Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/24/6536 |
| _version_ | 1797545017719390208 |
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| author | Chuanhui Li Xuewei Liu |
| author_facet | Chuanhui Li Xuewei Liu |
| author_sort | Chuanhui Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:09:44Z |
| format | Article |
| id | doaj.art-6bee2b89594844a1b1effa5c29384997 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:09:44Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6bee2b89594844a1b1effa5c293849972023-11-21T00:19:46ZengMDPI AGEnergies1996-10732020-12-011324653610.3390/en13246536Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural NetworkChuanhui Li0Xuewei Liu1School of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing 100083, ChinaSchool of Geophysics and Information Technology, China University of Geosciences (Beijing), Beijing 100083, ChinaGas 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.https://www.mdpi.com/1996-1073/13/24/6536gas hydratesaturationdeep learningrecurrent neural network |
| spellingShingle | Chuanhui Li Xuewei Liu Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network Energies gas hydrate saturation deep learning recurrent neural network |
| title | Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network |
| title_full | Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network |
| title_fullStr | Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network |
| title_full_unstemmed | Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network |
| title_short | Research on the Estimate of Gas Hydrate Saturation Based on LSTM Recurrent Neural Network |
| title_sort | research on the estimate of gas hydrate saturation based on lstm recurrent neural network |
| topic | gas hydrate saturation deep learning recurrent neural network |
| url | https://www.mdpi.com/1996-1073/13/24/6536 |
| work_keys_str_mv | AT chuanhuili researchontheestimateofgashydratesaturationbasedonlstmrecurrentneuralnetwork AT xueweiliu researchontheestimateofgashydratesaturationbasedonlstmrecurrentneuralnetwork |