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煤的CH_4与CO_2高压吸附行为及其应用
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摘要
运用煤岩学、地球物理学、物理化学等理论知识,结合压力计法高压吸附实验技术,对中国煤样的CH4和CO2的高压吸附热力学和动力学行为进行了实验与模拟研究,并探讨了其在煤层CO2封存与增强煤层气开采(CO2-ECBM)中的应用。研究选用7个中国煤样在35、45和55℃进行了高压吸附实验,压力最高至25MP。研究目的在于探索在潜在的CBM和CO2-ECBM开采压力和温度范围内(P>8MPa,T>35℃),压力计测量方法的准确性与重复性,并使用高斯误差传播理论对实验误差进行了计算。实验结果揭示了不同温度超临界CO2吸附等温线在压力高于超临界压力(7.38MPa)后特有的趋势,并证实了煤级与CO2和CH4最大吸附能力之间均呈现特定的“U”字形关系。基于过剩吸附实验数据,提出了一种计算煤层CO2储存能力的方法。为了评估和提高煤的高压CO2吸附等温线质量,使用活性碳开展了实验室间对比研究项目,对比结果揭示了气体纯度、样品水分含量、温度测量准确度等对实验误差的潜在影响。明确了当前高压吸附实验中存在的不足并提出了优化实验操作步骤的对策,为进一步研究超临界CO2在煤样上的吸附提供了参照基准。
Using a manometric experimental setup, experimental and modeling investigation of high-pressure CH4 and CO2 sorption thermodynamics and kinetics were performed on seven Chinese coals of different rank. The objective of this study was to explore the accuracy and reproducibility of the manometric method in the pressure and temperature range relevant for potential CBM and CO2-ECBM activities (P>8 MPa, T>35℃). Further, the upscaling and implementation of experimental results into reservoir simulation, CO2 coal seam sequestration and CO2-ECBM were discussed as well as maximum experimental errors were estimated using the Gauss error propagation theorem. The experimental data presented here was used to explicitly study the CO2 sorption behavior of Chinese coals in the elevated pressure range (up to 25 MPa) and the effects of temperature on supercritical CO2 sorption isotherms. To our knowledge the isotherms presented here are the first comprehensive sorption data of Chinese coal samples with pressure up to 25 MPa (250 bar) by manometric method which has not been explored previously. In order to assess and improve the quality of high-pressure sorption isotherms of carbon dioxide on coals, an inter-laboratory study ("Round Robin") was conducted among four European research laboratories. Supercrtical CO2 excess sorption isotherms were measured on Filtrasorb 400 (F400) activated carbon at 318 K. Inter-laboratory studies can help to identify and avoid pitfalls and to formulate standard procedures which can improve overall data quality significantly.
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