• Journal of the Korean Institute of Gas
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• v.24 no.4
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• pp.62-72
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• 2020

This study presents a methodology of history matching to evaluate the productivity of shale gas reservoir with high reliability and predict future production rate in the Horn-River basin, Canada. Sensitivity analysis was performed to analyze the effect of physical properties of shale gas reservoir on productivity. Based on the results, reservoir properties were classified into 4 cases and history matching were performed considering the classified 4 cases as objective function. The blind test was conducted using additional field production data for 3 years after the history matching period. The error of gas production rate in Case 1(all reservoir parameters), Case 2(influenced parameters for productivity), Case 3(controllable parameters), and Case 4(uncontrollable parameters) were 7.67%, 7.13%, 17.54%, and 10.04%, respectively. This means that it seems to be effective to consider all reservoir parameters in early period for 4 years but Case 2 which considered influenced parameters for productivity shows the highest reliability in predicting future production. The estimated ultimate recovery (EUR) of production well predicted using the Case 2 model was estimated to be 17.24 Bcf by December 2030 and the recovery factor compared to original gas in place (OGIP) was 32.2%.

• Journal of Korea Water Resources Association
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• v.35 no.1
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• pp.37-49
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• 2002

The application of conventional method for optimizing firm water supply and hydro-electric power generation has some limitation during abnormal or extreme drought periods. Hashimoto et al. (1982) suggested there risk evaluation criteria such as reliability, resilience, and vulnerability. These three criteria have been incorporated into a mixed-integer programming model for evaluating the possible performance of water- supply reservoir (Moy et al., 1986; Srinivasan et al., 1999). However, till now, these kind of researches have been conducted only for water-supply reservoir. Therefore there have been no other study for multi-purpose dam including hydro-electric power generation. This study presents an improved formulation of the previous model for evaluating a multi-purpose reservoir system operation considering water supply and hydro-electric power generation. The modified model was applied to the Daechung multi-purpose reservoir system in the Keum river basin to demonstrate the efficiency of the improved formulation.

• Journal of Korea Water Resources Association
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• v.37 no.10
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• pp.837-844
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• 2004

In this study, the efficient reservoir operation is studied by comparing results from reservoir operation using a basin drought forecasting and warning system with an existing reservoir operation rule. As a result, it is found that the reliability and average annual storage of reservoir operation using a basin drought forecasting and warning system and release coefficients is better than those of reservoir operation using the existing operation rule. The release coefficients for Yongdam dam located in the Geum river basin selected as a case study are found to be the most effective for the value of 0.95 for the drought watch, 0.9 for the drought warning and 0.85 for the drought emergency. The reservoir operation using a drought forecasting and warning enables the use of the limited water resources effectively during drought and will contribute the national water resources management.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.354-359
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• 2006

The analysis of large-scale water resources systems is often complicated by the presence of multiple reservoirs and diversions, the uncertainty of unregulated inflows and demands, and conflicting objectives. Reinforcement learning is presented herein as a new approach to solving the challenging problem of stochastic optimization of multi-reservoir systems. The Q-Learning method, one of the reinforcement learning algorithms, is used for generating integrated monthly operation rules for the Keum River basin in Korea. The Q-Learning model is evaluated by comparing with implicit stochastic dynamic programming and sampling stochastic dynamic programming approaches. Evaluation of the stochastic basin-wide operational models considered several options relating to the choice of hydrologic state and discount factors as well as various stochastic dynamic programming models. The performance of Q-Learning model outperforms the other models in handling of uncertainty of inflows.

• Proceedings of the Korean Environmental Health Society Conference
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• 2005.06a
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• pp.259-262
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• 2005

This study aims to identify the relationship between characteristics of aqueous organic matter and chlorination by-products formation potential according to temporal effect of Juam reservoir in Sun-Choen. The molecular weight distribution and chemical composition of precursors and their relationship with disinfection by-products(DBPs) were investigated. Most of the organic matters was responsible for the major DBP precursors in the raw water are small compounds with a molecular weight less than IKDa, Aromatic contents determined by SUVA correlated well with DBPs, THMs, and HAAs formation. Especially, THMFP/DOC showed better correlation with SUVA than HAAFP/DOC and DBPFP/DOC with SUVA in Juam reservoir. Therefore, effective removal of small molecules or hydrophobic organic matter prior to disinfection process will significantly reduce the DBP concentration in the finished water.

