4.7 Article

Asphaltene Deposition Preference and Permeability Reduction Mechanisms in Oil Reservoirs: Evidence from Combining X-ray Microtomography with Fluorescence Microscopy

期刊

ENERGY & FUELS
卷 31, 期 10, 页码 10467-10478

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.energyfuels.7b01389

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资金

  1. Chinese National Science and Technology Major Project [2016ZX05006001]
  2. National Natural Science Foundation [41330319]
  3. American Association of Petroleum Geologists Foundation

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Asphaltene deposition in oil reservoirs during acid stimulation, natural depletion, and CO2 injection may cause intense formation damage and reduced productivity. Gaining a better understanding of the asphaltene deposition mechanisms and their influence on the reservoir permeability reduction will contribute to the prevention of reservoir damage and the optimization of development schemes. Although numerous models and experiments have been applied to simulate the asphaltene deposition process and evaluate the reservoir permeability loss, few analyses have been performed on natural samples from oil reservoirs undergoing asphaltene deposition. Moreover, permeability reduction simulation due to asphaltene deposition has not yet been performed in three-dimensional (3D) microscale pore systems. In this work, sandstone samples were collected from natural oil reservoirs with asphaltene deposition and analyzed by both X-ray tomography and fluorescence microscopy to identify the asphaltene. A Navier-Stokes simulator and pore network model are used to study the 3D pore spaces and to calculate the permeabilities and pore radius distributions. Ideal asphaltene deposition models are applied in the 3D pore spaces to simulate the influences of surface adsorption and pore blockage on the permeability reduction. By comparing the calculation results of the ideal models and natural samples, we found that the asphaltene deposition is a coupled effect of the surface adsorption and the pore blockage, which causes a weaker permeability loss than that from the ideal single factor models.

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