Home ›
Entities
› academia
› Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery
Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery
Research article (Separation and Purification Technology, 2023) · cited 51× · AI/ML
Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery
Summary
Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery is a scholarly article[1].
Key Facts
Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery. Retrieved May 24, 2026, from https://4ort.xyz/entity/modelling-minimum-miscibility-pressure-of-co2-crude-oil-systems-using-deep-learning-tree-based-and-thermodynamic-models-
MLA“Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/modelling-minimum-miscibility-pressure-of-co2-crude-oil-systems-using-deep-learning-tree-based-and-thermodynamic-models-.
BibTeX@misc{4ortxyz_modelling-minimum-miscibility-pressure-of-co2-crude-oil-systems-using-deep-learning-tree-based-and-thermodynamic-models-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery}}, year = {2026}, url = {https://4ort.xyz/entity/modelling-minimum-miscibility-pressure-of-co2-crude-oil-systems-using-deep-learning-tree-based-and-thermodynamic-models-}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Modelling minimum miscibility pressure of CO2-crude oil systems using deep learning, tree-based, and thermodynamic models: Application to CO2 sequestration and enhanced oil recovery — https://4ort.xyz/entity/modelling-minimum-miscibility-pressure-of-co2-crude-oil-systems-using-deep-learning-tree-based-and-thermodynamic-models- (retrieved 2026-05-24)
