Reservoir Flow Field Characterization during Water Flooding: A Data Mining and Fuzzy Logic Approach.

Haicheng Liu, Shikai Tong, Weiyao Zhu, Fuyong Wang

Abstract read

In one paragraph

Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

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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

1 citing paper in PubMed.

Signal-Analytics Modeling of Fluorescence Time-to-Detection forACS omega · 2026
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

4 authors.

Haicheng LiuSchool of Energy Resource, China University of Geosciences (Beijing), Beijing 100083, China.

Shikai TongState Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China.

Weiyao ZhuSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Fuyong WangSchool of Resources and Safety Engineering, University of Science and Technology Beijing, Beijing 100083, China.ORCID https://orcid.org/0000-0003-1202-5283

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate characterization of reservoir flow fields is critical for enhancing oil recovery in water flooding oil fields. Current methodologies lack standardized criteria for selecting influential factors in flow field characterization. This study introduces a novel quantitative framework integrating data mining and fuzzy logic to address this gap. Principal component analysis (PCA) and Spearman's rank correlation coefficient are applied to identify key indices, forming a robust flow field characterization system. A dual subjective-objective weighting strategy, combining the analytic hierarchy process (AHP) with entropy weighting, determines comprehensive index weights. Statistical analysis of data distributionincluding quartile deviation, variance, and standard deviation, guides the selection of optimal membership functions for each index. Fuzzy logic is then employed to derive a quantitative characterization formula, while

Identifiers

PMID40521467

DOI10.1021/acsomega.5c01017

PMCPMC12163694

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LicenceCC BY-NC-ND

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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.