Evidence map›Paper›PMID 40683903›Full record

ArticleScientific reports2025

Modeling residue formation from crude oil oxidation using tree-based machine learning approaches.

Mohammad-Reza Mohammadi, Seyyed-Mohammad-Mehdi Hosseini, Behnam Amiri-Ramsheh, Saptarshi Kar, Ali Abedi, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mohammad-Reza MohammadiDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Seyyed-Mohammad-Mehdi HosseiniDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Behnam Amiri-RamshehDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Saptarshi KarCollege of Engineering and Technology, American University of the Middle East, 54200, Egaila, Kuwait.
Ali AbediCollege of Engineering and Technology, American University of the Middle East, 54200, Egaila, Kuwait.
Abdolhossein Hemmati-SarapardehDepartment of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. hemmati@uk.ac.ir.
Ahmad MohaddespourDepartment of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada. ahmad.mohaddespour@mail.mcgill.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In-situ combustion (ISC) offers a compelling solution for enhancing oil recovery, particularly for heavy crude oils. This process involves the oxidation and pyrolysis of hydrocarbons, generating heat and depositing fuel in the combustion front. In this work, the thermo-oxidative profiles and residue formation of crude oils during thermogravimetric analysis (TGA) were modeled using 3075 experimental data points from 18 crude oils with API gravities ranging from 5 to 42. Four advanced tree-based machine learning algorithms comprising gradient boosting with categorical features support (CatBoost), light gradient boosting machine (LightGBM), random forest (RF), and extreme gradient boosting (XGBoost) were utilized to develop accurate predictive models. The results indicated that CatBoost outperformed the other models, achieving mean absolute relative error (MARE) values of 4.95% for the entire dataset, 5.92% for the testing subset, and 4.71% for the training subset. Moreover, it recorded a determination coefficient (R

Indexed as

Crude oilIn-situ combustionMachine learningResidue formationThermal EORThermo-oxidative behavior

Identifiers

PMID40683903
PMCPMC12276359

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.