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Time-lapse earthquake difference prediction based on physics-informed long short-term memory coupled with interpretability boosting

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This method improves time-lapse seismic difference prediction accuracy, suggesting new approaches for reservoir monitoring.

The PI-LSTM model reduced the physical residual of North Sea field data from 62.3 kPa to 15.2 kPa—a 75.6% decrease.
In dual benchmark tests, PI-LSTM achieved time-lapse prediction accuracy of 0.71–2.1 ms, outperforming commercial software by 62.9%.
The interpretability enhancement reduced seasonal fluctuation of feature importance by 38%, improving model reliability.
For high-pressure reservoirs, it decreased wave equation residual to 18.6 kPa, significantly lower than traditional LSTMs.

Zhao et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This method improves time-lapse seismic difference prediction accuracy, suggesting new approaches for reservoir monitoring.

The PI-LSTM model reduced the physical residual of North Sea field data from 62.3 kPa to 15.2 kPa—a 75.6% decrease.
In dual benchmark tests, PI-LSTM achieved time-lapse prediction accuracy of 0.71–2.1 ms, outperforming commercial software by 62.9%.
The interpretability enhancement reduced seasonal fluctuation of feature importance by 38%, improving model reliability.
For high-pressure reservoirs, it decreased wave equation residual to 18.6 kPa, significantly lower than traditional LSTMs.

Zhao et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark