ArticleACS omega2024
ROP Prediction Method Based on PCA-Informer Modeling.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
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Who cites it
1 citing paper in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing the rate of penetration (ROP) is an effective means to improve the drilling efficiency. At present, the efficiency and accuracy of intelligent prediction methods for the rate of penetration still need to be improved. To improve the efficiency and accuracy of rate of penetration prediction, this paper proposes a ROP prediction model based on Informer optimized by principal component analysis (PCA). We take the Taipei Basin block oilfield as an example. First, we use principal component analysis to extract data features, transforming the original data into low-dimensional feature data. Second, we use the PCA-optimized data to build an Informer model for predicting ROP. Finally, combined with actual data and using the recurrent neural network (RNN) and long short-term memory (LSTM) as baselines, we perform algorithm performance comparative analysis using root-mean-square error (RMSE), mean absolute error (MAE), and coefficient of determination (
Identifiers
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Registered trials
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