Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization DOI Creative Commons
Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman

et al.

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 12, 2024

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) a new and advanced way automating experimental modelling. One part this AI approach the population search algorithms to fine-tune hyperparameters for better prediction performing. We Bayesian optimization first time find optimal learning parameters more precise neural network regressor goal leverage capability consider previous regression results improve output using three variograms as inputs one training, calculated from ore grades four orebodies, characterised by same genetic aspect. In comparison architectures, Bayesian-optimized demonstrably achieved superior Coefficient determination validation 78.36%. This significantly outperformed non-optimized wide, bilayer, tri-layer configurations, which yielded 32.94%, 14.00%, −46.03% determination, respectively. improved reliability demonstrates its superiority over traditional, regressors, indicating that incorporating can advance modelling, thus offering accurate intelligent solution, combining geostatistics specifically machine

Language: Английский

Application of Deep Learning for Reservoir Porosity Prediction and self Organizing Map for Lithofacies Prediction DOI
Mazahir Hussain, Shuang Liu, Wakeel Hussain

et al.

Journal of Applied Geophysics, Journal Year: 2024, Volume and Issue: 230, P. 105502 - 105502

Published: Aug. 31, 2024

Language: Английский

Citations

5
Influence of sea-level changes and dolomitization on the formation of high-quality reservoirs in the Cambrian Longwangmiao Formation, central Sichuan basin DOI Creative Commons

Yuru Zhao,

Da Gao, Ngong Roger Ngia

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2025, Volume and Issue: 15(5)

Published: April 29, 2025

Language: Английский

Citations

0
Lithofacies and sandstone reservoir characterization for geothermal assessment through artificial intelligence DOI Creative Commons
Zohaib Naseer, Muhsan Ehsan, Muhammad Ali

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105173 - 105173

Published: May 1, 2025

Language: Английский

Citations

0
An integrated comprehensive approach describing structural features and comparative petrophysical analysis between conventional and machine learning tools to characterize carbonate reservoir: A case study from Upper Indus Basin, Pakistan DOI
Zohaib Naseer,

Urooj Shakir,

Muyyassar Hussain

et al.

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2025, Volume and Issue: unknown, P. 103885 - 103885

Published: Feb. 1, 2025

Language: Английский

Citations

0
Evaluating the Ranikot formation in the middle Indus Basin, Pakistan as a promising secondary reservoir for development DOI Creative Commons
Muhsan Ehsan, Rujun Chen, Muhammad Ali

et al.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Journal Year: 2025, Volume and Issue: 11(1)

Published: March 24, 2025

Language: Английский

Citations

0
Exploring the Significance of Digitalized Logs and Seismics through Structural Modelling and Petrophysical Analyses: Case study: Neogene-Paleogene reservoirs of the Rio Del Rey Basin, Gulf of Guinea. Cameroon DOI Creative Commons

Mbouemboue Nsangou Moussa Ahmed,

Olugbenga A. Ehinola,

Wakwenmendam Nguet Pauline

et al.

Results in Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 100085 - 100085

Published: March 1, 2025

Language: Английский

Citations

0
A data-driven PCA-RF-VIM method to identify key factors driving post-fracturing gas production of tight reservoirs DOI Creative Commons
Yifan Zhao, Xiaofan Li, Lei Zuo

et al.

Energy Geoscience, Journal Year: 2025, Volume and Issue: unknown, P. 100411 - 100411

Published: April 1, 2025

Language: Английский

Citations

0
Optimization of entrainment and interfacial flow patterns in countercurrent air-water two-phase flow in vertical pipes DOI Creative Commons
Yongzhi Wang, Feng Luo, Zichen Zhu

et al.

Frontiers in Materials, Journal Year: 2024, Volume and Issue: 11

Published: Nov. 1, 2024

This study investigates countercurrent air-water two-phase flow in vertical pipes with inner diameters of 26 mm and 44 a height 2000 mm, under controlled conditions to eliminate heat mass transfer. Cutting-edge techniques were employed measure the liquid film thickness (δ) entrainment (e) within annular pattern. The methodology involved systematic comparative analysis experimental results against established models, identifying most accurate methods for predicting behavior. Specifically, Schubring et al. correlation was found accurately predict e pipes, while Wallis more pipes. Additionally, interfacial shear stress analyzed, confirming high precision δ parameters. research enhances understanding by providing reliable estimation different pipe emphasizes significance determining stress. Key findings include identification models sizes addressing challenges measuring conditions. study’s novelty lies its comprehensive existing leading improved predictions dynamics thereby contributing valuable insights into behavior geosciences environmental engineering.

Language: Английский

Citations

0
Geostatistics and artificial intelligence coupling: advanced machine learning neural network regressor for experimental variogram modelling using Bayesian optimization DOI Creative Commons
Saâd Soulaimani, Ayoub Soulaimani, Kamal Abdelrahman

et al.

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: Dec. 12, 2024

Experimental variogram modelling is an essential process in geostatistics. The use of artificial intelligence (AI) a new and advanced way automating experimental modelling. One part this AI approach the population search algorithms to fine-tune hyperparameters for better prediction performing. We Bayesian optimization first time find optimal learning parameters more precise neural network regressor goal leverage capability consider previous regression results improve output using three variograms as inputs one training, calculated from ore grades four orebodies, characterised by same genetic aspect. In comparison architectures, Bayesian-optimized demonstrably achieved superior Coefficient determination validation 78.36%. This significantly outperformed non-optimized wide, bilayer, tri-layer configurations, which yielded 32.94%, 14.00%, −46.03% determination, respectively. improved reliability demonstrates its superiority over traditional, regressors, indicating that incorporating can advance modelling, thus offering accurate intelligent solution, combining geostatistics specifically machine

Language: Английский

Citations

0