Title: Improved skeleton extraction method considering surface feature of natural micro fractures in unconventional shale/tight reservoirs
Abstract: Massive micro fractures (MFs) developed in the ultra-tight formations (such as shale/tight reservoirs), which provide preferential channels for the fluids flow. Accurate characterization of such pore-fracture systems and suitable pore network models are the fundamentals of pore structure characterization and micro scale flow simulation. Conventional medial axis (MA) skeleton extraction method cannot preserve the fracture surface feature and connectivity information, which is not suitable for accurate pore scale simulation for these porous media with MFs. In this paper, a new skeleton model was proposed to distinguish MFs from pore space via extraction of surface points set of MFs. In the procedure of points set extraction, we improved the classic “MA based” shrink method to “medial surface (MS) based” method for the MFs characterization through introducing a new set of skeleton points (i.e., surface points and edge points of the micro fractures). The former describes their apertures and the latter is used for collecting connectivity information and determining the extension ranges of the MFs. Comparison of connectivity index, fracture length, Euclidean distance showed enhanced effectiveness and accuracy of the proposed method. The proposed method was applied in four ideal models and one field shale core sample. Results show that the proposed skeleton model can show more comprehensible forms of the real connected junction instead of the conventional ideal model. The extracted skeleton can also satisfy demands of the traditional skeleton extraction model and preserve the topology of the original pore-fracture space. This work proposed a more accurate method for pore-scale modeling in cores with natural MFs, and potentially applicable for pore scale flow simulations for tight/shale reservoirs.
Publication Year: 2018
Publication Date: 2018-09-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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