58 research outputs found

    Study of Magnetic Interactions Between Ferromagnet, Antiferromagnet and Superconductor

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    Ph.DDOCTOR OF PHILOSOPH

    Influence factors of single well's productivity in the Bakken tight oil reservoir, Williston Basin

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    To find out key factors of productivity enhancement quickly and precisely in design and operation of horizontal well fracturing, three methods, including information amount theory, gray correlative method and orthogonal experimental design, were used to calculate and check the productivity parameters of fractured horizontal wells in the Bakken tight oil reservoir, so as to identify their correlations and influences on productivity. The results reveal stages of fracturing, fracture length, horizontal section length and permeability as primary parameters, and formation pressure, porosity, fracture width and in-situ oil viscosity as secondary parameters. The controllable parameters should be considered firstly. Similar influences are obtained from these three methods, indicating that they are extremely practical and consistent. Therefore, all three methods are effective to calculate and check the influences of fractured horizontal wells' productivity parameters in tight oil reservoirs. Key words: Bakken tight oil reservoir, fracturing, horizontal well, productivity, influence degree, information amount theory, gray correlation method, orthogonal experimental desig

    Evaluation of the Seasonal Thermal Environmental Benefits of Urban Green Space in the Core Areas of Urban Heat Island

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    The core areas of the urban heat island (CAUHI) are the most concentrated and closely associated with humans, and they are key to managing the urban heat island (UHI). It is widely acknowledged that one of the best ways to reduce the risk of UHI is the creation of urban green spaces (UGSs). However, most of the current studies are based on the grid or block scale to explore the impact of UGS on UHI. The key to mitigating the urban heat environment is to plan urban UGS rationally in the CAUHI and explore the thermal environmental benefits of UGS. This paper provides an assessment model for the thermal environmental advantages of UGS and uses ten UGS metrics as explanatory factors for seasonal land surface temperature (LST). It quantitatively evaluates the potential differences in landscape characteristics between LST and UGS under different seasons, as well as the seasonal impact on CAUHI. This study found the following: (1) The overall distribution pattern of CAUHI shows a characteristic of spreading from the central part to the surrounding area. Most of the extremely significant CAUHI is dispersed in the center and southeastern regions of the city, where there is a much greater density of impermeable surfaces and essentially no distribution of CAUHI on the natural surface represented by forest land and water bodies. (2) Except for the aggregation index (AI), correlation analysis revealed that other metrics were highly connected with LST. Among the metrics used in this study, the largest patch index (LPI) and landscape division index (DIVISION) had the highest significant correlation with LST. Patch density (PD) was strongly negatively correlated with LST, indicating that fragmented and complex UGS patches could promote vegetation cooling. (3) The green environmental benefit index (GEBI) results showed a significant degree of spatial and temporal variability in the extracted CAUHI. This study found higher GEBI values in the larger thermal patches and lower GEBI in the surrounding smaller patches. The highest mean GEBI was found in winter, at 0.6083, and the largest distribution of large high-value patches. This study revealed the geographical and temporal variability of UGS and CAUHI, and with the help of the constructed scientific evaluation model, it offered suggestions for the optimization of urban greenery

    Estimating Soil Salinity with Different Levels of Vegetation Cover by Using Hyperspectral and Non-Negative Matrix Factorization Algorithm

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    Hyperspectral technology has proven to be an effective method for monitoring soil salt content (SSC). However, hyperspectral estimation capabilities are limited when the soil surface is partially vegetated. This work aimed to (1) quantify the influences of different fraction vegetation coverage (FVC) on SSC estimation by hyperspectra and (2) explore the potential for a non-negative matrix factorization algorithm (NMF) to reduce the influence of various FVCs. Nine levels of mixed hyperspectra were measured from simulated mixed scenes, which were performed by strictly controlling SSC and FVC in the laboratory. NMF was implemented to extract soil spectral signals from mixed hyperspectra. The NMF-extracted soil spectra were used to estimate SSC using partial least squares regression. Results indicate that SSC could be estimated based on the original mixed spectra within a 25.76% FVC (R2cv = 0.68, RMSEcv = 5.18 g·kg−1, RPD = 1.43). Compared with the mixed spectra, NMF extraction of soil spectrum improved the estimation accuracy. The NMF-extracted soil spectra from FVC below 63.55% of the mixed spectra provided acceptable estimation accuracies for SSC with the lowest results of determination of the estimation R2cv = 0.69, RMSEcv = 4.15 g·kg−1, and RPD = 1.8. Additionally, we proposed a strategy for the model performance investigation that combines spearman correlation analysis and model variable importance projection analysis. The NMF-extracted soil spectra retained the sensitive wavelengths that were significantly correlated with SSC and participated in the operation as important variables of the model
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