A multi-mineral model for predicting petrophysical properties of complex metamorphic reservoirs:Case study of the Bozhong Depression,Bohai Sea

Interpreting reservoir properties through log data and logging responses in complex strata is critical for efficient petroleum exploitation,particularly for metamorphic rocks.However,the unsatisfactory accuracy of such interpretations in complex reservoirs has hindered their widespread application,resulting in severe inconvenience.In this study,we proposed a multi-mineral model based on the least-square method and an optimal principle to interpret the logging responses and petrophysical properties of complex hydrocarbon reservoirs.We began by selecting the main minerals based on a comprehensive analysis of log data,X-ray diffraction,petrographic thin sections and scanning electron microscopy(SEM)for three wells in the Bozhong 19-6 structural zone.In combination of the physical properties of these minerals with logging responses,we constructed the multi-mineral model,which can predict the log curves,petrophysical properties and mineral profile.The predicted and measured log data are evaluated using a weighted average error,which shows that the multi-mineral model has satisfactory prediction performance with errors below 11%in most intervals.Finally,we apply the model to a new well“x”in the Bozhong 19-6 structural zone,and the predicted logging responses match well with measured data with the weighted average error below 11.8%for most intervals.Moreover,the lithology is dominated by plagioclase,K-feldspar,and quartz as shown by the mineral profile,which correlates with the lithology of the Archean metamorphic rocks in this region.It is concluded that the multi-mineral model presented in this study provides reasonable methods for interpreting log data in complex metamorphic hydrocarbon reservoirs and could assist in efficient development in the future.

Standard-sample bracketing calibration method combined with Mg as an internal standard for silicon isotopic compositions using multi-collector inductively coupled plasma mass spectrometry

Silicon isotope analysis traditionally uses a standard-sample bracketing （SSB） method that relies upon greater instrument stability than can be consistently expected. The following proposed method reduces the level of instrumental stability required for the analysis process and provides a valid solution for high-precision and accurate studies of Si isotopic compositions. Rock samples were dissolved by using alkali fusion and acidification. Silicon isotopes were purified with an ion exchange resin. Interfering peaks for isotopes were separated by using a Nu Plasma 1700 multi-collector inductively coupled plasma mass spectrometry （MS） system in high-resolution mode （M/AM 〉 8000 RP）. Two magnesium isotopes （25Mg and 26Mg） and three silicon isotopes （28Si, 29Si, and 3;Si） were analyzed in the same data collection cycle. Mg isotopes were used as an internal standard to calibrate the mass discrimination effects in MS analysis of Si isotopes in combination with the SSB method in order to reduce the effects of MS interference and instrumental mass dis- crimination on the accuracy of measurements. The conventional SSB method without the Mg internal standard and the proposed SSB method with Mg calibration delivered consistent results within two standard deviations. When Mg was used as an internal standard for calibration, the analysis precision was better than 0.05 %0 amu.

A Highly Sensitive and Selective Spectrofluorimetric Method for the Determination of Arsenic at Pico-Trace Levels in Some Groundwater, Real, Environmental, Biological, Food and Soil Samples Using 2-(<i>α</i>-Pyridyl)-Thioquinaldinamide

