Nuclear Magnetic Resonance Permeability Research Articles

Nuclear Magnetic Resonance Logging-Based Permeability Classification Modeling for Tight Sandstone Reservoirs

The Upper Paleozoic tight sandstone reservoirs on the eastern margin of the Ordos Basin exhibit strong heterogeneity and complex pore structures, leading to poor correlation between porosity and permeability and insufficient accuracy in permeability calculations to meet the requirements of reservoir fine evaluation. Therefore, a new method for high-precision permeability calculation based on flow zone index (FZI) reservoir classification is proposed. This method determines the number of reservoir classifications based on the characteristics of the FZI normal probability distribution plot and establishes FZI division criteria for reservoir types. Classified reservoirs exhibit similar flow characteristics, significantly improving the correlation between permeability and porosity. Based on nuclear magnetic resonance (NMR) combined with mercury injection capillary pressure (MICP) experiments, a modeling method for calculating the flow zone index based on the geometric mean of NMR T2 is proposed. This method realizes continuous calculation of FZI based on NMR logging, reservoir classification, and permeability for the entire wellbore, thereby constructing a new permeability prediction method for tight sandstone reservoirs based on NMR logging and FZI classification. Actual application results demonstrate that the permeability calculated using NMR logging is in high agreement with the permeability analyzed from core data, with an average relative error of 45.8%, proving the effectiveness of the proposed method in this study.

Enhancing Methane Recovery with Cryogenic Liquid CO2 Cyclic Injection: Determination of Cyclic Injection Parameters.

Carbon dioxide (CO2) is both a primary greenhouse gas and a readily available energy source. In this study, a new underground coal permeability enhancement technique utilizing cryogenic liquid CO2 (L-CO2) cyclic injection is proposed. The key parameters that determine the feasibility of this technique are cycle period and cycle number within a fixed working period. The optimal value of these two parameters mainly depends on the pore structure evolution law of coal cores before and after cryogenic L-CO2 cyclic freeze–thaw. Accordingly, nuclear magnetic resonance (NMR) was employed to study the evolution characteristics of the fracture networks and pore structures in coal cores subjected to different freeze–thaw cyclic modes. The results demonstrated that the amplitude and width of all peaks of the T2 spectra of the three coal cores (lignite, gas coal, and 1/3 coking coal) increased with an increase in the number of injection cycles. Additionally, as the number of freeze–thaw cycles (Nc) increased, the total porosity and effective porosity of the coal cores increased linearly before stabilizing, while the residual porosity first steadily diminished and afterwards became constant. Furthermore, the variation in the total porosity and residual porosity of the coal cores continuously diminished with an increase in the level of metamorphism. The NMR permeability of the coal cores showed a similar pattern to the porosity. Accordingly, the optimal parameters for cryogenic L-CO2 cyclic injection during a complete working time were determined to be Nc = 4 and Pc = 30 min. A field test demonstrated that after L-CO2 cyclic freeze–thaw treatment, the average gas drainage concentration of a single borehole in the test region increased by 1.93 times, while the pure flow of a single gas drainage borehole increased by 2.21 times. Finally, the gas attenuation coefficient decreased from 0.036 to 0.012. We concluded that the proposed permeability enhancement technique transformed coal seams from hard-to-drain to drainable.

Fractal-based NMR permeability estimation in tight sandstone: A case study of the Jurassic rocks in the Sichuan Basin, China

