Natural Gamma Research Articles

Logging evaluation of total organic carbon and analysis of influencing factors in source rock of Zhu III sub-basin, Pearl river mouth basin, China

Total organic carbon (TOC) content of source rocks is usually determined by geochemical analysis of samples, but this process takes a long time and costs a lot. Using high-resolution and continuous logging information to evaluate the TOC content can solve the above problems. Previous studies have shown that there are certain differences in the response of logging curves such as spontaneous potential (SP), acoustic interval transit time (AC), natural gamma ray (GR), resistivity (Rt) and density (DEN) to TOC. Based on the response characteristics, this article uses the logging data of nine wells in Wenchang A and Wenchang B depressions to predict TOC by multiple regression method, and proves its accuracy by comparing with the density logging method and ΔlogR method. In addition, the influencing factors of logging evaluation are discussed: ① Type difference of parent rock affects the structural evolution and reservoir conditions of upper strata; ② Different clay mineral compositions lead to the difference in the distribution of argillaceous types and content, thus affecting the logging data. Therefore, clarifying the factors affecting logging evaluation and establishing effective prediction models in different blocks have important guiding significance for oil and gas exploration and development in the Zhu III sub-basin.

Prediction method and distribution characteristics of in situ stress based on borehole deformation—A case study of coal measure stratum in Shizhuang block, Qinshui Basin

A geometric equation of borehole deformation under stress was deduced based on the basic theory of elasticity. Subsequently, we established the quantitative relationship between the in situ stress and geometrical parameters of borehole deformation. Furthermore, we proposed an in situ stress prediction model based on borehole deformation. Additionally, numerical simulations of borehole morphology in different lithologies under in situ stress were conducted to analyze the deformation effect. Logging parameters that are sensitive to the shear wave time difference, such as longitudinal wave time difference, density, and natural gamma radiation, were selected for training using an artificial neural network (ANN) to predict the shear wave time difference. The results demonstrated that 1) combining the theoretical derivation and numerical simulation, the borehole geometry under stress was quasi-elliptic, and 2) compared with the existing shear wave time difference curve, the predicted geometry by the ANN was consistent with the actual geometry. Consequently, compared with the tested data from acoustic emission, the overall error of the in situ stress predicted using the new method was less than 9.2%. Moreover, the accuracy of the coal seam was the highest, wherein the average errors of the maximum and minimum horizontal principal stresses were 2.01 and 2.56%, respectively, which confirms the feasibility of the proposed method.

Identifying the carbonate interlayer using the horn response of electromagnetic wave resistivity logging while drilling combined with drilling and mud-logging data during geo-steering

Abstract The electromagnetic wave propagation resistivity (EWPR) and natural gamma rays (GR) are commonly used in logging while drilling (LWD) to identify the formation interface and for geo-steering. Because the GR logging value of a carbonate reservoir does not change significantly or not change at all, the horn effect of the EWPR is the main basis for identifying the formation interface. However, many factors influence the horn effect, so it is sometimes difficult to determine whether the sharper response of the EWPR is the horn signal or the real value of the thin interlayer. Therefore, based on a review of existing research results, in this paper, the EWR-PHASE4 LWD tool is taken as an example, and using a numerical forward simulation, the influences of different resistivity ratios of the surrounding rock to the target layers, the different transmitter spacings on the EWPR for different angles between the borehole trajectory and formation are investigated. The conditions required to generate horn signals at the formation interfaces and the influencing rules of the main influencing factors are summarized, providing a theoretical basis for correctly determining the formation occurrence. Since the drilling and mud-logging data can be used to determine the formation's properties directly, through application examples, this study provides a reference for identifying the carbonate interlayer and formation occurrence using a combination of EWPR with drilling and mud-logging data, which has a smaller uncertainty than the application of only EWPR.

