Nuclear Magnetic Resonance T2 Research Articles

Nuclear Magnetic Resonance T1–T2 Spectra in Heavy Oil Reservoirs

Low-field nuclear magnetic resonance (NMR) has been widely used in the petroleum industry for reservoir evaluation. Fluid properties and petrophysical parameters can be determined from NMR spectra, obtained from processing echo data measured from the NMR tool. The more accurate NMR spectra are, the higher the reliability of reservoir evaluation based on NMR logging is. The purpose of this paper is to obtain more precise T1–T2 spectra in heavy oil reservoirs, with focus on the T1–T2 data acquisition and inversion. To this end, four inversion algorithms were tested on synthetic T1–T2 data, their precision was evaluated and the optimal inversion algorithm was selected. Then, the sensitivity to various acquisition parameters (wait time and echo spacing) was evaluated with T1–T2 experiments using a disordered accumulation of glass beads with a diameter of 45 μm saturated with heavy oil and distilled water. Finally, the sensitivity to various inversion parameters (convergence tolerance, maximum number of iterations and regularization parameter) was evaluated using the optimal inversion algorithm. The results showed that the inverted T1–T2 spectra loss some relaxation information when the number of echo train is less than 7. The peak of the heavy oil signal gradually moves along the direction of increase in the T2 and the intensity of the heavy oil signal gradually decreases with increasing echo spacing. The echo spacing should be as small as possible for T1–T2 measurements in heavy oil reservoirs on the premise that the NMR instrument operates normally. A convergence tolerance that is too large or a maximum number of iterations that is too small may result in exiting the iteration prematurely during the inversion. A convergence tolerance of 1 × 10−7 and a maximum number of iterations of 30,000 are recommended for the inversion of the T1–T2 spectra. An appropriate regularization parameter is an important factor for obtaining accurate T1–T2 spectra from the optimal inversion algorithm.

Precise Size Control of the Growth of Fe3O4 Nanocubes over a Wide Size Range Using a Rationally Designed One-Pot Synthesis.

The physicochemical properties of spinel oxide magnetic nanoparticles depend critically on both their size and shape. In particular, spinel oxide nanocrystals with cubic morphology have shown superior properties in comparison to their spherical counterparts in a variety of fields, like, for example, biomedicine. Therefore, having an accurate control over the nanoparticle shape and size, while preserving the crystallinity, becomes crucial for many applications. However, despite the increasing interest in spinel oxide nanocubes there are relatively few studies on this morphology due to the difficulty to synthesize perfectly defined cubic nanostructures, especially below 20 nm. Here we present a rationally designed synthesis pathway based on the thermal decomposition of iron(III) acetylacetonate to obtain high quality nanocubes over a wide range of sizes. This pathway enables the synthesis of monodisperse Fe3O4 nanocubes with edge length in the 9-80 nm range, with excellent cubic morphology and high crystallinity by only minor adjustments in the synthesis parameters. The accurate size control provides evidence that even 1-2 nm size variations can be critical in determining the functional properties, for example, for improved nuclear magnetic resonance T2 contrast or enhanced magnetic hyperthermia. The rationale behind the changes introduced in the synthesis procedure (e.g., the use of three solvents or adding Na-oleate) is carefully discussed. The versatility of this synthesis route is demonstrated by expanding its capability to grow other spinel oxides such as Co-ferrites, Mn-ferrites, and Mn3O4 of different sizes. The simplicity and adaptability of this synthesis scheme may ease the development of complex oxide nanocubes for a wide variety of applications.

