Nuclear Magnetic Resonance Logging Research Articles

Evaluating the Zubair reservoir (Main Pay) in selected wells of the Rumaila Oilfield, southern Iraq, using nuclear magnetic resonance logging and petrophysical analysis data

Evaluating the Zubair reservoir (Main Pay) in selected wells of the Rumaila Oilfield, southern Iraq, using nuclear magnetic resonance logging and petrophysical analysis data

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.

Residual oil saturation in Pre-salt Brazilian carbonates: A multi-approach core-to-log case study

Accurate residual oil saturation (Sor) measurement is paramount for successful reservoir management and has significant economic implications. Considerable uncertainty surrounds its estimation due to inaccurate direct measurements and non-representative lab results. This case study combines well logging, petrography, RCAL, and SCAL to estimate and understand Sor in Pre-salt reservoirs in Santos Basin, Brazil. The results indicate that the distribution of Sor is strongly influenced by lithology. In-situ facies have higher Sor values than reworked facies due to their greater textural and porous complexity. It has been observed that laboratory experiments and well logging results show a correlation between irreducible water saturation, porosity, and permeability with Sor, thus validating the upscaling of laboratory data. The dielectric tool provides the most accurate results in Sor logging evaluation. This is due to its shallow depth of investigation, high sampling rate, and the elimination of uncertainty associated with electrical parameters and salinity used in the Archie equation. Nuclear magnetic resonance (NMR) logging can qualitatively identify regions with higher remaining oil, which can be correlated with areas of high Sor. Still, it cannot quantitatively directly assess residual saturation by the conventional cutoff approach. Conversely, machine learning results indicate that NMR T2 times can be used to predict Sor values obtained by dielectric logging.

High‐Resolution Direct Push NMR Tools for Groundwater Investigations

AbstractA small‐diameter nuclear magnetic resonance (NMR) technology optimized for use with direct push (DP) and cone penetrometer test (CPT) drilling has been developed. The DP NMR tool can be deployed through 2.25‐in. diameter DP and CPT drilling rods allowing high‐resolution NMR logging measurements to be acquired during retraction of drill rods. DP NMR technology runs from a person‐portable battery‐powered control unit and provides significantly higher resolution in both the spatial (vertical) dimension and in the time domain of the NMR measurement than previously available NMR technology. In this study, we summarize the development of two different DP NMR tools and demonstrate their application at different sites within the United States. We believe that this technology can provide a leap forward in adoption of NMR technology for high‐resolution hydrological and geophysical investigations in groundwater resources and environmental remediation applications.

A Method to Identify Pore Fluids in Heterogeneous Conglomerate Reservoirs Using a Nuclear Magnetic Resonance Log with Oil-Based Mud Invasion

Summary Nuclear magnetic resonance (NMR) logging has been extensively utilized for identifying pore fluids in recent years. However, after oil-based mud (OBM) invasion, OBM filtrate partially takes the place of the original fluid in the reservoir and the morphology of the NMR T2 (transverse relaxation time) spectrum changes. OBM invasion makes it difficult to identify the formation fluid using routine NMR fluid identification methods. In this study, we took heterogeneous conglomerate formation in the northwestern Junggar Basin as a case study and carried out core experiments under four conditions (viz., water-saturated, OBM displacing water, oil-saturated, and OBM displacing oil) and simulated the states of OBM invasion into water and hydrocarbon-bearing formation. Comparative analysis finds that when the OBM invades the water layer, the movable peak of the T2 spectrum primarily reflects the bulk relaxation characteristics of OBM filtrate, whereas when the OBM invades into the oil layer, the T2 spectrum may exhibit a three-peak distribution, where the first peak mainly indicates irreducible water, the second peak reflects OBM filtrate, and the third peak primarily reflects nondisplaced oil. To facilitate fluid identification, two T2 cutoffs are adopted to divide the T2 spectrum into three segments, and combined with the T2 geometric average, a fluid identification factor (ifluid) is proposed. Finally, identification criteria for reservoir type are established on the NMR logging and drillstem test data. The field application verifies the reliability of the proposed methods. These methods realize the identification of oil layers under OBM drilling and guide the subsequent production and development of oil reservoirs.

