[PDF][PDF] 基于深度双向循环神经网络的储层孔隙度预测
王俊， 曹俊兴， 周欣 - 地球物理学进展, 2022 - dsjyj.com.cn
摘要储层孔隙度是描述储层特征的重要参数之一, 根据测井资料进行准确的孔隙度预测对于储层
精细描述至关重要. 为此, 发展一种基于深度双向循环神经网络的储层孔隙度预测方法 …
精细描述至关重要. 为此, 发展一种基于深度双向循环神经网络的储层孔隙度预测方法 …
[HTML][HTML] 基于门控循环单元神经网络的储层孔渗饱参数预测
王俊， 曹俊兴， 尤加春， 刘杰， 周欣 - 石油物探, 2020 - xml-data.org
孔隙度, 渗透率和饱和度等物性参数是表征储层质量的重要参数, 也是储层评价的重要依据.
根据测井数据估算岩石的孔隙度, 渗透率和饱和度参数, 进而评价储层, 是测井解释的基本内容 …
根据测井数据估算岩石的孔隙度, 渗透率和饱和度参数, 进而评价储层, 是测井解释的基本内容 …
The construction of shale rock physics model and brittleness prediction for high-porosity shale gas-bearing reservoir
XP Pan, GZ Zhang, JJ Chen - Petroleum Science, 2020 - Springer
Due to the huge differences between the unconventional shale and conventional sand
reservoirs in many aspects such as the types and the characteristics of minerals, matrix …
reservoirs in many aspects such as the types and the characteristics of minerals, matrix …
Reservoir porosity prediction based on deep bidirectional recurrent neural network
J Wang, JX CAO, X ZHOU - Progress in Geophysics, 2022 - en.dzkx.org
Reservoir porosity is one of the critical parameters to describe reservoir characteristics. High-
efficiency and low-cost porosity prediction based on logging data is very important for fine …
efficiency and low-cost porosity prediction based on logging data is very important for fine …
Porosity prediction using a deep learning method based on bidirectional spatio-temporal neural network
J Wang, J Cao, S Yuan, H Xu, P Zhou - Journal of Applied Geophysics, 2024 - Elsevier
Deep learning is one of the best machine learning algorithms for modeling complex
mapping relationships between independent and dependent variables, and thus it can be …
mapping relationships between independent and dependent variables, and thus it can be …
An optimized neural network prediction model for reservoir porosity based on improved shuffled frog leaping algorithm
M Liu, D Yao, J Guo, J Chen - International Journal of Computational …, 2022 - Springer
Efficient and accurate porosity prediction is essential for the fine description of reservoirs, for
which an optimized BP neural network (BPNN) prediction model is proposed. Aiming at the …
which an optimized BP neural network (BPNN) prediction model is proposed. Aiming at the …
Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning
J Wang, J Cao - Energy, 2024 - Elsevier
The inversion of subsurface reservoir properties is of profound significance to the oil and gas
energy development and utilization. The strong heterogeneity and complex pore structure of …
energy development and utilization. The strong heterogeneity and complex pore structure of …
[HTML][HTML] 基于可变临界孔隙度模型的致密砂岩储层参数地震反演方法
巴晶， 方志坚， 符力耘， 郭强 - 地球物理学报, 2023 - geophy.cn
储层参数地震反演是通过地震观测数据定量评估储层物性及含油气性的重要途径.
中国西部致密砂岩油气分布范围广泛, 具有巨大的勘探开发潜力, 但其孔隙结构复杂 …
中国西部致密砂岩油气分布范围广泛, 具有巨大的勘探开发潜力, 但其孔隙结构复杂 …
[HTML][HTML] 核主成分分析法在测井浊积岩岩性识别中的应用
周游， 张广智， 高刚， 赵威， 易院平， 魏红梅 - 石油地球物理勘探, 2019 - html.rhhz.net
周游, 张广智, 高刚, 赵威, 易院平, 魏红梅. 核主成分分析法在测井浊积岩岩性识别中的应用.
石油地球物理勘探, 2019, 54 (3): 667-675. DOI: 10.13810/j. cnki. issn. 1000-7210.2019 …
石油地球物理勘探, 2019, 54 (3): 667-675. DOI: 10.13810/j. cnki. issn. 1000-7210.2019 …
[PDF][PDF] 基于数据分布域变换与贝叶斯神经网络的渗透率预测及不确定性估计
李明轩， 韩宏伟， 刘浩杰， 桑文镜， 袁三一 - 地球物理学报, 2023 - dsjyj.com.cn
摘要渗透率是储层评价和油气藏开发的关键参数. 传统测井方法与常规机器学习方法估算的渗透
率都是固定值. 但由于测井数据本身存在噪声, 渗透率的预测结果可能受到噪声的影响出现测量 …
率都是固定值. 但由于测井数据本身存在噪声, 渗透率的预测结果可能受到噪声的影响出现测量 …