Nuclear Magnetic Resonance Log Data Research Articles

Predicting the compressive strength of tight sandstone based on the low field NMR and pseudo-triaxial compression measurements

The compressive strength is very important for petroleum and other engineering studies. However, the effect of pore size and fluid distribution on the rock’s strength is not fully understood. We developed comprehensive research to study the controlling factors of the compressive strength based on low field nuclear magnetic resonance (NMR) measurements and pseudo-triaxial compression test for tight sandstones. The relationship between the compressive strength and the NMR obtained parameters are investigated completely, aiming for a better estimation of the compressive strength using the NMR data. The result shows that the rock’s strength is strongly controlled by the pore size distribution and the fluid existing state. Generally, the compressive strength is negatively correlated with the average transversal relaxation time, the movable water saturation, and the porosity, but positively correlated with the irreducible water saturation. The result reveals that the rock with larger pore radius and higher percentage of movable fluid is easier to reach the failure state. Further, the precision of the empirical model by multiple regression of the geometric mean of the relaxation time and the porosity is greatly improved compared with the model established by the brittle minerals, which is potentially to be use for geophysical prospecting when the NMR logging data is available.

Joint Inversion Method of Nuclear Magnetic Resonance Logging Multiwait Time and Multiecho Time Activation Data and Fluid Identification

Summary Nuclear magnetic resonance (NMR) logging is effective for reservoir evaluation; at present, NMR logging data acquisition parameters are primarily divided into dual wait time (TW) and dual echo time (TE) and then are analyzed, respectively. However, the interpretation results of the two activations are often inconsistent and confuse the identification and quantitative evaluation of reservoir fluids. Based on the principle of multi-TW and -TE activations of NMR logging, the relaxation mechanism is analyzed, and the relationship between the amplitude of the echo train and the pore structure, fluid types, and content of different activations is established. The joint system of the amplitude of echo trains in multiactivations is constructed. Then, the difference spectrum and the oil porosity of the flushed zone can be calculated by the least squares algorithm (LSQR). The fluid-saturated rock model is set, and the numerical simulation of NMR is used to verify the data joint inversion is correct and that the calculation result is more accurate than the previous time domain analysis (TDA) processing method. Moreover, the oil porosity of the flushed zone-deep induction resistivity crossplot is constructed and is also proposed to identify fluid. The above method was applied to the Yanchang Formation in the western Ordos Basin. Based on the joint inversion of NMR multi-TW and -TE logging data in the study area, the methodology yields more precise calculations of fluid volume and saturation compared with conventional approaches. The crossplots derived from these calculations demonstrate high efficacy in identifying fluid types; therefore, the method for fluid identification exhibits potential for practical application and holds considerable value for widespread adoption in the field.

Petrophysical insights into pore structure in complex carbonate reservoirs using NMR data

The study delves into pore structure attributes within the complex Eocene carbonate of an Indian offshore field, encompassing pore throat, radius and their characteristics. Nuclear Magnetic Resonance (NMR) experimental data reveals crucial insights into pore structures and fluid states. This study compares the NMR T2 distribution curve with capillary pressure data from the Mercury Injection Capillary Pressure (MICP) technique, deriving linear and nonlinear conversion coefficients to transform NMR T2 spectra into equivalent pore radius distribution. Pore radius-dependent porosity partitioning, linked to permeability and the distribution of irreducible water, is conducted utilizing NMR-derived data. Following the T2 cut-off analysis, a two-segment fractal analysis of NMR T2 distribution is also carried out. This analysis unveils associations between fractal dimensions and various petrophysical parameters, including permeability, porosity, T2LM, irreducible water saturation and R50. The NMR-derived pore radius distribution is mostly unimodal, occasionally slightly bimodal. Six different pore size classes (less than 0.05 μm to more than 5 μm) are analysed in relation to permeability, porosity and irreducible water. Small pores (<1 μm) contribute more to irreducible water with low porosity and permeability. The fractal dimension of large pores correlates strongly with porosity, permeability, T2LM, irreducible water and R50 suggesting significant impact on reservoir seepage capacity. In addition to porosity partitioning, the current study demonstrates effectiveness in modelling modified permeability and correlating it with in situ permeability when applied to field NMR log data from the study area. While numerous studies focus on sandstone, our study marks the pioneering attempt at a comprehensive analysis on complex carbonate reservoirs.