Deterministic and Stochastic Modeling in Prediction of Petrophysical Properties of an Albian Carbonate Reservoir in the Campos Basin (Southeastern Brazil)

Abstract —Permeability is one of the most significant and challenging parameters to estimate when characterizing an oil reservoir. Several empirical methods with geophysical borehole logs have been employed to estimate it indirectly. They include the Timur model, which uses conventional logs, and the Timur–Coates model, which uses the nuclear magnetic resonance log. The first goal of this study was to evaluate porosity, because it directly impacts permeability estimates. Deterministic and stochastic inversions were then carried out, as the main objective of this work was to estimate the permeability in a carbonate reservoir of the Campos Basin, Southeastern Brazil. The ridge regression scheme was used to invert the Timur and Timur–Coates equations deterministically. The stochastic inversion was later solved using fuzzy logic as the forward problem, and the Monte Carlo method was utilized to assess uncertainty. The goodness of fit for the estimations was all checked with porosity and permeability laboratory data using the Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), and Willmott’s agreement index (d). The results for the Timur model were R = 0.41; RMSE = 333.28; MAE = 95.56; and d = 0.55. These values were worse for the Timur–Coates model, with R = 0.39; RMSE = 355.28; MAE = 79.35; and d = 0.51. The Timur model with flow zones had R = 0.55; RMSE = 210.88; MAE = 116.66; and d = 0.84, which outperformed the other two models. The deterministic inversion showed, thus, little ability to adapt to the significant variations of the permeability values along the well, as can be seen from comparing these three approaches. However, the stochastic inversion using three bins had R = 0.35; RMSE = 320.27; MAE = 190.93; and d = 0.73, looking worse than the deterministic inversion. In the meantime, the stochastic inversion with six bins successfully adjusted the set of laboratory observations, because it provides R = 0.87; RMSE = 156.81; MAE = 74.60; and d = 0.92. This way, the last approach has proven it can produce a reliable solution with consistent parameters and an accurate permeability estimation.

Permeability Prediction of Carbonate Reservoir Based on Nuclear Magnetic Resonance (NMR) Logging and Machine Learning

Reservoir permeability is an important parameter for reservoir characterization and the estimation of current and future production from hydrocarbon reservoirs. Logging data is an important means of evaluating the continuous permeability curve of the whole well section. Nuclear magnetic resonance logging measurement results are less affected by lithology and have obvious advantages in interpreting permeability. The Coates model, SDR model, and other complex mathematical equations used in NMR logging may achieve a precise approximation of the permeability values. However, the empirical parameters in those models often need to be determined according to the nuclear magnetic resonance experiment, which is time-consuming and expensive. Machine learning, as an efficient data mining method, has been increasingly applied to logging interpretation. XGBoost algorithm is applied to the permeability interpretation of carbonate reservoirs. Based on the actual logging interpretation data, with the proportion of different pore components and the logarithmic mean value of T2 in the NMR logging interpretation results as the input variables, a regression prediction model is established through XGBoost algorithm to predict the permeability curve, and the optimization of various parameters in XGBoost algorithm is discussed. The determination coefficient is utilized to check the overall fitting between measured permeability versus predicted ones. It is found that XGBoost algorithm achieved overall better performance than the traditional models.

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.

Integration of electromagnetic, resistivity-based and production logging data for validating lithofacies and permeability predictive models with tree ensemble algorithms in heterogeneous carbonate reservoirs

This study develops an innovative workflow to identify discrete lithofacies distributions with respect to the well-log records exploiting two tree-based ensemble learning algorithms: extreme gradient boosting (XGBoost) and adaptive boosting (AdaBoost). In the next step, the predicted discrete lithofacies distribution is further assessed with well-log data using an XGBoost regression to predict reservoir permeability. The input well-logging records are gamma ray, neutron porosity, bulk density, compressional slowness, and deep and shallow resistivity. These data originate from a carbonate reservoir in the Mishrif Basin of southern Iraq's oilfield. To achieve a solid prediction of lithofacies permeability, random subsampling cross-validation was applied to the original dataset to formulate two subsets: training for model tuning and testing for the prediction of subsets that are not observed during the model training. The values for the total correct percentage (TCP) of lithofacies predictions for the entire dataset and testing subset were 98 and 93% using the XGBoost algorithm, and 97 and 89% using the AdaBoost classifier, respectively. The XGBoost predictive models led in attaining the least uncertain lithofacies and permeability records for the cored data. For further validation, the predicted lithofacies and reservoir permeability were then compared with porosity–permeability values derived from the nuclear magnetic resonance (NMR) log, the secondary porosity of the full-bore micro imager (FMI) and the production contribution from the production–logging tool (PLT). Therefore, it is believed that the XGBoost model is capable of making accurate predictions of lithofacies and permeability for the same well's non-cored intervals and other non-cored wells in the investigated reservoir.

