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Aquifer characterization of gas reservoirs using Ensemble Kalman filter and covariance localization
Kim, Sungil,Lee, Choongho,Lee, Kyungbook,Choe, Jonggeun Elsevier 2016 Journal of petroleum science & engineering Vol.146 No.-
<P><B>Abstract</B></P> <P>For decision making, it is necessary to predict reservoir behaviors using reliable models. We can forecast future performances with less uncertainty after reservoir characterization, which is an essential step for integrating available static and dynamic data in history matching. Ensemble Kalman filter (EnKF) is one of the powerful methods for reservoir characterization. It uses recursive updates and provides uncertainty assessment.</P> <P>EnKF has been rarely applied to characterization of gas reservoirs in spite of its active research for oil reservoirs. Gas reservoirs show typically high recovery and are less sensitive to permeability uncertainty. However, the recovery of gas reservoirs is severely affected by an aquifer, which has considerable uncertainty. Therefore, aquifer characterization is crucial for production management and uncertainty assessment of gas reservoirs.</P> <P>This paper presents a method to characterize permeability distribution and aquifer sizes of gas reservoirs using static data and production data available. Covariance localization is applied for taking account of proper relationship between well production data and the properties of grid cells. The proposed method manages not only permeability overshooting but also successful assimilation of permeability distribution. Besides, the aquifer factors come closer to the reference values as compared with a standard EnKF. Therefore, it obviously improves future predictions of gas and water productions.</P> <P><B>Highlights</B></P> <P> <UL> <LI> We characterize permeability distribution and aquifer sizes of gas reservoirs using only production data. </LI> <LI> We propose EnKF with covariance localization to characterize aquifer factors. </LI> <LI> The proposed method improves assimilation of permeability and predictions of gas and water rates. </LI> <LI> The proposed method characterizes performances of aquifer factors. </LI> </UL> </P>
Identification of a Transferrin Receptor-binding Peptide from a Phage-displayed Peptide Library
Sungil Kim(김성일),Suk-Jung Choi(최석정) 한국생명과학회 2008 생명과학회지 Vol.18 No.3
펩타이드 문고 기술을 이용하여 흑색종 세포주인 B16F10에 결합하는 펩타이드 리간드를 검색하였다. 먼저 세포 내부로 들어간 파지들을 선택하는 방법으로 두 번 검색한 후 표면에 결합한 파지들 가운데 트랜스페린 단백질을 이용하여 트랜스페린 수용체에 결합한 파지들만을 선별적으로 용출시키는 방법으로 세 번 검색하였다. 다음으로 이 두 가지 방법을 통해 선별된 파지들에 표현된 펩타이드들을 Pseudomonas exotoxin의 전이 영역과 촉매 영역에 융합시킨 재조합 독소들을 만들었다. B16F10 세포에 대한 각 재조합 독소의 활성을 측정하여 일곱 개의 클론을 선택한 후 염기서열을 분석하였다. 그 결과 그 가운데 한 클론에서 표현하는 펩타이드의 아미노산 서열이 사람의 트랜스페린과 유사한 서열을 갖는 것으로 확인되었다. 그 펩타이드를 화학적으로 합성한 후 항암제를 포함하는 리포솜에 붙여 실험한 결과 트랜스페린 수용체를 통해 치료물질을 전달할 수 있는 가능성을 지닌 것으로 평가되었다. Using a phage peptide library approach, we have isolated a peptide ligand that binds to transferrin receptor on the surface of human melanoma cell, B16F10. The library was first screened twice by recovering internalized phages and was further screened three times by competitively eluting transferrin receptor-specific phages with human transferrin among the phages bound to the cell surface. The peptides displayed by the selected phages were fused to translocation and catalytic domain of Pseudomonas exotoxin to prepare recombinant toxins. After estimating cytotoxicity of each recombinant toxin toward B16F10 cell, seven clones were selected. Sequence analysis revealed that one of the clones displayed a peptide which had a significant sequence homology with human transferrin. The peptide was chemically synthesized and was shown to be functional in delivering cytotoxic agents into B16F10 cell via interaction with transferrin receptor.
Kim, Sungil,Min, Baehyun,Kwon, Seoyoon,Chu, Min-gon Hindawi Limited 2019 Geofluids Vol.2019 No.-
<P>For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple data assimilation (ES-MDA), and improves the overall history matching performance. As a training dataset of the SDAE, the static reservoir models are realized based on multipoint geostatistics and contaminated with two types of noise: salt and pepper noise and Gaussian noise. The SDAE learns how to eliminate the noise and restore the clean reservoir models. It does this through encoding and decoding processes using the noise realizations as inputs and the original realizations as outputs of the SDAE. The trained SDAE is embedded in the ES-MDA. The posterior reservoir models updated using Kalman gain are imported to the SDAE which then exports the purified prior models of the next assimilation. In this manner, a clear contrast among rock facies parameters during multiple data assimilations is maintained. A case study at a gas reservoir indicates that ES-MDA coupled with the noise remover outperforms a conventional ES-MDA. Improvement in the history matching performance resulting from denoising is also observed for ES-MDA algorithms combined with dimension reduction approaches such as discrete cosine transform, K-singular vector decomposition, and a stacked autoencoder. The results of this study imply that a well-trained SDAE has the potential to be a reliable auxiliary method for enhancing the performance of data assimilation algorithms if the computational cost required for machine learning is affordable.</P>
Kim, Jeonghyo,Tran, Van Tan,Oh, Sangjin,Kim, Chang-Seok,Hong, Jong Chul,Kim, SungIl,Joo, Young-Seon,Mun, Saem,Kim, Myoung-Ho,Jung, Jae-Wan,Lee, Jiyoung,Kang, Yong Seok,Koo, Ja-Won,Lee, Jaebeom American Chemical Society 2018 ACS APPLIED MATERIALS & INTERFACES Vol.10 No.49
<P>Magnetic nanoparticles have had a significant impact on a wide range of advanced applications in the academic and industrial fields. In particular, in nanomedicine, the nanoparticles require specific properties, including hydrophilic behavior, uniform and tunable dimensions, and good magnetic properties, which are still challenging to achieve by industrial-scale synthesis. Here, we report a gram-scale synthesis of hydrophilic magnetic nanoclusters based on a one-pot solvothermal system. Using this approach, we achieved the nanoclusters with controlled size composed of magnetite nanocrystals in close-packed superstructures that exhibited hydrophilicity, superparamagnetism, high magnetization, and colloidal stability. The proposed solvothermal method is found to be highly suitable for synthesizing industrial quantities (gram-per-batch level) of magnetic spheres with unchanged structural and magnetic properties. Furthermore, coating the magnetic spheres with an additional silica layer provided further stability and specific functionalities favorable for biological applications. Using in vitro and in vivo studies, we successfully demonstrated both positive and negative separation and the use of the magnetic nanoclusters as a theragnostic nanoprobe. This scalable synthetic procedure is expected to be highly suitable for widespread use in biomedical, energy storage, photonics, and catalysis fields, among others.</P> [FIG OMISSION]</BR>