Fluid Identification Method of Nuclear Magnetic Resonance and Array Acoustic Logging for Complex Oil and Water Layers in Tight Sandstone Reservoir

In order to improve the logging interpretation accuracy for complex oil and water layers developed in tight sandstone reservoirs, this study takes the Chang 8 member of the Yanchang Formation in the Huanxian area as the research object, and two new fluid identification methods were constructed based on nuclear magnetic resonance (NMR) logging and array acoustic logging. Firstly, the reservoir characteristics of physical properties and conductivity were studied in the research area, and the limitations of conventional logging methods in identifying complex oil and water layers were clarified. Then, the sensitive parameters for identifying different pore fluids were established by analyzing the relationship between NMR logging and array acoustic logging with different pore fluids. On this basis, the fluid identification plate, composed of movable fluid apparent diffusion coefficient and effective porosity difference (Da-Δφe) by NMR logging data of D9TWE3 observation mode, and the other fluid identification plate, composed of apparent bulk modulus of pore fluid and elastic parameter sensitive factor (Kf-Fac), were constructed, respectively. Finally, these two fluid identification methods were used for reservoir interpretation of actual logging data. This study shows that the two new fluid identification methods constructed by NMR logging and array acoustic logging can effectively eliminate the interference of rock skeleton on logging interpretation, which make them more effective in identifying complex oil and water layers than the conventional logging method. Additionally, the two methods have their own advantages and disadvantages when used separately for interpreting complex oil and water layers, and the comprehensive interpretation of the two methods provides a technical development direction for further improving the accuracy of logging the interpretation of complex oil and water layers.

Simulation of nuclear magnetic resonance response based on 3D CT images of sandstone core

In recent years, nuclear magnetic resonance (NMR) logging has become increasingly prevalent in the characterization of rock properties such as porosity, permeability, saturation, and pore size distribution. However, interpreting such properties accurately from NMR logging data is challenging for some reservoirs. In particular, the impact of salinity, viscosity, and saturation on NMR measurements is not always clear, which can lead to inaccuracies in the resulting data interpretation. Properly accounting for these factors is essential in order to obtain accurate and reliable measurements for effective characterization of subsurface formations. This study utilized a random walk technique to simulate the NMR response of homogeneous sandstones using 3D CT images and conducted a sensitivity analysis under various salinity, crude oil viscosity, and water saturation conditions. The results indicate that the T2 relaxation time slightly shifts toward the short relaxation direction as the salinity of the formation water increases. In addition, longer echo intervals result in a more significant forward shift in the T2 value than shorter intervals. Whereas, for crude oil, the T2 relaxation time becomes shorter as its viscosity increases. Furthermore, the effect of echo interval on the forward T2 shift is less pronounced for crude oil than it is for formation water. Under water wet conditions, the T2 spectrum of crude oil exhibits a peak at the volume relaxation position. As the water saturation decreases, the left two peaks in the T2 spectrum shift toward shorter relaxation times. Under oil wet conditions, the T2 spectrum exhibits a complex three-peak structure. The method provides a physical basis for interpreting NMR macroscopic responses, and the simulated NMR responses can help identify fluids in reservoirs.

A Method of Inverting Rock Grain Size Based on Nuclear Magnetic Resonance Logging Data and Application

Rock grain size parameter is the key parameter of reservoir rock physics analysis. The study found that the relationship between the NMR T 2 spectrum and rock grain size distribution curve is directly related to NMR T 2 distribution and grain size distribution of rock, so you can use T 2 to retrieve the size distribution of rock NMR spectral data. Based on the calculation of the rock core experiment results of grain size distribution by NMR T 2 distribution using the conversion method of piecewise nonlinear calibration for each core, adjusting the calibration parameters to make the grain size distribution calculated with the core analysis of grain size distribution approximation, the error is minimum to obtain each core scale parameter conversion. In the end, the core parameters are classified according to the parameters of pore and permeability. Finally, according to the actual NMR T 2 distribution curve inversion of underground rock granularity, the inversion results contrast with the core analysis results, and the reliability of the method is verified to provide accurate, continuous rock size distribution profile analysis of geological reservoir rock physics.