A very simple, ultra-sensitive, highly selective and non-extractive new spectrofluorimetric method for the determination of arsenic at pico-trace levels using 2-(α-pyridyl)-thioquinaldinamide (PTQA) has been developed. PTQA has been proposed as a new analytical reagent for the direct non-extractive spectrofluorimetric determination of Arsenic (V). This novel fluorimetric reagent, PTQA becomes oxidized in a slightly acidic (0.025 - 0.1 M H2SO4) solution with Arsenic (V) in absolute ethanol to produce highly fluorescent oxidized product (λex = 303 nm;λem = 365 nm). Constant and maximum fluorescence intensities were observed over a wide range of acidity (0.025 - 0.1 M H2SO4) for the period between 2 min and 24 h. Linear calibration graphs were obtained for 0.001 - 800-μgL-1 of As, having a detection limit of 0.1-ngL-1;the quantification limit of the reaction system was found to be 1-ngL-1 and the RSD was 0% - 2%. A large excess of over 60 cations, anions and complexion agents (like, chloride, phosphate, azide, tartrate, oxalate, SCN, etc.) do not interfere in the determination. The developed method was successfully used in the determination of arsenic in several Certified Reference Materials (alloys, steels, ores, human urine, hair, nails, bovine liver and sediments) as well as in some biological fluids (human blood, urine, hair, nail and milk), soil samples, food samples (vegetables, fruits, rice, corn and wheat), solutions containing both arsenic (III) and arsenic (V) speciation and complex synthetic mixtures. The results of the proposed method for assessing biological, food and soil samples were comparable with both ICP-OES & AHG-AAS and were found to be in excellent agreement.

Elemental Concentrations in Biological Samples of Coronavirus Disease (COVID-19) and Other Pulmonary Disease Patients

Background: Recently, Coronavirus Disease 2019 (COVID-19) has been affected by the extreme acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has developed important global health anxiety. At this time, the treatment options for this disease are only moderately successful. Smoking has been related to COVID-19 and its mortality, and it has the potential to affect bacteriological and viral conversion, clinical effects, and treatment outcomes. Methods: The aim of this study was to determine the relationship between critical essential trace {zinc (Zn), iron (Fe), and copper (Cu)} and toxic {cadmium (Cd) and nickel (Ni)} elements in human biological samples such as scalp hair, serum, saliva, blood, nasal fluid, and sputum of smoking and nonsmoking male COVID-19 patients (n = 139, age range 25 - 38 years) from Hyderabad, Pakistan. For comparative purposes, the biological specimens of referent persons (n = 83), pulmonary Tuberculosis (T.B) patients (n = 67), Pneumonia (n = 56) of the same age groups were also be analyzed. Microwave oven mediated digestion method was employed, and digestion of samples was carried out with the help of 2:1 mixture solution of HNO3 (65%) and H2O2 (30%). Atomic absorption spectrometry was employed for the determination of elemental concentrations from the microwave oven employed digested samples. Results: The found average of essential elements (Zn, Fe, and Cu) in biological specimens of smoker and nonsmoker male COVID-19, T.B, and Pneumonia patients was found to lower, whilst cadmium and nickel were found to be higher when compared with samples from referents (p < 0.001). Conclusions: Improved elemental (Cu, Fe, Zn) concentrations may also decrease the risk of bacterial co-infection by enhancing the mucociliary clearance and respiratory epithelial barrier function, in addition to providing direct antibacterial effects against S. pneumoniae. Our findings also suggest that higher Cd and Ni concentrations are linked to cigarette smoking, which could lead to COVID-19 and other lung-infected diseased recurrences. However, further clinical and experimental research is required.

Prediction of Protein OmpH in Structure of C47-8 Pasteurella multocida

[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and protein characteristics of sequencing result of gene OmpH from PmC47-8 strain were analyzed. [Result] The similarities of gene OmpH from PmC47-8 with the published 81 OmpH genes were between 84% and 99%; a signal peptide was found with the cleavage sites between 20 and 21 in the polypeptide; secondary structure prediction showed that folding structure accounted for 49.8% and loop structure for 50.2%; it predicted that there were 7 O-glycosylation sites in OmpH protein with the amino acid residual sites of 2, 45, 48, 330, 716, 721, 723, respectively, and 2 N-glycosylation sites with the amino acid residual sites of 15 and 35. [Conclusion] This study lays the foundation for the study on the immunity of OmpH gene from yak.

Real-time crash prediction on freeways using data mining and emerging techniques

Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.

