Sharif Digital Repository

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... 

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods... 

Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods... 

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... 

One of the well-known online monitoring techniques used for quality control of bulk samples is Prompt Gamma Neutron Activation Analysis (PGNAA). PGNAA suffers from the so-called matrix effect problems such as density, thickness and moisture content of the sample under study. In this work, an Artificial Neural Network (ANN) model is introduced to deal with these effects. The required spectra for training and testing the proposed ANN model are obtained by Monte Carlo simulation of the gamma-ray spectra recorded in a PGNAA online analyzer system used in cement factories. The gamma-ray spectra related to given set of density, thickness and moisture content are corrected channel-to-channel using... 

Three distinct methods, namely, the spectral density, the multifractal random walk approach, and the multifractal detrended fluctuation analysis are utilized to study the properties of four distinct types of well logs from three oil and gas fields, namely, the natural gamma ray emission, neutron porosity, bulk density, and the sonic transient time logs. Such well logs have never been analyzed by the methods that we utilize in the present study. The results indicate that the well logs exhibit multifractal characteristics, and the estimated Hurst exponents by the three methods are close to each other. Using multifractal detrended fluctuation analysis and the shuffled and surrogated data, we... 

Three distinct methods, namely, the spectral density, the multifractal random walk approach, and the multifractal detrended fluctuation analysis are utilized to study the properties of four distinct types of well logs from three oil and gas fields, namely, the natural gamma ray emission, neutron porosity, bulk density, and the sonic transient time logs. Such well logs have never been analyzed by the methods that we utilize in the present study. The results indicate that the well logs exhibit multifractal characteristics, and the estimated Hurst exponents by the three methods are close to each other. Using multifractal detrended fluctuation analysis and the shuffled and surrogated data, we...