Evidence map›Paper›PMID 40880322›Full record

ArticlePloS one2025

Optimization of flooding parameters and enhanced-efficiency development for surface-modified nano-SiO₂ emulsion in low-permeability sandstone reservoirs.

Mengke Xin, Dingxue Zhang, Yan Zhang, Jun Chen, Linshuo Yan, Yiwei Qin, Qirui Zhang

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

The trial behind it

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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

2 citing papers in PubMed.

  1. Article
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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.

Mengke XinSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.
Dingxue ZhangSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.ORCID 0009-0008-1950-0167
Yan ZhangSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.
Jun ChenSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.
Linshuo YanSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.
Yiwei QinSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.
Qirui ZhangSchool of Petroleum Engineering, Yangtze University, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Low-permeability reservoirs, characterized by poor reservoir quality and pronounced heterogeneity, consistently encounter engineering challenges during waterflooding operations, such as suboptimal displacement efficiency and restricted sweep coverage. Nanoemulsions, through wettability alteration and enhancing macroscopic sweep efficiency, are widely applied in the efficient development of low-permeability reservoirs. However, current field applications in Reservoir G exhibit inefficacy, necessitating laboratory experiments to identify factors affecting poor injection performance and enhance oil recovery. This study conducted laboratory core displacement experiments using native cores from the target reservoir, integrated field operational data for injection parameter optimization, and implemented field applications. Results indicate: (1) The modified nano-SiO₂ emulsion, with an average particle size of 26.01 nm, is substantially below the pore-throat dimensions of reservoir cores; (2) Wettability reversal was confirmed through contact angle reduction on oil-wet substrates from 132.9° to 53.6°; (3) Optimal parameters-0.1 mL/min injection rate, 0.3 wt% concentration, and 0.3 PV slug volume-Utilizing dual-slug injection (0.15 PV × 2 + 0.1 PV water spacer) enhanced oil recovery by 18.83%. Segmented injection effectively mitigates particle adsorption-induced plugging and achieves deep reservoir penetration, outperforming single-slug injections. Field experimental results show that segmented injection with water spacers increased daily fluid production from 2.86 m³ to 5.22 m³ and daily oil production from 2.23 t to 4.3 t, retaliating a synergistic mechanism of the modified nano-SiO₂ emulsion in "performance-parameter-mode" optimization.

Indexed as

FloodsNanoparticlesOil and Gas FieldsSilicon DioxideEmulsionsParticle SizePermeabilityWettabilityEmulsionsSilicon Dioxide

Identifiers

PMID40880322
PMCPMC12396697

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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.