Evaluation of models for estimating hydraulic conductivity in glacial aquifers with NMR logging.

Nuclear magnetic resonance (NMR) logging is a promising method for estimating hydraulic conductivity (K). During the past ~60 years, NMR logging has been used for petroleum applications, and different models have been developed for deriving estimates of permeability. These models involve calibration parameters whose values were determined through decades of research on sandstones and carbonates. We assessed the use of five models to derive estimates of K in glacial aquifers from NMR logging data acquired in two wells at each of two field sites in central Wisconsin, USA. Measurements of K, obtained with a direct push permeameter (DPP), KDPP , were used to obtain the calibration parameters in the Schlumberger-Doll Research, Seevers, Timur-Coates, Kozeny-Godefroy, and sum-of-echoes (SOE) models so as to predict K from the NMR data; and were also used to assess the ability of the models to predict KDPP . We obtained four well-scale calibration parameter values for each model by using the NMR and DPP measurements in each well; and one study-scale parameter value for each model by using all data. The SOE model achieved agreement with KDPP that matched or exceeded that of the other models. The Timur-Coates estimates of K were found to be substantially different from KDPP . Although the well-scale parameter values for the Schlumberger-Doll, Seevers, and SOE models were found to vary by less than a factor of 2, more research is needed to confirm their general applicability so that site-specific calibration is not required to obtain accurate estimates of K from NMR logging data. This article is protected by copyright. All rights reserved.

Application of Multifractal Analysis Theory to Interpret T2 Cutoffs of NMR Logging Data: A Case Study of Coarse Clastic Rock Reservoirs in Southwestern Bozhong Sag, China

The Paleocene Kongdian Formation coarse clastic rock reservoir in Bozhong Sag is rich in oil and gas resources and has huge exploration potential. However, the coarse clastic rock reservoir has the characteristics of a complex pore structure and strong heterogeneity, which restrict the accuracy of evaluating the reservoir’s physical properties, such as porosity and permeability, for field evaluation. Nuclear magnetic resonance (NMR) technology has become a popular methods for unconventional reservoir evaluation because it can obtain abundant reservoir physical property information and because of its ability to identify fluid characteristics information. The transverse relaxation time (T2) cutoff (T2C) value is an important input parameter in the application of NMR technology. The accuracy of the T2C value affects the accuracy of the reservoir evaluation. The standard method for determining the T2C value requires a series of complicated centrifugation experiments in addition to the NMR experiments, and its application scope is limited by obtaining enough core samples. In this study, 14 core samples from the coarse clastic rock reservoir in the southwestern Bozhong sag of the Bohai Bay Basin were selected, and NMR measurements were carried out under the conditions of fully saturated water and irreducible water to determine the T2C value. Based on the multifractal theory, the NMR T2 spectrum of the saturated sample was analyzed, and the results show that the NMR T2 distribution of the saturated sample has multifractal characteristics, and the multifractal parameter Dq and the singular intensity range Δα have a strong correlation with the T2C value. Thus, based on multiple regression analyses of the multifractal parameters with the experimental T2C value of 10 core samples, we propose a method to predict the T2C value. After applying this method to 4 samples that were not used in the modeling, we confirmed that this method can be used to predict the T2C value of core samples. Furthermore, we expanded this method to the field application of a production well in Bozhong sag by adding an empirical index in the model. The new model can be used to directly calculate the T2C value of NMR logging data, and it does not require any other extra data, such as those from core analysis. This method is applicable in fast reservoir evaluations by only using NMR logging data in the field. The research results improve the accuracy of field NMR logging reservoir evaluations.

