Nuclear Magnetic Resonance Logging Research Articles

Comparison of different inversion methods of D-T2 two-dimensional nuclear magnetic resonance logging and applicability analysis

Comparison of different inversion methods of D-T2 two-dimensional nuclear magnetic resonance logging and applicability analysis

Nuclear Magnetic Resonance (NMR) Outputs Generation for Clastic Rocks Using Multi Regression Analysis, Examples from Offshore Western Australia

A large database of nuclear magnetic resonance (NMR) logging data from clastic rocks of offshore oil and gas fields of Western Australia was used to assess the performance of multi regression analysis (MRA) to calculate NMR log outputs from conventional well logs. This short paper introduces a set of MRA equations for the calculation of the NMR log outputs using conventional well logs as inputs. This study shows that unlike machine learning methods the MRA approach fails to predict most of the NMR log outputs with acceptable accuracy but can provide Coates and SDR permeabilities with R2 of more than 0.75.

Evaluation of slim-hole NMR logging for hydrogeologic insights into dolostone and sandstone aquifers

This study assesses the performance and limitations of slim-hole borehole nuclear magnetic resonance (NMR) technology from a hydrogeologic perspective in fractured, porous rock. NMR logging was carried out in dolomitic and sandstone bedrock boreholes at two research test sites in Ontario, Canada, where aquifer and aquitard units provide a range of clay contents as well as a variety of primary and secondary porosity types (e.g. discrete fractures, reefal structures, vugs and karstic conduits). Results were compared to core measurements, geophysical logs, and hydrogeophysical testing. The vertical response curve of the instrument tested was found to produce 60% of the signal from within a 0.2m span surrounding the measuring point. The repeatability of the total porosity measurements in stationary mode is excellent where the porosity is greater than 0.15. Below that threshold, repeatability is scattered at ±0.05 porosity about the mean, with the variability primarily within the clay- and capillary-bound fractions. The NMR porosity estimates agreed with core measurements to within ±0.04 porosity in both the dolostone and sandstone, but the correlation deteriorates in finely bedded lithologies, and where fracturing is present. Much of the discrepancy is attributed to scaling in a finely layered geologic sequence, as the core samples are much smaller than the entire volume measured with NMR probes. Data collection with the probe in motion (continuous logging) added variability to the response when compared to stationary recordings. Although broadscale trends were comparable, the details and depth-specific insights of the bound fluid fractions varied with logging rates. Overall, NMR provides a robust measurement of the bulk matrix porosity and pore size distribution of lithologies intersected, both of which are critically important parameters in understanding hydrogeologic conditions and contaminant distributions in layered sedimentary rock systems.

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.

Fractal Dimension Calculation Method from NMR Measurement and Its Application

Abstract The pore structure of organic shale with nano-scale pores is important for reservoir evaluation. Fractals shows the self-similarity of shale rocks and can reflect the pore structure. The fractal dimension of organic shale from nuclear magnetic resonance (NMR) measurement was studied and some equations were directly derived based on NMR transverse relaxation time (T2) distribution. In a case study, the fractal dimensions were calculated by the proposed method and numerical simulation, and the formation cementation index in Archie equation for saturation evaluation was calculated from the fractal dimension of NMR logging. The comparison of calculated water saturation and the core experiment result prove that the proposed method is more effective than the previous Archie’s equation with default cement index.

NMR T2 CUT OFF: WHICH ONE IS TO BE USED FOR APPLICATION?