Permeability is a crucial parameter evaluating tight sandstone reservoir. Previous permeability estimation methods based on nuclear magnetic resonance (NMR) are commonly suitable for well-sorted, highly porous and unconsolidated sandstone, but incurring large errors when applied to tight sandstone. This study introduces the fractal dimension based NMR permeability estimation for tight sandstone. Core permeability and NMR measurements were performed on thirty tight sandstone samples selected from the Jurassic rocks in the Sichuan Basin, China. We proposed two fractal-based NMR permeability models for tight sandstone in the studying area, which are considered as the modifications of the SDR and T-C models, examining the applications on core plug samples and comparing the results with other permeability models. Results show that tight sandstones have wider T2 distribution and weaker correlation between throat size and pore size than unconsolidated/clean sandstones. The proposed coefficient-based optimal fitting method can obtain more accurate fractal dimensions of micropores and macropores than these achieved by subjective and empirical methods and commonly used T2cutoff method. The proposed models introduced in this research have two advantages: (1) the models have high accuracy since the effective pore structure parameters (porosity, FFI/BVI and T2LM) are adopted. The complex pore structure of tight sandstones is considered by using fractal dimensions. (2) the models are easy to implement by following the concise formats of the Timur-Coates model and SDR model. In practice, the models can be applied to predict permeability of tight sandstone in the lab, or in the field requiring NMR T2 distributions as basic data, integrating core permeability to determine fitting parameters in the models.

Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability

Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability

Application of machine and deep learning techniques to estimate NMR-derived permeability from conventional well logs and artificial 2D feature maps

Nuclear magnetic resonance (NMR) logs can provide information on some critical reservoir characteristics, such as permeability, which are rarely obtainable from conventional well logs. Nevertheless, high cost and operational constraints limit the wide application of NMR logging tools. In this study, a machine learning (ML)-based procedure is developed for fast and accurate estimation of NMR-derived permeability from conventional logs. Following a comprehensive preprocessing on the collected data, the procedure is trained and tested on a well log dataset, with selected conventional logs as inputs, and NMR-derived permeability as target, shallow and deep learning (DL) methods are applied to estimate permeability from selected conventional logs through artificial production of NMR-derived information from the input data. Three supervised ML algorithms are utilized and evaluated, including random forest (RF), group method of data handling (GMDH), and one-dimensional convolutional neural network (1D-CNN). Additionally, a modified two-dimensional CNN (named as Residual 2D-CNN) is developed which is fed by artificial 2D feature maps, generated from available conventional logs. The hyper-parameters of the ML and DL models are optimized using genetic algorithm (GA) to improve their performances. By comparing the output of each model with the permeability derived from NMR log, it is illustrated that nonlinear machine and deep learning techniques are helpful in estimation of NMR permeability. The obtained accuracy of RF, GMDH, 1D-CNN and Res 2D-CNN models, respectively, is 0.90, 0.90, 0.91 and 0.97 which indicate that Res 2D-CNN model is the most efficient method among the other applied techniques. This research also highlights the importance of using generated feature maps for training Res 2D-CNN model, and the essential effect of the applied modifications (i.e., implementing residual and deeper bottleneck architectures) on improving the accuracy of the predicted output and reducing the training time.

Influencing mechanics and correction method of nuclear magnetic resonance measurement in igneous rocks reservoir

Nuclear magnetic resonance (NMR) measurement provides lithology-independent porosity and pore structure for sedimentary reservoirs. Recently, oil and gas were discovered in igneous rock formations, however, NMR logging porosity is substantially lower than the true porosity, and the T2 distributions does not characterize the pore-size distributions. These observations limit the use of NMR logging in igneous rock formations and new research is needed to be developed. To address this gap in the literature, using numerical simulation of NMR behavior, we conduct a theoretical study to examine the impact of magnetic susceptibility, echo spacing, and fluid type on the T2 distributions in igneous rock. The simulation results and laboratory experiment show that, the paramagnetic minerals affect the magnetization of fluids in pores and rate of the echo decay, and NMR porosity is thus significantly reduced. To effectively correct the NMR measurement, a new approach is proposed to improve the underestimation of NMR porosity and T2 distribution shifting to shorter times. For a pore model, the corrected porosity and T2 distribution are both in good agreement with the model. In a case study from Chepaizi area of Junggar Basin, China, the corrected NMR porosity matches well with core measurement, and the corrected T2 distribution shifts to longer times more than before. Moreover, bound fluid volume and movable fluid volume are both corrected, and the NMR permeability also agrees well with core laboratory measurement, which proves that the correction method effective. Therefore, this technology enables NMR log for evaluating the igneous rock reservoir quality.

