A new approach to efficient and safe gas production from unsealed marine hydrate deposits

Sun, Yi-Fei; Zhong, Jin‐Rong; Chen, Guangjin; Cao, Bo-Jian; Li, Rui; Chen, Daoyi

doi:10.1016/j.apenergy.2020.116259

Cited by 34 publications

(12 citation statements)

References 37 publications

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“…Deep learning provides a new perspective to solving the ERT inverse problem. The advantages of deep learning are manifested in two aspects: (1) deep learning is to learn the mapping law between relevant features and resistivity models from many data, which greatly reduces the interference of subjective factors; (2) deep learning is useful to establish a complex nonlinear mapping relationship between measurement data and resistivity model through a combination of multi-layer networks, which is suitable for the ERT inverse problem. A network is constructed in Tensorflow to achieve deep learning inversion.…”

Section: Deep Learning Inversion Methodsmentioning

confidence: 99%

“…The exploration and development of new energy has been very popular in recent times. As a new energy in the 21st century, the efficient and safe exploitation of hydrate is facing a tremendous challenge [1]. Submarine landslides, stratigraphic collapse, and methane leakage caused by hydrate development will cause significant harm to human safety and the environment [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

et al. 2022

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As a new energy source, gas hydrates have attracted worldwide attention, but their exploration and development face enormous challenges. Thus, it has become increasingly crucial to identify hydrate distribution accurately. Electrical resistivity tomography (ERT) can be used to detect the distribution of hydrate deposits. An ERT inversion network (ERTInvNet) based on a deep neural network (DNN) is proposed, with strong learning and memory capabilities to solve the ERT nonlinear inversion problem. 160,000 samples about hydrate distribution are generated by numerical simulation, of which 10% are used for testing. The impact of different deep learning parameters (such as loss function, activation function, and optimizer) on the performance of ERT inversion is investigated to obtain a more accurate hydrate distribution. When the Logcosh loss function is enabled in ERTInvNet, the average correlation coefficient (CC) and relative error (RE) of all samples in the test sets are 0.9511 and 0.1098. The results generated by Logcosh are better than MSE, MAE, and Huber. ERTInvNet with Selu activation function can better learn the nonlinear relationship between voltage and resistivity. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, the best choices for Relu, Selu, Leaky_Relu, and Softplus. Compared with Adadelta, Adagrad, and Aadmax, Adam has the best performance in ERTInvNet with the optimizer. Its average CC and RE of all samples in the test set are 0.9449 and 0.2301, respectively. By optimizing the critical parameters of deep learning, the accuracy of ERT in identifying hydrate distribution is improved.

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Section: Deep Learning Inversion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

et al. 2022

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“…In recent years, many scholars have proposed and discussed improved methods for NGH dissociation and gas production based on traditional DP and thermal stimulation, but most proposed methods are immature and still under study in the laboratory. , Above all, existing reviews involving conventional and new NGH exploitation methods and up-to-date NGH development characteristics in field trials are rarely found. Besides, less coupling analysis on multicomponent, multiphase, and multifield in HBS during production has been accomplished, and optimized matches of accumulation characteristics of HBS, well patterns, and their layouts, and NGH exploitation methods are urgently needed to evaluate the potential of NGH development as one specific project target . So, the contribution of our research is highlighted as follows: (1) reviewing nearly all the existing methods of gas recovery from HBS; (2) proposing a promising method for enhanced gas production from HBS based on theoretical analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, less coupling analysis on multicomponent, multiphase, and multifield in HBS during production has been accomplished, and optimized matches of accumulation characteristics of HBS, well patterns, and their layouts, and NGH exploitation methods are urgently needed to evaluate the potential of NGH development as one specific project target. 16 So, the contribution of our research is highlighted as follows:…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Methods of Gas Recovery from Hydrate-Bearing Sediments: A Review

et al. 2022

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Safe, efficient, and economical gas recovery from hydrate-bearing sediments (HBS) is a severe issue that determines if natural gas hydrate (NGH), as an alternative energy in the future as fossil fuels approach depletion, is applied. Researchers worldwide are committed to developing a safe, efficient, and economical method of gas recovery from HBS. However, until now, most methods are still being validated and not have not been identified to exploit NGH commercially. Therefore, it is appropriate and significant to discuss researchers’ achievements to exploit NGH and summarize their potential benefits and challenges. This paper introduces nearly all the conventional and latest NGH exploitation methods and reviews field trials’ development characteristics. On the basis of laboratory experiments and field trials, key challenges restricting safe and efficient NGH development, and the existing research gaps reflecting these challenges, are also presented from the gas production, security, and economic aspects. The unfavorable situation mainly comprises the insufficient sensible heat, extremely low thermal conductivity, and ultralow permeability of HBS, lower gas production rate and its intense fluctuations, higher water-to-gas ratio (WGR), geological deformation, and subsidence of HBS attributed to excessive sand production, driving the consideration of some possible solutions. Given this, a new method for enhanced gas production from HBS based on the combination of depressurization (DP) and an in-situ heat generation method is proposed. Benefiting from multiple theoretical enhancement mechanisms such as replenishing energy to HBS with in-situ heat generation powders, cementing and strengthening the HBS skeleton, improving the gas permeability in HBS, decreasing the WGR based on blocking water, and removing gas characteristics of hydration products, this method could be expected to achieve promising long-term performance of gas production, which will be theoretically and practically significant to study commercial gas production.

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“…Ding et al [8] studied the sand control in a natural gas hydrate reservoir in the South China Sea. Sun et al [9] proposed a new approach to efficient and safe gas production from unsealed marine hydrate deposits. Wang et al [10] established a new analysis method of cumulative gas production per unit pressure drop, and its effectiveness was verified by its application in the calculation of reserves of three gas reservoirs.…”

Section: Introductionmentioning

confidence: 99%

Research on Quantitative Evaluation Methods and Influencing Factors of Natural Gas Reservoir Development

Li¹,

Yu²,

Chen³

et al. 2021

Shock and Vibration

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Current methods of evaluating the effects of gas reservoir exploitation primarily focus on evaluating production statistics of a well or field, and they often have a certain subjectivity. In order to further analyze key indexes that affect a gas reservoir during production, this paper considered the characteristic parameters of different types of gas reservoirs and established an index system of evaluating a gas reservoir “exploitation effect” from the three aspects of gas reservoir characteristics, rock characteristics, and economic benefits. There are numerous factors affecting the exploitation effect of a gas reservoir. The relationships between different factors are complicated, and the evaluation result of each factor can also be vague. A fuzzy optimization theoretical model for gas reservoir evaluation and optimization was established on the basis of a quantitative study on the evaluation parameters in fuzzy mathematics. In order to clearly understand the influence of each factor on the gas reservoir exploitation effect, numerical simulation was used to perform a multifactor numerical simulation study of the quantitative sensitivity to determine the sequence of different influencing factors in terms of influencing the gas reservoir exploitation effect. This study provides a quantitative scientific basis for the evaluation of gas reservoir exploitation effect.

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A new approach to efficient and safe gas production from unsealed marine hydrate deposits

Cited by 34 publications

References 37 publications

Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

Optimization of Critical Parameters of Deep Learning for Electrical Resistivity Tomography to Identifying Hydrate

Recent Advances in Methods of Gas Recovery from Hydrate-Bearing Sediments: A Review

Research on Quantitative Evaluation Methods and Influencing Factors of Natural Gas Reservoir Development

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