Recent developments in petroleum industry have been witnessing the surge of the use of nuclear magnetic resonance (NMR) log. Despite some remaining problems the NMR technology appears to gain more acceptance as petrophysical tool for evaluating reservoir quality. Comprehensive formation evaluation requires determination of irreducible fluids, movable fluids, and permeability. However, rock heterogeneity introduces complexity in any formation evaluation activities. This can also cause problem for NMR log interpretation. In the presence of clays the most commonly used T2 cut off values, a constant value throughout a formation, seem to eventually yield inaccurate irreducible water saturation estimates, as well as other output such as permeability. This study focuses at finding a solution for finding the best way of choosing the most representative T2 cut off value to be used in NMR log interpretation. This is indeed a common pressing problem for heterogeneous formation rocks such as in the case of Tirrawarra sandstones used in this study. The main part of the study is devoted to comparison between the use of single averaged T2c value and establishment of empirical correlations enabling the provision of T2c for any level of heterogeneity (i.e. various levels of shaliness). The study however surprisingly shows that, in spite of the theoretical soundness of the empirical correlations established, simple averaging of T2c values yielded by a reliable method proves itself adequate. This conclusion therefore helps considerably in reducing complexity in NMR log interpretation.

Fluid substitution in NMR T2 distribution and resistivity independent saturation computation using synthetic capillary pressure data

Petrophysicists and reservoir engineers utilise the capillary pressure and saturation-height function for calculating the water saturation of any reservoir, at a given height above the free water level. The results have a big impact on reserve estimation. The majority of capillary pressure studies are carried out in labs with core data. Cores, on the other hand, are usually altered from their original state before being delivered to laboratories. Moreover, the accuracy of discrete sets of core data in describing entire reservoir parameters, is still up for debate. Prediction of the capillary pressure curve in reservoir condition is an important subject that is challenging to perform. The use of nuclear magnetic resonance (NMR) logs for oil and gas exploration has grown in popularity over the last few decades. However, most of the time the utilization of the data is limited for evaluating porosity-permeability, distributions and computation of irreducible water saturation. After the advent of fluid substitution methods, NMR T2 distributions may now be used to synthesize core equivalent capillary pressure curves. Using fluid substitution workflow, our study introduces a better approach for obtaining capillary pressure curves from the NMR T2 distribution. The available core data has been used to calculate calibration parameters for better saturation height modelling. The workflow introduces a novel approach in resistivity independent saturation computation. In the exploratory wells of our study area, computed water saturation derived from the saturation height function exhibits encouraging agreement with resistivity based water saturation from multi-mineral model. The NMR based saturation height modelling approach has been included in study area for the first time so far.

Evaluation of Carbonate Formation using both Gas While Drilling and Nuclear Magnetic Resonance Log (Case study, An Oilfield Southern Iraq)

Two wells close to each other (Well A and Well B) have been selected from the same oilfield in southern Iraq. Both wells were drilled through the Mishrif Reservoir, however, perforated within different sub-units. It was observed that one of the wells shows obvious flow naturally while the other did not notice any kind of flow.
 The type of hydrocarbon interpretation by Gas while drilling method (GWD), the results confirmed that both of these wells were located in oil-bearing reservoir with comparable gas profiles.
 However, petrophysical properties of the pore distribution and permeability using Neutron Magnetic Resonance (NMR) data in Techlog identified key differences in rock quality between the wells across the perforated intervals.
 Reservoir quality reveal very wide differences in performance in spite of both wells are penetrating the oil bearing layers within Mishrif reservoir.
 Keywords: Carbonate Formation, Gas While Drilling (GWD), NMR Log.

Gas Hydrate Saturation Estimates, Gas Hydrate Occurrence, and Reservoir Characteristics Based on Well Log Data from the Hydrate-01 Stratigraphic Test Well, Alaska North Slope

