Predicting adsorbed gas capacity of deep shales under high temperature and pressure: Experiments and modeling

Shangwen Zhou, Hongyan Wang, Bobo Li, Shuangshuang Li, Kamy Sepehrnoori, Jianchao Cai

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Abstract


Temperature and pressure conditions of deep shale are beyond experiment range, and the amount of adsorbed gas is difficult to determine. To predict the adsorbed gas content of deep shales under formation conditions, isothermal adsorption experiments and model building were conducted on shale samples from Longmaxi Formation in China. A temperature-dependent adsorption model based on the Langmuir equation is proposed, which can be well-fitted by observed isotherms with a high correlation coefficient. Based on the fitted parameters at 303.15 K, the isothermal adsorption curves at 333.15 K, 363.15 K, and 393.15 K are predicted, showing a good agreement with experimental curves available. Compared with previous prediction methods, the biggest advantage of the proposed method is that it can be carried out only based on one-time isothermal adsorption experiment. Based on the predictions, the downward trend of the excess adsorption curves will slow down under high temperature and pressure conditions, and when the pressure reaches a certain level (> 80 MPa), the temperature has little effect on the excess adsorption capacity. While for absolute adsorption, the gas adsorption reaches saturation much slowly at high temperature, it can also reach saturation under formation pressure. Under the burial depth of marine shale, temperature plays a major role in controlling the adsorbed gas, resulting in the decrease of adsorbed gas content in deep shale, and its ratio will further decrease as the depth increases.

Cited as: Zhou, S., Wang, H., Li, B., Li, S., Sepehrnoori, K., Cai, J. Predicting adsorbed gas capacity of deep shales under high temperature and pressure: Experiments and modeling. Advances in Geo-Energy Research, 2022, 6(6): 482-491. https://doi.org/10.46690/ager.2022.06.05


Keywords


Deep shale, shale gas, adsorbed gas, prediction method, model building

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DOI: https://doi.org/10.46690/ager.2022.06.05

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