Petrophysical modeling in the absence of core data in a small, marginal field in the Mumbai Offshore Basin, India

The best petrophysical models are based on direct measurements from the core. Unfortunately, core is not available in many cases, either for economic, logistical, or historical reasons. In this study, we needed to construct a detailed Field Development Plan (FDP) for the small, marginal B-9 field in the Western Offshore Basin, India, which did not merit core acquisition. The objective is to propose a workflow for building a petrophysical model with limited data sets instead of a typical FDP workflow. After analyzing the assumptions, limitations, and uncertainties involved in the petrophysical model, we used advanced petrophysical logs to reduce uncertainty and create a robust petrophysical model. We carried out a log-based petrophysical study to determine the volume of shale, porosity, saturation, and permeability. The advanced petrophysical logs including spectroscopy, nuclear magnetic resonance (NMR), formation pressures, and well testing data are used to calibrate the petrophysical model. Spectroscopy data are used to calibrate the mineralogical volumes and grain density, whereas the porosity is calibrated from NMR data. We calibrated log-derived permeability results with NMR permeability and mobility from well test data. We used heterogeneous rock analysis on petrophysical outputs to carry out petrophysical rock typing (PRT). This has helped in establishing the porosity-permeability relationship and saturation-height model for each PRT. In the absence of irreducible water saturation ([Formula: see text]) information from the core, NMR-derived [Formula: see text] is calculated and then used to calibrate the saturation model. Log-derived permeability and saturation are estimated, which agrees well with the available testing data. This provided a robust petrophysical model that served as a basis for geologic static and reservoir dynamic models. The gas-down-to and water-up-to methods are used to establish the contacts. The resulting saturation height model agreed well with the saturations derived from the log, which gave us confidence in our dynamic model.

Study on the Quantitative Characterization and Seepage Evolution Characteristics of Pores of Loaded Coal Based on NMR.

Quantitative characterization of the pore structure and gas seepage characteristics of loaded coal is of great significance to the study of high-efficiency gas drainage in coal seams. Aiming at the problem of imperfect characterizations of coal seepage characteristics based on nuclear magnetic resonance (NMR), a calculation method for the pore permeability of coal with different pore diameters is proposed. The pore structure and seepage characteristics of coal have been quantitatively studied using a nuclear magnetic resonance (NMR) system. The results show that with increasing external load, the proportion of the pore volume of the coal sample in the range of 0.01–0.52 μm gradually decreases, while that in the range of 5.11–352.97 μm increases. In this process, the porosity increases from 0.9967 to 1.0103%, the connectivity increases from 0.1718 to 0.2391, and the permeability increases from 2.64 × 10–6 to 8.20 × 10–6 μm2. The calculation of the coal sample connectivity and permeability using the improved NMR permeability component proves that 94.37–352.97 μm pores are the main channel of fluid flow. When the axial pressure increases, the coal body permeability in the aperture range of 94.37–352.97 μm rapidly increases. The improved permeability component calculation model can better reflect the variation law of pore permeability of the loaded coal body.

NMR-Based Study of the Pore Types’ Contribution to the Elastic Response of the Reservoir Rock

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of the petrophysical characteristics; and the results were compared with those from a deep learning network. Herein, NMR permeability was used as the estimation target and porosity was used as a benchmark to assess the reliability of the models. The final results indicated that by using the incremental porosity under the T2 curve, this curve could be synthesized using the seismic attributes. The results also proved the functionality of the selected statistical ensembles as reliable tools in the petrophysical characterization of the hydrocarbon reservoirs.