A Comprehensive Analysis of Health Risk due to Natural Outdoor Gamma Radiationin Southeast Haryana, India

A Comprehensive Analysis of Health Risk due to Natural Outdoor Gamma Radiationin Southeast Haryana, India

Methods for determining the characteristics of weathered granite reservoirs: application to Binxian Uplift, Dongying Sag, Bohai Bay Basin

AbstractWith the discovery of high-productivity oilfields in granite buried hills, it is necessary to systematically investigate the types of granite weathered reservoirs at different depths and their spatial distribution. However, previously subdivided reservoirs have been assumed to exhibit the same vertical zoning in different structural parts, contradicting the fact that the degree of weathering varies with the topography. In addition, comprehensive and quantitative methods for classifying reservoir types are lacking. Taking the Binxian Uplift of the Dongying depression in Bohai Bay Basin as an example, we therefore established a comprehensive identification standard for dividing granite reservoirs using lithology division, logging curve statistics, a dual-medium matrix–fracture model and seismic facies identification. Subsequently, by combining logging and seismic methods, the vertical stacking types and distribution properties of weathered granite reservoirs in various structural positions were analysed. The reservoirs were divided vertically into three zones: regolith, dissolution and fracture. Quantitative logging response standards for the different reservoirs were established using acoustic, density, natural gamma and resistivity logging. In terms of the seismic response, the regolith, dissolution and fracture zone corresponded to high-, medium- and low-amplitude seismic facies, respectively. A dynamic double-layer structure of the reservoir was established, comprising a completely weathered layer and a semi-weathered layer. The reservoir division method proposed in this paper can be used in other areas, and the research results can help promote the exploration of granite buried hill reservoirs.

Distribution of Upper Paleozoic Coal Seams in the Southeastern Ordos Basin

The Ordos Basin contains abundant coal resources in the Upper Paleozoic strata. They are shallowly buried or exposed in the periphery of the basin, forming recoverable coal mines and coalbed methane resources. For the deeply buried coal seams of the basin, however, due to complex changes and difficult mining, less attention was paid before. In recent years, with the exploitation of tight gas related to coal source rocks, a large amount of drilling and seismic data has been accumulated in the southeastern Ordos Basin, which makes it possible to study the distribution change and factors controlling Upper Paleozoic coal seams. Delineation of deep coal development and distribution is an important basis for coal, coalbed methane and related tight gas resources in the Upper Paleozoic. We use core, logging and seismic data to analyze the coal geophysics, thickness variation, paleotopography and sedimentary facies in the southeastern Ordos Basin. The lower part of the Shanxi Formation (Fm.) and the upper part of the Taiyuan Fm. in the southeastern part of the basin are characterized by low natural gamma ray, low density, high resistivity and high sonic logging values. As the coal velocity is very low and alternates with the high-velocity Taiyuan Fm. and Ordovician limestones, the two sets of coals below and above the Taiyuan limestones form an extremely strong-amplitude reflection in the seismic profile. The two sets of coals are cumulatively 1–10 m thick, averaging 2.4 m, and 4 m is the maximum induvial thickness. The individual coal seams thicker than 2 m cover an area of approximately 2500 km2, and the burial depths of the coal seam range from 2500 to 3000 m. The thick coal seams of the Taiyuan Fm. are mainly distributed proximally in the Ordovician paleokarst depression, while the Ordovician paleokarst depression without incised origin was deposited by distal coeval limestone rocks. There is a complementary relationship between the coal seams and the thickened zone of distributary channel sandstone in the Shanxi Fm.

One‐To‐One Coupling Between Southern Ocean Productivity and Antarctica Climate

AbstractThe Southern Ocean is supposed to play a crucial role in influencing atmospheric CO2 concentrations. However, the dynamic relationships among the rate of Southern Ocean upwelling, atmospheric CO2 concentration, and Antarctic climate at millennial timescales remain unclear. Here, we present high‐resolution color reflectance component b* and natural gamma radiation from the southern Scotia Sea sector of the Southern Ocean to reconstruct productivity over the past 160 ka. We find that these two independent productivity proxies, reflecting the signal of nutrient supply in this region, captured millennial‐scale upwelling that covaried in timing with Heinrich Stadials and Antarctic warming events, supporting the bipolar seesaw mechanism. The one‐to‐one coupling of variability between productivity and atmospheric CO2 concentrations reveals that the upwelling is closely linked to atmospheric CO2 and climate change.

