Shear Wave Velocity Prediction Based on the Long Short-Term Memory Network with Attention Mechanism

Author:

Fu Xingan12,Wei Youhua2,Su Yun2,Hu Haixia2

Affiliation:

1. College of Mathematics and Science, Chengdu University of Technology, Chengdu 610059, China

2. Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China

Abstract

Shear wave velocity (VS) is a vital prerequisite for rock geophysics. However, due to historical, cost, and technical reasons, the shear wave velocity of some wells is missing. To reduce the deviation of the description of underground oil and gas distribution, it is urgent to develop a high-precision neural network prediction method. In this paper, an attention module is designed to automatically calculate the weight of each part of the input value. Then, the weighted data are fed into the long short-term memory network to predict shear wave velocities. Numerical simulations demonstrate the efficacy of the proposed method, which achieves a significantly lower MAE of 38.89 compared to the LSTM network’s 45.35 in Well B. In addition, the relationship between network input length and prediction accuracy is further analyzed.

Funder

Creative Research Groups of the Natural Science Foundation of Sichuan

Publisher

MDPI AG

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