Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms
Lithology classification is crucial for efficient and sustainable resource exploration in the oil and gas industry. Missing values in well-log data, such as Gamma Ray (GR), Neutron Porosity (NPHI), Bulk Density (RHOB), Deep Resistivity (RS), Delta Time Compressional (DTCO), Delta Time Shear (DTSM),...
Saved in:
| Main Authors: | Sherly Ardhya Garini, Ary Mazharuddin Shiddiqi, Widya Utama, Alif Nurdien Fitrah Insani |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124005788 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Sequence-variable attention temporal convolutional network for volcanic lithology identification based on well logs
by: Hanlin Feng, et al.
Published: (2025-01-01) -
Identifying the lithologies and thicknesses of coal seam roofs and floors based on multiparameter logging of boreholes
by: Zhe LI, et al.
Published: (2024-12-01) -
A comparative petrophysical evaluation of the Abu Roash, Bahariya, and Kharita reservoirs using well-logging data, East El-Fayoum, Egypt
by: Mohamed Osman Ebraheem, et al.
Published: (2025-01-01) -
An intelligent lithology identification method for sandstone and mudstone strata and its applications: A case study of the Jurassic strata in the Lunnan area, Xinjiang, China
by: Ming CAI, et al.
Published: (2025-01-01) -
Reducing the uncertainty in the distribution of cm-scale rock properties in the near well-bore region
by: Seyed Ahmad Mortazavi, et al.
Published: (2025-01-01)