Yield strength prediction of rolled Al-(1.44-12.40)Si-0.7Mg alloy sheets under T4 condition

The effects of Si content on the microstructure and yield strength of Al-(1.44-12.40)Si-0.7 Mg(wt.%)alloy sheets under the T4 condition were systematically studied via laser scanning confocal microscopy(LSCM),DSC,TEM and tensile tests.The results show that the recrystallization grain of the alloy sheets becomes more refined with an increase in Si content.When the Si content increases from 1.44 to 12.4 wt.%,the grain size of the alloy sheets decreases from approximately 47 to 10μm.Further,with an increase in Si content,the volume fraction of the GP zones in the matrix increases slightly.Based on the existing model,a yield strength model for alloy sheets was proposed.The predicted results are in good agreement with the actual experimental results and reveal the strengthening mechanisms of the Al-(1.44-12.40)Si-0.7 Mg alloy sheets under the T4 condition and how they are influenced by the Si content.

Application of vertically integrated sampling approach to study of new production via ^(234)Th -^(238)U disequilibria

The spatial and temporal distributions of new production vary largely in different sea areas. To understand the level of new production in the sea area studied better, an estimate of new production must be obtained in large spatial and temporal scales. The ~234Th/ ~238U disequilibrium is an effective method for the study of new production. Two sampling strategies, vertically integrated sampling ap proach based on trapezoidal integration principle and discrete layer sampling approach, were compared in the studies of the xiamen Bay and the northern South China Sea. The scavenging fluxes and removal fluxes of ~234Th and the residence times for dissolved and particulate ~234Th were calculated. The coinci dent results from two Sampling approach suggest that vertically integrated sampling approach is not only effective and reliable, but also significantly reduces the number and volume of samples. It allows us to study new production by ba ^(234)Th - ^(238)U disequilibria in large spatial scale.

Bi-variable damage model for fatigue life prediction of metal components

Based on the theory of continuum damage mechanics,a bi-variable damage mechanics model is developed,which,according to thermodynamics,is accessible to derivation of damage driving force,damage evolution equation and damage evolution criteria. Furthermore,damage evolution equations of time rate are established by the generalized Drucker＇s postulate. The damage evolution equation of cycle rate is obtained by integrating the time damage evolution equations,and the fatigue life prediction method for smooth specimens under repeated loading with constant strain amplitude is constructed. Likewise,for notched specimens under the repeated loading with constant strain amplitude,the fatigue life prediction method is obtained on the ground of the theory of conservative integral in damage mechanics. Thus,the material parameters in the damage evolution equation can be obtained by reference to the fatigue test results of standard specimens with stress concentration factor equal to 1,2 and 3.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

Parameter Self - Learning of Generalized Predictive Control Using BP Neural Network

This paper describes the self—adjustment of some tuning-knobs of the generalized predictive controller(GPC).A three feedforward neural network was utilized to on line learn two key tuning-knobs of GPC,and BP algorithm was used for the training of the linking-weights of the neural network.Hence it gets rid of the difficulty of choosing these tuning-knobs manually and provides easier condition for the wide applications of GPC on industrial plants.Simulation results illustrated the effectiveness of the method.

Impact of Subsurface Entrainment on ENSO Prediction: 1997-98 El Nio

Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction.A new hybrid coupled model was used that considered the entrainment of subsurface temperature anomalies into the sea surface.The results showed that predictions were improved significantly in the new coupled model.The predictive correlation skill increased by about 0.2 at a lead time of 9 months,and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general.A detailed analysis of the 1997-98 El Nio hindcast showed that the new model was able to predict the onset,peak (both time and amplitude),and decay of the 1997-98 strong El Nio event up to a lead time of one year,factors that are not represented well by many other forecast systems.This implies,in terms of prediction,that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles.Improving the presentation of such effects in models would increase the forecast skill.