The Hydrate-01 Stratigraphic Test Well was drilled at the Kuparuk 7-11-12 site on the Alaska North Slope in December 2018. Sonic log data provide compressional (P) and shear (S) slowness from which we determine gas hydrate saturation (Sgh) estimates using effective medium theory. The sonic Sgh estimates compare favorably with Sgh estimated from resistivity and nuclear magnetic resonance (NMR) logs, showing that gas hydrate occupies up to approximately 90% of the pore space in the target reservoir sands. The informally named B1 sand (2294 feet below mean sea level) shows lower VP/VS ratios than the D1 sand (2770 feet below mean sea level), with the lower part of the B1 sand showing lower VP/VS ratios than the upper part of the B1 sand. This corresponds to a stiffer, or more “cemented”, behavior for the lower B1 sand and less cemented behavior for the D1 sand. This trend could be due to differences in the reservoirs themselves or in the gas hydrate morphology or to both factors. We observe that the presence of gas hydrate in the upper B1 sand has greater impact on hydraulic permeability (measurements suggest a greater difference between intrinsic and effective permeability) than in the D1 sand, possibly related to gas hydrate morphology but more likely due simply to higher gas hydrate saturations in the upper B1 sand. Analyses of Sgh relative to porosity, shale fraction, and intrinsic permeability show that reservoir quality (as represented by these three metrics) exerts control on gas hydrate saturation. Grain size and mineralogy data show somewhat smaller grains and better sorting in the D1 reservoir relative to the upper B1 reservoir and smaller grains and greater clay fraction in the lower B1 reservoir relative to the other two reservoir zones. Together, these data suggest that reservoir characteristics play a role in the observed VP/VS patterns, but gas hydrate morphology (possibly varying with saturation) must also be considered.

Quantifying motional dynamics in nuclear magnetic resonance logging

The motional dynamics of nuclear magnetic resonance (NMR) logging tools can significantly influence the measurement performance of such tools. NMR logging is used for geophysical evaluation in geological environments, primarily quantifying formation porosity and fluid volumes, as well as providing a qualitative estimation of permeability. NMR logging tools are conveyed via two main mechanisms; wireline logging and logging while drilling (LWD). We conduct detailed simulations to quantify the impact of tool motion on NMR measurements during logging. This involves conducting electromagnetic simulations which quantify the magnetic fields generated by a logging tool, and subsequently introducing motion profiles within the relevant spin dynamic calculations. This enables tool motional dynamics to be imposed on the signal acquisition. Several movement profiles are considered: linear axial movement to replicate wireline logging tool motion, as well as axial harmonic and lateral harmonic movement to simulate the shocks and vibrations experienced during logging while drilling. Lateral motion is observed to cause a greater degree of signal attenuation relative to axial motion due to the cylindrical shape of the excited volume. The magnitude of motion (e.g. the velocity of linear motion or the amplitude of harmonic motion) is demonstrated to increase the severity of signal attenuation, as expected. However, the frequency of harmonic motion demonstrates a more complex effect on the measured signal. The harmonic interaction between the motion frequency and measurement frequency (determined by the echo spacing) can cause wave interference which results in enhanced or diminished signal attenuation. Finally, we demonstrate that reducing both the magnetic field gradient as well as the echo spacing reduce the degree of signal attenuation observed during measurement. The results presented in this work demonstrate how the optimisation of key design parameters can be used to control the sensitivity of NMR logging tools towards motion.

Determination of pore size distribution in tight gas sandstones based on Bayesian regularization neural network with MICP, NMR and petrophysical logs

Mercury injection capillary pressure (MICP) is one of the most important means for quantitative study of pore structure, however, its application is often limited due to its expensive, destructive and dangerous characteristics. Nuclear magnetic resonance (NMR) then becomes a common alternative by log-linear conversion of transverse relaxation time (T2) spectrum into pseudo-MICP or pore size distribution (PSD) though the accuracy and generality is unconvincing. Petrophysical logs have rich geological-petrophysical information but are seldom used in pore structure research. In this paper, PSD determination of tight gas sandstones based on Bayesian regularization neural network (BRNN) with MICP, NMR and petrophysical logs is studied. A numerical workflow is developed to preprocess MICP and NMR experimental data and determine PSD. Subsequently, two BRNN models are constructed respectively with joint laboratory T2 spectrum and petrophysical logs as main inputs and pore radius and mercury saturation from MICP measurement as training target. It is revealed that, accurate PSD can be determined from prediction results of BRNN models, wherein the NMR-based BRNN can be employed to obtain PSD from more easily accessible NMR measurement and the log-based one is expected to be generalized in the field. Conversion of PSD by fitting pore radius with T2 through their piecewise linear or quintic polynomial relation in log-log coordinate is equivalent to underfitting or overfitting in accuracy of machine learning and is confined to laboratory NMR with joint MICP. Detailed discussion on BRNN modeling, pore radius fitting and the corresponding numerical workflow proposed here may provide valuable insight into petrophysical-log analysis and quantitative evaluation on pore structure of complex reservoirs.

