Zobrazeno 1 - 10 of 40 pro vyhledávání: '"Sunil Kwon"'
1
Akademický článek
Synthesis of Doped g‐C3N4 Photonic Crystals for Enhanced Light‐Driven Hydrogen Production from Catalytic Water‐Splitting
Autor: Simon Y. Djoko T., Sunil Kwon, Prasenjit Das, Vincent Weigelt, Warisha Tahir, Babu Radhakrishnan, Klaus Schwarzburg, Arne Thomas, Michael Schwarze, Reinhard Schomäcker
Publikováno v: Advanced Energy & Sustainability Research, Vol 5, Iss 12, Pp n/a-n/a (2024)
Dopants are frequently used to improve graphitic carbon nitride (gCN) photoactivity. As a doping source, phosphomolybdic acid (PMA) can activate doping sites inside the gCN lattice, resulting in 2D Mo:P‐gCN porous material. However, the gradual loa
Externí odkaz: https://doaj.org/article/756d9328ec294d62803599c109cb6ee8
Zobrazit plný text záznamu
2
Akademický článek
Chitin nanocrystals: A promising alternative to synthetic surfactants for stabilizing oil-in-water emulsions
Autor: Haithm Salah Hagar, Shiferaw Regassa Jufar, Jang Hyun Lee, Najib Al-mahbashi, M. Basheer Alameen, Sunil Kwon, Ahmad Hussaini Jagaba, Upaka Rathnayake
Publikováno v: Case Studies in Chemical and Environmental Engineering, Vol 8, Iss , Pp 100503- (2023)
Oil-in-water emulsions are used in a variety of applications, but they are often unstable and can break down over time due to different mechanisms such as flocculation and coalescence, as well as gravitational separation, like creaming and sedimentat
Externí odkaz: https://doaj.org/article/a858d5b0a0194450825b4c40efe84542
Zobrazit plný text záznamu
3
Akademický článek
Application of Machine Learning Method of Data-Driven Deep Learning Model to Predict Well Production Rate in the Shale Gas Reservoirs
Autor: Dongkwon Han, Sunil Kwon
Publikováno v: Energies, Vol 14, Iss 12, p 3629 (2021)
Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi-stage hydraulic fracturing. To overcome these limitations, this paper presents an alte
Externí odkaz: https://doaj.org/article/4c821827538b49c88677cfebdd13e7d2
Zobrazit plný text záznamu
5
Akademický článek
Comparative Study on Supervised Learning Models for Productivity Forecasting of Shale Reservoirs Based on a Data-Driven Approach
Autor: Dongkwon Han, Jihun Jung, Sunil Kwon
Publikováno v: Applied Sciences, Vol 10, Iss 4, p 1267 (2020)
Due to the rapid development of shale gas, a system has been established that can utilize a considerable amount of data using the database system. As a result, many studies using various machine learning techniques were carried out to predict the pro
Externí odkaz: https://doaj.org/article/b59d039e4bcc47bea559aae36376b65a
Zobrazit plný text záznamu
6
Maximizing Marginal Fields Profitability Via Drilling Multi-Lateral Wells to Reduce Well Count and Well Cost: Dual Lateral Well with Single ESP and Smart Lower Completion
Autor: Mahmoud Mohamed Abdelhay Mostafa, Sunil Kwon, Hyunjung Oh, Ali Mubarak Al Braiki, Fatema Al Shkeili, Ayman El Shahat
Publikováno v: Day 3 Wed, November 02, 2022.
Expensive drilling is highly impacting the overall marginal field profitability due to the low reserves and short well life. In this paper, we considered the case of a marginal field with multiple reservoirs stacked above each other with a thin dense
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::509cd754307a0bf118ceaa0822145267
https://doi.org/10.2118/211782-ms
Zobrazit plný text záznamu
10
Comprehensive Analysis for Production Prediction of Hydraulic Fractured Shale Reservoirs Using Proxy Model Based on Deep Neural Network
Autor: Dongkwon Han, Hanam Son, Woo-seong Jin, Sunil Kwon, Jeongwoo Kim
Publikováno v: Day 4 Thu, October 29, 2020.
The proposed method is a model using data-driven modeling. As many databases are available, we propose new alternatives to production analysis using exploratory data analysis (EDA) and deep neural network (DNN) techniques. It is an economical and tim
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ae06e681ae456868bd5e399c0e298b5b
https://doi.org/10.2118/201267-ms
Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání

Omezení vyhledávání
Plný text Recenzováno Digitální knihovna AV ČR
Zdroje