Complex impedance measurements have been performed on 14 shaly sand samples, Berea sandstone, and Ottawa sand‐bentonite packs in a frequency range of 10 Hz to 10 MHz, using both the two‐ and four‐electrode techniques. Measurements have been conducted at an effective radial stress varying from ambient pressure to 4000 psi for brine‐saturated oil‐wet and water‐wet samples. The dielectric permittivity is found to correlate with the clay volume fraction, the cation exchange capacity, and electrochemical potential of the rock samples and to depend strongly on the salinity of the brine used. Stress and wettability are shown to have a small influence on the dielectric constant of fully brine‐saturated rocks. A lower critical frequency is found to characterize the geometry of the pore space. Empirical correlations between the dielectric constant, frequency, permeability, cation exchange capacity, and porosity are presented for the shaly sands used in this study. These correlations provide a means of estimating important petrophysical parameters such as the permeability and the clay content from a nondestructive complex impedance sweep of shaly sands fully saturated with brine.

This study puts under scrutiny the unique relationship between molecular diffusion and electrical conductivity data for Berea, Okesa, Tallant, and Elgin sandstones ranging in permeability from 83 to 2502 md. The experimental setups used for generating the investigated data featured the use of a four-electrode circuit that canceled the effects of contact electrode polarization and a diffusion flow system that allowed on-line calibration and established a stable baseline. The effective molecular diffusion coefficients (D e ) for these porous media were estimated by matching simulated concentration profiles with measured ones. Tortuosity values were calculated by using molecular diffusivity models and their analogous electrical conductivity models. Tortuosity values calculated from diffusion measurements (using the Brakel and Heertjes model; Int. J. Heat Transfer 1974, 17, 1093 matched reasonably well with those values estimated using Pirson's electrical model (Geologic Well Log Analysis; Gulf Publishing: Houston, TX, 1983). These results indicate the superiority of the latter model over a large number of formation-factor-based models for estimating rock tortuosity. This study helped in the selection of adequate tortuosity models for characterizing sandstone rocks and verified the similarity between electrical conductivity and molecular diffusivity in sandstone rocks.

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This study introduces a general regression neural network (GRNN) model consisting of a one-pass learning algorithm with a parallel structure for estimating the minimum miscibility pressure (MMP) of crude oil as a function of crude oil composition and temperature. The GRNN model was trained with 91 samples and was successfully validated with a blind testing data set of 22 samples. The MMP for six of these data samples was experimentally measured at the Petroleum Fluid Research Centre at Kuwait University. The remaining data consisted of experimental MMP data collected from the literature. The GRNN model was used to estimate the MMP from the training data set with an average absolute error of 0.2 %. The GRNN model was used to predict the MMP for the blind test data set with an average absolute error of 3.3 %. The precision of the introduced model and models in the literature was evaluated by comparing the predicted MMP values with the measured MMP values and using training and testing data sets. The GRNN model significantly outperformed the prominent models that have been published in the literature and commonly used for estimating MMP. The use of the GRNN model was reliable over a large range of crude oil compositions, impurities, and temperature conditions. The GRNN model provides a cost-effective alternative for estimating the MMP, which is commonly, measured using experimental displacement procedures that are costly and time consuming. The results provided in this study support the use of artificial neural networks for predicting the MMP of CO 2 .