• Journal of the Korean Institute of Gas
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• v.21 no.3
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• pp.61-69
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• 2017

The commercial development of unconventional gas is pursued in North America because it is more feasible owing to the technology required to improve productivity. Shale reservoir have low permeability and gas production can be carried out through cracks generated by hydraulic fracturing. The decline rate during the initial production period is high, but very low latter on, there are significant variations from the initial production behavior. Therefore, in the prediction of the production rate using deterministic decline curve analysis(DCA), it is not possible to consider the uncertainty in the production behavior. In this study, production rate of the Eagle Ford shale is predicted by Arps Hyperbolic and Modified SEPD. To minimize the uncertainty in predicting the Estimated Ultimate Recovery(EUR), Monte Carlo simulation is used to multi-wells analysis. Also, kernel density function is applied to determine probability distribution of decline curve factors without any assumption.

• Journal of the Korean Institute of Gas
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• v.22 no.1
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• pp.64-77
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• 2018

This paper presents the prediction of the original gas in place(OGIP) by using the material balance method and decline curve analysis method with production history and pressure transient test data for four coalbed methane wells in the Horseshoe Canyon field. In this study, the conventional gas equation and the Jensen and Smith(J&S) equation were used to material balance analysis, and the Arps' empirical correlation and Khaled method were applied to decline curve analysis. From the results, the OGIP estimated from the conventional gas and the J&S method was small in difference as under 12%. Also, in case of decline curve analysis, it was found that the Khaled method has appropriated to calculate the OGIP, because the OGIP was estimated as unlimited value by the Arps' equation from the decline exponent of 1 - 3.5. The OGIP difference between conventional gas method and Khaled method was calculated as 8.67% ~ 31.04%, and those between J&S method and Khaled method was 13.67% ~ 26.49%.

• Journal of the Korean Institute of Gas
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• v.20 no.3
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• pp.1-11
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• 2016

Production data from hydraulically fractured well in coalbed methane (CBM) reservoirs was analyzed using decl ine curve analysis (DCA), flow regime analysis, and flowing material balance to forecast the production performance and to determine estimated ultimate recovery (EUR) and timing for applying the DCA. To generate synthetic production data, reservoir models were built based on the CBM propertie of the Appalachian Basin, USA. Production data analysis shows that the transient flow (TF) occurs for 6~16 years and then the boundary dominated flow (BDF) was reached. In the TF period, it is impossible to forecast the production performance due to the significant errors between predicted data and synthetic data. The prediction can be conducted using the production data of more than a year after reached BDF with EUR error of approximately 5%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.297-299
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• 2021

Predicting future productivity of tight oil is an important task for analyzing residual oil recovery and reservoir behavior. In general, productivity prediction is made using the decline curve analysis(DCA). In this study, we intend to propose an effective model for predicting future production using deep learning-based recurrent neural networks(RNN), LSTM, and GRU algorithms. As input variables, the main parameters are oil, gas, water, which are calculated during the production of tight oil, and the type curve calculated through various cluster analyzes. the output variable is the monthly oil production. Existing empirical models, the DCA and RNN models, were compared, and an optimal model was derived through hyperparameter tuning to improve the predictive performance of the model.

• Journal of Korea Trade
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• v.25 no.1
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• pp.1-17
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• 2021

Purpose - The global trend of protectionism has expanded since the onset of US President Donald Trump's administration in 2017. This global phenomenon has led to a significant reduction in world trade volume and a negative impact on economic development in some countries where the external sector accounts for a large proportion of GDP. Although Korea is a country vulnerable to this deteriorating trade environment, few studies have examined the relationship between protectionism and its business cycles based on Korean data. Thus, this paper investigates the impact of protectionism on Korea's business cycle. Design/methodology - To identify future implications, we conduct a structural vector autoregression (VAR) analysis using monthly Korean data from 1994 to 2015. Macroeconomic variables in the model include the industrial production index, inflation rates, exports (or net exports), interest rates, and exchange rates. For the identification of the shock reflecting the expansion of protectionism, we use an antidumping investigation (ADI) data. Since ADIs are followed generally by the imposition of antidumping tariffs, they have no contemporaneous impact on tariffs and are also contemporaneously exogenous to other endogenous variables in the VAR model. We examine two kinds of ADI shocks i) shocks on Korean exports imposed by Korea's trading partners (ADI-imposed shocks) and ii) shocks on imports imposed by the Korean government (ADI-imposing shocks). Findings - We find that Korea's exports decline sharply due to ADI-imposed shocks; the lowest point at the third month after the initial shock; and do not recover until 24 months later. Simultaneously, the inflation rate decreases. Therefore, the ADI-imposed shock can be regarded as a negative shock on the demand curve where both production and price decrease. In contrast, the ADI-imposing shock generates a different response. The net exports decline, but the inflation rate increases. These can be seen as standard responses with respect to the negative shock on the supply curve. Originality/value - We shed light on the relationship between protectionism and Korea's economic fluctuations, which is rarely addressed in previous studies. We also consider the effects of both protective policy measures on imports to Korea imposed by the Korean government and on policy measures imposed by Korea's trading partner countries on its exports.

• Journal of the Korean Institute of Gas
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• v.20 no.1
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• pp.13-22
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• 2016

This paper presents development of an appropriate procedure and flow chart to analyze shale gas production data obtained from a multi-fractured horizontal well according to flow characteristics in order to calculate an estimated ultimate recovery. Also, the technical considerations were proposed when a rate transient analysis was performed with field production data occurred to only $1^{st}$ transient flow. If production data show the $1^{st}$ transient flow from log-log and square root time plot analysis, production forecasting must be performed by applying different method as before and after of the end of $1^{st}$ linear flow. It is estimated by an area of stimulated reservoir volume which can be calculated from analysis results of micro-seismic data. If there are no bottomhole pressure data or micro-seismic data, an empirical decline curve method can be used to forecast production performance. If production period is relatively short, an accuracy of production data analysis could be improved by analyzing except the early production data, if it is necessary, after evaluating appropriation with near well data. Also, because over- or under-estimation for stimulated reservoir volume could take place according to analysis method or analyzer's own mind, it is necessary to recalculate it with fracture modeling, reservoir simulation and rate transient analysis, if it is necessary, after adequacy evaluation for fracture stage, injection volume of fracture fluid and productivity of producers.

• The Journal of Fisheries Business Administration
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• v.50 no.2
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• pp.41-56
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• 2019

The aim of this study is to analyze the productivity change of pollock enterprise by applying a mutually quadratic hyperbolic model and a bootstrapping model. This study used 20 units of pollock firms data (from 2013 to 2017). As a result of total productivity analysis of twenty pollock enterprises, total factor productivity was estimated to have decreased by 24.9% over the last five years (2013~2017). The main cause of this productivity decline was analyzed by technical change. In terms of annual productivity change, it showed decrease 3.0% in 2013~2014, 7.8% in 2014~2015, 4.5% in 2015~2016 and 4.7% in 2016~2017 respectively. In the analysis of productivity by corporation type, total factor productivity showed a significant decrease in both general corporation and external corporation, and productivity decrease (-29.3%) was larger than general corporation (-23.0%). In the productivity analysis by type of business, total factor productivity decreased significantly in the order of wholesale and commodity brokerage (-26.3%), food manufacturing (-25.1%) and fisheries (-15.3%). This decrease in productivity was caused by the technological change which indicates a downward shift in the production curve that is significant in all sectors.