درخت حوزه‌های تخصصی

Iran is one of the most important oil and gas producing countries in the world with 153.8 billion barrels of crude oil and 33.5 billion cubic meters of gas has 9.3% and 18% share of total oil and gas reservoirs, respectively. Rich hydrocarbon reservoirs along with a special geographical location are of the most important competitive advantages of Iran. The oil value chain has a special place in the social, economic structure and level of development of Iran. In policy-making, especially in global equations and in the long run, where uncertainty is an integral part it is necessary to pay attention to this area. As a strategic knowledge, futures studies can play an important role in mapping the future. In current study, the possible and plausible futures of Iran's fossil energies (oil and gas) in the 2035 horizon are presented in the form of four scenarios. In an environment where variables are dynamic and constantly changing and uncertainty is high, using scenario building methods is preferable for long-term horizons. In this study, 30 drivers with high uncertainty and impact on the future of Iranian oil and gas were produced. Using the cross impact analysis model and the balanced impact model, out of 41,472 possible scenarios, 10 scenarios with maximum compatibility were obtained, that presented in four scenarios namely: clean scenario (low carbon), a bipolar Middle East scenario, a cooperation and development scenario, and finally a postponed dream scenario.

The liberalization of natural gas markets and the emergence of gas hubs in recent decades have shifted the natural gas trade from the regional to the global trade. The growth and maturity of these hubs have weakened the previously established relationship between the natural gas price and the prices of crude oil and petroleum products. Therefore, predicting the price of gas as a strategic commodity has become more important for different countries. Using the neural network method, this paper attempts to provide a model for the monthly prediction of natural gas price. Based on the time series data from 2012 to April 2019 as neural network input, this model predicts the prices in five hubs and natural gas exchange centers in Europe. Based on the R2 performance evaluation index of 98% in the neural network model fitted based on the aforementioned data series, the neural network model has an acceptable performance in predicting the natural gas price. The results of this study show that using the artificial neural network (ANN) method, the gas prices in the European gas hubs, which are located in European Country, can be predicted with a high accuracy.