Laboratory Study of the Effect of Grain Size, Mineralogy and Fluid on Nuclear Magnetic Resonance T <sub>2</sub> Distribution

Nuclear magnetic resonance (NMR) is used to investigate key reservoir parameters, such as porosity and permeability. We focus here on unconsolidated sediments to calibrate NMR measurements in near surface environments and improve our understanding of porosity and permeability controls in sediments. Further, the effects of grain size and fluids on NMR relaxation time are poorly understood. We present here NMR T2 data interpretations by considering the effects of grain size, mineralogy, and fluid type in unconsolidated geologic materials. We find a linear relationship between grain size and the T2LM in water-saturated quartz, gypsum, and volcanic sands. Grain size and T2LM appear uncorrelated in oil saturated quartz and gypsum sands. Furthermore, T2LM is low in oil-saturated quartz sand, gypsum sand and volcanic sand which leads to an underestimation of NMR permeability. The relaxation times in gypsum, quartz sand, gypsum sand is much larger than in clay minerals and indicates large pore volumes that can easily be assessed using NMR. The relaxation times also depend on the grain size, as well as amount and distribution of paramagnetic impurities present. Our result provides important calibrations to improve NMR data interpretation for estimation of pore size distribution, porosity, and permeability in unconsolidated formations. This study can also be used as a baseline to study synthetic samples to investigate the effect of clay minerals and gypsum on NMR measurements.

Petrophysical properties difference of organic-rich and organic-poor shale reservoir using low-field nuclear magnetic resonance experiment in the Sangzhi block, South China

The petrophysical properties of rocks have an important influence on shale quality. To characterize the difference of petrophysical properties between organic-rich and organic-poor shale reservoirs, we used the low-field nuclear magnetic resonance (NMR) technique and field emission scanning electron microscopy analysis after argon-ion polishing or natural section to measure porosity and permeability from six core samples from well SY6 in the Sangzhi block, Northwest Hunan province. Some information about pore types, pore structure, residual porosity, movable porosity, and permeability based on the T2 spectrums’ difference of organic-rich and organic-poor shale samples were discussed. The shale sample test results show that the main pores size is mesopore, which provide most of shale gas reservoir space. The continuous peaks demonstrated the pores’ connectivity better than the isolated peaks, and shale gas can migrate freely between these connected pores and fractures. The permeability of all samples calculated by the classic Coates model is extremely low, which is not conducive to the migration of shale gas. We evaluated the dominating factors of NMR porosity and permeability and found that the relationships between NMR porosity and permeability and total organic carbon content, quartz minerals, and clay minerals are not clear, which may be a comprehensive influence. The research results have important guiding significance for shale reservoir quality evaluation in this area.

Evaluation of coal petrophysics incorporating fractal characteristics by mercury intrusion porosimetry and low-field NMR

Mercury intrusion porosimetry (MIP) and low-field nuclear magnetic resonance (NMR) were combined to investigate pore-fracture structure and fractal characteristics of coals (0.93% < Ro,m < 2.77%), and their impacts on coal permeability were assessed using two newly-proposed models. The results indicate that coals with type I mercury intrusion/extrusion curve are beneficial to gas flow due to well-developed micro-fractures (73.9–86.74%) and high pore-fracture connectivity, whereas coals with type II and III curves are less conducive to gas flow because of non-uniform pore-fracture structure and poor connectivity. Based on NMR analysis, pore-fracture structure of low-rank bituminous coals (0.9% < Ro,m < 1.2%) presents irregular three T2 peaks, whereas two and irregular three T2 peaks simultaneously exist in other coals (1.2% < Ro,m < 2.8%). The variation of coal composition and gas generation process may have complex effects on pore-fracture structure during the coalification process. Moreover, MIP fractal dimension ranges from 2.265 to 2.873, whereas NMR fractal dimension varies from 2.744 to 2.976. Due to different sample morphology and fractal estimation methods, most of MIP fractal dimension is larger than NMR fractal dimension. A modified Kozeny-Carman equation was used to calculate MIP permeability, ranging from 3.62 × 10−4 to 1.229 mD. The effect of mesopores on MIP permeability may be related to the gas flow pathway and interlinkage mechanism of adsorption pores and fractures. The NMR permeability was estimated by a movable porosity-permeability model (kN=0.0045∗eϕNM0.6851+0.037), ranging from 0.043 to 2.767 mD. The NMR permeability should be related to movable fluid space and pore-fracture connectivity, and micro-fractures can largely contribute to free fluid volume.

