Oil pollution is one of the main pollutions of the marine environment. With the year-on-year increase of our country's oil imports and maritime transportation, our country's offshore oil pollution has become more and more serious, causing serious harm to the ocean and the nearshore environment. According to the current situation of marine oil pollution, the article outlines the current situation, sources, hazards of marine oil pollution, and the methods currently used to deal with and prevent marine oil pollution in physics, chemistry and biology.
In order to improve measuring-temperature accuracy of the PT100 temperature sensor, we conduct multi-point calibration experiment. The BP neural network based on LM algorithm can process experimental data and the least square method can fit out more accurate formula that express the relationship between the temperature and resistance. It is available that this arithmetic that the interrelated experiment demonstrate its accuracy improve precision of the PT100 temperature sensor.This arithmetic can be applied to the calibration test.
Reservoir is the underground storage and accumulation place of oil and natural gas. The accuracy of reservoir heterogeneity evaluation has great economic value for correctly guiding the production and development of oil and natural gas. The high-order neural network method is used to comprehensively evaluate the heterogeneity of the reservoir. This method was applied to the evaluation of reservoir heterogeneity in the PK area. The results show that the heterogeneity of sandy clastic flow sand bodies is the weakest, the sandy landslide sand bodies are medium, and the turbidity current sand bodies are strongest. The evaluation method of reservoir heterogeneity based on high-order neural network technology effectively solves the problem of inconsistent conclusions of single-parameter evaluation of heterogeneity in conventional methods, and can quantitatively characterize the degree of reservoir heterogeneity.
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