Efficient selective attention LSTM for well log curve synthesis

TL;DR

This paper introduces an efficient LSTM model with selective attention for well log curve synthesis, significantly improving accuracy and computational efficiency over traditional methods, with practical field validation.

Contribution

It develops a novel LSTM-based approach incorporating self-attention to enhance prediction accuracy and reduce computational complexity in well log data synthesis.

Findings

Achieves higher accuracy than FCNN and vanilla LSTM methods.

Reduces computational complexity from O(n^2) to O(nlogn).

Validated through field experiments showing practical effectiveness.

Abstract

Non-core drilling has gradually become the primary exploration method in geological exploration engineering, and well logging curves have increasingly gained importance as the main carriers of geological information. However, factors such as geological environment, logging equipment, borehole quality, and unexpected events can all impact the quality of well logging curves. Previous methods of re-logging or manual corrections have been associated with high costs and low efficiency. This paper proposes a machine learning method that utilizes existing data to predict missing data, and its effectiveness and feasibility have been validated through field experiments. The proposed method builds on the traditional Long Short-Term Memory (LSTM) neural network by incorporating a self-attention mechanism to analyze the sequential dependencies of the data. It selects the dominant computational results in the LSTM, reducing the computational complexity from O(n^2) to O(nlogn) and improving model efficiency. Experimental results demonstrate that the proposed method achieves higher accuracy compared to traditional curve synthesis methods based on Fully Connected Neural Networks (FCNN) and vanilla LSTM. This accurate, efficient, and cost-effective prediction method holds a practical value in engineering applications.