Full‐Sized Pore Structure and Fractal Characteristics of Marine‐Continental Transitional Shale: A Case Study in Qinshui Basin, North China

Based on 10 shale samples collected from 4 wells in Qinshui Basin, we investigate the full‐sized pore structure and fractal characteristics of Marine‐Continental transitional shale by performing organic geochemistry, mineralogical composition, Nitrogen gas adsorption (N2 adsorption) and Nuclear Magnetic Resonance (NMR) measurements and fractal analysis. Results show that the TOC content of the shale samples is relatively high, with an average value of 2.44wt%, and the thermal evolution is during the mature‐over mature stage. The NMR T2 spectrum can be used to characterize the full‐sized pore structure characteristics of shale. By combining N2 adsorption pore structure parameters and NMR T2 spectrums, the surface relaxivity of samples are calculated to be between 1.7877 um/s and 5.2272 um/s. On this basis, the T2 spectrums are converted to full‐sized pore volume and surface area distribution curves. The statistics show that the pore volume is mainly provided by mesopore, followed by micropore, and the average percentages are 65.04% and 30.83% respectively; the surface area is mainly provided by micropore, followed by mesopore, and the average percentages are 60.8004% and 39.137% respectively; macropore contributes little to pore volume and surface area. The pore structure characteristics of shale have no relationship with TOC, but strong relationships with clay minerals content. NMR fractal dimensions Dmicro and Dmeso have strong positive relationships with the N2 adsorption fractal dimensions D1 and D2 respectively, indicating that Dmicro can be used to characterize the fractal characteristics of pore surface, and Dmeso can be used to characterize the fractal characteristics of pore structure. The shale surface relaxivity is controlled by multiple factors. The increasing of clay mineral content, pore surface area, pore surface fractal dimension and the decreasing of average pore size, will all lead to the decreasing of shale surface relaxivity.

Effects of boundary layer and stress sensitivity on the performance of low-velocity and one-phase flow in a shale oil reservoir: Experimental and numerical modeling approaches

Seepage flow is important because it is useful for production optimization and well spacing decisions. In this study, an enclosed high-precision seepage flow apparatus is used to determine the seepage flow characteristics of the Qianjiang shale reservoir in China under various pressures from 3 MPa to 24 MPa. The results revealed that the seepage flow curve has an inverse-spoon shape, which is completely different from the results for low-permeability and tight sandstone reservoirs, as reported in the literature. In addition, according to the full-scale pore size distribution technique, the modular automated processing system (MAPS), and the nuclear magnetic resonance (NMR) T2 spectrum before and after the seepage flow experiments, the seepage flow characteristics of the Qianjiang shale oil reservoir were not only caused by the boundary effect, but also dependent upon the stress sensitivity. It was also found that micro-fractures or macro-pore were compressed as the effective stress increased in the experiment. Moreover, a new non-Darcy model for a shale reservoir was derived, and the model was verified using experimental data. This model demonstrated it would be a good candidate for quantitative evaluation of affecting factors. It can also be applied to determine the main controlling factor. The calculated results showed that the shape of the nonlinear flow portion of the seepage flow curve for the Qianjiang shale oil reservoir was primarily dependent upon the effect of stress sensitivity. However, the linear flow portion of the seepage flow curve was dependent upon the combined effects of stress sensitivity and the boundary layer. Therefore, from the enhanced individual-well production point of view, the control of production pressure drops and selection of rational well spacing is important for the effective development of shale oil reservoirs.

