Petroleum University of Technology

About: Petroleum University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Enhanced oil recovery & Adsorption. The organization has 1392 authors who have published 2464 publications receiving 41455 citations. The organization is also known as: Abadan Institute of Technology.

Carbon Dioxide Separation from Flue Gases: A Technological Review Emphasizing Reduction in Greenhouse Gas Emissions

Abstract: Increasing concentrations of greenhouse gases (GHGs) such as CO2 in the atmosphere is a global warming. Human activities are a major cause of increased CO2 concentration in atmosphere, as in recent decade, two-third of greenhouse effect was caused by human activities. Carbon capture and storage (CCS) is a major strategy that can be used to reduce GHGs emission. There are three methods for CCS: pre-combustion capture, oxy-fuel process, and post-combustion capture. Among them, post-combustion capture is the most important one because it offers flexibility and it can be easily added to the operational units. Various technologies are used for CO2 capture, some of them include: absorption, adsorption, cryogenic distillation, and membrane separation. In this paper, various technologies for post-combustion are compared and the best condition for using each technology is identified.

Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir

Abstract: Multiphase flow meters (MPFMs) are utilized to provide quick and accurate well test data in numerous numbers of oil production applications like those in remote or unmanned locations topside exploitations that minimize platform space and subsea applications. Flow rates of phases (oil, gas and water) are most important parameter which is detected by MPFMs. Conventional MPFM data collecting is done in long periods; because of radioactive sources usage as detector and unmanned location due to wells far distance. In this paper, based on a real case of MPFM, a new method for oil rate prediction of wells base on Fuzzy logic, Artificial Neural Networks (ANN) and Imperialist Competitive Algorithm is presented. Temperatures and pressures of lines have been set as input variable of network and oil flow rate as output. In this case a 1600 data set of 50 wells in one of the northern Persian Gulf oil fields of Iran were used to build a database. ICA-ANN can be used as a reliable alternative way without personal and environmental problems. The performance of the ICA-ANN model has also been compared with ANN model and Fuzzy model. The results prove the effectiveness, robustness and compatibility of the ICA-ANN model.