同位素稀释-HRGC-LRMS法测定环境样品中二噁英类多氯联苯

多氯联苯(PCBs)是一类具有209种同类物的高毒性、难降解、强脂溶性和生物累积性的持久性有机污染物,自从1996年Jensen首次在鹰和鲱鱼中发现PCBs后[1,2],现在已在世界各地不同的环境介质中都发现有PCBs的存在[3].……

气相色谱-质谱分析气体样品的制备方法和技术

现代GC-MS仪器虽然具有较高的分辨能力和较高的灵敏度,但是对欲分析样品的要求也比较严格.诸如:进样量大小、组成组分的浓度范围、样品的物理状况、样品中的基体干扰等问题都是必须在进样之前调查清楚,才能选择正确的样品处理方法和技术并处理成GC-MS仪器能够直接进样测定的样品形式,获得正确的GC-MS分析测定结果.……

Separation density prediction of geldart A^(-)dense medium in gas-solid fluidized bed coal beneficiators

Gas-solid Fluidized Bed Coal Beneficiator(GFBCB)process is a crucial dry coal beneficiation fluidization technology.The work employs the GFBCB process alongside a novel Geldart A^(-)dense medium,consisting of Geldart A magnetite particles and Geldart C ultrafine coal,to separate small-size separated objects in the GFBCB.The effects of various operational conditions,including the volume fraction of ultrafine coal,the gas velocity,the separated objects size,and the separation time,were investigated on the GFBCB's separation performance.The results indicated that the probable error for 6∼3 mm separated objects could be controlled within 0.10 g/cm^(3).Compared to the traditional Geldart B/D dense medium,the Geldart A/A^(-)dense medium exhibited better size-dependent separation performance with an overall probable error 0.04∼0.12 g/cm^(3).Moreover,it achieved a similar separation accuracy to the Geldart B/D dense medium fluidized bed with different external energy for the small-size object beneficiation.The work furthermore validated a separation density prediction model based on theoretical derivation,available for both Geldart B/D dense medium and Geldart A/A^(-)dense medium at different operational conditions.

Relationship between serum calcium and CA 19-9 levels in colorectal cancer

AIM:To examine the calcium metabolism of colorectal cancer (CRC)in patients with colorectal cancer and control patients. METHODS:Seventy newly diagnosed CRC patients were included.The healthy control group was age and gender matched(n=32).Particular attention was devoted to the relationship between serum calcium of patients,and levels of AFP,CEA,carbohydrate antigen 19-9(CA 19-9)(that could be considered as prognostic factors).Furthermore,the Ca-sensing receptor(CaSR)gene A986S polymorphism was investigated in these patients,as well as the relationship between different CaSR genotypes and the data stated above. RESULTS:A lower level of ionized calcium(also corrected for albumin)was found in the serum of CRC patients with normal 25(OH)vitamin D levels.The ionized calcium concentration was inversely correlated with the serum level of CA.19-9.There was no difference in the distribution of CaSR genotypes,between CRC patients and general population.The genotypes did not correlate with other data examined. CONCLUSION:Based on these results,lower levels of serum calcium might be a pathogenic and prognostic factor in colorectal cancer.

Spherical Near-Field - Far-Field Transformation for Quasi-Planar Antennas from Irregularly Spaced Data

An effective near-field - far-field (NF - FF) transformation with spherical scanning for quasi-planar antennas from irregularly spaced data is developed in this paper. Two efficient approaches for evaluating the regularly spaced spherical samples from the nonuniformly distributed ones are proposed and numerically compared. Both the approaches rely on a nonredundant sampling representation of the voltage measured by the probe, based on an oblate ellipsoidal modelling of the antenna under test. The former employs the singular value decomposition method to reconstruct the NF data at the points fixed by the nonredundant sampling representation and can be applied when the irregularly acquired samples lie on nonuniform parallels. The latter is based on an iterative technique and can be used also when such a hypothesis does not hold, but requires the existence of a biunique correspondence between the uniform and nonuniform samples, associ- ating at each uniform sampling point the nearest irregular one. Once the regularly spaced spherical samples have been recovered, the NF data needed by a probe compensated NF - FF transformation with spherical scanning are efficiently evaluated by using an optimal sampling interpolation algorithm. It is so possible to accurately compensate known posi- tioning errors in the NF - FF transformation with spherical scanning for quasi-planar antennas. Some numerical tests assessing the accuracy and the robustness of the proposed approaches are reported.

