Integrated Machine Learning Model for Predicting Asphaltene Damage Risk and the Asphaltene Onset Pressure-Reference-Cited by-同舟云学术

Integrated Machine Learning Model for Predicting Asphaltene Damage Risk and the Asphaltene Onset Pressure

Published:2022-11-10 Issue:23 Volume:36 Page:14243-14252
ISSN:0887-0624
Container-title:Energy & Fuels
language:en
Short-container-title:Energy Fuels

Author:

Moncayo-Riascos Ivan¹^ORCID,Guerrero-Benavides Christian²,Giraldo Juliana³,Ramírez-Jaramillo Óscar¹,Rojas-Ruiz Fernando A.⁴^ORCID,Orrego-Ruiz Jorge A.⁴^ORCID,Cundar Cristiam³,Cañas-Marín Wilson A.⁴,Osorio Gallego Raul³

Affiliation:

1. Meridian Consulting−Ecopetrol S.A., Bogotá, Cundinamarca110231, Colombia

2. Halliburton Landmark, Bogotá, Cundinamarca111111, Colombia

3. Department of Reservoir and Recovery, Ecopetrol S.A., Bogotá, Cundinamarca110311, Colombia

4. Centro de Innovación y Tecnología ICP, Ecopetrol, Piedecuesta, Santander681018, Colombia

Funder

Ecopetrol

Instituto Colombiano del Petroleo

Publisher

American Chemical Society (ACS)

Subject

Energy Engineering and Power Technology,Fuel Technology,General Chemical Engineering

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.2c03319

Reference26 articles.

1. Characterization of Heteroatom Compounds in a Crude Oil and Its Saturates, Aromatics, Resins, and Asphaltenes (SARA) and Non-basic Nitrogen Fractions Analyzed by Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

2. Asphaltene precipitation in crude oils: Theory and experiments

3. Asphaltene Stability in Crude Oils

4. Rheological demonstration of alteration in the heavy crude oil fluid structure upon addition of nanoparticles

5. Adsorption-desorption of n–C7 asphaltenes over micro- and nanoparticles of silica and its impact on wettability alteration

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