New insights into spontaneous imbibition in tight oil sandstones with NMR

Previous research has confirmed that substantial yield has been recovered in the soaking period after hydraulic fracturing. Spontaneous imbibition (SI) is the primary mechanism responsible for the enhanced oil production and has become an effective oil recovery method with regards to unconventional reservoirs. Despite the importance of SI, an in-depth understanding of the features of SI in tight reservoirs is still inadequate. In this article, nuclear magnetic resonance (NMR) T2 was employed to study the dynamic behavior of imbibition in tight sandstone samples from Yanchang formation, Ordos Basin. Additionally, high-pressure mercury intrusion (HPMI) and the initial T2 of a core sample were combined to characterize pore structures of tight rocks and oil distributions in various pores at different imbibition stages. Then, the effects of gravity, anisotropic pore structure, and non-wetting phase (NWP) viscosity on imbibition recovery were determined via the variations in T2 spectra for each sample. The results show that the tight cores feature a multiscale pore structure. Sub-micropores is the dominant type providing the major contribution to oil recovery, while nanopores have largest imbibition efficiency due to the larger capillary force. Gravity of fluids has a non-trivial effect on SI and cannot be neglected in tight samples under two-ends-open (TEO) or one-end-open (OEO) conditions; this seemingly counterintuitive finding is because the capillary force is weakened by the resistance force caused by the intricate flow paths and other possible surface forces at the nanoscale. Imbibition performance in a tight medium is strongly related to its pore structure. The variations in directional permeabilities, relative permeabilities and capillary pressure functions resulting from highly anisotropic samples greatly affect both the ultimate oil recovery and imbibition rate for tight samples under TEO condition, but only impact imbibition rate under all-faces-open (AFO) condition. Furthermore, because tight oil has a narrow range of viscosity, the imbibition behavior in tight samples may not be sensitive to the oil viscosity. It would be hoped that the insights gained from this study can help to improve our understanding of imbibition characteristics in tight reservoirs and develop new scaling models for the predication of oil recovery.

Characterization of Pore Throat Size Distribution in Tight Sandstones with Nuclear Magnetic Resonance and High-Pressure Mercury Intrusion

Characterization of pore throat size distribution (PTSD) in tight sandstones is of substantial significance for tight sandstone reservoirs evaluation. High-pressure mercury intrusion (HPMI) and nuclear magnetic resonance (NMR) are the effective methods for characterizing PTSD of reservoirs. NMR T2 spectra is usually converted to mercury intrusion capillary pressure for PTSD characterization. However, the conversion is challenging in tight sandstones due to tiny pore throat sizes. In this paper, the linear conversion method and the nonlinear conversion method are investigated, and the error minimization method and the least square method are proposed to calculate the conversion coefficients of the linear conversion method and the nonlinear conversion method, respectively. Finally, the advantages and disadvantages of these two different conversion methods are discussed and compared with field case study. The research results show that the average linear conversion coefficients of the 20 tight sandstone core plugs collected from Yanchang Formation, Ordos Basin of China is 0.0133 μm/ms; the average nonlinear conversion coefficient is 0.0093 μm/ms and the average nonlinear conversion exponent is 0.725. Although PTSD converted from NMR spectra by the nonlinear conversion method is wider than that obtained from linear conversion method, the nonlinear conversion method can retain the characteristic of bi-modal distribution in PTSD.

Experimental investigation on the influence factors and oil production distribution in different pore sizes during CO2 huff-n-puff in an ultra-high-pressure tight oil reservoir

The Xinjiang tight oil reservoir has a low depletion recovery rate and high remaining oil content. The target reservoir has ultra-low permeability and complex pore structure. Therefore, performing water injection is very difficult, resulting in ineffective waterflooding. In this study, a series of experiments were conducted to study the effects of cycle numbers, production pressure, and permeability in CO2 huff-n-puff. The remaining oil production distribution in different pore sizes was also analyzed by the nuclear magnetic resonance (NMR) technique. The results showed that the cumulative oil recovery logarithmically increases with an increase in number of cycles, whereas, the oil recovery cycle decreases. Low production pressure and high permeability result in a higher oil recovery factor (ORF) of CO2 huff-n-puff. In addition, the optimal production pressure is 22 MPa. After the eighth cycle, the cumulative oil recovery can increase by 16.5%–44.5%, approximately 4.7–7.6 times that of the depletion recovery rate, indicating that CO2 huff-n-puff is a feasible way to enhance the ORF of a tight oil reservoir. HPR Chinaowever, the oil production in the first five cycles accounts for 84%–91.7% of the total enhanced recovery. Therefore, CO2 huff-n-puff operations should not have more than five cycles. NMR T2 spectrum analysis shows that in the first cycle, the oil in macro and medium pores (>50 ms) is produced first, followed by small pores (10–50 ms), accounting for 49.5%–79% and 8%–37% of the total ORF, respectively, whereas, oil from the micropores (<10 ms) is rarely produced. However, for the subsequent cycles, the oil production in macro pores and medium pores decreases while that in small pores and micropores increases. Thus, the small pores and micropores become the main oil producing areas.

