Ditapis dengan
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-quality hydrochemical data and verified by in-situ temperature measurements with a total of 208 data pairs of geochemical input parameters (Na+, K+, Ca2+, Mg2+, Cl−, SiO2, and pH) and reservoir temperatur…
- 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 challenges in analyzing and comprehending reservoir characteristics. Thin-section imaging offers a direct medium to observe these traits, yet the intrinsic compromise between image resolution and fie…
- 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 and geothermal energy exploitation. Popular methods are largely based on the analysis of lithological cores, well logs, and seismic inversion. These methods are reliable, but they are still time-consum…
- 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
Contributions of machine learning to quantitative and real-time mud gas data …
The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature on mud gas data revealed that the mud gas data is abundantly acquired during drilling but not sufficiently utilized in real time. There is the need to leverage the current advances in machine learning …
- Edisi
- Vol.16, December 2022
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 9 hlm PDF, 1.691 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
FracRough—Computer program to calculate fracture roughness from reservoir r…
Measuring roughness of fractures in buried rock is challenging, but important in estimating fracture shear strength and permeability. Here, we present FracRough, a first-of-its-kind computer program that was developed to calculate the joint roughness coefficient (JRC) of reservoir rock fractures from core exterior. Core is often acquired from oil and gas wells using particular drilling bits tha…
- Edisi
- Vol.9, March 2021
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 10 hlm PDF, 2.228 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A plugin for computing the pore/grain network tortuosity of a porous medium f…
Tortuosity is an important geometrical parameter of the pore or grain network in a porous medium. Here we present and discuss an implementation of a plugin to estimate the pore/grain network tortuosity of a porous medium sample. The tortuosity is estimated according to the geometric reconstruction algorithm that can be applied to 2D or 3D μCT image samples. To illustrate the tortuosity plugin …
- Edisi
- Vol.5, March 2020
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 14 hlm PDF, 6.599 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
A competitive ensemble model for permeability prediction in heterogeneous oil…
One important property of oil and gas reservoirs is permeability, which has proven to be difficult to predict. Empirical and regression models are the current industrial practice for predicting permeability due to high cost and time consumption associated with laboratory measurement. In recent times, machine learning algorithms have been employed for the prediction of permeability due to their …
- Edisi
- Vol.1, October 2019
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 13 hlm PDF, 3.657 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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