A Noise-robust Multi-head Attention Mechanism for Formation Resistivity Prediction: Frequency Aware LSTM

TL;DR

This paper introduces a frequency-aware LSTM with a dual-stream wavelet transformation and attention-based noise reduction, significantly improving formation resistivity prediction accuracy and noise robustness in electromagnetic well logging.

Contribution

The paper proposes a novel frequency-aware framework and temporal anti-noise block for LSTM, enhancing high-frequency feature learning and noise resistance in resistivity prediction.

Findings

Achieves 24.3% higher R2 than standard LSTM.

Reduces noise impact to about 1/8 of baseline.

Improves high-frequency feature learning and prediction accuracy.

Abstract

The prediction of formation resistivity plays a crucial role in the evaluation of oil and gas reservoirs, identification and assessment of geothermal energy resources, groundwater detection and monitoring, and carbon capture and storage. However, traditional well logging techniques fail to measure accurate resistivity in cased boreholes, and the transient electromagnetic method for cased borehole resistivity logging encounters challenges of high-frequency disaster (the problem of inadequate learning by neural networks in high-frequency features) and noise interference, badly affecting accuracy. To address these challenges, frequency-aware framework and temporal anti-noise block are proposed to build frequency aware LSTM (FAL). The frequency-aware framework implements a dual-stream structure through wavelet transformation, allowing the neural network to simultaneously handle high-frequency and low-frequency flows of time-series data, thus avoiding high-frequency disaster. The temporal anti-noise block integrates multiple attention mechanisms and soft-threshold attention mechanisms, enabling the model to better distinguish noise from redundant features. Ablation experiments demonstrate that the frequency-aware framework and temporal anti-noise block contribute significantly to performance improvement. FAL achieves a 24.3% improvement in R2 over LSTM, reaching the highest value of 0.91 among all models. In robustness experiments, the impact of noise on FAL is approximately 1/8 of the baseline, confirming the noise resistance of FAL. The proposed FAL effectively reduces noise interference in predicting formation resistivity from cased transient electromagnetic well logging curves, better learns high-frequency features, and thereby enhances the prediction accuracy and noise resistance of the neural network model.