Kinetic study of hydrogen tunnelling in meso-tetraphenylporphine by nuclear magnetic resonance lineshape analysis and selective T 1? -relaxation time measurements

1979 ◽  
Vol 75 ◽  
pp. 752 ◽  
Author(s):  
J�rgen Hennig ◽  
Hans-Heinrich Limbach
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Relaxation Time ◽  
Magnetic Resonance ◽  
Kinetic Study ◽  
Lineshape Analysis ◽  
Nuclear Magnetic

Textural and pore size analysis of carbonates from integrated core and nuclear magnetic resonance logging: An Arbuckle study

2015 ◽  
Vol 3 (1) ◽  
pp. SA77-SA89 ◽  
Author(s):  
John Doveton ◽  
Lynn Watney
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Relaxation Time ◽  
Magnetic Resonance ◽  
Pore Size ◽  
Relaxation Times ◽  
T2 Relaxation Time ◽  
T2 Relaxation ◽  
Nmr Logging ◽  
The Core ◽  
Nuclear Magnetic

The T2 relaxation times recorded by nuclear magnetic resonance (NMR) logging are measures of the ratio of the internal surface area to volume of the formation pore system. Although standard porosity logs are restricted to estimating the volume, the NMR log partitions the pore space as a spectrum of pore sizes. These logs have great potential to elucidate carbonate sequences, which can have single, double, or triple porosity systems and whose pores have a wide variety of sizes and shapes. Continuous coring and NMR logging was made of the Cambro-Ordovician Arbuckle saline aquifer in a proposed CO2 injection well in southern Kansas. The large data set gave a rare opportunity to compare the core textural descriptions to NMR T2 relaxation time signatures over an extensive interval. Geochemical logs provided useful elemental information to assess the potential role of paramagnetic components that affect surface relaxivity. Principal component analysis of the T2 relaxation time subdivided the spectrum into five distinctive pore-size classes. When the T2 distribution was allocated between grainstones, packstones, and mudstones, the interparticle porosity component of the spectrum takes a bimodal form that marks a distinction between grain-supported and mud-supported texture. This discrimination was also reflected by the computed gamma-ray log, which recorded contributions from potassium and thorium and therefore assessed clay content reflected by fast relaxation times. A megaporosity class was equated with T2 relaxation times summed from 1024 to 2048 ms bins, and the volumetric curve compared favorably with variation over a range of vug sizes observed in the core. The complementary link between grain textures and pore textures was fruitful in the development of geomodels that integrates geologic core observations with petrophysical log measurements.

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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