Variance Reduction Technique for Estimating Value-at-Risk based on the Cross - Entropy

Value-at-Risk （VaR） estimation via Monte Carlo （MC） simulation is studied here. The variance reduction technique is proposed in order to speed up MC algorithm. The algorithm for estimating the probability of high portfolio losses （more general risk measure） based on the Cross - Entropy importance sampling is developed. This algorithm can easily be applied in any light- or heavy-tailed case without an extra adaptation. Besides, it does not loose in the performance in comparison to other known methods. A numerical study in both cases is performed and the variance reduction rate is compared with other known methods. The problem of VaR estimation using procedures for estimating the probability of high portfolio losses is also discussed.

Predicting malignant transformation of esophageal squamous cell lesions by combined biomarkers in an endoscopic screening program

AIM To determine the association of p53, carcinoembryonic antigen(CEA) and CA19-9 protein expression with esophageal carcinogenesis.METHODS An iodine staining endoscopic screening program of esophageal lesions was carried out in the high-incidence area of Feicheng County, China. Seventy-seven patients with basal cell hyperplasia(BCH), 247 with low-grade dysplasia(LGD), 51 with high-grade dysplasia(HGD), 134 with invasive cancer, and 80 normal controls diagnosed by mucous membrane biopsy pathology were enrolled. Immunohistochemical detection of p53, CEA and CA19-9 proteins was performed. In the ROCcurve analysis, the expression of a single biomarker and the expression of a combination of biomarkers were used to predict the risk of these four esophageal lesions.RESULTS The positive rates of p53 protein expression in invasive cancer, HGD, LGD, BCH and the normal control groups were 53.0%, 52.9%, 35.6%, 27.3% and 20.0%, respectively; the positive rates of CA19-9 protein expression were 44.0%, 33.3%, 16.5%, 9.2% and 6.2%, respectively; the positive rates of CEA protein expression were 74.6%, 60.8%, 23.3%, 23.7% and 16.2%, respectively. The positive rates of the combined expression of the three biomarkers were 84.3%, 76.5%, 47.6%, 42.9% and 27.5%, respectively. In the receiver operating characteristic curves of the combination of the three biomarkers, the specificity was 88.8% for the normal controls, and the sensitivity was 58.2% for invasive cancer, 25.5% for HGD, 11.2% for LGD, and 6.5% for BCH.CONCLUSION p53, CEA and CA19-9 protein expression was correlated with esophageal carcinogenesis, and testing for the combination of these biomarkers is useful for identifying high-risk patients with precancerous lesions.

Comparison of fixed area and distance sampling methods in open forests:case study of Zagros Forest,Iran

The main aim of this study was to evaluate methods for fixed area and distance sampling in the Zagros open forest area in western Iran. Basic forest management and planning required appropriate quantitative and qualitative information. Two sampling methods were compared on the basis of the actual means of characteristics derived from the 100 % survey. In total, 37 sampling plots were systematically installed with a grid of 100 m × 100 m in the study area. Density, crown canopy, and basal area of the stands were measured. The 100 % survey showed that tree density above 12.5 cm diameter at breast height was 68.04 stem ha-1, basal area was 15.16 m2 ha-1 and crown canopy percentage was 35.71% ha-1. The values for the traits determined by the two sampling methods differed significantly （P = 0.05）. When the time required for the methods was compared, transect sampling required less than systematic-random sampling. Therefore, the transect sampling method was the more economical method for the Zagros open forests. The transect sampling method was statistically defensible and practical for quantitating characteristics of the Zagros open forests.