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Acoustic Prediction of a Multilateral-Well Unconventional Reservoir Based on a Hybrid Feature-Enhancement Long Short-Term Memory Neural Network

Overview of attention for article published in Frontiers in Energy Research, June 2022
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Title
Acoustic Prediction of a Multilateral-Well Unconventional Reservoir Based on a Hybrid Feature-Enhancement Long Short-Term Memory Neural Network
Published in
Frontiers in Energy Research, June 2022
DOI 10.3389/fenrg.2022.888554
Authors

Yating Hu, Ouyi Li, Lianteng Song, Zhonghua Liu, Qiong Zhang, Huilin Wu, Yan Wang, Yanru Zhang

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Earth and Planetary Sciences 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 July 2022.
All research outputs
#20,330,976
of 22,875,477 outputs
Outputs from Frontiers in Energy Research
#1,283
of 3,220 outputs
Outputs of similar age
#353,930
of 439,454 outputs
Outputs of similar age from Frontiers in Energy Research
#72
of 408 outputs
Altmetric has tracked 22,875,477 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,220 research outputs from this source. They receive a mean Attention Score of 1.7. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 439,454 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 408 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.