Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Jungtek Lim"'
Publikováno v:
Geofluids, Vol 2020 (2020)
This study proposes a deep neural network- (DNN-) based prediction model for creating synthetic log. Unlike previous studies, it focuses on building a reliable prediction model based on two criteria: fit-for-purpose of a target field (the Golden fiel
Externí odkaz:
https://doaj.org/article/87467de8e65b468b9439885614285d0d
Autor:
Sookyung Jeong, Juhee Jang, Indoo Park, Sun keun Jo, Jungtek Lim, Sangsoo Lee, Hae-seok Lee, Wonwook Oh
Publikováno v:
Microelectronics Reliability. 138:114704
Publikováno v:
SPE Journal. 24:2423-2437
SummaryDecline–curve analysis (DCA) is an easy and fast empirical regression method for predicting future well production. However, applying DCA to shale–gas wells is limited by long transient flow, a unique completion design, and high–density
Publikováno v:
Journal of Petroleum Science and Engineering. 171:1007-1022
Reservoir models are generated by geostatistics using available static data. However, there is inherent uncertainty in the reservoir models due to limited information. A number of reservoir models with equivalent probabilities are created to quantita
Publikováno v:
Energies, Vol 14, Iss 1499, p 1499 (2021)
Energies; Volume 14; Issue 5; Pages: 1499
Energies; Volume 14; Issue 5; Pages: 1499
It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the
Publikováno v:
Journal of Petroleum Science and Engineering. 209:109820
This study proposes a reliable evaluation method for three-phase saturation (water, gas hydrate (GH), and gas) evaluation during the GH dissociation core experiment using deep learning. A convolutional neural network (CNN) takes computed tomography (
Publikováno v:
Journal of Petroleum Science and Engineering. 149:340-350
Reservoir characterization is a key step to define the facies connectivity in channelized reservoirs. Recently, a new paradigm combining production data with geostatistics has been proposed. Pseudo-hard and -soft data are prepared from production-bas
Publikováno v:
Petroleum Geostatistics 2019.
Summary Shale resources are developed with high-density drilling and differ to predict future production rate with conventional methods such as decline curve analysis (DCA). We developed a prediction model for shale production using machine learning
Publikováno v:
Journal of Petroleum Science and Engineering. 195:107712
Slug liquid holdup, translational velocity, and slug length are representative slug characteristics. The precise prediction of these parameters is essential for the accurate calculation of the average liquid holdup and pressure gradient in pipes. Nev
Publikováno v:
Journal of Petroleum Science and Engineering. 191:107159
This study develops ensemble smoother–neural network (ES-NN) that combines an ensemble smoother (ES) with a convolutional autoencoder (CAE) to yield comparable performance at a lower computational cost to that of an ensemble smoother–multiple dat