Petroleum University of TechnologyPetroleum Business Review2645-47263220190601Prediction of Natural Gas Prices in European Gas Hubs Using Artificial Neural Network11411387810.22050/pbr.2019.113878ENMansourehRamPhD Student in Oil and Gas Economics, Faculty of Economics, Allameh Tabataba`i University, Tehran, IranAtefehTaklifAssistant Professor, Faculty of Economics, Allameh Tabataba`i University, Tehran, Iran0000-0003-3765-2339AliFaridzadAssociate Professor of Energy Economics, Faculty of Economics, Allameh Tabataba`i University, Tehran, IranJournal Article20190401The 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 develop a model of the monthly prediction of natural gas price. Based on the time series data from 2012 to April 2019 as the input to the neural network, this model predicts the prices in five hubs and natural gas exchange centers in Europe. Based on the <em>R</em><sup>2</sup> performance evaluation index of 98% of the neural network model fitted based on the aforementioned data series, the neural network model has 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 countries, can be predicted with a high degree of accuracy.https://pbr.put.ac.ir/article_113878_ab731d971bfeec367ff58cff3b9f9575.pdfPetroleum University of TechnologyPetroleum Business Review2645-47263220190601Gas Price Arbitration in The Light of Experts’ Role: A Conceptual Analysis with a Reference to Iran–Turkey Case152711286710.22050/pbr.2019.112867ENNiloofarHeydari RoochiMaster in Oil and Gas Law, University of Tehran, Tehran, IranNasrollahEbrahimiAssociate Professor, Faculty of Law and Political Science, University of Tehran, Tehran, Iran0000-0002-4942-0431Journal Article20190310<span class="H5CharChar"><span>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; if so, a new price formula should be established. Due to the complexity of the 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). 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.</span></span>https://pbr.put.ac.ir/article_112867_0ad75d53390edc40f39ca66aa9ae1166.pdfPetroleum University of TechnologyPetroleum Business Review2645-47263220190601Machine Learning Application in Stock Price Prediction: Applied to the Active Firms in Oil and Gas Industry in Tehran Stock Exchange294111280310.22050/pbr.2019.112803ENAli MohammadGhanbariAssistant Professor, Accounting Department, Petroleum Faculty of Tehran, Petroleum University of Technology, IranJournal Article20200612Stock 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.<br />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.https://pbr.put.ac.ir/article_112803_921152c8c7ba0d160be0a1e9b562888a.pdfPetroleum University of TechnologyPetroleum Business Review2645-47263220190601Determining the Most Important Components of the Petroleum Corporate Mission Statement Using Grey Systems Theory435411280110.22050/pbr.2019.112801ENAzadehDabbaghiAssistant Professor, Research Institute of Petroleum Industry (RIPI), Tehran, IranMaryamDehghanAssistant Professor, Department of Industrial Engineering, Faculty of Engineering, Robat Karim Branch, Islamic Azad University, Tehran, IranJournal Article20190411Strategic 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 literature review of strategic management. 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 of corporate mission statement in the petroleum industry 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.https://pbr.put.ac.ir/article_112801_1cc2cdffe7a2039f88bc72fca3f6395c.pdfPetroleum University of TechnologyPetroleum Business Review2645-47263220190601Legal Aspects of Technology Transfer Through Foreign Investment in Oil and Gas Industry556511280010.22050/pbr.2019.112800ENMostafaElsanAssociate Professor, International Trade Law and Intellectual Property Rights and Cyberspace Group, Shahid Beheshti University, Tehran, IranMehdiYousefiChehreghaniM.A in Private Law, Bu-Ali Sina University, Hamedan, IranJournal Article20190401<span class="H5CharChar"><span>Foreign investment contracts are one of the major ways to absorb the technology in oil and gas industry. Studies show that governments and investing companies prefer to gain more profit and control economic resources and political influence by investing financially and technically in developing countries than to buy 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 technology transfer is chosen with due regard to the country’s needs, requirements, and economic future. In this work, after examining the relationship between investment and technology transfer and its various methods in oil and gas industry, we will analyze the constraints on and the barriers to technology transfer through foreign investment in developing countries, including Iran, and provide a solution. </span></span>https://pbr.put.ac.ir/article_112800_98dad8d267920635fc39a486ecb3c6d0.pdfPetroleum University of TechnologyPetroleum Business Review2645-47263220190601Comparison of Stringency of the Environmental Requirements and Compliance with them in Upstream Sector of Oil and Gas in Iranian and American Law677511287010.22050/pbr.2019.112870ENHassanAziziPhD student in Law, Law Department, Allameh Tabataba’i University, Tehran, IranFaysalAmeriAssociate Professor, Private Law Department, Allameh Tabataba’i University, Tehran, IranAliEmami MeibodiAssociate Professor, Department of Energy Economics, Faculty of Economics, Allameh Tabataba’i University, Tehran, Iran0000-0002-1556-5042Journal Article20200130The purpose of this study is to compare the stringency of and compliance with environmental requirements in the upstream sector of oil and gas in law of Iran and the USA. This study is conducted using the mixed entanglement 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[1] in a similar research approved by the jurisdictions of 10 countries. The questions are prepared by a team of experts with international experience. The components of stringency of 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 human resources and costs 10 and 26 times more than those in Iran. The US, with 13 scores, is more stringent than Iran with 5 scores. In the project closure phase, Iran does not impose 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. On the whole, Iran gains 29 scores, and the United States obtains 42 scores. The recommendations are based on these two scores.
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[1]https://www.medicinehat.ca/home/showdocument?id=11659https://pbr.put.ac.ir/article_112870_566bf5b7ec4d313bfd2db028ed29605a.pdf