Artificial Intelligence Techniques For Recognition And Estimating Toxic Gas Hazards In Petroleum Industry

There are numerous life-threatening dangers for petroleum workers in the oil and gas industry. One of the most challenging is toxic gas hazards, which are extremely difficult to handle. In this research, we present a comparative study of machine learning methods related to the classification and estimation of toxic gas hazards in the oil and gas industry. The main focus of this study is a brief background of effective methods presented and their key advantages and disadvantages in reducing toxic gas hazards in petroleum operations. In contrast to traditional methods, it is clear that artificial intelligence-based methods are the most effective way to reduce challenges associated with toxic gas hazards in petroleum mines. In the future, we can investigate the effect of data type and size, along with different parameters, on predicting oil and gas outbursts and their pollution effects on petroleum sites.

کلیدواژه ها:

petroleum mines ، Artificial Intelligence ، Machine Learning ، Risk Mitigation ، danger Prediction