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Received：17 August 2020，
Published：28 February 2021
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作为页岩气资源勘探评价的核心基础，含气量的评价一直作为关键研究内容而受到高度关注。页岩气的成藏和富集是一个动态地质过程，游离和吸附状态天然气的同时存在及比例变化，导致了页岩中天然气赋存状态的复杂性。页岩含气机理与煤层气差异较大，直接和间接成因的页岩气类型各具不同的页岩油气形成条件和含气特点。垂向上的页岩含气相关指征曲线变化特点，可提供更多的沉积、含气及保存等信息。页岩含气量的获得方法可划分为6种基本类型，归属于3个可信度梯度级别，其中的现场解析法是含气量获取方法中的重要方法。在现场解析的升温过程中，只有当岩心在加热至地层温度前见解吸气者，通过线性或多项式逆向回归法计算出来的损失气量才具有明确物理意义。页岩的含气量受页岩的生气能力和强度控制，损失气、解吸气及残余气分别与吸附气和游离气存在内在联系。页岩吸附含气量和总含气量是页岩含气量地质评价中的重要参数，页岩气中游离气的占比不仅能反映页岩中天然气的赋存状态，而且更指示了页岩气的可采性。同时满足总含气量和游吸比双高目标的评价对象，是页岩气的有利目标。含气量、游吸比及可采系数等含气结构参数的同时使用，有助于更准确地进行页岩气评价。机器学习和大数据分析等提高了数据处理工作效率，智能评价是页岩含气量评价研究未来发展的重要方向。
The evaluation of gas content as the core of shale gas resource assessment has drawn great attention.Gas accumulation and enrichment in shale is a dynamic geological process that results in a complex occurrence of gas: free gas coexisting with adsorbed gas and their shifting proportions.The accumulation mechanism of shale gas is quite different from that of coalbed methane.Shale gas of direct or indirect origins can have quite different forming conditions and gas-bearing characteristics.The vertical variation characteristics of gas-content-related indicator curves can provide more information on sedimentation
gas content and reservoir preservation.There are
in essence
six kinds of shale gas content evaluation methods
falling into three credibility gradients.The field desorption method
among others
is the major one.According to the method
the lost gas amount of a core sample is physically meaningful only when it is the result of a linear or polynomial regressions of the gas amount desorbed from the sample before restored to its original ambient temperature (formation temperature) during evaluation.The gas content in shale is controlled by the shale's gas generation capacity and gas content.The lost gas
desorbed gas and residual gas are internally related to adsorbed gas and free gas respectively.The adsorbed gas content and total gas content of shale are important evaluation parameters for shale gas content.The ratio of free/adsorbed gas content in shale is an ideal indicator of gas occurrence and recoverability.Assessment targets with both high total gas content and high free/adsorbed gas ratio can be considered as promising.It is recommended to combine these parameters in shale gas evaluation to obtain more accurate results.Machine Learning and big data analysis are also proven to be useful in improving data processing efficiency of shale gas evaluation
indicating intelligent evaluation being one of the important evolving directions of shale gas content evaluation.
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