Gamma-radiation levels outdoors in Great Britain based on K, Th and U geochemical data

Gamma-rays from naturally occurring radionuclides are a major component of background radiation. They are an important tool for geology and are also important for radiation protection. In this paper we use over a quarter of a million geochemical measurements of concentrations of potassium, thorium and uranium in soils and in stream sediments to estimate outdoor gamma-ray dose rates across Great Britain. The soil concentrations are generally at a depth of 5–20 cm with some at 35–50 cm. Soil measurements will give spatially relatively precise estimates, but as soil data are not available for much of Scotland, stream sediment data are used there. Kriging methods are used to estimate surface concentrations of K, Th and U and dose rates are imputed from these concentrations. Our results are compared with measurement surveys of both outdoor and indoor gamma-ray dose rates. Recently there has been interest in exploring the carcinogenic risks of low dose radiation by investigating associations between childhood cancer rates and doses from natural background gamma radiation. To achieve adequate statistical power, such studies must be so large that it is impractical to assess exposures by direct measurements in the homes of study subjects. Instead the exposures must be modelled. The results presented here will be an important input to such work.

Adaptability analysis and optimization of log suite for uncased wellbores of shale gas reservoirs: A case study of Sichuan Basin, China

Significant progress has been made in the exploration and development of unconventional gas resources in China since the beginning of the 21st century. With a rapid increase in yield, the exploration and development of shale gas have been upgraded to a level of national strategy. Logging is one of the core technologies in gas exploration and development. However, logging technologies face the challenges of complex geological conditions and well casing environment, high temperature, high pressure, and strong heterogeneity. Despite the rich experience accumulated, unified understanding and implementation specifications are yet to be established for shale gas well logging. Given the analysis and comparison of the effectiveness and adaptability of logging technologies at different stages of shale gas exploration and development for the Sichuan Basin, this study optimizes the log suite to meet the demand for high-efficiency exploration and development of shale gas. According to the adaptability analysis of shale gas log suite, the mandatory logging items of exploratory wells should include caliper log (CAL), natural gamma ray spectrometry (NGS) log, spontaneous potential (SP) log, directional survey, borehole compensated sonic log, litho-density log (LDL), compensated neutron log (CNL), dual laterolog-microspherically focused log/dual induction-laterolog log/array induction log, temperature log, elemental capture spectroscopy (ECS) log, formation microimager (FMI) log, and array sonic log. The log suite of appraisal wells is almost the same as that for exploratory wells, excluding the ECS log and micro-resistivity scanning imaging. Meanwhile, for the logging of horizontal/highly-deviated holes, the log suite of vertical wells should be referred to according to specific well conditions.

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

The heterogeneity of lithofacies types, combination modes, and sedimentary model of lacustrine shale restricted by high‐frequency sequence

The second Member of the Funing Formation in the Haian Sag of the Subei Basin is a set of organic‐rich fine‐grained sedimentary rocks, which is a key interval for shale oil exploration. Under the premise of establishing a high‐frequency sequence framework, the fine description of the lithofacies change characteristics of the continental fine‐grained sedimentary rocks is the basis for the prediction of shale oil favourable areas and the optimization of the production horizons. This study is established through wavelet transformation of the natural gamma curve of the high‐frequency sequence framework of the second Member of the Funing Formation, using thin‐section observation, total organic carbon analysis, and X‐ray diffraction experiments to study the lithofacies types and characteristics of fine‐grained sedimentary rocks, and discuss the combination of lithofacies under the high‐frequency sequence framework Mode and influencing factors. The results show that the second Member of Funing Formation is composed of three‐level sequence of low‐level system tract (LST), transgressive system tract (TST), and early high‐level system tract (EHST), corresponding to 6 fourth‐level sequences and 10 fifth‐level sequences. According to the division scheme of “abundance of organic matter + mineral composition + sedimentary structure”, it is found that 12 types of lithofacies were developed in the fine‐grained sedimentary rocks of the second Member of Funing Formation. The lithofacies type is affected by the palaeoenvironment. The organic matter facies are mainly controlled by palaeo‐water depth and palaeo‐redox conditions, the components are mainly controlled by palaeo‐water depth and palaeo‐salinity, and the tectonic facies are mainly controlled by palaeo‐redox and palaeo‐salinity. The lithofacies are regularly superimposed in the high‐frequency sequence framework, and three combination modes of A, B, and C are developed. Among them, the type A lithofacies are developed in the low‐stand system tract, and the type B lithofacies are developed in the transgressive system. The C‐type lithofacies assemblage developed near the largest flooding surface and the early highstand system tract, and combined with palaeoenvironment analysis to establish a lithofacies sedimentary model.

