Abstract: A method of subsurface formation characterization is described. The method includes obtaining spectral induced polarization (SIP) measurements of a volume of a subsurface formation at a plurality of frequencies to determine a frequency-dependent complex (FDC) impedance value of a matrix material in the volume of the subsurface formation. The method further includes determining whether the volume is a low resistivity pay (LRP) zone with a formation resistivity index?2, by analyzing the FDC impedance value of the matrix material, and identifying the volume of the subsurface formation as the LRP zone when the FDC impedance exhibits a dispersion at increasing frequencies. The FDC impedance value is substantially constant before the dispersion and increases by at least one order of magnitude over one order of magnitude of the increasing frequencies in the dispersion.

Abstract: A method to measure acid diversion efficiency in rock samples. The method includes measuring a first porosity and a permeability of the rock samples. The method includes flooding a core region of the rock sample with a hydrochloric acid (HCl) system to create a wormhole, wherein during the flooding the HCl system penetrates from a first end of the rock sample to a second end of the rock sample to form the wormhole. The method includes measuring a second porosity, a pore size distribution, and a diffusion tortuosity of the rock samples with NMR. The method includes establishing a diversion index (DI) based off a ratio between the diffusion tortuosity in a first direction and the diffusion tortuosity in a second direction. The method includes determining the acid diversion efficiency based off the diversion index.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for assessing an optimal acid injection rate in the process of hydrocarbon formation stimulation. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, introduction of a semi-empirical correction based on comparison of the downhole conditions with the library of laboratory experiments where such corrections were measured, extrapolation of the library data to the real downhole conditions. The improved values of the diffusion coefficients are applied in determining wormhole regime conditions that are optimal in terms of acid consumption per a unit of stimulated yield of the hydrocarbon.