Research on porosity characterization methods of shale oil reservoirs in Lianggaoshan Formation, Sichuan Basin

Shale oil, an important component of unconventional oil and gas resources, mainly exists in the storage spaces such as shale pores, microfractures, etc. Porosity is commonly used to quantitatively describe the storage space of shale oil and is a key parameter in reservoir evaluation. However, there are significant differences in the results by existing experimental methods for porosity measurement, and moreover, it is difficult to compare the porosity obtained by the experimental measurement method with the logging calculation method. It is urgent to explore reasons for the differences in porosity measurement between various porosity experiments and logging calculations of the shale oil reservoir, and propose an effective method for shale oil reservoir to characterize porosity. In this research, core samples of shale oil reservoirs from the Lianggaoshan Formation of the Sichuan Basin were selected to measure the porosity by means of experimental methods including helium gas charging, saturation liquid method, nuclear magnetic resonance (NMR), etc. Meanwhile, porosity was calculated using the combination method of lithology scanning (LS) logging and conventional logging as well as the NMR logging method. Subsequently, porosity experimental results and logging calculation results were compared to clarify the applicability of various porosity characterization methods. The research results indicate that: 1) The porosity measurement results by the saturation liquid method and the NMR experimental method are close, both greater than that using the helium gas charging method; 2) The hydrogen signal of the dry-state sample is significant in the NMR experiment, mainly originating from organic matter and clay minerals; 3) The NMR short relaxation component in the water-saturated state primarily reflects the signal of organic matter and clay mineral matrix, while the long relaxation component reflects the pore fluid component; 4) After deducting the NMR signal of the dry-state core, the core NMR porosity measurement results under the water-saturated state agree well with that using the saturation liquid method, which is an indicative of effective reservoir porosity; 5) The NMR logging is limited by its echo spacing and cannot reflect the signal from organic matter and the crystal water in clay minerals at T2 &amp;lt; 0.3 ms. Taken together, the porosity measurement method of subtracting the dry-state NMR signal from the water-saturated state NMR signal is considered effective and can be used to reflect the porosity of shale oil reservoirs in the Lianggaoshan Formation of the Sichuan Basin.

New Insight Into Data Processing of Nuclear Magnetic Resonance Logging for Pore Structure Characterization in Tight Sandstone Reservoirs

New Insight Into Data Processing of Nuclear Magnetic Resonance Logging for Pore Structure Characterization in Tight Sandstone Reservoirs

Evaluation of glutenite reservoir using nuclear magnetic resonance T2 spectrum of oil-based drilling cuttings

Cores and cuttings are the only materials that directly show characteristics of glutenite reservoirs, and most wells have few or no cores. This raises the question of whether cuttings can be used to evaluate glutenite reservoir. In this study, six key issues related to the application of cuttings in evaluating reservoir firstly were analyzed. Then glutenite reservoir of Baikouquan Formation in Jungar Basin was taken as the research object, and experiments of cuttings and cores were carried out. Repeated experiments confirmed the stability of cuttings performance. T2 spectrum of glutenite cuttings after treatment exhibits a single peak with a relatively short T2 time, which indicates pore structure and sorting of rock change due to mechanical effects and gravity. By comparing T2 spectra of cores and artificially crushed debris, it can be found that sandstone cuttings are less affected by mechanical effects, while for glutenite, macropores decrease a lot, mesopores increase, and micropores have few changes. Afterwards, a method of cuttings depth correction is proposed based on comparing the morphology of the spectra corresponding to micropores in T2 spectra of cuttings and nuclear magnetic resonance (NMR) logging. Finally, irreducible water saturation and porosity are calculated and corrected using T2 spectrum of cuttings, and results are in agreement with those obtained from logging data, which confirm the feasibility of evaluation method using cuttings.

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.

Classification of Reservoirs and Fluids in Tight Sandstone Based on the NMR Logging Technique-A Case Study of the Upper Paleozoic in the Ordos Basin.