An Intelligent Denoising Method for Nuclear Magnetic Resonance Logging Measurement Based on Residual Network

Nuclear magnetic resonance (NMR) is an important geophysical technique for the structure and properties measurement of porous media. The small-diameter NMR logging technology has been emerged as a useful tool in terms of evaluating the shallow surface hydrogeological parameters. However, the NMR logging is always suffering from the low signal-to-noise radio (SNR). This paper proposes a denoising network for NMR logging signal based on the combination of the convolutional neural network (CNN) and residual learning, called as Dn-ResNet. Firstly, a large amount training datasets are constructed according to the characteristics of the NMR logging signal and noise, which is used to train the network. Secondly, the useful features of the NMR logging signals are learned by the network during training processing, in which the residual learning mechanism is employed to improve the training efficiency and the denoising performance. As a result, the Dn-ResNet may realize adaptive denoising without manual filter parameters tuning, prior knowledge of NMR logging signals and preprocessing of the original noisy signals. The performance of the proposed denoising network is demonstrated on both the synthetic NMR logging signals and field data. The effective signals are obtained from the noisy signals at different SNRs. In addition, an attempt to use the well-trained Dn-ResNet to deal with a single record provides similar results compared to 200 stacks. It shows that the number of stacks may be reduced to shorten the measurement time and improve the measurement efficiency. The results show the effectiveness and robustness of the proposed approach, which enables the technology of deep learning to be developed in application of NMR logging data processing.

Variational Mode Decomposition for NMR Echo Data Denoising

Nuclear magnetic resonance (NMR) relaxometry, a noninvasive and nondestructive method, is a key technique for unconventional reservoir evaluation. Echo data detected from NMR instrument, a kind of weak signal, however, is characterized by a low signal-to-noise ratio (SNR). The achievement of NMR relaxation spectra inversion of a high precision, for echo data with a low SNR, is a challenge, which will also affect the unconventional reservoir evaluation of NMR logging. In this article, a variational mode decomposition (VMD) method was proposed for NMR echo data denoising. NMR echo data were decomposed into an ensemble of intrinsic mode functions (IMFs) by the VMD method. The IMF number is an important parameter for VMD, and VMD results are different with various IMF numbers. An optimal selection method for the IMF number was proposed. The decomposed IMFs compose of signals with different frequencies. Noise is from high-frequency signal, but valuable data are from low-frequency signal. The effective IMFs in VMD were selected and summed as the denoised echo data. Numerical simulations and field NMR logging data processing were undertaken to evaluate the NMR echo data denoising effectiveness and practicality of the proposed VMD-based method. The results showed that the inverted NMR spectra exhibit a higher quality after the VMD-based denoise, compared with those for raw echo data and after the empirical mode decomposition (EMD) denoise. This indicates that a higher denoising quality is achieved by the VMD-based method than by the EMD-based method for NMR echo data.

Nuclear magnetic resonance pore radius transformation method and fluid mobility characterization of shale oil reservoirs

The mobility of fluid is important for evaluating shale oil reservoirs, and it is closely related to the pore structure. Nuclear magnetic resonance (NMR) can effectively reveal the pore size distribution from nanometer to micron, providing a way to study the relationship between pore structure and mobility. However, the transverse relaxation time (T2) of NMR also contains bulk relaxation and diffusion relaxation, which will affect the accuracy of pore structure calculation. To characterize the mobility of shale oil by NMR, we proposed a pore radius transformation method with relaxation correction. The NMR simulations were carried out based on different digital core models, which were constructed according to X-CT results of shale cores. And the comparisons were performed to verified the accuracy and efficiency of the presented method. Then, the relation of geometric means of pore radius and mobility was studied through NMR experiments, and the NMR logging data of well J31 were processed by proposed method. The results show that the relative errors between corrected T2 distributions and surface relaxation distributions are smaller, and the geometric means of pore radius after relaxation correction are closer to those of digital core models, which proves the efficiency of the presented method. The logarithm of geometric mean of pore radius obtained from T2 distribution of shale core NMR experiment is positively correlated with S1/TOC, which can represent shale mobility. The processing results of NMR logging data in well J31 show two sections with large geometric means of pore radius, indicating that they have good mobility, which are consistent with the oil test results.

