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基于CNN-LSTM混合神经网络的储层孔隙度预测模型 构建及应用.

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Title: 基于CNN-LSTM混合神经网络的储层孔隙度预测模型 构建及应用.
Alternate Title: Construction and application of a reservoir porosity prediction model based on CNN-LSTM hybrid neural network.
Authors: 范伟1 fan_wei@cnpc.com.cn, 孙红华1, 杨 丹2, 窦松江3, 程兴春1, 马丽杰1
Source: Mud Logging Engineering. 2025, Vol. 36 Issue 4, p13-28. 9p.
Database: Academic Search Ultimate
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  Data: 基于CNN-LSTM混合神经网络的储层孔隙度预测模型 构建及应用.
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  Data: Construction and application of a reservoir porosity prediction model based on CNN-LSTM hybrid neural network.
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  Data: <searchLink fieldCode="JN" term="%22Mud+Logging+Engineering%22">Mud Logging Engineering</searchLink>. 2025, Vol. 36 Issue 4, p13-28. 9p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=192324162
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        Value: 10.3969/j.issn.1672‐9803.2025.04.003
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      – Code: fre
        Text: French
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        PageCount: 9
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      – TitleFull: 基于CNN-LSTM混合神经网络的储层孔隙度预测模型 构建及应用.
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            NameFull: 孙红华
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              Text: 2025
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              Y: 2025
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