A new electrical formation factor model for bimodal carbonates: numerical studies using dual-pore percolation network

作者：Tang, Y. B.^*; Li, M.; Bernabe, Y.; Tang, H. M.; Li, X. F.; Bai, X. Y.; Tao, Z. W.

来源：Geophysical Journal International, 2015, 201(3): 1456-1470.

DOI：10.1093/gji/ggv073

• Numerical approximations and analysis
• Downhole methods
• Electrical properties
• Microstructures
• Permeability and porosity
• Fracture and flow

In this paper, we modelled the electrical transport behaviour of bimodal carbonate rocks from a reservoir in China using dual-pore networks. One basic assumption, generally supported by experimental data and microstructure observations in the reservoir samples, was that the low porosity, monomodal rocks had the same properties and structure as the microporous matrix of the high porosity, bimodal samples. We assumed that the matrix was homogeneous and always interconnected but that the connectivity and the pore size distribution of macropore system was randomly variable. Both pore systems were supposed to act locally as 'in parallel' electrical conductors, an approach previously used by Bauer et al. Hence, the effect of matrix properties, macropore size distribution and connectivity on electrical properties of bimodal rocks could be modelled and investigated. We simulated electrical current through 3-D, simple cubic and body-centred cubic networks with different coordination numbers, different pipe radius distributions of macropore system and different matrix properties. The main result was that the formation factor of dual-pore network obeyed a 'universal' scaling relationship (i.e. independent of lattice type). Based on this result, we extended the power-law model derived by Bernab, et al. for monomodal porous media. We developed methods for evaluating the scale-invariant pore structure parameters in the model using conventional core analysis and satisfactorily tested the proposed model against experimental data from the Chinese reservoir as well as some other previously published data sets.

• 出版日期2015-6
• 单位中国石油大学（北京）; MIT; 西南石油大学