Insight into the Pore Characteristics of a Saudi Arabian Tight Gas Sand Reservoir

The petrophysical characterization of tight gas sands can be affected by clay minerals, gas adsorption, microfractures, and the presence of high-density minerals. In this study, we conducted various petrophysical, petrographic, and high-resolution image analyses on Saudi Arabian tight sand in order to understand how a complex pore system responds to measurement tools. About 140 plug samples extracted from six wells were subjected to routine core analyses including cleaning, drying, and porosity–permeability measurements. The porosity–permeability data was used to identify hydraulic flow units (HFU). In order to probe the factors contributing to the heterogeneity of this tight sand, 12 subsamples representing the different HFUs were selected for petrographic study and high-resolution image analysis using SEM, quantitative evaluation of minerals by scanning electron microscope (QEMSCAN), and micro-computed tomography (µCT). Nuclear magnetic resonance (NMR) and electrical resistivity measurements were also conducted on 56 subsamples representing various lithofacies. NMR porosity showed good agreement with other porosity measurements. The agreement was remarkable in specific lithofacies with porosity ranging from 0.1% to 7%. Above this range, significant scatters were seen between the porosity methods. QEMSCAN results revealed that samples with &lt;7% porosity contain a higher proportion of clay than those with porosity &gt;7%, which are either microfractured or contain partially dissolved labile minerals. The NMR T2 profiles also showed that samples with porosity &lt;7% are dominated by micropores while samples with porosity &gt;7% are dominated by macropores. Analysis of the µCT images revealed that pore throat sizes may be responsible for the poor correlation between NMR porosity and other porosity methods. NMR permeability values estimated using the Shlumberger Doll Research (SDR) method are fairly correlated with helium permeability (with an R2 of 0.6). Electrical resistivity measurements showed that the different rock types fall on the same slope of the formation factors versus porosity, with a cementation factor of 1.5.

Analysis of Petrophysical Characteristics and Water Movability of Tight Sandstone Using Low-Field Nuclear Magnetic Resonance

Low-field nuclear magnetic resonance (NMR) coupled with gas–water centrifugation (GWC) was performed to investigate petrophysical characteristics and water movability of the Chang 7 tight sandstone. Microscopic observation, mineral composition analysis and grain size analysis were applied to determine lithology and pore characteristics. Then, the controls of petrophysical properties and geological factors on the water movability were discussed. The results indicate that the T2 distribution of the Chang 7 tight sandstone can be divided into three groups according to the shape, range and amplitude of T2 spectrum. The removable water volume increases with the centrifugal force, but its gradient presents a remarkable difference among the different reservoir types. The final removable water volume (Φmov) at centrifuge force of 400 psi (average value of 5.28%) is approximately half of the total pore volume (porosity). The T2 geometric mean (T2gm), fractal dimension (Df) and NMR permeability calculated from NMR data can be used to characterize the pore structure and petrophysical properties of tight sandstone. The reservoir quality index can also be estimated based on the NMR permeability models. Removable water volume under the different centrifugation conditions is positively related to porosity, permeability and T2gm and negatively related to Df. Furthermore, removable water volume under different centrifugation conditions is positively related to detrital mineral (quartz + feldspar) content and grain size but negatively related to clay content.