Study on the logging response characteristics and the quantitative identification method of solid bitumen at different thermal evolution stages

Solid bitumen is the intermediate products during the oil and gas generation, and widely exists in petroliferous basins all over the world. Besides damaging the reservoir physical properties, solid bitumen affects the logging parameters and is hard to be identified by the conventional logging method. In this study, two different thermal evolution stages of solid bitumen, separately from the Devonian outcrop profile and the Well ST3 in the northwestern Sichuan Basin, are taken as an example. Through a series of analyses on the porosity, permeability, resistivity, S-wave time difference, P-wave time difference and nuclear magnetic resonance (NMR) before and after bitumen dissolution, the logging response characteristics of solid bitumen at different thermal evolution stages are clarified, and the logging quantitative identification method of solid bitumen is established. The results are as below: (1) The solubility of solid bitumen is linearly correlated with the increment of porosity, permeability, P-wave time difference and S-wave time difference, and exponentially correlated with the decrement of resistivity. (2) Under the equal solid bitumen content and with the increase of thermal evolution, the influence of solid bitumen on porosity and permeability gradually decreases, while the influence on acoustic time difference and resistivity gradually increases. (3) Two kinds of NMR logging interpretation models for bitumen reservoir are established based on the NMR response characteristics of solid bitumen at different thermal evolution stages. The solid bitumen from low thermal evolution stage has a strong response in NMR, while that from high thermal evolution stage is otherwise. The Peak 1 (0.1 ms – 10 ms) of the T2 spectrum can respond to most of the low thermal evolution stage solid bitumen, but just a few of the high thermal evolution stage solid bitumen. (4) The NMR logging quantitative calculation methods on different thermal evolution stage solid bitumen are established respectively to solve the difficult issues of quantitative identification of solid bitumen. All the researches above have important guiding significance for the logging quantitative calculation of solid bitumen and the selection of effective oil/gas test layers.

Assessment of natural gas hydrate reservoirs at Site GMGS3-W19 in the Shenhu area, South China Sea based on various well logs

To obtain the characteristics of the gas hydrate reservoirs at GMGS3-W19, extensive geophysical logging data and cores were analyzed to assess the reservoir properties. Sediment porosities were estimated from density, neutron, and nuclear magnetic resonance (NMR) logs. Both the resistivity and NMR logs were used to calculate gas hydrate saturations, the Simandoux model was employed to eliminate the effects of high clay content determined based on the ECS and core data. The density porosity was closely in agreement with the core-derived porosity, and the neutron porosity was higher while the NMR porosity was lower than the density porosity of sediments without hydrates. The resistivity log has higher vertical resolution than the NMR log and thus is more favorable for assessing gas hydrate saturation with strong heterogeneity. For the gas hydrate reservoirs at GMGS3-W19, the porosity, gas hydrate saturation and free gas saturation was 52.7%, 42.7% and 10%, on average, respectively. The various logs provide different methods for the comprehensive evaluation of hydrate reservoir, which supports the selection of candidate site for gas hydrate production testing.©2022 China Geology Editorial Office.

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.

