Predictive pore pressure modeling using well-log data in the West Baram Delta, offshore Sarawak Basin, Malaysia DOI Creative Commons
Dejen Teklu Asfha, Haylay Tsegab Gebretsadik, Abdul Halim Abdul Latiff

et al.

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

Published: Dec. 1, 2024

Language: Английский

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
Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models DOI Creative Commons

M.A. Ogundero,

Taiwo Adelakin,

Kehinde Orolu

et al.

ABUAD Journal of Engineering Research and Development (AJERD), Journal Year: 2025, Volume and Issue: 8(1), P. 292 - 306

Published: April 24, 2025

Sand production is one of the major challenges in oil and gas industry, impacting operational integrity economic efficiency extraction activities. This study focuses on predicting Reservoir Flow Capacity (RFC) sandstone formations by analyzing geological petrophysical properties critical to reservoir performance mechanical stability. It also identified key factors that impact stability during production. Given a large number input variables enclose environmental factors, set correlation these conditions provide profound analysis reveal patterns within data. With following supervised machine learning algorithms: Random Forest, Artificial Neural Network (ANN) Support Vector Regression (SVR); modeled RFC. The algorithms were selected for their ability model complex relationships characterization, with Forest excelling high-dimensional data handling, ANN pattern learning, SVR regression-based predictions. Model evaluation using R-Squared metrics showed possesses good level accuracy 0.9573 RFC, compared which had values 0.9390 0.7294 respectively. variations from actual hence was not very useful our Further developed models revealed formation thickness, permeability are most parameters influencing flow capacity overall rock

Language: Английский

Citations

0
Sand production during hydrocarbon exploitation: mechanisms, factors, prediction, and perspectives DOI

Haoze Wu,

Shui‐Long Shen, Annan Zhou

et al.

Geoenergy Science and Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 213954 - 213954

Published: May 1, 2025

Language: Английский

Citations

0
Determination of well stability and sand risk minimization parameters for gas condensate field conditions using geomechanical and CT-based approaches DOI Creative Commons
V. V. Khimulia,

Yury Kovalenko,

V. I. Karev

et al.

Journal of Rock Mechanics and Geotechnical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
Predictive pore pressure modeling using well-log data in the West Baram Delta, offshore Sarawak Basin, Malaysia DOI Creative Commons
Dejen Teklu Asfha, Haylay Tsegab Gebretsadik, Abdul Halim Abdul Latiff

et al.

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

Published: Dec. 1, 2024

Language: Английский

Citations

1