Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network

Chuanhui Li,Xuewei Liu

doi:10.3390/en13246536

Chuanhui Li, Xuewei Liu

Open Access

https://doi.org/10.3390/en13246536

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Journal: Energies	Publication Date: Dec 11, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: China University of Geosciences (Beijing)

Abstract

Gas hydrate saturation is an important index for evaluating gas hydrate reservoirs, and well logs are an effective method for estimating gas hydrate saturation. To use well logs better to estimate gas hydrate saturation, and to establish the deep internal connections and laws of the data, we propose a method of using deep learning technology to estimate gas hydrate saturation from well logs. Considering that well logs have sequential characteristics, we used the long short-term memory (LSTM) recurrent neural network to predict the gas hydrate saturation from the well logs of two sites in the Shenhu area, South China Sea. By constructing an LSTM recurrent layer and two fully connected layers at one site, we used resistivity and acoustic velocity logs that were sensitive to gas hydrate as input. We used the gas hydrate saturation calculated by the chloride concentration of the pore water as output to train the LSTM network. We achieved a good training result. Applying the trained LSTM recurrent neural network to another site in the same area achieved good prediction of gas hydrate saturation, showing the unique advantages of deep learning technology in gas hydrate saturation estimation.

Highlights

Gas hydrate is an ice-like crystalline solid, formed by water molecules and methane molecules under low temperature and high pressure
We constructed an long short-term memory (LSTM) network prediction model that included an LSTM recurrent layer and two dense layers (Figure 9), where xi is the standardized input sequence sample of the resistivity and p-wave velocity; yi is the output saturation sample; LSTMi is the LSTM neuron that makes up the LSTM recurrent layer, which has the exact structure in Figure 3; oi is the output of the LSTM neuron; Ci and hi have the same meanings as in Equations (1)–(5)
The overall change trend of the predicted value of gas hydrate saturation hydrate saturation obtained by the LSTM recurrent neural network was reasonable, and the obtained by the LSTM recurrent neural network was reasonable, and the prediction was basically prediction was basically consistent with the 21 measured values of site SH7

Summary

Introduction

Gas hydrate is an ice-like crystalline solid, formed by water molecules and methane molecules under low temperature and high pressure. Recurrent neural network, which is suitable saturation, we adopted the long short‐term memory (LSTM) recurrent neural network, which is for processing sequential data to apply to apply the well that logs are sensitive to gas hydrate. This method suitable for processing sequential data to to logs the well that are sensitive to gas hydrate.

LSTM Recurrent Neural Network

C Ct t C

Geological

Results

Findings

Discussion

Conclusions