The Upper Paleozoic strata in the Ordos Basin are dominated by tight sandstone gas reservoirs characterized by strong heterogeneity and widely distributed aquifers. Laterally, there is no evidently continuous distribution of water and gas, and longitudinally, the gas and aquifers are mostly in isolated intersections without uniform gas-water contact, rendering difficulties for effectively identifying the reservoir and fluid properties by using traditional mud logging technology. To solve these problems mentioned, the reservoirs were classified by integrating the T2 spectra of cores obtained through nuclear magnetic resonance (NMR) logging, regional geological characteristics, and gas test results. It is indicated in the result that the "three-division" method can reflect the type of storage space, and the structural movement and sedimentary thickness in the basin are primary factors for the formation of reservoirs of various types. The "nine-division" method is closely related to characteristics of "initial tightening and subsequent accumulation" of the basin and the gas test results, which can give insights into natural gas charging status in the tight sandstone samples, thereby effectively identifying fluid properties.

Permeability evaluation based on Nuclear Magnetic Resonance analysis for gas hydrate reservoir

Permeability (or hydraulic conductivity) is one of the most important parameters for analyzing hydraulic behavior of underground. For example, in the geotechnical engineering, permeability is necessary to calculate water inflow and draw down of water level during excavation, to predict the deformation time due to the consolidation, and to evaluate the barrier of the ground for radioactive waste disposal. In the petroleum industry, permeability is considered an essential parameter which is directly linked to the productivity of oil and natural gas. Therefore, predicting permeability using wireline logging or logging while drilling (LWD) is required during the assessment of the reservoir. Nuclear Magnetic Resonance (NMR) logging is performed at this point. Low-field NMR measurements are sensitive to the hydrogen proton, 1H, in liquid water but not to the 1 H bond in solid form, making it possible to obtain the bulk water volume. In addition, the hydrogen proton NMR- T2 (transverse or spin-spin) relaxation time distribution is used to infer pore-size information and to estimate permeability [1, 2]. The major methods used for estimating permeability, k, are the Schlumberger Doll Research (SDR) model and Timur-Coates (TC) model [3, 4]:&#x0D; Where fNMR is the NMR-estimated (effective) porosity. SDR model uses the averaged value of the T2 distribution plotted on log-scale of time and matches it to the experimental reference permeability with the fitting parameter a [mD/(ms)2]. The TC model seeks to differentiate between the fraction of free (mobile) fluid volume (FFV) and bound fluid volume (BFV) and estimates permeability with FFV/BFV ratio and the material constant c [m2/s2]. The permeability in the research and development of methane hydrate, which is expected to be a next-generation resource, has been estimated using above methods. Methane hydrate is an ice-like solid crystalline structure of water and methane molecules. Because hydrate is solid, NMR is not sensitive, the initial (effective) permeability can be estimated by NMR analyzer. On the other hand, A strong disconnect (up to 2 orders of magnitude) between NMR-based estimates of permeability with those determined from a laboratory study of pressure cores from the same reservoir has further cast uncertainty in the application of borehole NMR data in hydrate evaluation [5, 6]. Recently, authors provided comparisons between NMR-derived values and direct fluid flow estimates of permeability in naturally occurring hydrate-bearing sediment [7, 8]. On the basis of the NMR measurement for hydrate-bearing pressure core sediments, which were recovered from the Gulf of Mexico and Alaska North Slope, the following new hydraulic radius model was proposed [7].&#x0D; This equation estimates the specific surface based on the NMR signal and has been shown to successfully estimate the initial permeability of a hydrate reservoir by expressing a hydraulic radius model based on Kozeny's equation [9]. However, the proposed equation is not universal and has been found to overestimate for clayey and silty hydrate free sediment. This study summarizes the methods and parameters used to estimate permeability using NMR signals from hydrate-bearing sediment obtained from logging and laboratory tests for the Gulf of Mexico and Alaska North Slope. In addition, we reports parameters for predicting model for muddy sediments which was recovered from overburden of hydrate-reservoir in the Hyuganada sea in 2022 Japan [10, 11]. When using the hydraulic radius model to predict permeability of hydrate-bearing sediment, reasonable estimates can be obtained by setting the shape factor for grains Cs = 1/2 (for a circular grain) and the shape factor for the flow path cn = 4 (for the cylinder network), respectively. Although, for hydrate free muddy sediment, Cs = 1/3 for parallel plate grain shape and cn = 2 for the fracture network model matched its permeability. Furthermore, the conversion parameter from T2 relaxation time to the pore size for hydrate free muddy sediments r2 = 0.009 (mm/s) was confirmed based on the result of mercury injection capillary pressure analysis, which is lower than r2 = 0.0325 (mm/s) of hydrate-bearing sediments.

