Saeid Ahadi; َAndisheh Alimoradi; Hamid Sarkheil; Mahyar Kalhor Mohammadi; Mahdi Fathi
Abstract
The process of extraction and exploitation of oil and gas resources requires the cycle of production, sending, and recycling of drilling mud or drilling fluid, so achieving the right combination of drilling mud and it’s recycling as an essential and fundamental matter in the industrial oil and ...
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The process of extraction and exploitation of oil and gas resources requires the cycle of production, sending, and recycling of drilling mud or drilling fluid, so achieving the right combination of drilling mud and it’s recycling as an essential and fundamental matter in the industrial oil and gas and also the environment. Therefore, determining the level of contamination of heavy metals and organic matter in the drilling mud and drilling cuttings can be necessary so that intelligent methods to estimate these contaminants can be indirectly effective. This study tried to estimate the contamination rate of drilling cuttings, despite the formation parameters of 10 oil wells drilled at different depths (66 data sets), using the regression learning limit of an artificial neural network. A total of 60 data sets were prepared to estimate the rate of change in the concentration of heavy metals, polycyclic aromatic hydrocarbons in the learning and testing process, and another six sets of data related to a well that randomly selected and used in the artificial neural network validation process. Limit learning regression algorithm for ten heavy elements and ten aromatic compounds contaminating cutting and drilling mud on two different data sets in a drilling area in one of the oil fields in southern Iran was evaluated. The results are suitable for estimating the contamination of drilling cuttings and in subsequent environmental protection processes. Such as the process of contamination and recycling of drilling mud will play an efficient role.
Hamid Sarkheil; Zeinab Karimi Asl; Mohammad Talaeian Araghi
Abstract
Introduction: The problem of noise pollution is the most severe problem that most people encounter with in public. The purpose of this study was to investigate the amount of noise pollution caused by Tehran metro train traffic at the time of arrival and departure at the underground stations of ...
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Introduction: The problem of noise pollution is the most severe problem that most people encounter with in public. The purpose of this study was to investigate the amount of noise pollution caused by Tehran metro train traffic at the time of arrival and departure at the underground stations of Darvazeh Doulat and Tajrish.Material and methods: This study was conducted in the summer and autumn of 1398 in Darvazeh Doulat and Tajrish stations of Tehran Metro Line 1. The maximum and minimum values of noise pollution Intensity were recorded at specific distances from the edge of the platforms in the morning and evenings. Once every three days, 48 stations were measured and analyzed. In the continuation of the research in Tajrish Station, the distribution of noise pollution has been estimated by performing limited impressions using artificial neural network.Results and discussion: The values measured at specific distances from the edge of the platform show that the amount of noise pollution from the arrival of trains to the platform, the simultaneous entry of two trains to the platform, the arrival of non-stop trains, and crossing the platform in most times, was higher than standard. In some harvesting stations, it reached over 100 decibels, which indicates the critical situation of noise pollution in these subway platforms.Conclusion: The methods used in the present study have effectively estimated the amount of noise pollution in subway platforms. By identifying critical areas and providing measures to manage noise pollution, the damage on the health of citizens caused by noise pollution can be managed.
Hamid Sarkheil; Meisam Fathi Nooran; Mojtaba Kalhor; Yousef Azimi; Mohammad Talaeian Araghi
Abstract
Introduction: The process of extracting and exploiting oil and gas resources requires a cycle of production, delivery, and recycling of drilling mud or drilling fluid to achieve the right composition of drilling mud and recycling it as a fundamental issue in the oil industry and also, the environment. ...
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Introduction: The process of extracting and exploiting oil and gas resources requires a cycle of production, delivery, and recycling of drilling mud or drilling fluid to achieve the right composition of drilling mud and recycling it as a fundamental issue in the oil industry and also, the environment. For this purpose, waste management methods are usually used. Material and methods: In this study, an alternative method was chosen instead of the conventional method of stabilization/solidification as a drilling waste management process that is environmentally and economically viable. A total of 80 samples were taken from four types of samples taken from nine levels in an oil well in the south Azadegan field, including mud-cutting mixture, washed cutting, fixed mud-cutting mixture by cement (1:12 portion), and fixed mud-cutting mixture by cement and silica (1:12 and 1:400 portion). Results and discussion: Samples were prepared in different types of washed, with drilling fluid, stabilized, with cement, and stabilized with cement and sodium silicate to assess the concentration of heavy metals and polycyclic aromatic hydrocarbons (PAHs), as well as the salinity. The concentration of heavy metals in the samples showed that no waste management process is required to control or reduce the metals in the samples, but salinity and PAHs require treatment at a depth of 1800 m and more. Conclusion: Summarizing the results of the analyzes showed that the washing process is better from the environmental point of view than the conventional method of stabilization/solidification because the contaminants have been removed from the drill bit, which will be utilized as the final product of this process. However, in the stabilization/solidification method, the pollutants are not purified. Therefore, the washing process was able to remove the contaminant from the excavation wells, so that the washed samples were according to the standard soil of Iran until the end of the standard drilling operation.
Hamid Sarkheil; Maryam Fakhari; Behzad Rayegani; Javad Bodagh Jamali
Abstract
Introduction: Tehran metropolis, with an area of 750 km2, a population of more than 8 million people, and about 4 million vehicles is associated with the problem of air pollution. A thorough study of the spatial distribution of pollutants such as CO and NO2 in Tehran is significant for identifying ...
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Introduction: Tehran metropolis, with an area of 750 km2, a population of more than 8 million people, and about 4 million vehicles is associated with the problem of air pollution. A thorough study of the spatial distribution of pollutants such as CO and NO2 in Tehran is significant for identifying the risks, probabilities, and risks of these contaminants. Therefore, mathematical and computational methods such as the confidence level method can be useful. The main goals of this research were to investigate the changes in air pollution levels in terms of CO and NO2 concentration, study the radius of impacts of fixed pollution stations, and calculate the level of reliability by investigating the probability of air pollution and the map of the risk of air pollution in different parts of the urban area of Tehran. Material and methods: In this study, Tehran's air pollution data in October, November, and December 2017 was used in spatial modeling. Using geostatistics and indicator kriging methods, data were analyzed and maps of the distribution of pollution concentration, and also two-dual maps (0 and 1) of the probability of pollution and risk of pollution in Tehran's for the study period were produced by ArcGIS Software. Results and discussion: The resulting maps showed the highest NO2 emissions areas (Ghaem Park, Razi Park, and the municipality of district 16) and areas with the least risk of NO2 pollution (Shahid Beheshti University, Pasdaran, Science, and Technology University, and Shad Abad). Moreover, the highest CO emission areas were the municipality of districts 11, 15, and 16, Ray station, Sharif University, Fatah Square, Health Park, and Razi Park). Aghdasyeh station, Shahid Beheshti University, municipality of district 2, Rose Park, Science and Technology University, Golbargh, Shad Abad, and Masoudieh had the lowest CO emissions. Conclusion: The indicator kriging was a useful method for assessing the risk of contamination by providing a possibility map. The hazardous maps produced in this study were useful tools for identifying areas with CO and NO2 contaminations. The results of this study can play an effective role in urban management decisions by correctly identifying the amount of air pollution in an appropriate spatial distribution.
