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Reservoir numerical simulation technology is an important means of reservoir analysis， and it is a powerful tool for oilfield developers to design development schemes， proceed with dynamic tracking adjustment， and enhance oil recovery of reservoirs. In order to clarify the future development directions of reservoir numerical simulation technology in Shengli Oilfield， this paper reviewed the development process of reservoir numerical simulation application technology and independent intellectual property software in Shengli Oilfield over the past 60 years and summarized the innovative progress in numerical simulation application technology in many fields since the “13th Five-Year Plan in 2011-2015” period， which is applicable to current development and geological characteristics of Shengli Oilfield. The fields covered fine reservoir description in ultra-high water-cut stage， pressure flooding development in low-permeability reservoirs， heterogeneous combination flooding， multiple thermal combination flooding in heavy oil reservoirs， high-pressure CO2 miscible flooding， and integrated development of shale oil by large-scale fracturing. Then， this paper introduced the development and application of several reservoir numerical simulation software with Shengli characteristics， involving water flooding， chemical flooding， and micro and intelligent simulation. Finally， it was pointed out that under the current development situation of Shengli Oilfield， the reservoir numerical simulation is facing great challenges in terms of refinement， scale， efficiency， and collaboration approach， and the numerical simulation of reservoirs should be further developed towards integration， parallelization， and intelligence， so as to provide technical support for improving the quality and efficiency of Shengli Oilfield development.

The content of ethanol in wine can affect its quality. Some edible alcohol contains small amounts of methanol, and its accumulation can cause serious health problems. Therefore, it is very important to test the contents of methanol and ethanol in alcohol products. In this paper, a rapid method for the determination of methanol and ethanol by ambient corona discharge ionization mass spectrometry was developed. The method is based on the interaction of water radical cation with methanol and ethanol, respectively. In the positive ion mode, the water dimer radical cation (H2O)2+· (m/z 36) with ambient corona discharge ionization source was prepared by using ambient corona discharge ionization source, and it was introduced into the mass spectrometer with methanol or ethanol through a dual-channel low-energy corona discharge ionization device. The two reacted with each other to form the corresponding water radical cationic complex [CH3OH+H2O]+· (m/z 50) and [CH3CH2OH+H2O]+· (m/z 64), which realized the direct and rapid qualitative and quantitative analysis of methyl alcohol and ethanol in different kinds of alcoholic beverages. The result showed that the limits of detection and the limits of quantification of methanol and ethanol were 0.011 and 0.004 mg/L, 0.038 and 0.014 mg/L, respectively. The correlation coefficients of the target ionic strength curves obtained by the interaction of different concentrations of standard methanol and ethanol with water free radical cation could reach to 0.990 4 and 0.991 1, respectively. By using this method, no methanol was detected in common alcoholic drinks, and the ethanol content in liquor was higher than beer. In the actual work, the method has good practicability through the experimental exploration and verification of different wines. This method does not require sample pretreatment, has the advantages of simple operation and fast analysis, and is especially suitable for the detection of aqueous system. This study has important guiding significance for the quantitative detection and analysis of methanol and ethanol in complex system, provides technical support for the detection of alcoholic beverages and food, and also provides an effective strategy for the quantitative detection of the content of small and medium molecular alcohols in actual samples.

Near-surface air temperature (Ta) estimation by geostationary meteorological satellites is mainly carried out under clear-sky conditions. In this study, we propose an all-weather Ta estimation method utilizing FY-4A Advanced Geostationary Radiation Imager (AGRI) and the Global Forecast System (GFS), along with additional auxiliary data. The method includes two neural-network-based Ta estimation models for clear and cloudy skies, respectively. For clear skies, AGRI LST was utilized to estimate the Ta (Ta,clear), whereas cloud top temperature and cloud top height were employed to estimate the Ta for cloudy skies (Ta,cloudy). The estimated Ta was validated using the 2020 data from 1211 stations in China, and the RMSE values of the Ta,clear and Ta,cloudy were 1.80 °C and 1.72 °C, while the correlation coefficients were 0.99 and 0.986, respectively. The performance of the all-weather Ta estimation model showed clear temporal and spatial variation characteristics, with higher accuracy in summer (RMSE = 1.53 °C) and lower accuracy in winter (RMSE = 1.88 °C). The accuracy in southeastern China was substantially better than in western and northern China. In addition, the dependence of the accuracy of the Ta estimation model for LST, CTT, CTH, elevation, and air temperature were analyzed. The global sensitivity analysis shows that AGRI and GFS data are the most important factors for accurate Ta estimation. The AGRI-estimated Ta showed higher accuracy compared to the ERA5-Land data. The proposed models demonstrated potential for Ta estimation under all-weather conditions and are adaptable to other geostationary satellites. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV3h and PWV6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV3h product performed slightly better than PWV6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases. [ABSTRACT FROM AUTHOR]

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1. [ABSTRACT FROM AUTHOR]