Petroleum Business Review (PBR)

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.

Unlike many arbitrations, the arbitration procedure for price reviews in gas industry contracts is not based on allegations that one party has breached a contract or otherwise committed a legal wrong. Instead, arbitrators are asked to determine whether the economics of a contract has been changed in the market, and thus should be adjusted and, if so, to establish a new price formula. Due to the complexity of revision of gas prices, the gas experts have significant role in these arbitrations. Regarding the role of gas experts, gas price arbitrations have specific nature. In this article, the nature of these arbitrations is examined to establish whether they are arbitration or expert determination as one of Alternative Dispute Resolutions (ADR). As Iran has the second proven natural gas resources worldwide and is the third natural gas producer in the world, so, Iran- Turkey case will come under scrutiny in this article.

Stock price prediction is one of the crucial concepts in finance area. Machine learning can provide the opportunity for traders and investors to predict stock prices more accurately. In this paper, Closing Price is dependent variable and First Price, Last Price, Opening Price, Today’s High, Today’s Low, Volume, Total Index of Tehran Stock Exchange, Brent Index, WTI Index and Exchange Rate are independent variables. Seven different machine learning algorithms are implemented to predict stock prices. Those include Bayesian Linear, Boosted Tree, Decision Forest, Neural Network, Support Vector, and Ensemble Regression. The sample of the study is fifteen oil and gas companies active in the Tehran Stock Exchange. For each stock the data from the September 23, 2017 to September 23, 2019 gathered. Each algorithm provided two metrics for performance: Root Mean Square Error and Mean Absolute Error. By comparing the aforementioned metrics, the Bayesian Linear Regression had the best performance to predict stock price in the oil and gas industry in the Tehran Stock Exchange.

Strategic management contexts usually define a couple of activities, including preparing a mission statement, which is one of the essential parts in developing the strategic plan of an organization. Numerous researches in the strategic management literature have expressed the attributes of an effectively written mission statement for a firm in general. Although the corporate mission statement and its components vary from industry to industry, none of the researchers have specifically studied the components of a corporate mission statement in the petroleum industry. In this study, the general components of the corporate mission statement were extracted and listed based on the strategic management literature review. Then, the most important components of the corporate mission statement specific to the petroleum industry were selected using the industry experts’ opinions. The grey systems theory was utilized to aggregate the expert judgments that are qualitative in nature. Fourteen components for the petroleum industry corporate mission statement were selected as the research results. Whether developing a new business or reformulating direction for an ongoing company in the petroleum industry, these specific components should be included in the content of the corporate mission statement.

Foreign investment contracts are one of the major ways to absorb the technology in the oil and gas industry. Studies show that governments and investing companies prefer to gain more profit, control economic resources and political influence by investing financially and technically in developing countries unless buying foreign technology and intellectual property rights directly. On the other hand, for developing countries, the sum of foreign capital and technology within a contract is a great opportunity for advancement, provided that the choice of technology transfer is made with due regard to the country's needs, requirements and economic future. In this article, after examining the relationship between investment and technology transfer and its various methods in the oil and gas industry, we will analyze the constraints and barriers of technology transfer through foreign investment in developing countries (including Iranian law) and provide a solution. Keywords: Foreign Investment, Technology Transfer, Oil & Gas Industry, Mutual Trade, Intellectual Property Rights.

The purpose of this study is to compare stringency of and compliance with environmental requirements in upstream sector of oil and gas in law of Iran and the USA. This study is conducted using the inter-spinned mix method (qualitative – quantitative). In the qualitative part, library studies are used. In the quantitative part, the studied society is a group of six professionals in the field of oil and gas, who filled out a questionnaire consisted of 34 questions prepared by the Worley Parsons in a similar research approved by jurisdictions of ten countries. The questions are prepared by a team of experts with international experience. The components of stringency and compliance with law during the phases of approval, operation, and closure of a hydrocarbon project are studied using the Delphi method. At the level of stringency, the environmental assessment in the US is carried out with 10 times more human resources and 26 times more costs than in Iran. In the US, with 13 scores, it is more stringent than Iran with 5 scores. In the project closure phase, Iran does not put in place any obligations for rehabilitation and restoration. At the level of compliance, the Construction Environmental Management Plan (CEMP) is mandatory in both countries. In Iran, the list of violations and their consequences will not be published. The US regulatory mechanisms of restoration are an appropriate model. Totally, Iran gains 29 and the United States 42 scores. The recommendations are based on these two scores.