Pore-structure characterization by combined laboratory nuclear magnetic resonance and spectral induced polarization: A case study of Kansas carbonates

2017 ◽  
Author(s):  
Fan Zhang ◽  
Chi Zhang ◽  
Lynn Watney
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Pore Structure ◽  
Induced Polarization ◽  
Structure Characterization ◽  
Spectral Induced Polarization ◽  
Nuclear Magnetic

Estimating permeability of sandstone samples by nuclear magnetic resonance and spectral-induced polarization

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. E215-E226 ◽  
Author(s):  
Andreas Weller ◽  
Sven Nordsiek ◽  
Wolfgang Debschütz
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Current Flow ◽  
Pore Space ◽  
Induced Polarization ◽  
Western Desert ◽  
Permeability Prediction ◽  
Spectral Induced Polarization ◽  
Space Geometry ◽  
Nuclear Magnetic

Two techniques to estimate permeability are compared in this paper: nuclear magnetic resonance (NMR) and spectral-induced polarization (SIP). Both methods are based on relaxation processes. NMR records the relaxation of hydrogen nuclei after excitation in an external magnetic field. The phenomenon of induced polarization can be characterized by a relaxation of ions after excitation by an electric field. Hydrogen nuclei are concentrated in the pore water, the current flow is restricted to the pore space for most reservoir rocks, and permeability is related to the pore space geometry. Based on the similarity between fluid movement and current flow in the pore space, different relations have been published linking parameters derived from NMRand SIP data to predict permeability. NMR, SIP and permeability data have been acquired on 53 sandstone samples of the cretaceous Bahariya Formation (Western Desert, Egypt) including 27 samples showing a lamination that causes anisotropy. We compare the applicability of known and generalized relations for permeability prediction including isotropic and anisotropic samples. Because NMR relaxation ignores directionality of pore space geometry, the known relations provide only a weak accuracy in permeability estimation. The integrating parameters derived from a Debye decomposition of SIP data are partly sensitive to anisotropy. A generalized power-law relation using resistivity, chargeability, and mean relaxation time provide a reliable permeability prediction for isotropic and anisotropic samples.

Determination of nuclear magnetic resonance T2 cutoff value based on multifractal theory — An application in sandstone with complex pore structure

Geophysics ◽  
2015 ◽  
Vol 80 (1) ◽  
pp. D11-D21 ◽  
Author(s):  
Xinmin Ge ◽  
Yiren Fan ◽  
Xuejuan Zhu ◽  
Yiguo Chen ◽  
Runze Li
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Pore Structure ◽  
Water Saturation ◽  
Influential Factors ◽  
Structure Characterization ◽  
Cutoff Value ◽  
Irreducible Water Saturation ◽  
Multifractal Theory ◽  
Nuclear Magnetic

The cutoff value of nuclear magnetic resonance (NMR) transversal relaxation time [Formula: see text] is vital for pore structure characterization, permeability prediction, and irreducible water saturation calculation. Conventional default values often lead to inaccurate results for rocks with complex pore structure. Based on NMR experiments and multifractal theory, we have developed an effective statistical method to predict [Formula: see text] cutoff values without other petrophysical information. The method is based on multifractal theory to analyze the NMR [Formula: see text] spectrum with the assumption that the [Formula: see text] spectrum is an indicator of pore size distribution. Multifractal parameters, such as multifractal dimension, singularity strength, and mass exponent, are calculated to investigate the multifractal behavior of [Formula: see text] spectrum via NMR experiments and a dyadic scaling-down algorithm. To obtain the optimal [Formula: see text] cutoff value, the rotation speed and time of centrifugation are enlarged increasingly to optimal centrifugal state. A predicating model for [Formula: see text] cutoff value based on multiple linear regressions of multifractal parameters was proposed after studying the influential factors. On the basis of the multifractal analysis of NMR [Formula: see text] spectrum, a reasonable predication model for [Formula: see text] cutoff value was rendered. Upon testing, the predicted results were highly consistent with the experimental results.

scholarly journals Estimation of the permeability of hydrocarbon reservoir samples using induced polarization and nuclear magnetic resonance methods

Geophysics ◽  
2019 ◽  
Vol 84 (2) ◽  
pp. MR73-MR84 ◽  
Author(s):  
Fatemeh Razavirad ◽  
Myriam Schmutz ◽  
Andrew Binley
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Relaxation Time ◽  
Magnetic Resonance ◽  
Mechanistic Model ◽  
Transverse Relaxation Time ◽  
Induced Polarization ◽  
Permeability Model ◽  
Transverse Relaxation ◽  
Hydrocarbon Reservoir ◽  
Nuclear Magnetic

We have evaluated several published models using induced polarization (IP) and nuclear magnetic resonance (NMR) measurements for the estimation of permeability of hydrocarbon reservoir samples. IP and NMR measurements were made on 30 samples (clean sands and sandstones) from a Persian Gulf hydrocarbon reservoir. We assessed the applicability of a mechanistic IP-permeability model and an empirical IP-permeability model recently proposed. The mechanistic model results in a broader range of permeability estimates than those measured for sand samples, whereas the empirical model tends to overestimate the permeability of the samples that we tested. We also evaluated an NMR permeability prediction model that is based on porosity [Formula: see text] and the mean of the log transverse relaxation time ([Formula: see text]). This model provides reasonable permeability estimations for the clean sandstones that we tested but relies on calibrated parameters. We also examined an IP-NMR permeability model, which is based on the peak of the transverse relaxation time distribution, [Formula: see text] and the formation factor. This model consistently underestimates the permeability of the samples tested. We also evaluated a new model. This model estimates the permeability using the arithmetic mean of log transverse NMR relaxation time ([Formula: see text]) and diffusion coefficient of the pore fluid. Using this model, we improved estimates of permeability for sandstones and sand samples. This permeability model may offer a practical solution for geophysically derived estimates of permeability in the field, although testing on a larger database of clean granular materials is needed.

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