In Situ CH4–CO2 Dispersion Measurements in Rock Cores

Injection of carbon dioxide (CO2) into a natural gas reservoir is an emerging technology for enhanced natural gas recovery (EGR) realizing increased natural gas production whilst sequestering the injected CO2. However, given that CO2 and natural gas are completely miscible, simulation of potential EGR scenarios is required to determine when breakthrough of CO2 will occur at the natural gas production wells. For such reservoir simulations to be reliable (independent of software used), accurate dispersion data between CO2 and natural gas at relevant reservoir conditions are required. To this end, we apply one-dimensional magnetic resonance imaging (MRI) to quantify this dispersion process in situ in both sandstone and carbonate rock cores. Specifically we apply the SPRITE MRI sequence (Balcom et al. in J Magn Reson Ser A 123(1):131–134, 1996. https://doi.org/10.1006/jmra.1996.0225 ) to facilitate quantitative axial profiles of methane (CH4) content during core flooding processes between CO2 and CH4. Simultaneously we measure, using infrared, the effluent CO2 and CH4 concentrations enabling ex situ dispersion measurements. Via comparison with the corresponding MRI data, the erroneous contributions to dispersion from entry/exit effects and mixing in piping to and from the rock core holder are quantified. Furthermore, we demonstrate how nuclear magnetic resonance T2 measurements can be uniquely used to probe the pore size occupancy of the CH4 during the core flooding process.

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

Influence of Pluronic F127 microenvironments on the photochemical nitric oxide release from S-nitrosoglutathione

Poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO) (F127) hydrogels have been used to deliver nitric oxide (NO) topically in biomedical applications. Here, the effect of F127 microenvironments on the photochemical NO release from S-nitrosoglutathione (GSNO) was investigated in F127 solutions 7.6 wt% 15 wt% and 22.5 wt% at 15 °C and 37 °C. Small-angle X-ray Scattering (SAXS) and Differential Scanning Calorimetry (DSC) measurements, along with proton Nuclear Magnetic Resonance (1H NMR) spectral shifts and T2 relaxation data at six different concentration-temperature conditions, allowed identifying F127 microphases characterized by: a sol phase of unimers; micelles in non-defined periodic order, and a gel phase of cubic packed micelles. Kinetic measurements showed that GSNO photodecompositon proceeds faster in micellized F127 where GSNO is segregated to the intermicellar microenvironment. Real time kinetic monitoring of NO release and T2 relaxation profiles showed that NO is preferentially partitioned into the hydrophobic PPO cores of the F127 micelles, with the consequent decrease in its rate of release to the gas phase. These results show that F127 microphases affect both the kinetics of GSNO photodecomposition and the rate of NO escape and can be used to modulate the photochemical NO delivery from F127/GSNO solutions.

Pore-scale monitoring of CO2 and N2 flooding processes in a tight formation under reservoir conditions using nuclear magnetic resonance (NMR): A case study

Recent reports have demonstrated that gas injection can improve oil recovery of tight reservoirs after natural depletion, with major projects in progress worldwide. There is however a lack of understanding of the gas displacement behaviors at pore-scale especially under reservoir conditions. Herein we conducted an experimental investigation of CO2 and N2 flooding in the Lucaogou tight formation at 35.0 MPa and 80 °C. The flooding dynamics were continuously monitored by use of low-field nuclear magnetic resonance (NMR) to map the displacement processes at pore-scale and quantify the reduction of oil saturation in-situ. We observed that the overall oil recoveries of CO2 and N2 flooding were 38.4% and 35.1%, respectively. Due to the interactions between the oil phase and CO2, a uniform displacement along the core plug was yielded by CO2 flushing, indicated by the 1D spatial distribution of local oil saturation and 2D transverse images. Interestingly, during CO2 flooding, the major reduction of NMR T2 signals occurred in the large pores (4.2 ms < T2 < 252.4 ms), whereas in the case of N2 flooding, the main change was found both in small pores (T2 < 4.5 ms) and large pores (4.5 ms < T2 < 432.3 ms). The results of this study supplement earlier observations from pore scale and can provide insights into oil recovery improvement of tight reservoirs.