Abstract 2820: Innate immune system in a bag: A novel immunotherapy comprising NK cells, Vδ1 and Vδ2 T-cells

Abstract The recent successes of chimeric antigen receptor (CAR) T cells in the treatment of hematological malignancies have led to an explosion of adoptive cell therapies for cancer. Currently approved therapies are still limited to patient-autologous αβ T cells. Although successful in some settings, this approach comes with challenges including associated toxicities, high production costs and the requirement to gene edit cells to avoid graft vs host disease in an allogeneic setting. In contrast to αβ T cells, innate immune cells such as Natural Killer (NK) cells and gamma delta (γδ) T cells offer opportunities to develop MHC unrestricted, fully allogeneic, off-the-shelf immunotherapies without the need for gene editing. Whilst NK cells offer great cytotoxic potential, control of MHC dysregulation and stress sensing receptors, γδ T cells on the other hand combine the power of a TCR with a variety of natural cytotoxicity receptors and the capability to orchestrate adaptive immune responses even in an HLA-mismatched context. Lately, Vδ1 T-cells have gained significant attention in the field of cancer immunotherapy based on their evolutionary conserved role in cancer immunosurveillance. Multiple groups are developing protocols to grow clinical grade innate immune cells at scale for the treatment of cancer. Each protocol is designed to grow one specific effector cell type of adequate quality. Despite their unique potential, each cell type faces its own challenges for clinical use. A treatment combining a variety of innate cells could be desirable to mitigate for these limitations. We have developed a first in class, scalable expansion process to simultaneously grow NK cells, Vδ1 and Vδ2 T-cells from a healthy donor leukapheresis in a single culture vessel. This 12-day process induces proliferation of all three cell types, resulting in a viable and cryo-preservable drug product where all cells express markers associated with great cytotoxicity, such as CD56 and NKp30. Named after the high expression of CD56, GDX056 shows great efficacy against haematological and solid tumor cells in-vitro. Integrating a viral transduction step into the expansion process allows us to generate a CAR modified tri-cellular product which shows unprecedented cytotoxicity against target cells at E:T ratios as low as 1:100. In an in vivo NALM-6 leukemia model all cell types show engraftment but different body distribution and persistence over time. Whilst efficacious, GDX056 also serves as a great research tool to study each cell type side-by-side. In summary, we have developed an easily manufacturable, first of its kind cellular product combining all major MHC unrestricted cell types. Whilst Vδ1 T cells and NK cells demonstrate anti-cancer activities, Vδ2 T cells are strong mediators of anti-bacterial biology, hence GDX056 potentially offers a unique combinatorial immunotherapy to be considered in scenarios where patients are prone to infections. Citation Format: André Simoes, Mihil Patel, Amy Lane, Sam Illingworth, Sarah Edwards, Istvan Kovacs, Michael Koslowski, Oliver Nussbaumer. Innate immune system in a bag: A novel immunotherapy comprising NK cells, Vδ1 and Vδ2 T-cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2820.

Study of certain congenital malformations due to low-level radiation exposures from high background radiation areas

The coastal regions of some states (Kerala, Tamil Nadu, Andhra Pradesh and Orissa) in India are known to have plenty of thorium-rich minerals, therefore possess high external gamma radiation fields, and are identified as Natural High Background Radiation Areas. There are concerns about congenital malformations due to the high background radiation prevailing in the region.Therefore, scientists find interest to study the possible biological effects of low-level exposures on the local populace who are living with generations under these extraordinary radiation fields. The present study is an attempt to find the correlation between the incidences of certain congenital malformations through a 1:3 matched case-control study. A total of 75 subjects (58 mental retardation cases and 17 Cleft lip/palate cases) along with 225 controls were selected for the investigations. Indoor and outdoor gamma dose rates were measured using a halogen-quenched GM tube-based survey meter, while the indoor inhalation dose rates were measured based on the radon and progeny measurements using the twin cup dosimeters equipped with LR-115 Type II solid-state nuclear track detectors. Conditional logistic regression analysis was used to find out the correlation between the radiation dose with congenital malformation. This study found an insignificant risk of cleft lip/palate (but not for mental retardation) from the exposures to natural high external gamma radiation fields in the study area. Therefore, the results of the investigation indicate no explicit risk of congenital malformations by the high background natural radiation prevailing in the region.