Characterization of Volcanic Reservoirs Requires Comprehensive NMR Tests

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper IPTC 19378, “Comprehensive Evaluation of NMR Characteristics of Complex Volcanic Reservoirs With Different Types of Rock Lithology,” by Junchang Sun, Shijie Zhang, Jieming Wang, Hekun Guo, Chun Li, Hongcheng Xu, Sinan Zhu, and Kai Zhao, PetroChina, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission. This paper discusses a study undertaken to gain better understanding of nuclear magnetic resonance (NMR) characteristics of volcanic reservoirs with different lithologies. Results of the study confirmed that accurate identification of reservoir-interval lithology, achieved through comprehensive laboratory NMR investigation of the volcanic rock, is a primary prerequisite of correct interpretation of NMR logging of volcanic reservoirs. The research results play an important role in guiding in-depth understanding of NMR characteristics of volcanic reservoirs and the implementation of NMR reservoir evaluation in corresponding areas. Introduction The introduction of NMR to the petroleum industry in the 1990s led to a breakthrough in logging. NMR can distinguish between movable and irreducible fluid, and is currently the only logging technology that can determine reservoir permeability. When used to interpret and evaluate complex reservoirs such as volcanic rocks and low-resistivity oil and gas layers, NMR provides logging analysts with new ideas and techniques to deal with various difficulties. By analyzing the NMR relaxation-time (T2) spectrum of rock plugs at a fully brine-saturated state, the distribution of reservoir pore size can be evaluated, and important reservoir evaluation parameters, including effective porosity, NMR permeability, and movable fluid saturation, can be obtained. Numerous studies have shown that, compared with sandstones and carbonates, volcanic reservoirs are much more complex and heterogeneous because of the special eruption diagenesis mechanism, many types of rock lithology, various mineral compositions, and a broad range of pore sizes. The models currently used for NMR logging interpretation are suitable only for sedimentary reservoirs. Consequently, accurate characterization of volcanic reservoirs using advanced NMR logging requires a comprehensive laboratory NMR investigation of volcanic rock. Materials and Methods For the study described in the complete paper, NMR sample analysis technology was adopted to perform NMR measurements and other related tests on a total of 108 low-permeability rock plugs from the Xushen reservoir of the Daqing field, the Changling reservoir of the Jilin field, and the Dixi reservoir of the Xinjiang volcanic gas field. The rock plugs comprised nine types of lithology representing the main producing formation lithology from the three reservoirs. Tests included CT scans, thin-section petrography, mercury injection, and mineral-composition analysis. Centrifuge tests were conducted with maximum centrifugal forces up to 500 psi to explore the suitable capillary pressure for T2cutoff determination. The paper includes a step-by-step description of the experimental procedure.

An Improved NMR Permeability Model for Macromolecules Flowing in Porous Medium

The extraction of macromolecules from nano-self-assembled material can be used as a laboratory model for enhancing oil recovery in reservoirs. By combining Darcy’s law and Poiseuille equation, an improved nuclear magnetic resonance (NMR) permeability model, suitable for macromolecular flow in mesopores is obtained. The calibration coefficients in the Coates equation are expressed in terms of the physical parameters of pore throat ratio rb/rt, tortuosity, and thickness of bond film in the improved model. The results show that the proportion of irreducible fluid to total fluid obtained through NMR characterization can reflect the variation tendency of irreducible macromolecule and water. By simplifying the pores of the extracted samples, the thickness model of irreducible macromolecule and water is established with the total thicknesses calculated as 1.482 nm, 1.585 nm, 1.674 nm, and 1.834 nm. The corresponding permeability results obtained from the NMR characterization (KNMR) are 7.39 mD, 6.02 mD, 5.27 mD, and 6.25 mD. The permeability results obtained from mercury intrusion experiment (KHG) are 5.10 mD, 4.73 mD, 5.82 mD, and 5.56 mD, and those from the Darcy flow experiment (KD) are 4.1 mD and 5.19 mD. The absolute deviation between KNMR and KHG varies from 0.69 to 2.29 mD, while that between KNMR and KD is 1.58 mD. This method can be applied to the enhanced recovery of shale oil.

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

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.