Study on Inversion Algorithm of NMR T2 Spectrum

Nuclear magnetic resonance (NMR) logging technology is a kind of technology developed gradually in recent decades, which has great significance for the development of logging technology. Nuclear magnetic resonance logging technology compared to traditional logging technology, not only has the ideal signal - hydrogen nuclei, and the exploration of the original signal is not affected by the influence of the rock skeleton, is the only provides information that is unrelated to formation rock porosity, can better provide the formation permeability, movable fluid, oil and gas content, such as information, It is a kind of logging method suitable for low porosity and low permeability and complex special lithology, and overcomes the defects of traditional logging methods .With the development of NMR theory, NMR has been applied to the analysis of well logging data. The original NMR logging data are the spin-echo string observed by the spin echo string, and its amplitude decays with time. The T2 spectrum can be inversely calculated from the measured logging data, and then the T2 spectrum can be further solved to obtain key data such as permeability and porosity. In this paper, a simulated T2 spectrum is constructed by forward modeling, and the inversion is carried out under different SNR. On this basis, the specific relationship between the optimal number of singular values retained and the change of SNR is summarized, and an improved singular value decomposition algorithm is proposed. Inverse problem can be converted to nonlinear fitting optimization problems with nonnegative constraints, linear truncation algorithm on calculation precision has increased significantly, compared with the traditional singular value decomposition inversion algorithm, a new algorithm of signal-to-noise ratio on the solution of smaller and has better stability, and through the improvement of nonnegative constraint processing structure was more rapid inversion iteration algorithm.

Classification of Tight Sandstone Reservoirs Based on the Nuclear Magnetic Resonance T2 Distribution: A Case Study on the Shaximiao Formation in Central Sichuan, China

The classification of tight sandstone reservoirs is of significance for hydrocarbon exploration and production. Different from conventional reservoirs, tight sandstone reservoirs are characterized by a complex pore structure and strong heterogeneity. Tight sandstone reservoir classification may not be reliable with conventional reservoir classification methods. In this paper, a classification method of tight sandstone reservoirs was proposed on the basis of the nuclear magnetic resonance (NMR) transverse relaxation (T2) distribution, and it was employed for reservoir classification on the Shaximiao Formation in central Sichuan, China. As a result of the complex pore structure of a tight sandstone reservoir, the NMR T2 distribution is characterized by the presence of multiple peaks. Therefore, the three-peak Gaussian function was used to fit the T2 distribution and obtain the characteristic parameters. On the basis of high-pressure mercury intrusion (HPMI) experiments, the correlation between the characteristic parameters of the NMR T2 distribution and pore structure parameters and petrophysical properties was analyzed, and then the optimal characteristic parameters of the NMR T2 distribution were selected according to the results of correlation analysis and used to establish the pore structure index. In combination of petrophysical properties with the pore structure index, the model for classification of tight sandstone reservoirs was established by the naive Bayesian method based on hierarchical clustering, and the accuracy of the model was verified by k-fold cross-validation. Finally, the effectiveness of the proposed method was verified using the actual NMR logging data in conjunction with the oil test data. The results showed that the method for classification of tight sandstone reservoirs based on the NMR T2 distribution can provide effective guidance for production capacity evaluation.

Formation Evaluation Using NMR, Mud Gas, and Triple-Combo Data – A Norwegian Logging-While-Drilling Case History