NMR Logging Application for Complicated Reservoir Formation Evaluation of M Oil Field in Middle East

There are many carbonate oil fields laying-out in and around Middle-East region, which are the hot productive spot with the huge crude oil reserves. However, these reservoirs also face the challenges of complex lithology, diverse pore configuration, and strong formation heterogeneity to affect the field operation performance. Take Iraq oil companies as an example, before joint operations with overseas oil companies from 2010, only the conventional logging technologies were mainly applied on site, but which can not provide enough support for the development of oil field. After 2010, the advanced logging technologies, such as Nuclear Magnetic Resonance (NMR) logging which are superior to conventional logging in some aspects, are introduced in order to support oil fields development effectively, and which actually makes good performance to direct the identification of non-pay zones, hidden effective zones and micro-fracture distributions. Technical analysis is based on M Oil Field in Iraq to share the experiences of NMR logging application from the view of reservoir engineering in the complicated carbonate reservoir.

Spatial assessment of gross vertical reservoir heterogeneity using geostatistics and GIS-based machine-learning classifiers: A case study from the Zubair Formation, Rumaila oil field, southern Iraq

The study of oil-field reservoir heterogeneity is an important task in the oil industry as it affects waterflooding, developing injection production systems, and optimizing hydrocarbon production. In this study, vertical reservoir heterogeneity was quantified using the Lorenz statistical index, empirical Bayesian kriging, and seven machine-learning classifiers (Classification and Regression Trees, Boosted Regression Trees, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, and Support Vector Machine with three different kernels (linear, radial, and polynomial) under the geographic information system platform. The main pay zone of the Zubair Formation in the Rumaila oil field from southern Iraq was used as a case study. The degree of heterogeneity was first quantified using the Lorenz index, and a borehole-heterogeneity inventory location map was prepared according to the determined Lorenz index. Information about five factors influencing the heterogeneity, namely, porosity, permeability, volume of shale, reservoir-unit thickness, and depth to the top of reservoir unit, was collected based on available cores, nuclear magnetic resonance log, gamma-ray logs, and drilling-information logs. Factors from these sources were interpolated to show their spatial distribution using the empirical Bayesian kriging technique. The relationship between the borehole inventory map of vertical heterogeneity and the five factors was examined using the seven machine-learning classifiers. Two statistical-error measures, namely, accuracy and Cohen's kappa, were used to verify the performance of the classifiers in both training and testing stages. Results proved that Random Forest, Support Vector Machine with radial kernel function, and Logistic Regression were the best models. The probabilities of the best performance models were then interpolated and classified into five heterogeneity zones: Very low, low, moderate, high, and very high. The high-very high classes for each of these models approximately occupy 60% of the oil field and are mainly distributed in the middle and north of the field, whereas the other classes encompass about 40% of the field and mostly occur in the south. This distribution of classes is most likely related to the distribution and complexity of former depositional environments. • The spatial vertical heterogeneity of a clastic reservoir unit is quantified in this study. • Five factors including porsoity and permeability are used as influencing factors for modeling heterogeneity. • The high zone of heterogeneity encompasses 60% of the reservoir unit.

Characterization of porous media by nuclear magnetic resonance beyond fast diffusion limit with numerical simulation

Nuclear magnetic resonance logging is an important formation detection technique in the petroleum industry, which is always used to detect formation pore structure and fluid identification. However, conventional nuclear magnetic resonance characterization methods assume that the formation pore medium are in fast diffusion regime. In complex formations, non-fast diffusion regimes (intermediate diffusion and slow diffusion) are also present. In the previous paper [13], a new method for characterizing porous media with non-fast diffusion regime and uniform pore size distribution by T2-T2 pulse sequence is proposed. In this research, the T2-T2 signals of periodic stacked pore models beyond fast diffusion regime are numerically simulated by the random walk algorithm, and the pore size - surface relaxivity maps are respectively inverted, the relationship between pore structure and correlation maps are discussed. The results provide theoretical foundation for characterizing pore structure beyond fast diffusion limit.