Genetic analysis and identification of low‐resistivity gas reservoirs of southwestern Sulige gas field in China

AbstractSulige gas field is the largest natural gas producing area in China. Due to the “water blocking” effect, the gas–water layer significantly influences the development of gas reservoirs. The existence of low‐resistivity gas layers in the He 8 member of the Shihezi Formation in the southwestern Sulige gas field makes it challenging to distinguish the gas layers from the gas–water layers using conventional identification methods. To effectively identify the low‐resistivity gas layers, their genetic mechanisms are analyzed by studying their lithology and pore structural characteristics based on the well logging and core experimental data. The low‐resistivity gas layers are main affected by three causes: (1) electrical conductivity of argillaceous laminae in sandstone, (2) additional conductivity of clay minerals, and (3) high bound water saturation caused by the development of micropores and clay minerals in sandstone. Herein, to effectively distinguish the low‐resistivity gas layer from the gas–water layer based on the genetic mechanism of the low‐resistivity gas layer, first, a gas‐bearing index was constructed to characterize the gas‐bearing properties of the reservoir using the neutron logging and density logging curves after eliminating the influence of the dispersed shale. Second, a model to calculate the bound water saturation was constructed by selecting sensitive parameters with respect to the causes of bound water. Then, two plots, namely the gas‐bearing index–natural gamma relative value cross‐plot and the bound water content–porosity cross‐plots, were constructed using the gas testing data, and the identification standard of the low‐resistivity gas and gas–water layers was established. The interactive identification of the two cross‐plots effectively distinguished the low‐resistivity gas layer from the gas‐water layer, thereby providing a basis for understanding the distribution of gas and water in the southwestern Sulige gas field, which may guide further exploration and the deployment of the development well pattern.

An Optimized Neural Network Prediction Model for Reservoir Porosity Based on Improved Shuffled Frog Leaping Algorithm

Efficient and accurate porosity prediction is essential for the fine description of reservoirs, for which an optimized BP neural network (BPNN) prediction model is proposed. Aiming at the problem that the BPNN is sensitive to initialization and converges to local optimum easily, an improved shuffled frog leaping algorithm (ISFLA) is proposed based on roulette and genetic coding. Firstly, a roulette mechanism is introduced to improve the selection probability of elite individuals, thus enhancing the global optimization ability. Secondly, a genetic coding method is carried out by making full use of effective information such as the global and local optimal solutions and the boundary values of subgroups. Subsequently, the ISFLA algorithm is verified on 12 benchmark functions and compared with four intelligent optimization algorithms, and experimental results show its good optimization performance. Finally, the ISFLA algorithm is applied to the optimization of initial weights and thresholds of the BPNN, and a new model named ISFLA_BP is proposed to study the porosity prediction problem. The logging data is preprocessed by grey correlation analysis and deviation normalization, and then the effective prediction of porosity is achieved by natural gamma, density and other relevant parameters. The performance of ISFLA_BP model is compared with the standard three-layer BPNN and four BPNN parameter optimization methods based on swarm intelligence algorithms. Experimental results show that the proposed model has higher training accuracy, stability and faster convergence speed, with a mean square error of 0.02, and its prediction accuracy for porosity is higher than that of the other five methods.

Evaluation of fractures using conventional and FMI logs, and 3D seismic interpretation in continental tight sandstone reservoir

Abstract Due to the complex pore structures and strong heterogeneity of fractured reservoirs, it is a hot and difficult point in petroleum geology to identify fractures using logging principles. In this paper, taking the tight sandstone reservoir of the Chang 8 Member in the Huanjiang Oilfield as an example, field outcrops, cores, thin sections, and logging identification methods were used for quantitative description and fine logging evaluation of fractures. The research shows that high-angle, medium-low-angle, near-vertical, and horizontal fractures are developed in the Chang 8 Member of the Huanjiang Oilfield. The main ones are high-angle fractures, followed by horizontal fractures with a low degree of fillings. Under the constraints of core and imaging logging data, three fracture sensitivity logging parameters of acoustic wave time difference, natural gamma, and dual induction-octalateral resistivity were optimized, and a comprehensive fracture probability index was proposed. Seventy-nine fracture development intervals were identified based on log curve characteristics and fracture probability indexes. The coincidence rate of fracture logging identification results with the core observation and imaging logging interpretation is 80.6%. The research results can provide a theoretical basis for the efficient development of fractured continental tight sandstone reservoirs in similar areas.

