4.6 Article

Thermodynamic Modeling of Phase Behavior in Shale Media

期刊

SPE JOURNAL
卷 21, 期 1, 页码 190-207

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SOC PETROLEUM ENG
DOI: 10.2118/176015-PA

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  1. Reservoir Engineering Research Institute

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In conventional permeable media, once pore volume (PV) is known, the amount of fluid-in-place can be estimated. This is because the fluid is locally homogeneous, pores are generally larger than 100 nm, and surface adsorption is negligible. In shale media, in addition to PV, knowledge of pore-size distribution, total organic content, and chemistry of the rock is required. Fluid molecules in shale media can be found in three different states: (1) free molecules in the pores, (2) adsorbed molecules on the pore surface, and (3) dissolved molecules in the organic matter. Of the three, the first two mechanisms are discussed in the literature. In this work, we compute for the first time the amount of dissolved molecules. To compute the fluids in shale media, we divide the pores into sizes greater than 10 nm and sizes less than 10 nm. In pores greater than 10 nm, the interface curvature affects phase behavior, and fluid phases are homogeneous. Therefore, they can be described by conventional equations of state. Our calculations show that retrograde condensation increases in nanopores; the upper dewpoint increases, and the lower dewpoint decreases. These calculations are supported by experimental measurements. Gas solubility in water and liquid normal decane shows a modest increase with curvature. In pores less than 10 nm, the fluids become inhomogeneous, and the direct use of conventional equations of state cannot be applied even with adjusted critical pressure and temperature. We suggest the use of molecular modeling. A model such as the Langmuir adsorption isotherm is merely a curve fitting of the data. We use available data in shale media, which are mainly limited to excess sorption of methane and carbon dioxide, to compare to our thermodynamic model computations. This is the first attempt to compare measured data in shale and predictions that are based on the integration of molecular modeling and classical thermodynamic modeling.

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