Porosity prediction in tight sandstone reservoirs based on a one–dimensional convolutional neural network–gated recurrent unit model

Shi, Su-Zhen; Shi, Gui-Fei; Pei, Jin-Bo; Li-Li,; Zhao, Kang; He, Ya-Zhou

doi:10.1007/s11770-023-1044-9

Appl. Geophys.

2023

DOI: 10.1007/s11770-023-1044-9

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Porosity prediction in tight sandstone reservoirs based on a one–dimensional convolutional neural network–gated recurrent unit model

Su-Zhen Shi,

Gui-Fei Shi,

Jin-Bo Pei

et al.

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2024

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Cited by 2 publications

References 19 publications

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Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Olutoki,

Elsaadany,

Siddiqui

et al. 2024

Results in Engineering

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Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Olutoki,

Elsaadany,

Siddiqui

et al. 2024

Results in Engineering

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Porosity Prediction and Uncertainty Estimation in Tight Sandstone Reservoir Using Non-Deterministic XGBoost

Mohammad Hossain,

Hermana,

Oluwadamilola Olutoki

2024

IEEE Access

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Porosity prediction in tight sandstone reservoirs based on a one–dimensional convolutional neural network–gated recurrent unit model

Cited by 2 publications

References 19 publications

Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zealand

Porosity Prediction and Uncertainty Estimation in Tight Sandstone Reservoir Using Non-Deterministic XGBoost

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