Semi-Automated Rock Classification for Permeability Estimation Using High-Resolution Computed Tomography Scan Images, Core Photos, and Well Logs

Summary Reliable estimation of petrophysical properties can be challenging, especially in geological formations with rapid variation in the spatial distribution of rock components. The spatial distribution of rock components, also known as rock fabric, is often not captured by conventional well logs that are typically used for the estimation of petrophysical properties. The aforementioned challenge is rooted in the limited vertical resolution of conventional well logging tools. Alternatively, specialized tools and techniques, such as nuclear magnetic resonance (NMR) and computed tomography (CT) scan images, can provide information on the variation in the rock fabric of geological formations. The objectives of this paper are (a) to use 2D CT scan images and core photos, conventional well logs, NMR logs, and core-measured properties for semi-automated rock classification, (b) to develop class-based rock physics models for enhanced petrophysical properties estimation, and (c) to provide a method to expedite the detection of quantitative image-based rock classes in cored wells. First, we conducted conventional formation evaluation (CFE) for the initial assessment of petrophysical properties. Then, we implemented three different rock classification techniques for class-based estimation of petrophysical properties. The first of the rock classification techniques uses routine core analysis (RCA) data to define hydraulic units. The second rock classification technique uses NMR data to characterize the changes in pore-size distribution of the evaluated formation. The last rock classification technique integrates quantitative image-based features from CT scan images and core photos with NMR data. Finally, the obtained rock classes from the abovementioned rock classification techniques are used to derive class-based permeability models. We applied the proposed workflow to a pilot well drilled in a saline water aquifer formation that will be used for CO2 injection and storage in the Northern Lights carbon capture and storage (CCS) project. The extracted image-based rock fabric features were in agreement with the visual aspect of the evaluated depth intervals. The detected rock classes captured the fluid-flow behavior using a permeability-based cost function in two of the implemented rock classification techniques, the variation in petrophysical and compositional properties through well logs, and quantitative rock fabric of the evaluated depth interval through the core image data. Finally, the use of class-based rock physics models improved permeability estimates, decreasing the mean relative error by up to 37% compared with formation-based permeability estimates from a conventional method (formation-based porosity-permeability correlations). One of the key contributions of the proposed workflow is that it integrates conventional well logs, core-measured properties, NMR logs, and high-resolution image data. As a result, the obtained integrated rock classes capture key petrophysical and geological parameters of the evaluated depth intervals that are typically not included in rock classification efforts. The obtained integrated rock classes can potentially improve the development of accurate geological models, which are used in simulation efforts as a screening tool for the selection of geological formations for CO2 storage as well as for storage capacity, selection of CO2 injection intervals, and containment forecasting.

Characterizing facies and porosity-permeability heterogeneity in a geothermal carbonate reservoir with the use of NMR-wireline logging data

If they are to be economically and technically sustainable, geothermal projects require the production of hot fluid at high flow rates over a 30-year thermal lifetime. The combined use of multiple logging tools in making petrophysical assessments of reservoir quality helps to optimize drilling in areas of high geothermal potential. The present paper focuses on four geothermal wells intercepting Middle Jurassic (Dogger) carbonate rocks of the Paris Basin where for the first time Nuclear Magnetic Resonance (NMR) log data have been successfully used to investigate reservoir porosity-permeability heterogeneities. A total of ten facies have been identified from recovered cuttings and cores and grouped into four facies associations along a schematic carbonate ramp profile. From the wells studied, four reservoir units exhibiting porosities exceeding 15% and permeabilities of up to 1 Darcy (D) were traced in the Calcaires de Comblanchien, the Oolithe Blanche and the Calcaires marneux à Pholadomyes formations. The well's sub-horizontal trajectory and well-logs correlations between two wells, made it possible a priori to identify porous and permeable layers extending over at least 500 m and up to 2000 m within the Bathonian reservoir, providing useful pointers for further 3D reservoir geomodeling. Permeabilities derived from well testing proved to be overestimated when compared with NMR-derived permeabilities, illustrating the upscaling problem that is invariably a challenge in carbonate systems. NMR can be combined with production logging tool (PLT), that provides data on the distribution and thickness of productive layers, to give indications for example about continuous permeability record along the geothermal wells or about the proportion of micro and macroporosities in rocks. Based on the geological classification derived from examination of cores and cuttings, four rock-types (including mean T2 pore-size distributions) have been identified and attributed to a given sedimentary facies and depositional environment by extending a clustering method to NMR log distributions from wells.

