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Long short-term memory autoencoder architecture for automated supervisory quality control of well logging data
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DOI：10.1016/j.jtice.2026.106851.png)
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Abstract
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• Automated quality control is performed using LSTM-autoencoder on multi-sensor logs. • Reconstruction error metrics are improved through structured preprocessing. • Kolmogorov Smirnov test (KS) confirms separability of clean and anomalous logs. • Tuning for detection of irregularities is enabled by adjustable thresholds. • Automation is enhanced, manual oversight reduced, subsurface models improved.
Keywords:
LSTM-autoencoder
Well logging quality control
Anomaly detection
Reconstruction error
Petrophysical data QC
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