University of Technology
    We carry out a series of cross-correlation analysis of raw well-log data, in order to study the possible connection between natural gamma ray (GR) logs and other types of well logs, such as neutron porosity (NPHI), sonic transient time (denoted usually by DT), and bulk density (RHOB) of oil and gas reservoirs. Three distinct, but complementary, methods are used to analyze the cross correlations, namely, the multifractal detrended cross-correlation analysis (MF-DXA), the so-called Qcc(m) test in conjunction with the statistical test-the χ2(m) distribution-and the cross-wavelet transform (XWT) and wavelet coherency. The Qcc(m) test and MF-DXA are used to identify and quantify the strength of...