Shear wave velocity prediction based on LSTM and its application for morphology identification and saturation inversion of gas hydrate

You, Jiachun; Cao, Junxing; Wang, Xingjian; Liu, Wei

doi:10.1016/j.petrol.2021.109027

Cited by 23 publications

(8 citation statements)

References 39 publications

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“…The proposed new NN reduced the error of the CNN by 38 times. A gas hydrate morphology and saturation prediction behavior were proposed by You . Their findings suggest that machine learning techniques using the LSTM method perform better than LSF in the predictions of the shear wave velocity and hydrate morphologies.…”

Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

“…GHBS samples from the Nankai Trough of Japan were used to develop a machine learning model for the estimation of hydrate reservoirs’ tensile and shear strength . GHBS data from the Korean East Sea region, Shenhu area, South China Sea (SH7), and Alaminos Canyon (Block 21) are practical field data used to develop machine learning models for predicting hydrate sediments, gas hydrate saturation, and morphologies. − In areas such as Ulleung Basin, Korea, Mackenzie Delta (Canada), and on the Alaska north slope (USA), reservoir data have been used to develop machine learning models to characterize gas hydrate reservoirs and determine and estimate the hydrate rocks’ mineral compositions …”

Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

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Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO 2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results.

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Section: Machine Learning In Gas Hydrate Applicationsmentioning

confidence: 99%

Section: Machine Learning In Hydrate Field Data Predictionmentioning

confidence: 99%

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Osei,

Bavoh,

Lal

2024

ACS Omega

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“…Characterizing hydrate growth habits in fine-grained sediments is crucial for developing hydrate deposits. However, it is challenging to obtain high-saturation hydrates in fine-grained sedimentary sediments in the laboratory as those found in nature. In recent years, machine learning techniques (e.g., neural network, support vector machine, and Markov chain) have been widely used in the oil and gas industry to process geophysical data for pattern recognition or parameter prediction. − However, there are few applications in hydrate studies. Machine learning shows better generalization performance and compensates for gaps in researchers’ knowledge of physical laws.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

“…In recent years, machine learning techniques (e.g., neural network, support vector machine, and Markov chain) have been widely used in the oil and gas industry to process geophysical data for pattern recognition or parameter prediction. − However, there are few applications in hydrate studies. Machine learning shows better generalization performance and compensates for gaps in researchers’ knowledge of physical laws.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

Review and Outlook of Visual and Geophysical Detections for the Pore-Scale Morphology of Gas Hydrates

Liu,

Zhang,

2023

Energy Fuels

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“…Feng et al (2021) used random forest to predict well logs and evaluated simultaneously the uncertainty of the prediction. You et al (2021) proposed to use transfer learning to improve the performance of LSTM and applied the prediction results to morphology identification and saturation inversion of gas hydrate. Wang et al (2021) proposed a spatialtemporal neural network (STNN) algorithm that combines the advantages of CNN and LSTM.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Prediction of a Multilateral-Well Unconventional Reservoir Based on a Hybrid Feature-Enhancement Long Short-Term Memory Neural Network

Song

et al. 2022

Front. Energy Res.

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Due to the complexity of unconventional reservoir measurement, log data acquired are often incomplete, which results in inaccurate formation evaluation and higher operational risks. Common solutions, such as coring, are typically high cost related while not being sufficiently representative. In recent years, neural network has received increasing attention given its strong ability in data prediction. Nevertheless, most neural networks only focus on one specific feature of the selected data, thus prohibiting their prediction accuracy for reservoir logs where data are often dominated by more than one key feature. To address this challenge, a novel multi-channel hybrid Long Short-Term Memory (LSTM) neural network for effective acoustic log prediction is proposed. The network combines Convolutional Neural Network (CNN) and LSTM, where CNN is used to extract spatial features of the logs and LSTM network extracts temporal features with the assistance of an adaptive attention mechanism implemented for key feature recognition. In addition, the strong heterogeneity of unconventional reservoirs also increases the difficulty of prediction. Therefore, according to the characteristics of the unconventional reservoir, we designed three feature enhancement methods to mine the hidden information of logs. To prove the performance of the proposed network, a case study is presented with data acquired from Jimusar Oilfield, one of the largest unconventional reservoirs in China. Four groups of experiments are conducted, and the proposed network is employed for acoustic log prediction. The predicted results are validated against measurement (R2: 92.27%, 91.42%, 93.31%, and 92.03%; RMSE: 3.32%, 3.92%, 3.06%, and 3.53%). The performance of the proposed network is compared to other networks such as CNN, LSTM, CNN-LSTM, and random forest (RF). The comparisons show that the proposed network has the highest accuracy level of prediction, which means it provides an effective approach to complement missing data during complicated unconventional reservoir measurement and, therefore, could be of significant potential in energy exploration.

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Shear wave velocity prediction based on LSTM and its application for morphology identification and saturation inversion of gas hydrate

Cited by 23 publications

References 39 publications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Research Advances in Machine Learning Techniques in Gas Hydrate Applications

Review and Outlook of Visual and Geophysical Detections for the Pore-Scale Morphology of Gas Hydrates

Acoustic Prediction of a Multilateral-Well Unconventional Reservoir Based on a Hybrid Feature-Enhancement Long Short-Term Memory Neural Network

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