• Journal of Korean Society on Water Environment
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• v.25 no.5
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• pp.651-660
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• 2009

Detailed flow analysis in river is essential to increase the accuracy of water quality simulation since flow variation depends on many factors such as cross sections, channel slopes, and bed materials. In the QUAL2E stream water quality simulation model, the hydraulic coefficients are assigned to the reach that is collection of computational element using the hydraulic coefficient. This study developed a module that can incorporate the results of non-uniform flow analysis and assign such information to each individual element. Model application focused on the upstream of the Hoengseong reservoir including the reservoir where significant flow change is expected. Comparing with original QUAL2E model the developed module improved the result of water quality simulation without considering the relation of flow velocity and flow depth in terms of flow rates.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2003.10a
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• pp.495-498
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• 2003

In order to analyze water supply capacity in estuary barrage dam, a system was developed in which base model was consisted of daily water balance model and daily inflow model. Agricultural water demand to paddy fields and domestic and industrial water demand were considered in this daily water balance model. Also outflow volume through sluice gate and inside water level at time to start outflow was conditioned initially to simulate reservoir storage. The DAWAST model was selected to simulate daily reservoir inflow in which return flows from agricultural, domestic and industrial water were included to simulate runoff. Using this developed system, water supply capacity in the Keum river estuary reservoir was analyzed.

• Journal of Environmental Impact Assessment
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• v.20 no.4
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• pp.575-583
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• 2011

In this study, the plausible grid size was estimated to increase for efficiency of reservoir management using 3 dimensional water quality model. To validate utilization of a real time water quality management tool, ELCOM-CAEDYM model was applied to Soyang reservoir in korea. 100m grid size can represent the real topography and take out exact analysis results. $400{\times}400m$ grid can be easily used to analysis because of data capacity. Consequently, the grid size of 200m or 300m was recommended to establish 3D model considering the required simulation time and the irrelevance between horizontal grid size and vertical distribution for temperature and turbidity analysis.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.59-64
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• 2002

An expert system, to assist dam managers for five dams along the Saikawa River, has been developed with a primary objective of achieving swift and accurate reservoir operation decision-makings during floods. The expert system is capable of supporting on decision-makings upon establishment of flood management procedure and release/storage planning. Furthermore, an attempt was made to improve reservoir inflow prediction models for better supporting capability. As a result, accuracy on prediction of inflow up to 7 hours ahead was improved, which is important for flood management of the five dams, using neural network. The neural network inflow prediction models were developed for each types of floods caused by frontal rainfalls, snowmelt and typhoons, after extracting relevant meteorological factors for each.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.2
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• pp.197-206
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• 2013

Recently, climate change causes climatic anomaly such as global warming, the typhoon and severe rain storm etc. and it brings damage frequently. Climate change and global warming are prevalent all over the world in this century and many researchers including hydrologists have studied on the climate change. In this study, Seonakdong river watershed in the Nakdong river basin was selected as a study area. Real-time monitoring system was used to draw the rating curves, which has 0.78 to 0.96 of $R^2$. To predict runoff change in Seonakdong river watershed caused by climate change, the change in hydrologic runoff were predicted using the watershed model, SWAT. As a result, the runoff from the Seonakdong river watershed was increased by up to 45 % in summer. Because of the non-point sources from the farmland and the urban area, the water quality will be affected by the climate change. In this study, the operating plan of the water gates in Seonakdong river will be suggested by considering the characteristics of the watershed runoff due to the climate change. The optimal watergate opening plan will solve the water pollution problems in the reservoir-like river.