Insights into the pore structure and oil mobility in fine-grained sedimentary rocks: The Lucaogou Formation in Jimusar Sag, Junggar Basin, China

Unconventional shale/tight oil resources in fine-grained sedimentary rocks play essential roles in global energy structure. Fine-grained sedimentary rocks are characterized by complex pore assemblage and connectivity. Insights into the links between pore structure and movable oil are important for exploration and production of tight/shale oil or gas in fine-grained sedimentary rocks. A series of analyses including thin sections, mercury injection capillary pressure (MICP), and nuclear magnetic resonance (NMR) were carried out to unravel the linkage between pore structure and oil movability. Various centrifugation speeds were used in the NMR tests to remove the movable oil to obtain the movable oil saturation corresponding to various centrifugation speed. Fractal dimension is calculated from NMR T2 spectrum. Then four types of pore structures were divided according to the parameters derived from the MICP and NMR results, and the characteristics of various pore structure are summarized. From type Ⅰ to type Ⅳ, the threshold pressure, irreducible water saturation (Swi), and the fractal dimension increased while the SHgmax, rapex, T2gm, porosity, permeability, and movable oil saturation decreased. Then the key factors controlling the movable oil saturation were investigated, including permeability, porosity, T2gm, rapex, and mineral composition of the rocks. The movable oil saturation is dominated by porosity, T2gm, fractal dimension, and rapex. Then a quantitative evaluation model of the movable oil saturation was established. In addition, the NMR logs and the derived parameters were utilized to evaluate the reservoir quality and pore structures in a single well. At last the T2 value of 25 ms (the ratio of the T2 components >25 ms to the total T2 components) was used to calculate the movable oil saturation in the upper member of the Lucaogou Formation, and the T2 value of 28 ms was used for the lower member of the Lucaogou Formation in the Jimusar Sag. The results are testified by the high-speed (10000 r/min) centrifugation NMR measurements. The results above help improve understanding of tight/shale oil enrichment and provide a new model for calculating the movable oil saturation in fine-grained sedimentary rocks.

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.

Lithofacies Architecture and Distribution Patterns of Lacustrine Mixed Fine-Grained Rocks —a Case Study of Permian Lucaogou Formation in Jimsar Sag, NW China

As the controlling effect of complex lithofacies of lacustrine mixed fine-grained rocks on the shale oil sweet spot remains unclear, core, outcrop, general logging, nuclear magnetic resonance (NMR) logging, testing, and production data were used to study the types, combination pattern, and genesis of lithofacies architectures of lacustrine mixed fine-grained rocks in the study area by lithofacies hierarchy analysis, X-ray fluorescence (XRF) logging, UAV, and 3D geological modeling. The research shows that: 1) According to lithology and sedimentary structure, the mixed fine-grained rocks can be divided into 13 lithofacies types of different origins in 5 sub-categories and 2 categories. 2) UAV photography was combined with a traditional field survey to characterize the 3D spatial distribution of lithofacies architecture of the Lucaogou Formation on the outcrop, and it is found that the lithofacies architecture patterns of mixed fine-grained rocks include three types: gradual change type, abrupt change type, and special type. The gradual change type with higher sand development degree and symmetrical lithofacies architecture has a high quality reservoir with dissolution pores, and is mixed beach-bar sand in the mixed zone. It is high in development degree and often appears as several similar cycles stacking over each other. The abrupt change type can be subdivided into two sub-types, asymmetric and smaller in reservoir thickness. It is very high in development degree and often comes in several similar cycles. The special type belongs to thick clastic rock relatively independent in the mixed fine-grained rocks with a high development degree of sand. The sand is a higher quality reservoir with properties of tight reservoir. It often appears as stacking of single cycle sand. 3) The different lithofacies architectures in the mixed fine-grained rocks have significant differences in distribution. The gradual change type is mainly composed of mudstone, dolomitic siltstone, and sandy dolomite, dolomitic siltstone, and mudstone, and appears in lenticular shape overlapping with each other on the plane. The abrupt change type is made up of felsic siltstone, dolomitic siltstone, sandy dolomite, and mudstone, and appears as isolated thin layers on the plane. The special type is mainly composed of mudstone and felsic siltstone, and mudstone, and turns up as lenses of different sizes on the plane.