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  • Frontiers Media SA  (3)
  • Zhang, Yongan  (3)
  • Unknown  (3)
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  • Frontiers Media SA  (3)
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  • Unknown  (3)
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  • 1
    Online Resource
    Online Resource
    Gated recurrent unit neural network (GRU) based on quantile regression (QR) predicts reservoir parameters through well logging data
    Frontiers Media SA ; 2023
    In:  Frontiers in Earth Science Vol. 11 ( 2023-1-17)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 11 ( 2023-1-17)
    Abstract: The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    DOI: 10.3389/feart.2023.1087385
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741235-0
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  • 2
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    An adaptive ensemble learning by opposite multiverse optimizer and its application in fluid identification for unconventional oil reservoirs
    Frontiers Media SA ; 2023
    In:  Frontiers in Earth Science Vol. 11 ( 2023-7-26)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 11 ( 2023-7-26)
    Abstract: Unconventional reservoirs are rich in petroleum resources. Reservoir fluid property identification for these reservoirs is an essential process in unconventional oil reservoir evaluation methods, which is significant for enhancing the reservoir recovery ratio and economic efficiency. However, due to the mutual interference of several factors, identifying the properties of oil and water using traditional reservoir fluid identification methods or a single predictive model for unconventional oil reservoirs is inadequate in accuracy. In this paper, we propose a new ensemble learning model that combines 12 base learners using the multiverse optimizer to improve the accuracy of reservoir fluid identification for unconventional reservoirs. The experimental results show that the overall classification accuracy of the adaptive ensemble learning by opposite multiverse optimizer (AIL-OMO) is 0.85. Compared with six conventional reservoir fluid identification models, AIL-OMO achieved high accuracy on classifying dry layers, oil–water layers, and oil layers, with accuracy rates of 94.33%, 90.46%, and 90.66%. For each model, the identification of the water layer is not accurate enough, which may be due to the classification confusion caused by noise interference in the logging curves of the water layer in unconventional reservoirs.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    DOI: 10.3389/feart.2023.1116664
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741235-0
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  • 3
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    Online Resource
    DataSheet_2.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Immunology Vol. 14 ( 2023-6-26)
    Details
    In: Frontiers in Immunology, Frontiers Media SA, Vol. 14 ( 2023-6-26)
    Abstract: Antibacterial peptide has been widely developed in cultivation industry as feed additives. However, its functions in reducing the detrimental impacts of soybean meal (SM) remain unknown. In this study, we prepared nano antibacterial peptide CMCS-gcIFN-20H (C-I20) with excellent sustained-release and anti-enzymolysis, and fed mandarin fish ( Siniperca chuatsi ) with a SM diet supplemented with different levels of C-I20 (320, 160, 80, 40, 0 mg/Kg) for 10 weeks. 160 mg/Kg C-I20 treatment significantly improved the final body weight, weight gain rate and crude protein content of mandarin fish and reduced feed conversion ratio. 160 mg/Kg C-I20-fed fish maintained appropriate goblet cells number and mucin thickness, as well as improved villus length, intestinal cross-sectional area. Based on these advantageous physiological changes, 160 mg/Kg C-I20 treatment effectively reduced multi-type tissue (liver, trunk kidney, head kidney and spleen) injury. The addition of C-I20 did not change the muscle composition and muscle amino acids composition. Interestingly, dietary 160 mg/Kg C-I20 supplementation prevented the reduction in myofiber diameter and change in muscle texture, and effectively increased polyunsaturated fatty acids (especially DHA + EPA) in muscle. In conclusion, dietary C-I20 in a reasonable concentration supplementation effectively alleviates the negative effects of SM by improving the intestinal mucosal barrier. The application of nanopeptide C-I20 is a prospectively novel strategy for promoting aquaculture development.
    Type of Medium: Online Resource
    ISSN: 1664-3224
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fimmu.2023.1197767
    DOI: 10.3389/fimmu.2023.1197767.s001
    DOI: 10.3389/fimmu.2023.1197767.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2606827-8
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