Prediction of asphaltene stability in crude oils using machine learning algorithms-Reference-Cited by-同舟云学术

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Prediction of asphaltene stability in crude oils using machine learning algorithms

Published:2023-04 Issue: Volume:235 Page:104784
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Ali Syed Imran,Lalji Shaine Mohammadali,Awan Zahoor,Qasim Muhammad,Alshahrani Thamraa,Khan Firoz,Ullah Sami,Ashraf Almas

Funder

Princess Nourah Bint Abdulrahman University

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference55 articles.

1. Experimental results of pipeline dewatering through surfactant injection;Dall'Acqua;J. Pet. Sci. Eng.,2017

2. Experiences in numerical simulation of wax deposition in oil and multiphase pipelines: theory versus reality;Leporini;J. Pet. Sci. Eng.,2019

3. Critical analysis of different techniques used to screen asphaltene stability in crude oils;Ali;Fuel,2021

4. Removal and prevention of asphaltene deposition during oil production: a literature review;Gharbi;J. Petrol. Sci. Eng.,2017

5. Remediation of asphaltene and other heavy organic deposits in oil wells and in pipelines;Mansoori;SOCAR Proc.,2010

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1. Predicting Petroleum SARA Composition from Density, Sulfur Content, Flash Point, and Simulated Distillation Data Using Regression and Artificial Neural Network Techniques;Processes;2024-08-20

2. Asphaltene Stability Prediction Using Hybrid Artificial Neural Network Modeling Approach;SPE Nigeria Annual International Conference and Exhibition;2024-08-05

3. Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets;Journal of the American Chemical Society;2024-07-31

4. Stability Analysis of Breakwater Armor Blocks Based on Deep Learning;Water;2024-06-13

5. Application of Intercriteria and Regression Analyses and Artificial Neural Network to Investigate the Relation of Crude Oil Assay Data to Oil Compatibility;Processes;2024-04-12

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