• Journal of the Korean Institute of Gas
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• v.21 no.2
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• pp.10-19
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• 2017

This paper presents the status and characteristics of oil and gas industry and the guideline for investment of producing asset or petroleum and natural gas rights(PNG rights) in Canada. The Western Canadian Sedimentary Basin(WSCB) consists of around 11 main formations, and petroleum has actively been produced at the Montney, Cardium, Viking and Bakken formation. However, the drilling activity declined to 1,917 in Q1, 2016 from 5,724 in Q1, 2014 and 3,365 in Q1, 2015 which dropped 67% and 43% respectively because of the low oil price since 2014. Also, the price of oil and gas asset decreased 34~47% on reserves and production base, and the PNG rights for development decreased 81~97% based on total bidding price, bidding area and unit bidding price. Therefore, it is very favorable environment for Korean companies entering into the Canadian petroleum business especially in PNG rights acquisition which needs smaller investment compare to asset acquisition and shows sharpest value depreciation.

• Korea Trade Review
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• v.46 no.2
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• pp.19-38
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• 2021

This study aims to analyze the foreign asset leverage effect on Korean oil & gas companies' foreign profits and to maintain the appropriate foreign asset volume for reducing exchange risk. For a long time, large Korean companies, including oil companies, overheld foreign currency liabilities. For this reason, most large companies have been burdened to hedge exchange risk and this excess limit holding deteriorated total profit and reduced foreign currency asset management efficiency. Our paper proceeds in presenting a three-stage analysis considering diversified exchange risk factors through estimation on transformation of foreign transactions a/c including annual trends of foreign asset and industry specifics. We also supplement incomplete the estimation method through a practical hedging case investigation. Our research parts are differentiated on the analyzing four periods considering period-specifics The FER value of the oil firms ranged from -0.3 to +2.3 over the entire period. The results of the FER Value are volatile and irregular; those results do not represent the industry standard comparative index. The Korean oil firms are over the credit limit without accurate prediction and finance high interest rate funds from foreign-owned banks on the basis on a biased relationship. Since the IMF crisis, liabilities of global firms have decreased. Above all, oil firms need to finance a minimum limit without opportunity losses on the demand forecast and prepare for uncertainty in the market. To reduce exchange risk from the over-the-limit position, we must consider factors that affect the corporate exchange risk on the entire business process, including the contract phase.

• Proceedings of the KSME Conference
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• 2008.11b
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• pp.2001-2006
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• 2008

The most concerning issue of these days is the energy crisis by increasing threat of dependency on foreign oil and its volatility itself. In the situations, the PHEV is drawing attention for the next generation's car which could give a chance to decrease the dependency on foreign oil. As well as, the Korean electric power infrastructure is a strategic national asset that is under utilized most of the time. With the proper changes in the operational paradigm, it could generate and deliver necessary energy to charge the PHEVs which could penetrate the market within few years. In doing so, it would reduce greenhouse gas emissions, improve the economics of the electricity industry, and reduce the energy dependency. This paper investigate the technical potential and impacts of using the existing idle capacity of the electric infrastructure in conjunction with the emerging PHEVs technology to meet the majority of daily energy needs of the Korean LDV fleet.

• Fisheries and Aquatic Sciences
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• v.25 no.9
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• pp.473-488
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• 2022

To have a better understanding of the impact of the PETRONAS oil and gas platform on commercial fisheries activities, Universiti Malaysia Terengganu (UMT) examined two approaches which are data collection from satellite and data collection from fishermen and anglers. By profiling the anglers who utilize reefed oil and gas structures for fishing, it can determine if the design and location of the reef platforms will benefit or negatively impacts those anglers and fisherman. Furthermore, this assessment will be contributing to the knowledge regarding the value of offshore oil and gas platforms as fisheries resources. Collectively, the apparent fishing activity data included, combined with the findings in the reefing viability index will help to inform PETRONAS's future decommissioning decisions and may help determine if the design and proposed locations for future rigs-to-reefs candidates would benefit commercial fishing groups, further qualifying them as appropriate artificial reef candidates. The method applied in this study is approaching by using a data satellite known as Google's Global Fishing Watch technology, which is one of the applications to measure commercial fishing efforts around the globe. The apparent commercial fishing effort around the selected twelve PETRONAS platforms was analyzed from January 2012 to December 2018. Using the data collection from fishermen which is the total estimation of commercial fish value cost (in Malaysia ringgit, MYR [RM]) in Peninsular Malaysia Asset, Sabah Asset, and Sarawak Operation region. The data were extracted every month from 2016 to 2018 from the National Oceanic and Atmospheric Administration database. Most of the selected platforms that show a high frequency of vessels around the year are platform KP-A, platform BG-A and platform PL-B. The estimated values of commercial fishes varied between platforms, with ranged from RM 10,209.92 to RM 89,023.78. Thus, platforms with high commercial fish value are selected for reefing in-situ and will serve multi-purposes and benefit the locals as well as the country. The current study has successfully assessed the potential reefing area of the Malaysian offshore environment with greater representativeness and this paper focused on its potential as a new fishing ground.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.4_2
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• pp.569-581
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• 2020

Since the dehydration packages of offshore plant deal directly with oil & gas, there is a great risk of fire and explosion during operation. Therefore, this study performed risk assessment through HAZard & OPerability (HAZOP) for solid desiccant dehydration package that can remove water component of natural gas in offshore floating liquefied natural gas (LNG) production facilities below 0.1 ppmv. The risk matrix was determined by dividing the likelihood and the severity into five levels separately by asset, life, environment and reputation. The piping & instrumentation diagram (P&ID) of the dehydration package was divided into 9 nodes. Total 22 deviations were assessed in consideration of the adsorption and desorption conversion cycle. A risk assessment based on deviations revealed 14 major hazards. Three representative types of hazards were open/close failure of the control valve, control failure of the heater, and abnormal operation of the regeneration gas cooler. Finally, we proposed the installation of additional safety devices to improve safety against these major hazards, such as safety instrumented functions, alarms, etc.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.