• The Korean Journal of Petroleum Geology
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• v.14 no.1
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• pp.36-44
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• 2008

Natural gas in tight reservoirs, one of unconventional hydrocarbon resources, has become a significant exploration and exploitation targets. Tight gas reservoirs are the gas-bearing rocks that commonly have a permeability of less than 0.1 millidarcy (mD). Tight gas reservoirs are characterized by extensive and deep locations as well as abnormal pressure such as over- or under-pressure. The tight gas reservoirs are independent of structural or stratigraphic traps, whereas conventional gases normally occur at these traps. Tight gas reservoirs can be productive when stimulated by hydraulic fracturing. Better production areas within the tight reservoir beds are referred to as sweet spots that are commonly caused by natural fractures, which should be understood and identified to enhance the recovery of the gas from tight reservoirs. The exploration and production techniques allow the commercial production of tight gas, one of environmentally friendly resources. Slant and horizontal wells have best production when they intersect the fractures. Gas production from the tight reservoirs has rapidly grown in U.S. and Canada. Indeed, the U.S. gas production of tight sandstones increases from 11.1% in 1990 to 24.1% in 2005. The presence of tight gas reservoirs has been suggested on the Korean offshore block 6-1. Paradigm shift from conventional to unconventional tight reservoir is required to develop the tight gas from the block.

• 한국석유지질학회:학술대회논문집
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• 2005.09a
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• pp.54-57
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• 2005

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.20-30
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• 2014

To develop a unconventional gas reservoir, an analysis of tight rock property are required. Especially, conventional measurements are difficult to be applied to unconventional resources such as tight gas reservoir because the permeability are extremely low compared to a conventional gas reservoir. In this study, an apparatus was developed for measuring low permeability and porosity based on a pressure pulse decay method under unsteady state conditions. The apparatus was applied for measuring the porosity and permeability of tight sand core samples from Gyeongsang basin in Korea. As a validation of the measurement, regression analysis was carried out using the dimensionless pseudo-pressure between the measured data and analytical solution. The results show the correlation coefficients above 0.96. Therefore, it is believed that the apparatus has a high accuracy.

• Tunnel and Underground Space
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• v.23 no.3
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• pp.169-179
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• 2013

Well testing in unconventional reservoirs, such as tight or shale gas formations, presents considerable challenges. It is difficult to estimate the reservoir properties in ultra-low permeability formation because of poor inflow prior to stimulation and excessive test duration. Moreover, radial flow may not develop in hydraulically fractured horizontal wells. For these reasons, the cost of test is high and the accuracy is relatively low. Accordingly, industry is turning to an alternate testing method, diagnostic fracture injection test (DFIT), which is conducted prior to the main hydraulic fracture treatments. Nowadays, DFIT are regarded as the most practical way to obtain good estimates of reservoir properties in unconventional reservoirs. Various methods may be used for interpreting DFIT data. This paper gives an explanation of those methods in detail and examines three actual field data. These show how various analysis methods can be applied to consistently interpret fracture closure pressure and time, as well as before and after closure flow regimes and reservoir properties from field data.

• The Korean Journal of Petroleum Geology
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• v.4 no.1_2 s.5
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• pp.27-39
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• 1996

In the Cretaceous, the Gulf Coast Basin evolved as a marginal sag basin. Thick clastic and carbonate sequences cover the disturbed and diapirically deformed salt layer. In the Cretaceous the salinities of the Gulf Coast Basin probably matched the Holocene Persian Gulf, as is evidenced by the widespread development of supratidal anhydrite. The major Lower Cretaceous reservoir formations are the Cotton Valley, Hosston, Travis Peak siliciclastics, and Sligo, Trinity (Pine Island, Pearsall, Glen Rose), Edwards, Georgetown/Buda carbonates. Source rocks are down-dip offshore marine shales and marls, and seals are either up-dip shales, dense limestones, or evaporites. During this period, the entire Gulf Basin was a shallow sea which to the end of Cretaceous had been rimmed to the southwest by shallow marine carbonates while fine-grained terrigengus clastics were deposited on the northern and western margins of the basin. The main Upper Cretaceous reservoir groups of the Gulf Coast, which were deposited in the period of a major sea level .rise with the resulting deep water conditions, are Woodbinefruscaloosa sands, Austin chalk and carbonates, Taylor and Navarro sandstones. Source rocks are down-dip offshore shales and seals are up-dip shales. Major trap types of the Lower and Upper Cretaceous include salt-related anticlines from low relief pillows to complex salt diapirs. Growth fault structures with rollover anticlines on downthrown fault blocks are significant Gulf Coast traps. Permeability barriers, up-dip pinch-out sand bodies, and unconformity truncations also play a key role in oil exploration from the Cretaceous Gulf Coast reservoirs. The sedimentary sequences of the major Cretaceous reseuoir rocks are a good match to the regressional phases on the global sea level cuwe, suggesting that the Cretaceous Gulf Coast sedimentary stratigraphy relatively well reflects a response to eustatic sea level change throughout its history. Thus, of the three main factors controlling sedimentation (tectonic subsidence, sediment input, and eustatic sea level change) in the Gulf Coast Basin, sea-level ranks first in the period.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.9
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• pp.973-980
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• 2015

With their wide geographical distribution, unconventional resources are continuously compared against conventional resources, but their development is expanding because TRRs (Technical Recoverable Resources) are similar to conventional resources. In particular, there is active development of unconventional gas resources such as shale gas, tight gas, CBM (coalbed methane) and gas hydrate. However, it is difficult to calculate the material properties of unconventional resources, especially the gas content, with current geophysical logging technology. Additionally, some overseas companies have monopolies on related equipment and materials. Therefore, this study developed a reservoir PCS (Pressure Core Sampler). It can collect core samples without gaseous loss by maintaining high pressure from the moment the core is sampled and record pressure and temperature in real time. Successful performance testing was also carried out for official verification of the manufactured PCS. The reservoir PCS will contribute to the acquisition of geophysical well logging data as well as accurate and reliable cores.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.484-487
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• 2022

This study suggests an XAI-based machine learning method to predict the productivity of tight oil reservoirs according to the production period. The XAI algorithm refers to interpretable artificial intelligence and provides the basis for the predicted result and the validity of the derivation process. In this study, we proposed a supervised learning model that predicts productivity in the early and late stages of production after performing data preprocessing based on field data. and then based on the model results, the factors affecting the productivity prediction model were analyzed using XAI.

• Economic and Environmental Geology
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• v.43 no.2
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• pp.85-100
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• 2010

Devonian Cairn Formation is one of the important hydrocarbon reservoirs in Alberta, Canada. However, the Cairn Formation, outcropped in the study area, is not prospective reservoir with poor porosity and permeability by some late diagenetic processes. In this study, geochemical characteristics of the Cairn Formation were studied to use these preliminary results for advanced geological and geophysical petroleum explorations in the near future. Rock-Eval pyrolysis showed that total organic carbon content is less than 0.3 wt.%, indicating a minor amount of bitumen and/or other hydrocarbons. The carbonates in the Cairn Formation are mainly composed of subhedral and anhedral dolomites. Pore sizes in the carbonate are various, ranging from nanometer to micrometer. Clastic sediments increase in the upper and lower parts of the Cairn Formation, probably due to changing its depositional conditions. The Cairn Formation can also be divided into several intervals based on Ca/Mg ratio in dolomite and degree of amount of calcite. These could be formed by different sedimentary environment, degree of cementation and recrystallization, different saline/fresh water, etc.

• 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.