Logging evaluation of pore structure and reservoir quality in shale oil reservoir: The Fengcheng Formation in Mahu Sag, Junggar Basin, China

The Permian Fengcheng Formation in the Mahu Sag of the Junggar Basin is characterized by frequent changes of mineral compositions and lamina structure. The alkali lacustrine shale reservoir shows strong heterogeneity and anisotropy, it is particularly important to clarify the relationship between pore structure and reservoir quality. The pore characteristics were investigated by using thin sections, Scanning Electron Microscope (SEM), and Nuclear Magnetic Resonance (NMR) analyses. The pore types are mainly composed of inorganic pores, organic pores, and microfractures. The storage space is dominated by inorganic pores and microfractures. The pore structure was divided into four types by NMR parameters including T2 spectral morphology, T2gm, total porosity, and movable fluid porosity. Combined with core NMR experiments and two-dimensional (2D) NMR logs, 21 ms was adopted as the T2cutoff value to represent the demarcation between movable and bound fluids. From Type I to Type IV, the energy clusters reflect the movable fluids including movable oil and movable water gradually decreasing in the upper right corner. The energy clusters reflect the bound fluids including clay-bound water and bitumen gradually increasing in the lower left corner in the T1-T2 map. The results show that the heterogeneity of mineral compositions and lamina structures are the key factors directly leading to the complexity and distribution of pore structure. Quartz with strong compaction resistance helps keep a large number of primary interparticle pores, and feldspar dissolution pores caused by organic acids are present. Dolomite intercrystalline pores and dissolution pores provide a large amount of storage space for the shale, while the cementation of calcite reduces pore spaces. The increase of felsic mineral content is conducive to improving the pore structure and oil-bearing potential of oil shale reservoir, while clay minerals and calcite are contrary. There are abundant primary interparticle pores and dissolution pores in the layered siliceous (felsic) shales, and they consist of the upper sweet spot in the Fengcheng Formation. The longitudinal time (T1) and transverse time (T2) spectra are bimodal with high total porosity and movable oil content, reflecting high reservoir quality and oil-bearing property. Laminated dolomite shales contain dolomite intercrystalline pores and dissolution pores, and they consist of the lower sweet spot in the Fengcheng Formation. The results above provide a new perspective on the evaluation of pore structure and reservoir quality of shales in the context of an alkaline lake.

Formation Characterization and Type Prediction Based on Geophysical Well Log Data in Horizontal Well: A Case Study of Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin

The role of the horizontal well in developing unconventional oil and gas reservoirs is particularly significant. Different from vertical wells, horizontal wells are greatly affected by many factors, e.g., well track, surrounding mudstone, resistivity, and pore structure heterogeneity in horizontal and vertical directions. These make it difficult to evaluate reservoir parameters and determine optimized test layers. In order to improve formation evaluation in horizontal wells, it is necessary to carry out the research of analyzing formation anisotropy, predict physical property parameters, and classify formation to determine high-quality intervals. In this study, taking Triassic Chang 8 Formation in Shunning Region, Central Ordos Basin as an example, 40 core samples were drilled and applied for nuclear magnetic resonance (NMR), mercury injection capillary pressure (MICP), and resistivity experiments. The porosity, permeability, resistivity, and pore structure anisotropy are analyzed. Results illustrate that the physical properties and pore structure in horizontal direction are superior to vertical direction. Meanwhile, NMR log loses its role in pore structure characterization in horizontal wells. Afterward, methods of correcting interval transit time (DT) and resistivity anisotropy are raised, and models of predicting formation physical property parameters, such as porosity, permeability, and water saturation, are established. A parameter, named as the formation type indicator (FTI), is proposed to reflect reservoir oil-bearing properties and pore structure. Finally, our target horizontal intervals are classified into four types, and the highest-quality “sweet spot” is determined.

A study on the temperature sensitivity of NMR porosity in porous media based on the intensity of magnetization

The measurement of nuclear magnetic resonance (NMR) porosity is affected by temperature. Without considering the impact of NMR logging tools, this phenomenon is mainly caused by variations in magnetization intensity of the measured system due to temperature fluctuations and difference between the temperature of the porous medium and calibration sample. In this study, the effect of temperature was explained based on the thermodynamic theory, and the rules of NMR porosity responses to temperature changes were identified through core physics experiments. In addition, a method for correcting the influence of temperature on NMR porosity measurement was proposed, and the possible factors that may affect its application were also discussed.