Outputs (5)

Capsule network with using shifted windows for 3D human pose estimation (2025)
Journal Article

3D human pose estimation (HPE) is a vital technology with diverse applications, enhancing precision in tracking, analyzing, and understanding human movements. However, 3D HPE from monocular videos presents significant challenges, primarily due to sel... Read More about Capsule network with using shifted windows for 3D human pose estimation.

A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction Using GPR (2024)
Presentation / Conference Contribution

Permeability is a critical parameter in reservoir engineering and hydrocarbon extraction, yet its prediction remains challenging due to inherent uncertainties in subsurface data. While Gaussian Process Regression (GPR) has proven effective in predict... Read More about A Novel Fuzzy Logic Framework for Model Reliability Evaluation in Permeability Prediction Using GPR.

Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements (2024)
Presentation / Conference Contribution

This study investigates the challenges of permeability prediction in reservoir engineering, focusing on addressing uncertainties inherent in the data and modelling process, and leveraging Nuclear Magnetic Resonance (NMR) log data from the Northern Se... Read More about Uncertainty-Aware Reservoir Permeability Prediction using Gaussian Processes Regression and NMR Measurements.

A Novel Framework for Reservoir Permeability Prediction Using GPR with Grey Relational Grades and Uncertainty Quantification (2024)
Presentation / Conference Contribution

Reservoir permeability prediction is crucial for hydrocarbon exploration and production. Traditional methods have limitations, and Gaussian Process Regression (GPR) offers a powerful alternative. However, GPR can be sensitive to kernel parameters. Th... Read More about A Novel Framework for Reservoir Permeability Prediction Using GPR with Grey Relational Grades and Uncertainty Quantification.

Machine Learning in Oil and Gas Exploration: A Review (2024)
Journal Article
Lawal, A., Yang, Y., He, H., & Baisa, N. L. (2024). Machine Learning in Oil and Gas Exploration: A Review. IEEE Access, 12, 19035-19058. https://doi.org/10.1109/access.2023.3349216

A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and re... Read More about Machine Learning in Oil and Gas Exploration: A Review.