ArticleScientific data2019
An X-ray computed micro-tomography dataset for oil removal from carbonate porous media.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Full scale, microscopically resolved tomographies of sandstone and carbonate rocks augmented by experimental porosity and permeability values.Scientific data · 2023Article
- Effects of the Pore Morphology on Multiphase Fluid Displacement in Porous Media-A High-Resolution Modeling Investigation.ACS omega · 2023Article
- Deep Learning Driven Noise Reduction for Reduced Flux Computed Tomography.Sensors (Basel, Switzerland) · 2021Article
- Pore-scale investigation of the use of reactive nanoparticles for in situ remediation of contaminated groundwater source.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study reveals the pore-scale details of oil mobilisation and recovery from a carbonate rock upon injection of aqueous nanoparticle (NP) suspensions. X-ray computed micro-tomography (μCT), which is a non-destructive imaging technique, was used to acquire a dataset which includes: (i) 3D images of the sample collected at the end of fluid injection steps, and (ii) 2D radiogram series collected during fluid injections. The latter allows monitoring fluid flow dynamics at time resolutions down to a few seconds using a laboratory-based μCT scanner. By making this dataset publicly available we enable (i) new image reconstruction algorithms to be tested on large images, (ii) further development of image segmentation algorithms based on machine learning, and (iii) new models for multi-phase fluid displacements in porous media to be evaluated using images of a dynamic process in a naturally occurring and complex material. This dataset is comprehensive in that it offers a series of images that were captured before/during/and after the immiscible fluid injections.
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Registered trials
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