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.

Long Short-Term Memory and Variational Autoencoder With Convolutional Neural Networks for Generating NMR T2 Distributions

Downhole nuclear magnetic resonance (NMR) logs acquired in the borehole environment are valuable for subsurface characterization because they contain information about the pore size distribution, fluid composition, fluid saturation, fluid mobility, formation permeability, and porosity. NMR log acquisition can be challenging due to operational and financial constraints. Recently, NMR T2 distributions of the subsurface were generated by processing conventional well logs using deeplearning neural-network (NN) models. This improves the accessibility to subsurface pore size distributions. We implement two neural-network models, variational autoencoder-based NN with convolutional layers and long short-term memory (LSTM), to generate NMR T2 distributions from formation mineral content and fluid saturation logs. Prediction performance is evaluated for the entire NMR T2 spectrum ranging from 0.3 to 3000 ms as well as for T2 spectra within four bins obtained by dividing the entire spectrum into four equal parts. Each bin represents a specific pore size range. In terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , both the models have prediction performances above R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.75 for the entire spectrum. The best prediction performance is achieved for the bin of size ranging from 2.7 to 28 ms, representing pore diameters from 10 to 100 nm. The performance of this bin in terms of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> is 0.78. The LSTM model is highly sensitive to noise in T2 distribution used during the training, and both the models are robust to noise in the conventional input logs.

A new method for measuring shale porosity with low-field nuclear magnetic resonance considering non-fluid signals

Abstract Porosity is a key parameter for the evaluation of potential shale oil and gas resources. Low-field nuclear magnetic resonance (NMR) is a rapid technique for measuring shale porosity without causing damage to the testing samples. Previous NMR methods for porosity characterization are mainly based on the NMR T2 distribution of saturated oil shale, but for shales with high total organic carbon (TOC) and clay mineral contents, not all signals of NMR detection originate from the pore fluid. Nine continental shales in Damintun Sag were selected and subjected to pyrolysis, X-ray diffraction (XRD), mercury injection capillary pressure (MICP), weighing before and after fluid saturation, and NMR tests to improve the accuracy of NMR porosity measurements. According to the NMR T2 distribution and T1–T2 map of shale with different oil contents (original, extracted, and saturated oil states), we revealed that the non-fluid signals (kerogen and structural water) accounted for 15.77%–43.10% (with a mean of 28.87%) of saturated oil shale. A stronger NMR signal intensity was observed for the extracted shale with higher TOC and clay mineral contents. A ΔT2 distribution of oil present in the pores of oil-saturated shale was constructed based on the difference between the oil-saturated shale and extracted shale and was combined with the calibration equation of oil (the relationship between oil volume and its NMR signal intensity) to directly evaluate the porosity. The porosity that was calculated based on the ΔT2 distribution versus the weighing method had an absolute error of ≤0.7%, and the relative error was

Near-infrared spectroscopy detects woody breast syndrome in chicken fillets by the markers protein content and degree of water binding