The presence of gas in hydrocarbon reservoirs has a distinct effect on commonly used porosity logging measurements such as density, neutron, and nuclear magnetic resonance (NMR). Although the effect of gas on NMR measurements is well understood, it remains difficult to estimate true formation porosity and hydrocarbon (HC) saturation exclusively from NMR data in gas-bearing formations. The hydrogen index (HI) of gas is much smaller than one and causes an underestimation of the measured NMR porosity. Unless temperature, pressure, HC composition, and saturation in the volume sensed by NMR are known accurately, compensating for the effect of gas is not easily accomplished. This paper compares different approaches for NMR gas zone analysis, and it introduces a new method integrating NMR logging data with a HI curve derived from mud gas surface logging. The results are connected to a full and consistent petrophysical reservoir description. Data and results from two wells drilled in a North Sea clastic reservoir are presented. The reservoir is dominated by thick units of sandstone, deposited in a submarine turbidite fan, with high porosity and high permeability. The wells were logged with triple-combo (i.e., gamma ray, density, neutron, and resistivity), NMR, and formation tester (FT) tools while drilling across gas, oil, and water intervals. Basic mud gas data are available from surface logging. Uncorrected NMR porosity shows an average porosity underestimation of 6 p.u. in the gas zone compared to triple-combo computer-processed interpretation (CPI) processing. The standalone evaluation of NMR data by a T2 cutoff approach and dual wait-time (DTW) data processing reduces the average porosity mismatch but results in zones of under- and overcorrection of the gas effect. Combining NMR DTW data with density improves the results and reduces the average porosity mismatch to less than 2 p.u. Compositional information from the mud gas data validates an inferred trend of fluid property variation in gas and oil zones across the reservoir and is used to derive a continuous HI log for further improving porosity and gas saturation estimation. Complementary to the results of the integrated data evaluation, we show independent results from mud gas data evaluation using a recently implemented method that provides independent porosity, permeability, and saturation indexes. Adverse conditions for the different approaches, including invasion, variations in mineralogy, and the limited vertical resolution of mud gas data, are discussed. Finally, the benefits and potential of combining NMR, mud gas, and triple-combo data are summarized.

Integrated Reservoir Characterization Using Unsupervised Learning on Nuclear Magnetic Resonance (NMR) T1-T2 Logs

A novel interpretation workflow was developed using an automated unsupervised learning algorithm on nuclear magnetic resonance (NMR) T1-T2 log data to quantify fluid-filled porosity and saturation, producible oil volumes, and to characterize matrix pore sizes and formation wettability. Core porosity and saturation measurements, scanning electron microscope images (SEM), Rock-Eval pyrolysis, wettability measurements, and mercury injection capillary pressure (MICP) tests are compared with the NMR interpretation for calibration and validation. Understanding in-situ fluid types and volumetrics is key for reservoir characterization. The traditional static formation evaluation model based on triple-combo logs (density, neutron, resistivity, and gamma ray) has been widely used to characterize formations to provide cost-effective answers of lithology, total porosity, and water saturation. Nevertheless, the dynamic result from production often shows quite a different water cut than total water saturation because the static model cannot distinguish immobile hydrocarbons from producible oil. NMR log data show unique signatures of formation fluids, such as gas, immobile hydrocarbon, producible oil, T1-T2 immobile, and free water. The NMR data also provide a method to interpret fluid and matrix properties, including fluid viscosity, pore geometry, and fluid-pore interaction. However, due to the downhole environment and the resolution limitation of the logging tool, the signatures of the fluids are not always well separated. It is challenging to visually separate the signal contributions of different formation fluids on T1-T2 maps. An automated unsupervised learning algorithm based on non-negative matrix factorization (NMF) and hierarchical clustering (Venkataramanan et al., 2018) is implemented in the new workflow to separate T1-T2 signatures of different pore fluids, enabling fluid typing and providing quantitative fluid-filled porosities and associated saturations. T1-T2 signatures of separated fluids are used to characterize fluid mobility, pore sizes, and formation wettability. The new approach is successfully applied to multiple wells for a field case study to characterize the saturation and producibility of hydrocarbon and water, which routine petrophysical models are unable to distinguish. Results are corroborated with dynamic production data showing high free water and high residual oil. This is also validated by routine and special core analyses. Integration of NMR, MICP, and SEM gives pore-body and pore-throat-size distributions with body-to-throat ratio (BTR), increasing the precision of estimated formation permeability. A high T1/T2 ratio of the oil suggests that the formation is partially oil-wet. The wettability results from NMR are consistent with the core wettability test and production results. Understanding which portion of a reservoir contains mobile fluids impacts target zone selection and reserves estimation.

