Reservoir evaluation using petrophysics informed machine learning: A case study
We propose a novel machine learning approach to improve the formation evaluation from logs by integrating petrophysical information with neural networks using a loss function. The petrophysical inf…
- Edisi
- Vol.5, December 2024
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 18 hlm PDF, 19.964 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Rapid identification of high-quality marine shale gas reservoirs based on the…
The identification of high-quality marine shale gas reservoirs has always been a key task in the exploration and development stage. However, due to the serious nonlinear relationship between the lo…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 6 hlm PDF, 1.091 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Hydrocarbon detections using multi-attributes based quantum neural networks i…
A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon dete…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 8 hlm PDF, 3.472 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Application of neural network to speed-up equilibrium calculations in composi…
Injecting carbon dioxide (CO2) into reservoirs is a widely recognized method for enhanced oil recovery (EOR) and carbon storage. This study introduces an innovative Artificial neural network (ANN)-…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 1.456 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Reconstruction of reservoir rock using attention-based convolutional recurren…
The digital reconstruction of reservoir rock or porous media is important as it enables us to visualize and explore their real internal structures. The reservoir rocks (such as sandstone and carbon…
- Edisi
- Vol.24, December 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 18 hlm PDF, 14.609 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Improved reservoir characterization of thin beds by advanced deep learning ap…
Targeting reservoirs below seismic resolution presents a major challenge in reservoir characterization. High-resolution seismic data is critical for imaging the thin gas-bearing Khadro sand facies …
- Edisi
- Vol.23, September 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 15.238 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Fracture density reconstruction using direct sampling multiple-point statisti…
The aim of this work is to present a methodology for the reconstruction of missing fracture density within highly fractured intervals, which can represent preferential fluid flow pathways. The lack…
- Edisi
- Vol.22, June 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 5.089 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
AnnRG - An artificial neural network solute geothermometer
Solute artificial neural network geothermometers offer the possibility to overcome the complexity given by the solute-mineral composition. Herein, we present a new concept, trained from high-qualit…
- Edisi
- Vol.20, December 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 4.961 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Super-resolution in thin section of lacustrine shale reservoirs and its appli…
Lacustrine shale reservoirs present intricate attributes such as the prevalence of lamination, rapid sedimentary phase transitions, and pronounced heterogeneity. These factors introduce substantial…
- Edisi
- Vol.19, September 2023
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 27.763 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
Enhancing reservoir porosity prediction from acoustic impedance and lithofaci…
Inferring underground porosity and evaluating its spatial distribution is of great significance in a wide range of Earth sciences and engineering, including hydrocarbon reservoir characterization a…
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 7.361 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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