Unlocking thin sand potential: a data-driven approach to reservoir characterization and pore pressure mapping DOI Creative Commons
Muhsan Ehsan, Rujun Chen, Umar Manzoor

et al.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Journal Year: 2024, Volume and Issue: 10(1)

Published: Oct. 4, 2024

Language: Английский

Deep Learning-Driven Analysis of Petrophysical Dynamics in Pay Zone Quality and Reservoir Characterization DOI
Changsheng Deng, Yongke Wang,

Wu Mi

et al.

Natural Resources Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 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
Revolutionizing hydrogen storage: Predictive modeling of hydrogen-brine interfacial tension using advanced machine learning and optimization technique DOI
Hung Vo Thanh

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: 128, P. 406 - 424

Published: April 17, 2025

Language: Английский

Citations

0
Gas well productivity prediction based on fractional Fourier transform DOI Creative Commons
Chengyi Zheng, Jun Tang, M. Li

et al.

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

Published: April 19, 2025

Language: Английский

Citations

0
Research on the prediction method of mineral fraction content of lacustrine shale based on AlexNet DOI
Haibo Liao,

Zhen Li,

Hongqi Liu

et al.

Petroleum Science and Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 20

Published: April 29, 2025

Language: Английский

Citations

0
Data-Driven Explainable Machine Learning Approaches for Predicting Hydrogen Adsorption in Porous Crystalline Materials DOI
Hung Vo Thanh, Zhenxue Dai, Mohammad Rahimi

et al.

Journal of Alloys and Compounds, Journal Year: 2025, Volume and Issue: unknown, P. 180709 - 180709

Published: May 1, 2025

Language: Английский

Citations

0
Integrating petrophysical data into efficient iterative cluster analysis for electrofacies identification in clastic reservoirs DOI Creative Commons

Mohammed A. Abbas,

Watheq J. Al‐Mudhafar, Aqsa Anees

et al.

Energy Geoscience, Journal Year: 2024, Volume and Issue: 5(4), P. 100341 - 100341

Published: Sept. 6, 2024

Language: Английский

Citations

2
Applications of Machine Learning in Sweet-Spots Identification: A Review DOI

Hasan Khanjar

SPE Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 17

Published: Oct. 1, 2024

Summary The identification of sweet spots, areas within a reservoir with the highest production potential, has been revolutionized by integration machine learning (ML) algorithms. This review explores advancements in sweet-spot techniques driven ML, analyzing 122 research papers published OnePetro, Elsevier, ScienceDirect, SpringerLink, GeoScienceWorld, and MDPI databases last 10 years. provides comprehensive analysis ML applications highlights best practices data collection, preprocessing, feature engineering, model selection, training, validation, optimization, evaluation. paper categorizes discusses different types used algorithms into six groups, analyzes combinations frequently for training visualizes distribution input parameters features each main categories. It also examines frequency target variables these models. In addition, it various supervised unsupervised key studies offering valuable insights researchers.

Language: Английский

Citations

2
The role of stylolites as a fluid conductive, in the heterogeneous carbonate reservoirs DOI Creative Commons

Mohammad Nikbin,

Reza Moussavi‐Harami, Naser Hafezi Moghaddas

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 14, 2024

Language: Английский

Citations

1
Organic richness and maturity modeling of cretaceous age Chichali shales for enhanced hydrocarbon exploration in Punjab platform, Pakistan DOI Creative Commons
Qadeer Ahmad, Muhammad Iqbal Hajana, Shamshad Akhtar

et al.

Journal of Petroleum Exploration and Production Technology, Journal Year: 2024, Volume and Issue: 14(10), P. 2687 - 2701

Published: Aug. 16, 2024

This study employs comprehensive source rock evaluation using seismic inversion, rock-eval pyrolysis, organic petrography and basin modeling techniques. The kerogen type is determined by the Van Krevelen diagram, confirming Bahu-01, Nandpur-01 Zakria-01 wells have III, whereas Panjpir-01 well exhibits II to III. TOC was calculated core data from (34) samples employing geochemistry technique post stack applied on 2D data. Bahu-01 indicates poor potential, with an average value of 0.34%. In contrast, represent moderate good richness, values 1.25% 1.36%, respectively. However, a 0.72%, fair richness. Maturity estimation reveals that Panjpir-01, vitrinite reflectance (%Ro) below 0.50, indicating immature rock. %Ro for 0.63, maturity in early oil window, peak generation occurring during Eocene age. Finally, proves mature western part only future hydrocarbon exploration should be focused area. integrated approach novel Punjab Platform. diverse methodologies enhanced our understanding about characteristics pursuing resources. An will also provide valuable insights numerous other basins worldwide.

Language: Английский

Citations

0