☆ 4.5 Article

Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2023)

Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

Jakub Wazny et al.

Summary: The use of Artificial Neural Networks (ANNs) provides a reliable solution for supplementing missing data in geophysical applications. By training the network on a reliable set of borehole data values and applying it to unknown wells, synthetic values of missing geophysical parameters can be created.

ACTA GEOPHYSICA (2021)

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Article Green & Sustainable Science & Technology

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Ahmed Gowida et al.

SUSTAINABILITY (2020)

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Article Geochemistry & Geophysics

Application of conventional propagation resistivity logging for formation boundary identification in geosteering

Zhen Qin et al.

JOURNAL OF GEOPHYSICS AND ENGINEERING (2017)

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Article Energy & Fuels

Predicting Log Data by Using Artificial Neural Networks to Approximate Petrophysical Parameters of Formation

M. Baneshi et al.

PETROLEUM SCIENCE AND TECHNOLOGY (2013)

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Resistivity Log Prediction in Horizontal Low Formation Quality Well Using Data-Driven Robust Models

相关参考文献

Estimation of electrical resistivity using artificial neural networks: a case study from Lublin Basin, SE Poland

New Computational Artificial Intelligence Models for Generating Synthetic Formation Bulk Density Logs While Drilling

Application of conventional propagation resistivity logging for formation boundary identification in geosteering

Predicting Log Data by Using Artificial Neural Networks to Approximate Petrophysical Parameters of Formation

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