The muscle syndrome woody breast (WB) impairs quality of chicken fillets and is a challenge to the poultry meat industry. There is a need for online detection of affected fillets for automatic quality sorting in process. Near-infrared spectroscopy (NIRS) is a promising method, and in this study we elucidate the spectral properties of WB versus normal fillets. On a training set of 50 chicken fillets (20 normal, 30 WB), we measured NIR, nuclear magnetic resonance (NMR) T2 relaxation distributions, and crude chemical composition. NIRS could estimate protein in the fillets with an accuracy of ±0.64 percentage points. T2 distributions showed that there was a larger share of free water in WB fillets. This difference in water binding generated a shift and narrowing of the water absorption peak in NIR around 980nm, quantified by a bound water index (BWI). The correlation between BWI and T2 distributions was 0.78, indicating that NIRS contains information about degree of water binding. Discriminant analysis showed that NIRS obtained 100% correct classification of normal versus WB on the training set, and 96% correct classification on a test set of 52 fillets. The main reason for why NIRS can successfully discriminate between WB and normal fillets is the methods sensitivity to both protein content and degree of water binding in the muscle, both established markers for WB. The classification model can be based on NIR spectra only, calibration against protein is not needed. The affected muscle tissue associated with the WB syndrome is unevenly distributed in the fillets, and this heterogeneity was characterized by NIRS and NMR. Clear differences in water binding properties were found between the superficial 1cm layer and the deeper layer at 1 to 2cm depth. Significant differences in protein estimates by NIRS at different measurement points along the chicken fillets were obtained for WB fillets. The findings suggest how to obtain optimal sampling with NIRS for best possible discrimination between WB and normal breast fillets.

CO2-brine injectivity tests in high co2 content carbonate field, sarawak basin, offshore east Malaysia

We conducted relatively long duration core-flooding tests on three representative core samples under reservoir conditions to quantify the potential impact of flow rates on fines production/permeability change. Supercritical CO2 was injected cyclically with incremental increases in flow rate (2─14 ml/min) with live brine until a total of 7 cycles were completed. To avoid unwanted fluid-rock reaction when live brine was injected into the sample, and to mimic the in-situ geochemical conditions of the reservoir, a packed column was installed on the inflow accumulator line to pre-equilibrate the fluid before entering the core sample. The change in the gas porosity and permeability of the tested plug samples due to different mechanisms (dissolution and/or precipitation) that may occur during scCO2/live brine injection was investigated. Nuclear magnetic resonance (NMR) T2 determination, X-ray CT scans and chemical analyses of the produced brine were also conducted. Results of pre- and post-test analyses (poroperm, NMR, X-ray CT) showed no clear evidence of formation damage even after long testing cycles and only minor or no dissolution (after large injected pore volumes (PVs) ~ 200). The critical flow rates (if there is one) were higher than the maximum rates applied. Chemical analyses of the core effluent showed that the rock samples for which a pre-column was installed do not experience carbonate dissolution.

An Investigation into Different Correlation Methods between NMR T2 Distributions and Primary Drainage Capillary Pressure Curves Using an Extensive Sandstone Database

Many workers have recognised the link between Nuclear Magnetic Resonance (NMR) derived T2 distributions and pore size distributions in reservoir rocks. This property has been used to develop models to obtain primary drainage capillary pressure curves from T2 distributions. These models often assume that the rocks pore space resembles a simple bundle of capillary tubes. They do not consider the existence of multiple pore body connections and pore body restrictions/throats. The most successful models utilise variable scaling factors to convert T2 times to pore diameters and hence capillary pressure. The variable scaling factor approach recognises the existence of variable surface relaxivity throughout the pore space due to variations in mineralogy and pore topography. This investigation uses SCAL data from the ART NMR Sandstone Rock Catalogue to obtain core calibrated variable scaling factors for 174 reservoir sandstone samples. The depositional environments for these samples include; aeolian, fluvial, coastal and shallow and deep marine. The samples used have a wide variety of mineralogy, diagenetic overprints and cover six orders of magnitude in absolute permeability. Three different methods for obtaining the scaling factors are presented and the relative merits of each discussed. A global model to predict capillary pressure from NMR T2 distributions in reservoir sandstones has been developed using correlations between the variable scaling factors and permeability.