A Novel Method for Conventional Logging Prediction of Brittleness Index: A Case of Tight Sandstone in Western Ordos Basin

Brittleness index is an important parameter for the exploration and development of tight sandstone reservoir. In this paper, a new method for calculating brittleness index using conventional logging data was established to find the high-quality exploration and development target area of tight sandstone reservoir in He8 Member of Permian in the western margin of the Ordos Basin. Firstly, the mineral composition and content of rock samples in the study area were analyzed by rock mineral X-ray diffraction, and the brittleness index was calculated according to the mineral content with the average of 70.85%. Then, three kinds of brittleness index logging prediction models were obtained by conventional logging curves such as natural gamma and compressional wave interval transit time. Each prediction model was corrected by the brittleness index calculated according to the X-ray diffraction experiment, and the correlation coefficients were 0.83, 0.58, and 0.71, respectively. Finally, through multiple regression, the comprehensive equation for brittleness index prediction was established by combining these three kinds of models. According to the example of the specific well in the study area, the brittleness index predicted by conventional logging is highly matched with the interpretation results of array acoustic logging with the correlation coefficient of 0.90, which means the accuracy of the comprehensive equation has been verified. The new method can evaluate reservoir brittleness by conventional logging data in the absence of rock sample test data and popularize the application of brittleness index in oil and gas field exploration and development.

Radiological Hazard Related with Natural Radioactivity in Natural Gas Transportation—A Case Study from Poland

Activity concentration of 210Pb in black powder and gamma radiation dose rate related to natural gas transportation were discussed. As part of the research, the content of radiolead (210Pb) in black powder, spent filter containers, radon (222Rn) activity concentration in natural gas and gamma radiation dose were measured around selected points of natural gas transportation infrastructure in Poland. The content of some heavy metals was also analyzed. The average concentrations of 222Rn and 210Pb ranged from 30 to 1400 Bq/m3 and from around 450 to 16,000 Bq/kg, respectively. The external exposure to gamma radiation was low; the gamma radiation dose rate was at the level of background radiation or slightly exceeded it. The research demonstrated that the content of 210Pb in black powder is strongly related to radon concentration in natural gas, the volumetric flow rate of the transported natural gas and solid fraction content. Some black powder samples should be classified based on 210Pb content as low-radioactive waste.

Middle Miocene-Pleistocene Magneto-Cyclostratigraphy from IODP Site U1501 in the Northern South China Sea

Site U1501 of International Ocean Discovery Program (IODP) Expedition 368 locates on a broad regional basement high in the northern margin of the South China Sea (SCS). This study refines the chronostratigraphy of the upper 160 m sedimentary succession from Hole U1501C using paleomagnetic measurements and cyclostratigraphic analysis on the Natural Gamma Radiation (NGR) data. Rock magnetic analysis displays that the magnetic signal of the sediments is mainly carried by single-domain (SD) and multi-domain (MD) magnetite. A total of 12 geomagnetic reversals are identified and correlated to the geomagnetic polarity time scale (GPTS) in Geologic Time Scale 2020 (GTS 2020), combining biostratigraphic data and planktonic foraminiferal oxygen records. The Milankovitch cycles of 405-kyr long orbital eccentricity, ∼100-kyr short orbital eccentricity, and obliquity cycles are identified in the NGR profile. A 15.54 Myr astronomical time scale is constructed by tuning the short eccentricity cycles filtered from the NGR profiles to the La2010 astronomical solution with the constraints of the magnetostratigraphic results, biostratigraphic age datum and planktonic foraminiferal oxygen records. This new high-resolution age model provides a new temporal constraint on the tectonic and paleoenvironmental evolution in the South China Sea.

Lithology Identification of Uranium-Bearing Sand Bodies Using Logging Data Based on a BP Neural Network

Lithology identification is an essential fact for delineating uranium-bearing sandstone bodies. A new method is provided to delineate sandstone bodies by a lithological automatic classification model using machine learning techniques, which could also improve the efficiency of borehole core logging. In this contribution, the BP neural network model for automatic lithology identification was established using an optimized gradient descent algorithm based on the neural network training of 4578 sets of well logging data (including lithology, density, resistivity, natural gamma, well-diameter, natural potential, etc.) from 8 boreholes of the Tarangaole uranium deposit in Inner Mongolia. The softmax activation function and the cross-entropy loss function are used for lithology classification and weight adjustment. The lithology identification prediction was carried out for 599 samples, with a prediction accuracy of 88.31%. The prediction results suggest that the model is efficient and effective, and that it could be directly applied for automatic lithology identification in sandstone bodies for uranium exploration.