Pore structure characterization and its effect on methane adsorption in shale kerogen

Wang, Tianyu; Tian, Shouceng; Liu, Qingling; Li, Gensheng; Sheng, Mao; Ren, Weiqing; Zhang, Panpan

doi:10.1007/s12182-020-00528-9

Cited by 55 publications

(36 citation statements)

References 55 publications

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“…Fitting the excess adsorption obtained by molecular simulation and comparing the fitting results to molecular simulation can further verify the accuracy of the model. The SDR and VD models were used to fit the excess adsorption isotherm obtained by Wang et al using molecular simulation. The correlation coefficients of the two fittings were high.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Therefore, we can assume that the adsorbed phase volume is constant during the adsorption process and does not change with the pressure. However, this kind of assumption is currently only carried out in molecular simulations. ,,, Although a lot of previous studies ,,,,,− approximated the volume of micropores (pore size of <2 nm) in the shale to the adsorbed phase volume, the reliability of this kind of assumption to the adsorbed phase volume still cannot be verified. In some studies, the absolute adsorption divided by a fixed adsorbed volume can recover the adsorbed phase density as a function of the pressure, but the absolute adsorption is still obtained by the fixed density model; obviously, the recovery is unreasonable. ,, As in the latest study by Xiong et al, the fixed density isotherm adsorption model (Langmuir model and Ono–Kondo model) was used to fit the excess adsorption isotherm to obtain the absolute adsorption capacity and the absolute adsorption capacity divided by a fixed adsorbed phase volume to obtain the adsorbed phase density as a function of the pressure.…”

Section: Introductionmentioning

confidence: 99%

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Improved Methane Adsorption Model in Shale by Considering Variable Adsorbed Phase Density

Kong¹,

Fan²,

Xiao

et al. 2021

Energy Fuels

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Numerous models have been used to describe the isotherm adsorption of supercritical methane in porous media. Many models assume that the adsorbed phase density does not change with pressure during the adsorption process. However, recent studies show that this assumption is unreasonable, and the resulting error is enormous. Therefore, we propose an improved isotherm adsorption model in shale that assumes that the adsorbed phase density keeps changing and that adsorbed phase volume remains constant during the adsorption process [the variable density adsorption (VD) model]. A logarithmic function is used to describe the change of the adsorbed phase density during the adsorption process. The product of the adsorbed phase density and volume is used to calculate the adsorption capacity. The fitting results for large amounts of methane adsorption data show that this assumption is reasonable. The fitting results are consistent with the molecular simulation, and it will be more convenient to obtain the truly adsorbed phase volume and density. The adsorbed phase volume and density obtained by the VD model show a good positive correlation with the total organic carbon, specific surface area, and micropore volume, which indicates the rationality of adsorption parameters fitted by the model. As a result of the correct calculation of the adsorption phase density, the gas in place (GIP) obtained by the VD model is lower than the supercritical Dubinin–Radushkevich model. The new model proposed this time provides a new tool for the study of shale methane isotherm adsorption and a new model for the calculation of GIP. Using this model, the adsorbed phase density and volume of methane can be obtained more conveniently and accurately. This will be a milestone in the VD model.

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Section: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Methane Adsorption Model in Shale by Considering Variable Adsorbed Phase Density

Kong¹,

Fan²,

Xiao

et al. 2021

Energy Fuels

View full text Add to dashboard Cite

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“…For shale micropore description, the following references should be consulted [109][110][111][112][113]. These techniques have been used for the determination of micropore volume and to obtain a pore-size distribution of the micropores [114].…”

Section: Dubinin-radushkevichmentioning

confidence: 99%

Use of Gas Adsorption and Inversion Methods for Shale Pore Structure Characterization

Medina-Rodriguez

Alvarado

2021

Energies

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The analysis of porosity and pore structure of shale rocks has received special attention in the last decades as unconventional reservoir hydrocarbons have become a larger parcel of the oil and gas market. A variety of techniques are available to provide a satisfactory description of these porous media. Some techniques are based on saturating the porous rock with a fluid to probe the pore structure. In this sense, gases have played an important role in porosity and pore structure characterization, particularly for the analysis of pore size and shapes and storage or intake capacity. In this review, we discuss the use of various gases, with emphasis on N2 and CO2, for characterization of shale pore architecture. We describe the state of the art on the related inversion methods for processing the corresponding isotherms and the procedure to obtain surface area and pore-size distribution. The state of the art is based on the collation of publications in the last 10 years. Limitations of the gas adsorption technique and the associated inversion methods as well as the most suitable scenario for its application are presented in this review. Finally, we discuss the future of gas adsorption for shale characterization, which we believe will rely on hybridization with other techniques to overcome some of the limitations.

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“…The networks establishment needs to determine the weights and thresholds based on the dataset [21]. We calculate massive production data with different geological and fracturing parameters based on our previous numerical production model [22,23] as the dataset. Detailed information of the numerical model can be found in our previous study [8].…”

Section: Data Preparation and Preprocessingmentioning

confidence: 99%

Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

Wang

Shi

et al. 2021

Applied Sciences

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Predicting shale gas production under different geological and fracturing conditions in the fractured shale gas reservoirs is the foundation of optimizing the fracturing parameters, which is crucial to effectively exploit shale gas. We present a multi-layer perceptron (MLP) network and a long short-term memory (LSTM) network to predict shale gas production, both of which can quickly and accurately forecast gas production. The prediction performances of the networks are comprehensively evaluated and compared. The results show that the MLP network can predict shale gas production by geological and fracturing reservoir parameters. The average relative error of the MLP neural network is 2.85%, and the maximum relative error is 12.9%, which can meet the demand of engineering shale gas productivity prediction. The LSTM network can predict shale gas production through historical production under the constraints of geological and fracturing reservoir parameters. The average relative error of the LSTM neural network is 0.68%, and the maximum relative error is 3.08%, which can reliably predict shale gas production. There is a slight deviation between the predicted results of the MLP model and the true values in the first 10 days. This is because the daily production decreases rapidly during the early production stage, and the production data change greatly. The largest relative errors of LSTM in this work on the 10th, 100th, and 1000th day are 0.95%, 0.73%, and 1.85%, respectively, which are far lower than the relative errors of the MLP predictions. The research results can provide a fast and effective mean for shale gas productivity prediction.

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Pore structure characterization and its effect on methane adsorption in shale kerogen

Cited by 55 publications

References 55 publications

Improved Methane Adsorption Model in Shale by Considering Variable Adsorbed Phase Density

Improved Methane Adsorption Model in Shale by Considering Variable Adsorbed Phase Density

Use of Gas Adsorption and Inversion Methods for Shale Pore Structure Characterization

Productivity Prediction of Fractured Horizontal Well in Shale Gas Reservoirs with Machine Learning Algorithms

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