Experimental Study of the Pore Structure Deterioration of Sandstones under Freeze‐Thaw Cycles and Chemical Erosion

The issue of rock deterioration in chemical environments has drawn much attention in recent years in the rock engineering community. In this study, a series of 30 freeze‐thaw cycling tests are conducted on sandstone samples soaked in H2SO4 solution and in pure water, prior to the application of nuclear magnetic resonance (NMR) on the rock specimens. The porosity of the sandstone, the distribution of transverse relaxation time T2, and the NMR images are acquired after each freeze‐thaw cycle. The pore size distribution curves of the sandstone after freeze‐thaw cycles, four categories of pore scale, and the features of freeze‐thaw deterioration for pores of different sizes in H2SO4 solution and pure water are established. The result shows that, with the influence of the acid environment and the freeze‐thaw cycles, the mass of the samples largely deteriorates. As the freeze‐thaw cycles increase, the porosity of rocks increases approximately linearly. The distribution of the NMR T2 develops gradually from 4 peaks to 5 or even to 6. Magnetic resonance imaging (MRI) dynamically displays the process of the freeze‐thaw deterioration of the microstructure inside the sandstones under acid conditions. The results also show pore expansion in rocks under the coupling effects of chemistry and the freeze‐thaw cycles, which differ largely from the freeze‐thaw deterioration of the rock specimens placed in pure water.

Nuclear magnetic resonance T2 cutoffs of coals: A novel method by multifractal analysis theory

Nuclear magnetic resonance (NMR) has been widely used in petrophysical characterization of coals. For NMR experimental data analysis, transverse relaxation time (T2) cutoff value is a key parameter to identify the form of movable and irreducible fluids, and to evaluate permeability and full-scale pore size distribution (PSD). Conventionally, the T2 cutoff value is procured by a series of centrifugal experiments, which is much complicated and time consuming, and is also hard to be used in fields such as well logging. Thus, a convenient and practical method is needed for T2 cutoff value prediction. Based on series of centrifugal experiments, this study firstly determined an optimal centrifugal pressure of 1.38 MPa for T2 cutoff value calculation. The results from centrifugal experiments show that the T2 cutoff values of bituminous coals and anthracite coals in the range of 0.62–15.11 ms. Then, the multifractal analysis theory is introduced into the estimation of T2 cutoff values of coals. The results showed that the NMR T2 distribution of 100% water-saturated coal is multifractal, and the multifractal parameters of multifractal dimension (Dq) and singularity strength range (Δα) can be used to evaluate the T2 cutoff value of coals. Finally, a new multifractal-based NMR T2 cutoff calculation model was provided, and the model was verified by centrifugal experimental data of six coal samples. It is concluded that the provided multifractal analysis method is effective, convenient and independent of any other experiments. The model derived from the study of coal, is also applicable for researches of other rock types such as shales and so on.

Characterization of pore size distributions of shale oil reservoirs: A case study from Dongying sag, Bohai Bay basin, China

Abstract Shale oil reservoirs characterized by complex pore structures typically contain a wide pore size range from nanometers to millimeters. The pore size distribution (PSD) is one of the most critical parameters affecting fluid storage and transport properties in shale oil reservoirs. Quantitatively and accurately determining PSD is of great importance for evaluating the shale oil occurrence state and movable fluid volume. In this study, four major techniques, including nitrogen adsorption (NA), scanning electron microscopy (SEM), mercury injection capillary pressure (MICP) and nuclear magnetic resonance (NMR), were adopted to determine pore sizes in ten shale oil reservoir samples from Dongying sag, Bohai Bay basin, China. NMR T2 spectra were converted to PSDs by using a new subsection calibration method based on the NA and SEM measurements; using the linear and power exponent conversion models, these two models are compared. The results show that the subsection calibration is an effective method for shale NMR T2 spectrum conversion. Shale oil reservoir T2 spectra can be converted to full-scale PSDs by combining NA micropore ( 1000 nm) in shale oil reservoirs; however, they are mainly connected by small-scale throats. Therefore, the comprehensive characterization of shale oil reservoir pore structures may be efficiently and accurately determined by a combination of NMR and MICP.