Characterization of Tight Gas Sandstone Properties Based on Rock Physical Modeling and Seismic Inversion Methods

Han Jin, Cai Liu and Zhiqi Guo ()
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Han Jin: College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China
Cai Liu: College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China
Zhiqi Guo: College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China

Energies, 2023, vol. 16, issue 22, 1-18

Abstract: Tight sandstones produce an increasing amount of natural gas worldwide. Apart from identifying the gas enrichment, the predictions of lithology and permeable zones are crucial for the prediction of tight gas sandstones. In the present study, a seismic inversion method is developed based on rock physical modeling, by which it is possible to directly predict the lithology and pore structure in tight formations. The double-porosity model is used as a modeling tool in considering complex pore structures. Based on the model, the microfracture porosity is then predicted using logging data, which are used as a factor to estimate microfractures. Parameters representing the lithology and pore structure are proposed and estimated using logging data analyses and rock physical modeling based on the framework of the Poisson impedance. Thereafter, a new AVO equation is established and extended to the form of an elastic impedance for a direct prediction of the lithology and pore structure parameters. Real data applications show that the indicators of lithology and permeable zones are consistent with the production status. They agree with the petrophysical properties measured in wellbores, thereby proving the applicability of the proposed method for the effective characterization of tight gas sandstones.

Keywords: lithology indicator; pore structure; rock physics model; seismic inversion; tight gas sandstone reservoir (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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