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

и другие.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Год журнала: 2024, Номер 10(1)

Опубликована: Дек. 1, 2024

Язык: 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

и другие.

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер unknown, С. 103885 - 103885

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

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

и другие.

ABUAD Journal of Engineering Research and Development (AJERD), Год журнала: 2025, Номер 8(1), С. 292 - 306

Опубликована: Апрель 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

Язык: Английский

Процитировано

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

Haoze Wu,

Shui‐Long Shen, Annan Zhou

и другие.

Geoenergy Science and Engineering, Год журнала: 2025, Номер unknown, С. 213954 - 213954

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

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

и другие.

Geomechanics and Geophysics for Geo-Energy and Geo-Resources, Год журнала: 2024, Номер 10(1)

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1