Application of nuclear magnetic resonance permeability models in tight reservoirs

AbstractTight reservoir permeability with values ranging from a few nD–0.1 mD is a challenging parameter to measure. Since the 1970s, many correlations were applied to estimate the permeability of tight formations using the nuclear magnetic resonance (NMR) technique. Due to increasing interest in tight reservoir recovery, each year many papers are published on the Timur‐Coates and Kenyon models and their modification on estimating the NMR permeability of tight reservoirs. It brings up the question of which is the most accurate, reliable, and feasible model to be used for predicting tight NMR permeability.The first part of this paper is dedicated to introducing the existing models for NMR‐based estimates of tight reservoir permeability. The second part compares these models by applying them on tight reservoirs from North America, Asia, and Europe. NMR experiments were conducted on 150 cores. NMR T2 relaxation times were measured and effective and total porosities, the geometric mean of relaxation time (T2gm), irreducible bulk volume (BVI), and free fluid index (FFI) were calculated. For validation of the models, the cross plots of NMR permeability versus the Klinkenberg gas permeability are presented. Comparison of the model is based on the calculated standard error of these two independently measured permeabilities.This paper is beneficial in understanding existing tight reservoir NMR permeability models. The results can be used as a guide for choosing the best NMR permeability estimation model for tight reservoirs data.

Effects of nano-pore system characteristics on CH4 adsorption capacity in anthracite

This study aims to determine the effects of nanoscale pores system characteristics on CH4 adsorption capacity in anthracite. A total of 24 coal samples from the southern Sichuan Basin, China, were examined systemically using coal maceral analysis, vitrinite reflectance tests, proximate analysis, ultimate analysis, low-temperature N2 adsorption–desorption experiments, nuclear magnetic resonance (NMR) analysis, and CH4 isotherm adsorption experiments. Results show that nano-pores are divided into four types on the basis of pore size ranges: super micropores ( 100 nm). Super micropores, micropores, and mesopores make up the bulk of coal porosity, providing extremely large adsorption space with large internal surface area. This leads us to the conclusion that the threshold of pore diameter between adsorption pores and seepage pores is 100 nm. The “ink bottle” pores have the largest CH4 adsorption capacity, followed by semi-opened pores, whereas opened pores have the smallest CH4 adsorption capacity which indicates that anthracite pores with more irregular shapes possess higher CH4 adsorption capacity. CH4 adsorption capacity increased with the increase in NMR porosity and the bound water saturation. Moreover, CH4 adsorption capacity is positively correlated with NMR permeability when NMR permeability is less than 8 × 10–3 md. By contrast, the two factors are negatively correlated when NMR permeability is greater than 8 × 10–3 md.

Effects of internal gradients on pore-size distribution in shale

In porous media, effects of internal gradient caused by magnetic susceptibility contrast between the matrix and infilling fluid shifts transverse relaxation time (T2) to shorter time, resulting in imprecise classification of pore sizes. In shale, details of the effects of paramagnetic and clay minerals that induce the internal gradients are poorly understood. We measured a brine mixture with a granular form of minerals, including kaolinite, montmorillonite, glauconite, illite, pyrite, celadonite, and chamosite, to infer contributions of each to T2 relaxation in shale. Nuclear magnetic resonance (NMR) response of these samples was used to calculate the total mean logarithm of T2 (T2ML) and volume of water in the samples. Additionally, physical properties such as magnetic susceptibility, specific surface area, and elemental composition were acquired. Pore geometry, magnitude of internal gradients, pore-size distributions, and secular regimes in which the media relaxed (based on their shortest characteristic time scales) were characterized from these properties. These time scales measure the behavior of the relaxation regimes such as longitudinal relaxation time-to-T2 ratio and dependence of relaxation rate on echo spacing (tE). Glauconite illite, pyrite, chamosite, and kaolinite grouped into a small-pore system, whereas montmorillonite and celadonite grouped into large-pore systems. A free diffusion regime governed secular relaxation for kaolinite; a motional-averaging regime governed montmorillonite, celadonite, and large tE chamosite; whereas a localization regime dominated the rest of the minerals. We conclude that understanding the effects of internal gradients in shale is important to quantify the shortening of T2ML, which leads to overestimation of bulk fluid volume and, thus, underestimating NMR permeability when used as an input into permeability estimator equations.