Evidence map›Paper›PMID 40287534›Full record

ArticleScientific reports2025

Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs.

Sayed Gomaa, Ahmed Ashraf Soliman, Mohamed Mansour, Fares Ashraf El Salamony, Khalaf G Salem

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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 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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  3. 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

5 authors.

Sayed GomaaMining and Petroleum Engineering Department, Faculty of Engineering, Al-Azhar University, Cairo, Egypt. ElsayedGomaa.2214@azhar.edu.eg.
Ahmed Ashraf SolimanPetroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE), El Sherouk City, Cairo, Egypt.
Mohamed MansourPetroleum Engineering and Gas Technology Department, Faculty of Energy and Environmental Engineering, British University in Egypt (BUE), El Sherouk City, Cairo, Egypt.
Fares Ashraf El SalamonyArtificial Intelligence Department, Faculty of Informatics and Computer Science, British University in Egypt (BUE), El Sherouk City, Cairo, Egypt.
Khalaf G SalemDepartment of Reservoir Engineering, South Valley Egyptian Petroleum Holding Company (GANOPE), Cairo, Egypt. Khalaf.SaIb@pme.suezuni.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Waterflooding is the most widely used improved oil recovery technique. Predicting the overall oil recovery resulting from waterflooding in oil reservoirs is crucial for effective reservoir management and appropriate decision-making. Machine learning (ML) techniques present resourceful and fast-track tools, aiding in predicting oil recovery, which is time-consuming and costly to accomplish by simulation studies. In this paper, four machine learning models: artificial neural network (ANN), Random Forest (RF), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) are applied to estimate the overall oil recovery (R) of water flooding. Initially, statistical methods were employed to analyze the input data before applying machine learning techniques. These models take into consideration the mobility ratio (M), reservoir permeability variation (V), water-oil production ratio (WOR), and initial water saturation (S

Indexed as

Artificial neural networkInitial water saturationMachine learningMobility ratioOil recoveryReservoir permeability variationWaterfloodingWater-oil ratio

Identifiers

PMID40287534
PMCPMC12033285

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Registered trials

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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.