ArticleScientific reports2024
Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A hybrid simulation-machine learning proxy model for waterflood design optimization in the Bahariya Formation.Scientific reports · 2026Article
- Neural network and regression approaches for predicting bubble point pressure in oil reservoirs.Scientific reports · 2026Article
- Soft computing models for predicting polymer viscosity across wide thermal and ionic-strength conditions.Scientific reports · 2026Article
- Effects of Graphene-Based Nanomaterials on Anaerobic Digestion of Thermally Hydrolyzed Municipal Sewage Sludge.Materials (Basel, Switzerland) · 2025Article
- Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties.Scientific reports · 2025Article
- Machine learning models for estimating the overall oil recovery of waterflooding operations in heterogenous reservoirs.Scientific reports · 2025Article
- Deep learning predictions on a new dataset: Natural gas production and liquid level detection.PloS one · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH
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