Research On Key Technologies Of Partial Porous Externally Pressurized Gas Bearing

With the development of industry, the oil industry has become an important part of the country’s comprehensive national strength. Terrestrial oil produced during the PAHs (Polycyclic aromatic hydrocarbons, PAHs) and other pollutants through volatilization, floor crude oil, oily wastewater, etc. Research for the distribution of space mainly for research into the law among the three rivers area media, etc., for oil exploration area in a multi-media environment pollutants migration and transformation distribution changes with temperature, wind direction and other meteorological conditions, and using a variety of characterization methods for ecological risk assessment of spatial and temporal differences in regional comprehensive quantitative characterization have limited research results.In this paper, based on study of Hu Yan typical PAHs in the terrestrial oil exploration area of medium between the migration transformation rule, add the temperature and wind speed and other meteorological conditions, three medium BETR reference Mackay (Berkeley-Trent) river model, implementation based on the month change in PAHs multiple medium (air, water, soil, sediment) hybrid grid model simulation. Through the study of regional multi-media environment (air, water, soil, sediment) to establish BETR (Berkeley-Trent) grid model to study land-based oil exploration area PAHs multi-media migration and transformation, fate and spatial distribution of environmental behavior. Make the results more reliable, and the research of PAHs in the area of time and space environment quality and human health risk level of quantitative characterization of more practical reference value.The research results show that enters the environment system of crude oil is the main way of the ground, followed by the exhaust gas emissions into the atmosphere, from 2009 to 2014, the atmosphere, water, soil and sediment phase PAHs concentration in stable state, the ups and downs were 7.8%,0.01%,0.02%and 7.8% respectively. This is mainly because oil production in the stationary phase since 2009, the source of PAHs in the medium emissions is relatively stable, as time goes on PAHs concentration gradually decreases, the main input pathway for soil emissions, output pathway for atmospheric advection, the main migration pathways for migration from the soil to the atmosphere. In the atmosphere, polymer PAHs tetracyclic above, namely FLA, BaP, BKF, DBA, the proportion accounted for more than 85% of the maximum value, PAHs concentration in the atmosphere is low. PAHs in soil by BaP and multi containing three benzene (ANA, ANY, FLU) based, distribution and balance, are at about 30%. And the water phase in Bicyclic, tricyclic mainly, namely NAP, ANA, ANY, FLU and PHE. In the sediment phase, IPY, DBA, BPE and other high molecular weight contaminants for, they were easier to adsorption on sediments, and difficult to degrade.In this study, BETR grid model simulation results were based on the data, combined with fuzzy theory, probabilistic characterization of the uncertainty analysis, by constructing fuzzy environmental quality standards and health risk assessment, to build environmental quality assessment framework based on model of BETR-MFG(IV) human health risk assessment framework, and using the built method to make space-time multi-media ecological risk assessment to the 2009-2014 PAHs within the study area. Evaluation results show that, BaP and BaA have the highest comprehensive environmental risk level, reached moderate risk or low risk, the environmental quality risk mainly comes from the oil and coke combustion. In 16 kinds of PAHs, BaP, BKF, CHR and DBA of the non-carcinogenic risk is unacceptable, the remaining 12 PAHs non carcinogenic risk is completely acceptable; BaA, BbF, BkF, BaP, CHR, IPY, NAP and DBA have cancer risks cannot be ignored, and the majority of non-acceptance, but in addition to NAP to outside, the rest are highly carcinogenic risk level of pollutants existing in the study area. Based on the above results of large amounts of data, which could not be displayed by simple table and simulation diagram, with the passage of time, spatial variation of each region are not the same, so this paper attempts to build adatabase with a large number of simulations and Arcgis, accuratly simulating and evaluating results and the corresponding trends intuitily, for policymakers to make decisions quickly with most convenient form.