عباس سلحشور

China, which was one of the least developed countries in the 20th century, after extensive reforms in 1979, entered the path of serious economic development and was recognized as an emerging economic power in the world since 2000. After 23 years have passed since then, China has become an important actor in the world with its high gross domestic product and as a commodity production factory in the world, as well as a huge need for energy and introducing itself as a big investor. Creation, especially in regions with a special geopolitical and geoeconomic position. Among these countries, we can mention the countries of the Middle East and especially the Persian Gulf region, which have a large energy capacity to be present and invest and export energy to China. The Middle East region, with its many advantages, such as energy and special geographical location, etc., is one of the regions of interest to China. But being in the complex region of the Middle East always has challenges for actors, especially emerging powers such as China, which this article tries to address. In this regard, the author tries to find the answer to the following basic question with an analytical-descriptive method in this article: What are the challenges facing China's extensive presence in the Middle East region? The data of this research has been collected in a library-document way.

China, as an emerging power that has been recognized by the world since the beginning of the 21st century, has actually stepped on the path of economic development since the end of the 70s with fundamental reforms. This country, which was initially the place of investment for western companies, has now taken the initiative of investing in other regions of the world and is implementing large projects that focus on two issues, energy and transportation. are.China is currently seeking to expand its presence in other regions of the world with a large economy and as the largest factory producing goods and with a population of more than 1.3 billion people. In the meantime, the Middle East region, with its many advantages, such as energy and special geographical location, etc., is one of the areas of interest to China. In this regard, the author tries to find the answer to the following basic question with an analytical-descriptive method in the present article titled "Geopolitical analysis of the process of China's presence in the Middle East region in economic and political dimensions":How is China's presence in the Middle East from a geopolitical point of view? In this article, the conclusion has been reached that the geopolitical relations between China and the Middle East are mostly based on geo-economics, and in this regard, China has tried to avoid entering into security-political conflicts in this region as much as possible. The data of this research has been collected in a library-document way.

Permeability is an important feature of oil and gas reservoirs that is difficult to predict. At present, experimental and regression models are used to predict permeability. On the other hand, increasing the accuracy of permeability prediction for points that do not have a core sample is of particular importance in analyzing reservoir behavior. In recent times, due to better predictability, machine learning algorithms have been used to predict permeability. In this study, a new group machine learning model for permeability prediction in oil and gas tanks is introduced. In this method, the input data is labeled using log lithology information and separated into a number of clusters and each cluster was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we design a group model using augmented decision tree (ETR), decision tree (DTR) regression, and enhanced gradient (GBR) algorithms. And petrophysical data, we were able to dramatically improve the accuracy of the prediction as well as the mean square error and predict the permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.

Determination of rock types is of special importance in the construction of static and dynamic models of hydrocarbon reservoirs. Estimating the properties of reservoir rocks increases the accuracy in predicting the amount of reservoir storage and its performance. Numerous models have been proposed by experts to determine the types of reservoir rocks. But most of the proposed models are based on conventional methods based on engineering and geology of carbonate reservoir rocks. Therefore, using a machine learning method to determine rock species in comparison with previous methods and comparing its efficiency and performance with other methods seems necessary. In this study, core and log data in maroon oil reservoir after preparation were match using Dynamic Time Series (DTW) technique for depth matching. The brain data were then clustered by the non-supervised machine learning method. The kernel data clustering process was also performed by conventional model-based methods such as flow zone index method (FZI) and Winland. Then, the clustering results were validated and compared with kmeans, FZI and Winland methods by having the lithology information of the logs. The kmeans method with a 93.5% accuracy criterion succeeded in performing the highest cluster resolution, which showed that the kmeans data-based machine learning method is a suitable alternative to conventional model-based methods for clustering rock typing.

Permeability is one of the important characteristics of oil and gas reservoirs that is difficult to predict. In the present solution, experimental and regression models are used to predict permeability, which includes time and high costs associated with laboratory measurements. Recently, machine learning algorithms have been used to predict permeability due to better predictability. In this study, a new ensemble machine learning model for permeability prediction in oil and gas reservoirs is introduced. In this method, the input data are labeled using the lithology information of the logs and divided into a number of categories and each category was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we were able to predict the accuracy of such a square mean error by designing a group model using ETR, DTR, GBR algorithms and petrophysical data. Improve dramatically and predict permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.

Mining is regarded as one of the most polluting sources of environment due to the production of toxic acid wastewater. Considering the environmental impacts of mining in the stage of mine production planning development can potentially reduce the harmful effects of this industry on the environment. In this research, attentions have been paid to the planning of production in Sungun Copper mine looking at parameters and environmental impacts. For this propose, field studies were conducted and chemical analysis, as well as exploratory and mapping data for planning of environmental production have been carried out, and various sections of ore have been evaluated with a view of the production of waste water. After modeling the mineral deposit and providing a block model, toxic elements content was determined for each block using the Kriging method, and the weight and effect of the blocks were determined using the entropy method. The block model in contrast to conventional production planning methods that include only grade of minerals, considers the wastewater production capacity of each block and its degree of pollution. Finally, by designing a production planning based on the environmental impacts of the toxic elements prevailing in Sungun copper mine eight operational phases were designed for this deposit. In this planning, blocks in the first pitt with a mean pollution level of 1141 and those in the final pit with an average of 52 have been found to represent the highest and lowest potential, respectively, producing harmful wastewater into the environment.