Evidence map›Paper›PMID 40676071›Full record

ArticleScientific reports2025

Parallel and distributed chimp-optimized LSTM for oil well-log reconstruction in China.

Zisong Wang, Zhiliang Cheng, Wenxiang Wang, Xiujian Ding, Lu Xia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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.

  1. 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

5 authors.

Zisong WangSchool of Civil Engineering and Transportation, Weifang University, Weifang, 261061, Shandong, China. wangzisong0033@gmail.com.
Zhiliang ChengSchool of Civil Engineering and Transportation, Weifang University, Weifang, 261061, Shandong, China.
Wenxiang WangSchool of Civil Engineering and Transportation, Weifang University, Weifang, 261061, Shandong, China.
Xiujian DingChina University of Petroleum (East China) Geological College, Qingdao, 266000, Shandong, China.
Lu XiaSchool of Petroleum Engineering, Shandong University of Petrochemical Technology, Dongying, 257000, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Well-log analysis contributes significantly to effective oil and gas extraction, but inconsistent logs may render subsequent geological analyses useless. This study tackles this problem by devising a deep Long Short-Term Memory (LSTM) model that uses the new Parallel and Distributed Chimp Optimization Algorithm (PDCOA). PDCOA's primary goal is to speed up the process of hyperparameter tuning for LSTMs by letting them work in parallel and across multiple computers, with separate groups of computers communicating with each other regularly to ensure the system is diverse and reliable. It is designed for reconstructing missing well-log data, showing that the proposed method is more scalable, efficient, and accurate as a predictor. This feature makes it a valuable tool for geological interpretation and estimating hydrocarbon resources.

Indexed as

Chimp optimization algorithmLong short-term memoryOil and gas extractionParallel computingWell log analysis

Identifiers

PMID40676071
PMCPMC12271472

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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.