Development of a new approach using an artificial neural network for estimating oil formation volume factor at bubble point pressure of Egyptian crude oil-Reference-Cited by-同舟云学术

Development of a new approach using an artificial neural network for estimating oil formation volume factor at bubble point pressure of Egyptian crude oil

Published:2022-08 Issue: Volume: Page:
ISSN:1018-3639
Container-title:Journal of King Saud University - Engineering Sciences
language:en
Short-container-title:Journal of King Saud University - Engineering Sciences

Author:

Gouda Abdelrahman,Attia Attia Mahmoud

Publisher

Elsevier BV

Subject

General Engineering,General Chemical Engineering,Catalysis,Civil and Structural Engineering,Electrical and Electronic Engineering,Mechanical Engineering,Environmental Engineering

Reference81 articles.

1. Integration of self organizing map with MLFF neural network to predict oil formation volume factor: North Africa crude oil examples;Algdamsi;International Petroleum Technology Conference, OnePetro.,2020

2. Alkinani, H.H., Al-Hameedi, A.T.T., Dunn-Norman, S., et al., 2019. Prediction of lost circulation prior to drilling for induced fractures formations using artificial neural networks. SPE Oklahoma City Oil and Gas Symposium, Society of Petroleum Engineers.

3. Pressure-volume-temperature correlations for Saudi crude oils;Al-Marhoun,1985

4. PVT correlations for Middle East crude oils;Al-Marhoun;J. Pet. Technol.,1988

5. Evaluation of empirically derived PVT properties for Middle East crude oils;Al-Marhoun;J. Pet. Sci. Eng.,2004

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of Hydrodynamic Parameters of the State of the Bottomhole Zone of Wells Using Machine Learning Methods;Georesursy;2024-03-30

2. Review of modeling schemes and machine learning algorithms for fluid rheological behavior analysis;Journal of the Mechanical Behavior of Materials;2024-01-01