An unsupervised clustering method for nuclear magnetic resonance transverse relaxation spectrums based on the Gaussian mixture model and its application

To make the quantitative results of nuclear magnetic resonance (NMR) transverse relaxation (T2) spectrums reflect the type and pore structure of reservoir more directly, an unsupervised clustering method was developed to obtain the quantitative pore structure information from the NMR T2 spectrums based on the Gaussian mixture model (GMM). Firstly, We conducted the principal component analysis on T2 spectrums in order to reduce the dimension data and the dependence of the original variables. Secondly, the dimension-reduced data was fitted using the GMM probability density function, and the model parameters and optimal clustering numbers were obtained according to the expectation-maximization algorithm and the change of the Akaike information criterion. Finally, the T2 spectrum features and pore structure types of different clustering groups were analyzed and compared with T2 geometric mean and T2 arithmetic mean. The effectiveness of the algorithm has been verified by numerical simulation and field NMR logging data. The research shows that the clustering results based on GMM method have good correlations with the shape and distribution of the T2 spectrum, pore structure, and petroleum productivity, providing a new means for quantitative identification of pore structure, reservoir grading, and oil and gas productivity evaluation.

A novel method for NMR data denoising based on discrete cosine transform and variable length windows

Raw nuclear magnetic resonance (NMR) data detected exhibits a low signal-to-noise ratio (SNR). The inversion results suffer from the low SNR of echo data. It is therefore essential to denoise the data before NMR data inversion, to improve the accuracy of inverted NMR spectrum. In this paper, a denoising method was firstly proposed based on discrete cosine transform (DCT), whereby four window schemes were designed into one window, multiple non-overlapping windows of equal length, multiple overlapping windows of equal length, and variable length windows. The effectiveness of four schemes was verified and compared by numerical simulations, and it indicates that Denoising Fig. 5 with variable length windows is the optimum. Core experimental data and NMR logging data were used to verify the practicality of Denoising Fig. 5. The results indicated that Denoising Fig. 5 can be effectively used to reduce the noise of actual NMR data, and the processed NMR spectrum with Denoising Fig. 5 is more reliable. • Discrete Cosine Transform was first applied to the noise reduction of NMR echo data. • Variable Length Windows scheme can achieve the best denoising effect. • The proposed method can effectively improve the application effect of NMR logging.

Pore-Structure Characterization of a Complex Carbonate Reservoir in South Iraq Using Advanced Interpretation of NMR Logs

The Rumaila Field is in southeast Iraq and contains multiple reservoir intervals, including the Upper Cretaceous Mishrif carbonate reservoir, one of the major reservoirs in the world, that has been producing for more than 50 years. One of the key challenges in the Mishrif is to characterize the pore-structure distinction between primary and secondary porosity. The secondary porosity in the form of large pores, if present, dominates the petrophysical properties, especially permeability. Advanced logs, e.g., nuclear magnetic resonance (NMR) and image logs, can be used to understand the variations in pore structure, both qualitatively and quantitatively. In this paper, we focused primarily on four new wells with very comprehensive logging and coring programs. NMR logs were acquired using different tools and pulse sequences. This resulted in uncertainty in porosity and T2 distributions and, consequently, complications in the NMR interpretation. We observed two key issues: porosity deficit due to lack of polarization and T2 distribution truncation due to the low number of echoes. We used a single pore model to reproduce the NMR response in different pore sizes and fluid types for different pulse sequences. The results showed that the NMR response, especially in water-filled (water-based-mud filtrate) large pores, is sensitive to polarization time, echo spacing, and tool gradient strength. NMR log data confirmed the modeling results. We recommended an optimum pulse sequence and tool characteristics to fully capture the heterogeneous rock and fluid system in this carbonate reservoir. NMR logs, when available, were the primary tools to identify the large pores. We present a consistent workflow for NMR log analysis that was developed to identify and quantify large pores and extended to all wells in the field. We used advanced NMR interpretation techniques, e.g., factor analysis (NMR FA) (Jain et al., 2013), in a series of oil wells drilled with water-based mud. Using factor analysis, we identified a cutoff value of 847 ms for large pore volumes. In this manuscript, we also present an integration of laboratory measurements, e.g., NMR, mercury intrusion capillary pressure (MICP) data, whole-core CT scanning, and thin-section analysis, in our interpretation workflow. We also compared the large pore volume from image logs with NMR logs and other laboratory data and observed very consistent results. All the available information was integrated to build an “NMR-based” petrophysical model for porosity, rock type, permeability, and saturation determination. The NMR-based model was very comparable with the classic flow zone indicator (FZI) rock typing. The results of this study were used to modify the NMR acquisition program in the field and to build a petrophysical model based on only NMR and image log measurements for carbonate reservoirs. In this paper, we will discuss NMR modeling and corresponding log data from various wells to confirm the results. Furthermore, we will present a novel interpretation workflow integrating laboratory measurements and log data, which led to the modification of the NMR acquisition program in the field and the creation of a data-driven petrophysical model based on only NMR and image log measurements for carbonate reservoirs.

Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio

While the well log-based lithology classification has been extensively utilized in reservoir characterization, the classification of carbonate sub-facies remains challenging due to the subtle nuances in conventional well-logs. The nuclear magnetic resonance (NMR) log provides extra information of pore size and pore geometry features, improving differentiating carbonate sub-facies. Here we explore the feasibility of using the ratio between NMR longitudinal relaxation time and transverse relaxation time as a potential lithology indicator to determine carbonate sub-facies. We analyzed a series of logging data and corresponding core samples of Arbuckle Group carbonate containing mudstone, packstone, grainstone, incipient breccia, and breccia in northern Kansas for the characteristics of longitudinal relaxation times, transverse relaxation times, and longitudinal over transverse relaxation time ratios. The results show that mudstone, packstone, and grainstone exhibit high, intermediate, and low longitudinal over transverse relaxation time ratios, respectively, while incipient breccia and breccia have a wide range of longitudinal over transverse relaxation time ratios. Furthermore, we evaluated the potential of using longitudinal over transverse relaxation time ratios to classify carbonate sub-facies using multivariate analysis. By adding longitudinal over transverse relaxation time ratios to neutron porosity, total gamma-ray, and conductivity logs as inputs of automated facies classification, the prediction error decreased, especially for incipient breccia. On the contrary, when photoelectric log and computed gamma-ray are also available, adding longitudinal over transverse relaxation time ratios does not improve the accuracy of sub-facies classification. Our results suggest that longitudinal over transverse relaxation time ratio is an independent lithology indicator. However, it cannot replace other logs like gamma-ray and photoelectric logs in classifying carbonate sub-facies. Our study provided valuable evidence and credible elucidation of the importance and physicochemical mechanism of longitudinal over transverse relaxation time ratios, which is essential for deciphering NMR logging data in carbonate reservoirs. Cited as : Zhang, F., Zhang, C. Evaluating the potential of carbonate sub-facies classification using NMR longitudinal over transverse relaxation time ratio. Advances in Geo-Energy Research, 2021, 5(1): 87-103, doi: 10.46690/ager.2021.01.09

Reservoir evaluation method for complex resistivity using the borehole–surface electromagnetic method: A case study of an igneous reservoir in the K exploration area, China

Electrical well-logging techniques, combined with electrical conductivity models, have been widely used to estimate oil saturation levels in hydrocarbon reservoirs. For complicated and heterogeneous petroleum reservoirs, however, evaluation results are inevitably limited by well locations. Recent studies have indicated that the borehole–surface electromagnetic method (BSEM) can detect electrical characteristics distal from a well, and it can be applied to delineate the boundaries of complicated reservoirs. Therefore, in the study described here, a new method to estimate reservoir oil saturation, based on the BSEM, has been developed. In this work, we deployed the BSEM in a designated study area, obtaining complex resistivity and polarizability data, through the constrained inversion of the re-weighted regularized conjugate gradient method. Based on the petrophysical analyses, we developed a new saturation evaluation model, composed of a parallel circuit connection between formation water and the polarization induced by the porous medium containing clay minerals. The effectiveness of the new model was verified with a synthetic dataset generated using Archie's law and the improved Dias model. We found that the target reservoir oil saturation results calculated using the new oil saturation model were in good agreement with applicable nuclear magnetic resonance log data, thus verifying the validity of this new BSEM evaluation technique.